All Configurations

This page covers the different ways of configuring your job to write/read Hudi tables. At a high level, you can control behaviour at few levels.

• Hudi Table Config: Basic Hudi Table configuration parameters.
• Environment Config: Hudi supports passing configurations via a configuration file hudi-defaults.conf in which each line consists of a key and a value separated by whitespace or = sign. For example:

hoodie.datasource.hive_sync.mode               jdbc
hoodie.datasource.hive_sync.jdbcurl            jdbc:hive2://localhost:10000
hoodie.datasource.hive_sync.support_timestamp  false

It helps to have a central configuration file for your common cross job configurations/tunings, so all the jobs on your cluster can utilize it. It also works with Spark SQL DML/DDL, and helps avoid having to pass configs inside the SQL statements.

Hudi always loads the configuration file under default directory file:/etc/hudi/conf, if exists, to set the default configs. Besides, you can specify another configuration directory location by setting the HUDI_CONF_DIR environment variable. The configs stored in HUDI_CONF_DIR/hudi-defaults.conf are loaded, overriding any configs already set by the config file in the default directory.

• Spark Datasource Configs: These configs control the Hudi Spark Datasource, providing ability to define keys/partitioning, pick out the write operation, specify how to merge records or choosing query type to read.
• Flink Sql Configs: These configs control the Hudi Flink SQL source/sink connectors, providing ability to define record keys, pick out the write operation, specify how to merge records, enable/disable asynchronous compaction or choosing query type to read.
• Write Client Configs: Internally, the Hudi datasource uses a RDD based HoodieWriteClient API to actually perform writes to storage. These configs provide deep control over lower level aspects like file sizing, compression, parallelism, compaction, write schema, cleaning etc. Although Hudi provides sane defaults, from time-time these configs may need to be tweaked to optimize for specific workloads.
• Reader Configs: Please fill in the description for Config Group Name: Reader Configs
• Metastore and Catalog Sync Configs: Configurations used by the Hudi to sync metadata to external metastores and catalogs.
• Metrics Configs: These set of configs are used to enable monitoring and reporting of key Hudi stats and metrics.
• Record Payload Config: This is the lowest level of customization offered by Hudi. Record payloads define how to produce new values to upsert based on incoming new record and stored old record. Hudi provides default implementations such as OverwriteWithLatestAvroPayload which simply update table with the latest/last-written record. This can be overridden to a custom class extending HoodieRecordPayload class, on both datasource and WriteClient levels.
• Kafka Connect Configs: These set of configs are used for Kafka Connect Sink Connector for writing Hudi Tables
• Amazon Web Services Configs: Configurations specific to Amazon Web Services.
• Hudi Streamer Configs: These set of configs are used for Hudi Streamer utility which provides the way to ingest from different sources such as DFS or Kafka.

note

In the tables below (N/A) means there is no default value set

Externalized Config File

Instead of directly passing configuration settings to every Hudi job, you can also centrally set them in a configuration file hudi-defaults.conf. By default, Hudi would load the configuration file under /etc/hudi/conf directory. You can specify a different configuration directory location by setting the HUDI_CONF_DIR environment variable. This can be useful for uniformly enforcing repeated configs (like Hive sync or write/index tuning), across your entire data lake.

Hudi Table Config

Basic Hudi Table configuration parameters.

Hudi Table Basic Configs

Configurations of the Hudi Table like type of ingestion, storage formats, hive table name etc. Configurations are loaded from hoodie.properties, these properties are usually set during initializing a path as hoodie base path and never changes during the lifetime of a hoodie table.

Basic Configs

Config Name	Default	Description
hoodie.bootstrap.base.path	(N/A)	Base path of the dataset that needs to be bootstrapped as a Hudi table Config Param: BOOTSTRAP_BASE_PATH
hoodie.compaction.payload.class	(N/A)	Payload class to use for performing merges, compactions, i.e merge delta logs with current base file and then produce a new base file. Config Param: PAYLOAD_CLASS_NAME
hoodie.database.name	(N/A)	Database name. If different databases have the same table name during incremental query, we can set it to limit the table name under a specific database Config Param: DATABASE_NAME
hoodie.record.merge.mode	(N/A)	org.apache.hudi.common.config.RecordMergeMode: Determines the logic of merging updates COMMIT_TIME_ORDERING: Using transaction time to merge records, i.e., the record from later transaction overwrites the earlier record with the same key. EVENT_TIME_ORDERING: Using event time as the ordering to merge records, i.e., the record with the larger event time overwrites the record with the smaller event time on the same key, regardless of transaction time. The event time or ordering fields need to be specified by the user. CUSTOM: Using custom merging logic specified by the user. Config Param: RECORD_MERGE_MODE Since Version: 1.0.0
hoodie.record.merge.strategy.id	(N/A)	Id of merger strategy. Hudi will pick HoodieRecordMerger implementations in hoodie.write.record.merge.custom.implementation.classes which has the same merger strategy id Config Param: RECORD_MERGE_STRATEGY_ID Since Version: 0.13.0
hoodie.table.checksum	(N/A)	Table checksum is used to guard against partial writes in HDFS. It is added as the last entry in hoodie.properties and then used to validate while reading table config. Config Param: TABLE_CHECKSUM Since Version: 0.11.0
hoodie.table.create.schema	(N/A)	Schema used when creating the table Config Param: CREATE_SCHEMA
hoodie.table.index.defs.path	(N/A)	Relative path to table base path where the index definitions are stored Config Param: RELATIVE_INDEX_DEFINITION_PATH Since Version: 1.0.0
hoodie.table.keygenerator.class	(N/A)	Key Generator class property for the hoodie table Config Param: KEY_GENERATOR_CLASS_NAME
hoodie.table.keygenerator.type	(N/A)	Key Generator type to determine key generator class Config Param: KEY_GENERATOR_TYPE Since Version: 1.0.0
hoodie.table.legacy.payload.class	(N/A)	Payload class to indicate the payload class that is used to create the table and is not used anymore. Config Param: LEGACY_PAYLOAD_CLASS_NAME Since Version: 1.1.0
hoodie.table.metadata.partitions	(N/A)	Comma-separated list of metadata partitions that have been completely built and in-sync with data table. These partitions are ready for use by the readers Config Param: TABLE_METADATA_PARTITIONS Since Version: 0.11.0
hoodie.table.metadata.partitions.inflight	(N/A)	Comma-separated list of metadata partitions whose building is in progress. These partitions are not yet ready for use by the readers. Config Param: TABLE_METADATA_PARTITIONS_INFLIGHT Since Version: 0.11.0
hoodie.table.name	(N/A)	Table name that will be used for registering with Hive. Needs to be same across runs. Config Param: NAME
hoodie.table.ordering.fields	(N/A)	Comma separated fields used in records merging comparison. By default, when two records have the same key value, the largest value for the ordering field determined by Object.compareTo(..), is picked. If there are multiple fields configured, comparison is made on the first field. If the first field values are same, comparison is made on the second field and so on. Config Param: ORDERING_FIELDS
hoodie.table.partial.update.mode	(N/A)	This property when set, will define how two versions of the record will be merged together when records are partially formed Config Param: PARTIAL_UPDATE_MODE Since Version: 1.1.0
hoodie.table.partition.fields	(N/A)	Comma separated field names used to partition the table. These field names also include the partition type which is used by custom key generators Config Param: PARTITION_FIELDS
hoodie.table.precombine.field	(N/A)	Comma separated fields used in preCombining before actual write. By default, when two records have the same key value, the largest value for the precombine field determined by Object.compareTo(..), is picked. If there are multiple fields configured, comparison is made on the first field. If the first field values are same, comparison is made on the second field and so on. Config Param: PRECOMBINE_FIELD
hoodie.table.recordkey.fields	(N/A)	Columns used to uniquely identify the table. Concatenated values of these fields are used as the record key component of HoodieKey. Config Param: RECORDKEY_FIELDS
hoodie.table.secondary.indexes.metadata	(N/A)	The metadata of secondary indexes Config Param: SECONDARY_INDEXES_METADATA Since Version: 0.13.0
hoodie.timeline.layout.version	(N/A)	Version of timeline used, by the table. Config Param: TIMELINE_LAYOUT_VERSION
hoodie.archivelog.folder	archived	path under the meta folder, to store archived timeline instants at. Config Param: ARCHIVELOG_FOLDER
hoodie.bootstrap.index.class	org.apache.hudi.common.bootstrap.index.hfile.HFileBootstrapIndex	Implementation to use, for mapping base files to bootstrap base file, that contain actual data. Config Param: BOOTSTRAP_INDEX_CLASS_NAME
hoodie.bootstrap.index.enable	true	Whether or not, this is a bootstrapped table, with bootstrap base data and an mapping index defined, default true. Config Param: BOOTSTRAP_INDEX_ENABLE
hoodie.bootstrap.index.type	HFILE	Bootstrap index type determines which implementation to use, for mapping base files to bootstrap base file, that contain actual data. Config Param: BOOTSTRAP_INDEX_TYPE Since Version: 1.0.0
hoodie.datasource.write.hive_style_partitioning	false	Flag to indicate whether to use Hive style partitioning. If set true, the names of partition folders follow <partition_column_name>=<partition_value> format. By default false (the names of partition folders are only partition values) Config Param: HIVE_STYLE_PARTITIONING_ENABLE
hoodie.partition.metafile.use.base.format	false	If true, partition metafiles are saved in the same format as base-files for this dataset (e.g. Parquet / ORC). If false (default) partition metafiles are saved as properties files. Config Param: PARTITION_METAFILE_USE_BASE_FORMAT
hoodie.populate.meta.fields	true	When enabled, populates all meta fields. When disabled, no meta fields are populated and incremental queries will not be functional. This is only meant to be used for append only/immutable data for batch processing Config Param: POPULATE_META_FIELDS
hoodie.table.base.file.format	PARQUET	Base file format to store all the base file data. Config Param: BASE_FILE_FORMAT
hoodie.table.cdc.enabled	false	When enable, persist the change data if necessary, and can be queried as a CDC query mode. Config Param: CDC_ENABLED Since Version: 0.13.0
hoodie.table.cdc.supplemental.logging.mode	DATA_BEFORE_AFTER	org.apache.hudi.common.table.cdc.HoodieCDCSupplementalLoggingMode: Change log capture supplemental logging mode. The supplemental log is used for accelerating the generation of change log details. OP_KEY_ONLY: Only keeping record keys in the supplemental logs, so the reader needs to figure out the update before image and after image. DATA_BEFORE: Keeping the before images in the supplemental logs, so the reader needs to figure out the update after images. DATA_BEFORE_AFTER(default): Keeping the before and after images in the supplemental logs, so the reader can generate the details directly from the logs. Config Param: CDC_SUPPLEMENTAL_LOGGING_MODE Since Version: 0.13.0
hoodie.table.format	native	Table format name used when writing to the table. Config Param: TABLE_FORMAT
hoodie.table.initial.version	NINE	Initial Version of table when the table was created. Used for upgrade/downgrade to identify what upgrade/downgrade paths happened on the table. This is only configured when the table is initially setup. Config Param: INITIAL_VERSION Since Version: 1.0.0
hoodie.table.log.file.format	HOODIE_LOG	Log format used for the delta logs. Config Param: LOG_FILE_FORMAT
hoodie.table.multiple.base.file.formats.enable	false	When set to true, the table can support reading and writing multiple base file formats. Config Param: MULTIPLE_BASE_FILE_FORMATS_ENABLE Since Version: 1.0.0
hoodie.table.timeline.timezone	LOCAL	User can set hoodie commit timeline timezone, such as utc, local and so on. local is default Config Param: TIMELINE_TIMEZONE
hoodie.table.type	COPY_ON_WRITE	The table type for the underlying data. Config Param: TYPE
hoodie.table.version	NINE	Version of table, used for running upgrade/downgrade steps between releases with potentially breaking/backwards compatible changes. Config Param: VERSION
hoodie.timeline.history.path	history	path under the meta folder, to store timeline history at. Config Param: TIMELINE_HISTORY_PATH
hoodie.timeline.path	timeline	path under the meta folder, to store timeline instants at. Config Param: TIMELINE_PATH

Advanced Configs

Config Name	Default	Description
hoodie.datasource.write.drop.partition.columns	false	When set to true, will not write the partition columns into hudi. By default, false. Config Param: DROP_PARTITION_COLUMNS
hoodie.datasource.write.partitionpath.urlencode	false	Should we url encode the partition path value, before creating the folder structure. Config Param: URL_ENCODE_PARTITIONING
hoodie.datasource.write.slash.separated.date.partitioning	false	Flag to indicate whether to use slash separated date partitioning. If set to true, date partition values in yyyy-MM-dd format will be transformed to yyyy/MM/dd directory structure. By default false. Cannot be used together with hive-style partitioning. Config Param: SLASH_SEPARATED_DATE_PARTITIONING
hoodie.table.partition_extractor_class		Class which implements PartitionValueExtractor to extract the partition values, default is inferred based on partition configuration. Config Param: PARTITION_EXTRACTOR_CLASS

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Spark Datasource Configs

These configs control the Hudi Spark Datasource, providing ability to define keys/partitioning, pick out the write operation, specify how to merge records or choosing query type to read.

Read Options

Options useful for reading tables via read.format.option(...)

Basic Configs

Config Name	Default	Description
hoodie.datasource.read.begin.instanttime	(N/A)	Required when hoodie.datasource.query.type is set to incremental. Represents the completion time to start incrementally pulling data from. The completion time here need not necessarily correspond to an instant on the timeline. New data written with completion_time >= START_COMMIT are fetched out. For e.g: ‘20170901080000’ will get all new data written on or after Sep 1, 2017 08:00AM. Config Param: START_COMMIT Since Version: 0.9.0
hoodie.datasource.read.end.instanttime	(N/A)	Used when hoodie.datasource.query.type is set to incremental. Represents the completion time to limit incrementally fetched data to. When not specified latest commit completion time from timeline is assumed by default. When specified, new data written with completion_time <= END_COMMIT are fetched out. Point in time type queries make more sense with begin and end completion times specified. Config Param: END_COMMIT Since Version: 0.9.0
hoodie.datasource.read.incr.table.version	(N/A)	The table version assumed for incremental read Config Param: INCREMENTAL_READ_TABLE_VERSION Since Version: 1.0.0
hoodie.datasource.read.streaming.table.version	(N/A)	The table version assumed for streaming read Config Param: STREAMING_READ_TABLE_VERSION Since Version: 1.0.0
hoodie.datasource.write.precombine.field	(N/A)	Comma separated list of fields used in preCombining before actual write. When two records have the same key value, we will pick the one with the largest value for the precombine field, determined by Object.compareTo(..). For multiple fields if first key comparison is same, second key comparison is made and so on. This config is used for combining records within the same batch and also for merging using event time merge mode Config Param: READ_PRE_COMBINE_FIELD
hoodie.datasource.query.type	snapshot	Whether data needs to be read, in incremental mode (new data since an instantTime) (or) read_optimized mode (obtain latest view, based on base files) (or) snapshot mode (obtain latest view, by merging base and (if any) log files) Config Param: QUERY_TYPE Since Version: 0.9.0

Advanced Configs

Config Name	Default	Description
as.of.instant	(N/A)	The query instant for time travel. Without specified this option, we query the latest snapshot. Config Param: TIME_TRAVEL_AS_OF_INSTANT
hoodie.datasource.read.paths	(N/A)	Comma separated list of file paths to read within a Hudi table. Config Param: READ_PATHS Since Version: 0.9.0
hoodie.datasource.merge.type	payload_combine	For Snapshot query on merge on read table. Use this key to define how the payloads are merged, in 1) skip_merge: read the base file records plus the log file records without merging; 2) payload_combine: read the base file records first, for each record in base file, checks whether the key is in the log file records (combines the two records with same key for base and log file records), then read the left log file records Config Param: REALTIME_MERGE
hoodie.datasource.query.incremental.format	latest_state	This config is used alone with the 'incremental' query type.When set to 'latest_state', it returns the latest records' values.When set to 'cdc', it returns the cdc data. Config Param: INCREMENTAL_FORMAT Since Version: 0.13.0
hoodie.datasource.read.create.filesystem.relation	false	When this is set, the relation created by DefaultSource is for a view representing the result set of the table valued function hudi_filesystem_view(...) Config Param: CREATE_FILESYSTEM_RELATION Since Version: 1.0.0
hoodie.datasource.read.extract.partition.values.from.path	false	When set to true, values for partition columns (partition values) will be extracted from physical partition path (default Spark behavior). When set to false partition values will be read from the data file (in Hudi partition columns are persisted by default). This config is a fallback allowing to preserve existing behavior, and should not be used otherwise. Config Param: EXTRACT_PARTITION_VALUES_FROM_PARTITION_PATH Since Version: 0.11.0
hoodie.datasource.read.file.index.list.partitions.from.catalog	false	Controls whether partition listing is obtained from the catalog instead of listing the file system to avoid recursively listing of large number of directories. Enabling this can reduce large amount of listing calls and speed up the queries for very large tables. This is only necessary when MDT is not enabled on the dataset as otherwise the MDT can provide the partition listing faster and without any actual listing on the file system. Config Param: FILE_INDEX_PARTITION_LISTING_VIA_CATALOG Since Version: 1.2.0
hoodie.datasource.read.file.index.listing.mode	lazy	Overrides Hudi's file-index implementation's file listing mode: when set to 'eager', file-index will list all partition paths and corresponding file slices w/in them eagerly, during initialization, prior to partition-pruning kicking in, meaning that all partitions will be listed including ones that might be subsequently pruned out; when set to 'lazy', partitions and file-slices w/in them will be listed lazily (ie when they actually accessed, instead of when file-index is initialized) allowing partition pruning to occur before that, only listing partitions that has already been pruned. Please note that, this config is provided purely to allow to fallback to behavior existing prior to 0.13.0 release, and will be deprecated soon after. Config Param: FILE_INDEX_LISTING_MODE_OVERRIDE Since Version: 0.13.0
hoodie.datasource.read.file.index.listing.partition-path-prefix.analysis.enabled	true	Controls whether partition-path prefix analysis is enabled w/in the file-index, allowing to avoid necessity to recursively list deep folder structures of partitioned tables w/ multiple partition columns, by carefully analyzing provided partition-column predicates and deducing corresponding partition-path prefix from them (if possible). Config Param: FILE_INDEX_LISTING_PARTITION_PATH_PREFIX_ANALYSIS_ENABLED Since Version: 0.13.0
hoodie.datasource.read.file.index.optimize.listing.using.path.filter	false	Controls whether file listing is done using the HoodieROTablePathFilter. This is mainly necessary when the metadata table is not enabled or corrupted and the job is doing recursive calls to fetch the partition paths and the dataset has multiple versions of the same file in the same partition and it could lead to Out of Memory on the driver if the dataset is too large. Another important limitation is that this config should not be used if there are bootstrap files present in the file system. NOTE: Only works for COW tables with snapshot queries. Config Param: FILE_INDEX_LIST_FILE_STATUSES_USING_RO_PATH_FILTER Since Version: 1.2.0
hoodie.datasource.read.incr.fallback.fulltablescan.enable	true	When doing an incremental query whether we should fall back to full table scans if file does not exist. Config Param: INCREMENTAL_FALLBACK_TO_FULL_TABLE_SCAN_FOR_NON_EXISTING_FILES
hoodie.datasource.read.incr.fallback.fulltablescan.enable	true	When doing an incremental query whether we should fall back to full table scans if file does not exist. Config Param: INCREMENTAL_FALLBACK_TO_FULL_TABLE_SCAN
hoodie.datasource.read.incr.filters		For use-cases like DeltaStreamer which reads from Hoodie Incremental table and applies opaque map functions, filters appearing late in the sequence of transformations cannot be automatically pushed down. This option allows setting filters directly on Hoodie Source. Config Param: PUSH_DOWN_INCR_FILTERS Since Version: 0.9.0
hoodie.datasource.read.incr.path.glob		For the use-cases like users only want to incremental pull from certain partitions instead of the full table. This option allows using glob pattern to directly filter on path. Config Param: INCR_PATH_GLOB Since Version: 0.9.0
hoodie.datasource.read.incr.skip_cluster	false	Whether to skip clustering instants to avoid reading base files of clustering operations for streaming read to improve read performance. Config Param: INCREMENTAL_READ_SKIP_CLUSTER Since Version: 1.0.1
hoodie.datasource.read.incr.skip_compact	false	Whether to skip compaction instants and avoid reading compacted base files for streaming read to improve read performance. Config Param: INCREMENTAL_READ_SKIP_COMPACT Since Version: 1.0.1
hoodie.datasource.read.partition.value.extractor.enabled	false	This config helps whether PartitionValueExtractor interface can be used for parsing partition values from partition path. When this config is enabled, it uses PartitionValueExtractor class value stored in the hoodie.properties file. Config Param: USE_PARTITION_VALUE_EXTRACTOR_ON_READ Since Version: 1.2.0
hoodie.datasource.read.schema.use.end.instanttime	false	Uses end instant schema when incrementally fetched data to. Default: users latest instant schema. Config Param: INCREMENTAL_READ_SCHEMA_USE_END_INSTANTTIME Since Version: 0.9.0
hoodie.datasource.read.table.valued.function.filesystem.relation.subpath		A regex under the table's base path to get file system view information Config Param: FILESYSTEM_RELATION_ARG_SUBPATH Since Version: 1.0.0
hoodie.datasource.read.table.valued.function.timeline.relation	false	When this is set, the relation created by DefaultSource is for a view representing the result set of the table valued function hudi_query_timeline(...) Config Param: CREATE_TIMELINE_RELATION Since Version: 1.0.0
hoodie.datasource.read.table.valued.function.timeline.relation.archived	false	When this is set, the result set of the table valued function hudi_query_timeline(...) will include archived timeline Config Param: TIMELINE_RELATION_ARG_ARCHIVED_TIMELINE Since Version: 1.0.0
hoodie.datasource.streaming.startOffset	earliest	Start offset to pull data from hoodie streaming source. allow earliest, latest, and specified start instant time Config Param: START_OFFSET Since Version: 0.13.0
hoodie.enable.data.skipping	true	Enables data-skipping allowing queries to leverage indexes to reduce the search space by skipping over files Config Param: ENABLE_DATA_SKIPPING Since Version: 0.10.0
hoodie.file.index.enable	true	Enables use of the spark file index implementation for Hudi, that speeds up listing of large tables. Config Param: ENABLE_HOODIE_FILE_INDEX
hoodie.read.timeline.holes.resolution.policy	FAIL	When doing incremental queries, there could be hollow commits (requested or inflight commits that are not the latest) that are produced by concurrent writers and could lead to potential data loss. This config allows users to have different ways of handling this situation. The valid values are [FAIL, BLOCK, USE_TRANSITION_TIME]: Use FAIL to throw an exception when hollow commit is detected. This is helpful when hollow commits are not expected. Use BLOCK to block processing commits from going beyond the hollow ones. This fits the case where waiting for hollow commits to finish is acceptable. Use USE_TRANSITION_TIME (experimental) to query commits in range by state transition time (completion time), instead of commit time (start time). Using this mode will result in begin.instanttime and end.instanttime using stateTransitionTime instead of the instant's commit time. Config Param: INCREMENTAL_READ_HANDLE_HOLLOW_COMMIT Since Version: 0.14.0
hoodie.schema.on.read.enable	false	Enables support for Schema Evolution feature Config Param: SCHEMA_EVOLUTION_ENABLED
hoodie.spark.polaris.catalog.class	org.apache.polaris.spark.SparkCatalog	Fully qualified class name of the catalog that is used by the Polaris spark client. Config Param: POLARIS_CATALOG_CLASS_NAME Since Version: 1.1.0

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Write Options

You can pass down any of the WriteClient level configs directly using options() or option(k,v) methods.

inputDF.write()
.format("org.apache.hudi")
.options(clientOpts) // any of the Hudi client opts can be passed in as well
.option(DataSourceWriteOptions.RECORDKEY_FIELD_OPT_KEY(), "_row_key")
.option(DataSourceWriteOptions.PARTITIONPATH_FIELD_OPT_KEY(), "partition")
.option(HoodieTableConfig.ORDERING_FIELDS(), "timestamp")
.option(HoodieWriteConfig.TABLE_NAME, tableName)
.mode(SaveMode.Append)
.save(basePath);

Options useful for writing tables via write.format.option(...)

Basic Configs

Config Name	Default	Description
hoodie.datasource.hive_sync.mode	(N/A)	Mode to choose for Hive ops. Valid values are hms, jdbc and hiveql. Config Param: HIVE_SYNC_MODE
hoodie.datasource.write.partitionpath.field	(N/A)	Partition path field. Value to be used at the partitionPath component of HoodieKey. Actual value obtained by invoking .toString() Config Param: PARTITIONPATH_FIELD
hoodie.datasource.write.precombine.field	(N/A)	Comma separated list of fields used in preCombining before actual write. When two records have the same key value, we will pick the one with the largest value for the precombine field, determined by Object.compareTo(..). For multiple fields if first key comparison is same, second key comparison is made and so on. This config is used for combining records within the same batch and also for merging using event time merge mode Config Param: ORDERING_FIELDS
hoodie.datasource.write.precombine.field	(N/A)	Comma separated list of fields used in preCombining before actual write. When two records have the same key value, we will pick the one with the largest value for the precombine field, determined by Object.compareTo(..). For multiple fields if first key comparison is same, second key comparison is made and so on. This config is used for combining records within the same batch and also for merging using event time merge mode Config Param: PRECOMBINE_FIELD
hoodie.datasource.write.recordkey.field	(N/A)	Record key field. Value to be used as the recordKey component of HoodieKey. Actual value will be obtained by invoking .toString() on the field value. Nested fields can be specified using the dot notation eg: a.b.c Config Param: RECORDKEY_FIELD
hoodie.datasource.write.secondarykey.column	(N/A)	Columns that constitute the secondary key component. Actual value will be obtained by invoking .toString() on the field value. Nested fields can be specified using the dot notation eg: a.b.c Config Param: SECONDARYKEY_COLUMN_NAME
hoodie.write.record.merge.mode	(N/A)	org.apache.hudi.common.config.RecordMergeMode: Determines the logic of merging updates COMMIT_TIME_ORDERING: Using transaction time to merge records, i.e., the record from later transaction overwrites the earlier record with the same key. EVENT_TIME_ORDERING: Using event time as the ordering to merge records, i.e., the record with the larger event time overwrites the record with the smaller event time on the same key, regardless of transaction time. The event time or ordering fields need to be specified by the user. CUSTOM: Using custom merging logic specified by the user. Config Param: RECORD_MERGE_MODE Since Version: 1.0.0
hoodie.clustering.async.enabled	false	Enable running of clustering service, asynchronously as inserts happen on the table. Config Param: ASYNC_CLUSTERING_ENABLE Since Version: 0.7.0
hoodie.clustering.inline	false	Turn on inline clustering - clustering will be run after each write operation is complete Config Param: INLINE_CLUSTERING_ENABLE Since Version: 0.7.0
hoodie.datasource.hive_sync.enable	false	When set to true, register/sync the table to Apache Hive metastore. Config Param: HIVE_SYNC_ENABLED
hoodie.datasource.hive_sync.jdbcurl	jdbc:hive2://localhost:10000	Hive metastore url Config Param: HIVE_URL
hoodie.datasource.hive_sync.metastore.uris	thrift://localhost:9083	Hive metastore url Config Param: METASTORE_URIS
hoodie.datasource.meta.sync.enable	false	Enable Syncing the Hudi Table with an external meta store or data catalog. Config Param: META_SYNC_ENABLED
hoodie.datasource.write.hive_style_partitioning	false	Flag to indicate whether to use Hive style partitioning. If set true, the names of partition folders follow <partition_column_name>=<partition_value> format. By default false (the names of partition folders are only partition values) Config Param: HIVE_STYLE_PARTITIONING
hoodie.datasource.write.operation	upsert	Whether to do upsert, insert or bulk_insert for the write operation. Use bulk_insert to load new data into a table, and there on use upsert/insert. bulk insert uses a disk based write path to scale to load large inputs without need to cache it. Config Param: OPERATION Since Version: 0.9.0
hoodie.datasource.write.table.type	COPY_ON_WRITE	The table type for the underlying data, for this write. This can’t change between writes. Config Param: TABLE_TYPE Since Version: 0.9.0

Advanced Configs

Config Name	Default	Description
hoodie.datasource.hive_sync.serde_properties	(N/A)	Serde properties to hive table. Config Param: HIVE_TABLE_SERDE_PROPERTIES
hoodie.datasource.hive_sync.table_properties	(N/A)	Additional properties to store with table. Config Param: HIVE_TABLE_PROPERTIES
hoodie.datasource.overwrite.mode	(N/A)	Controls whether overwrite use dynamic or static mode, if not configured, respect spark.sql.sources.partitionOverwriteMode Config Param: OVERWRITE_MODE Since Version: 0.14.0
hoodie.datasource.partition_extractor_class	(N/A)	PartitionValueExtractor implementation used by Spark datasource write/read paths to parse partition values from partition paths. Config Param: PARTITION_EXTRACTOR_CLASS Since Version: 1.2.0
hoodie.datasource.write.partitions.to.delete	(N/A)	Comma separated list of partitions to delete. Allows use of wildcard * Config Param: PARTITIONS_TO_DELETE Since Version: 0.9.0
hoodie.datasource.write.payload.class	(N/A)	Payload class used. Override this, if you like to roll your own merge logic, when upserting/inserting. This will render any value set for PRECOMBINE_FIELD_OPT_VAL in-effective Config Param: PAYLOAD_CLASS_NAME
hoodie.datasource.write.table.name	(N/A)	Table name for the datasource write. Also used to register the table into meta stores. Config Param: TABLE_NAME Since Version: 0.9.0
hoodie.write.record.merge.custom.implementation.classes	(N/A)	List of HoodieMerger implementations constituting Hudi's merging strategy -- based on the engine used. These record merge impls will filter by hoodie.write.record.merge.strategy.idHudi will pick most efficient implementation to perform merging/combining of the records (during update, reading MOR table, etc) Config Param: RECORD_MERGE_IMPL_CLASSES Since Version: 0.13.0
hoodie.write.record.merge.strategy.id	(N/A)	ID of record merge strategy. Hudi will pick HoodieRecordMerger implementations in hoodie.write.record.merge.custom.implementation.classes which has the same merge strategy id Config Param: RECORD_MERGE_STRATEGY_ID Since Version: 0.13.0
hoodie.datasource.compaction.async.enable	true	Controls whether async compaction should be turned on for MOR table writing. Config Param: ASYNC_COMPACT_ENABLE
hoodie.datasource.hive_sync.auto_create_database	true	Auto create hive database if does not exists Config Param: HIVE_AUTO_CREATE_DATABASE
hoodie.datasource.hive_sync.base_file_format	PARQUET	Base file format for the sync. Config Param: HIVE_BASE_FILE_FORMAT
hoodie.datasource.hive_sync.batch_num	1000	The number of partitions one batch when synchronous partitions to hive. Config Param: HIVE_BATCH_SYNC_PARTITION_NUM
hoodie.datasource.hive_sync.bucket_sync	false	Whether sync hive metastore bucket specification when using bucket index.The specification is 'CLUSTERED BY (trace_id) SORTED BY (trace_id ASC) INTO 65536 BUCKETS' Config Param: HIVE_SYNC_BUCKET_SYNC
hoodie.datasource.hive_sync.create_managed_table	false	Whether to sync the table as managed table. Config Param: HIVE_CREATE_MANAGED_TABLE
hoodie.datasource.hive_sync.database	default	The name of the destination database that we should sync the hudi table to. Config Param: HIVE_DATABASE
hoodie.datasource.hive_sync.ignore_exceptions	false	Ignore exceptions when syncing with Hive. Config Param: HIVE_IGNORE_EXCEPTIONS
hoodie.datasource.hive_sync.partition_extractor_class	org.apache.hudi.hive.MultiPartKeysValueExtractor	Class which implements PartitionValueExtractor to extract the partition values, default is inferred based on partition configuration. Config Param: HIVE_PARTITION_EXTRACTOR_CLASS
hoodie.datasource.hive_sync.partition_fields		Field in the table to use for determining hive partition columns. Config Param: HIVE_PARTITION_FIELDS
hoodie.datasource.hive_sync.password	hive	hive password to use Config Param: HIVE_PASS
hoodie.datasource.hive_sync.skip_ro_suffix	false	Skip the _ro suffix for Read optimized table, when registering Config Param: HIVE_SKIP_RO_SUFFIX_FOR_READ_OPTIMIZED_TABLE
hoodie.datasource.hive_sync.support_timestamp	false	‘INT64’ with original type TIMESTAMP_MICROS is converted to hive ‘timestamp’ type. Disabled by default for backward compatibility. NOTE: On Spark entrypoints, this is defaulted to TRUE Config Param: HIVE_SUPPORT_TIMESTAMP_TYPE
hoodie.datasource.hive_sync.sync_as_datasource	true	Config Param: HIVE_SYNC_AS_DATA_SOURCE_TABLE
hoodie.datasource.hive_sync.sync_comment	false	Whether to sync the table column comments while syncing the table. Config Param: HIVE_SYNC_COMMENT
hoodie.datasource.hive_sync.table	unknown	The name of the destination table that we should sync the hudi table to. Config Param: HIVE_TABLE
hoodie.datasource.hive_sync.use_jdbc	true	Use JDBC when hive synchronization is enabled Config Param: HIVE_USE_JDBC
hoodie.datasource.hive_sync.username	hive	hive user name to use Config Param: HIVE_USER
hoodie.datasource.insert.dup.policy	none	Note This is only applicable to Spark SQL writing.<br />When operation type is set to "insert", users can optionally enforce a dedup policy. This policy will be employed when records being ingested already exists in storage. Default policy is none and no action will be taken. Another option is to choose "drop", on which matching records from incoming will be dropped and the rest will be ingested. Third option is "fail" which will fail the write operation when same records are re-ingested. In other words, a given record as deduced by the key generation policy can be ingested only once to the target table of interest. Config Param: INSERT_DUP_POLICY Since Version: 0.14.0
hoodie.datasource.meta_sync.condition.sync	false	If true, only sync on conditions like schema change or partition change. Config Param: HIVE_CONDITIONAL_SYNC
hoodie.datasource.write.commitmeta.key.prefix	_	Option keys beginning with this prefix, are automatically added to the commit/deltacommit metadata. This is useful to store checkpointing information, in a consistent way with the hudi timeline Config Param: COMMIT_METADATA_KEYPREFIX Since Version: 0.9.0
hoodie.datasource.write.drop.partition.columns	false	When set to true, will not write the partition columns into hudi. By default, false. Config Param: DROP_PARTITION_COLUMNS
hoodie.datasource.write.insert.drop.duplicates	false	If set to true, records from the incoming dataframe will not overwrite existing records with the same key during the write operation. <br /> Note Just for Insert operation in Spark SQL writing since 0.14.0, users can switch to the config hoodie.datasource.insert.dup.policy instead for a simplified duplicate handling experience. The new config will be incorporated into all other writing flows and this config will be fully deprecated in future releases. Config Param: INSERT_DROP_DUPS Since Version: 0.9.0
hoodie.datasource.write.keygenerator.class	org.apache.hudi.keygen.SimpleKeyGenerator	Key generator class, that implements org.apache.hudi.keygen.KeyGenerator Config Param: KEYGENERATOR_CLASS_NAME Since Version: 0.9.0
hoodie.datasource.write.keygenerator.consistent.logical.timestamp.enabled	false	When set to true, consistent value will be generated for a logical timestamp type column, like timestamp-millis and timestamp-micros, irrespective of whether row-writer is enabled. Disabled by default so as not to break the pipeline that deploy either fully row-writer path or non row-writer path. For example, if it is kept disabled then record key of timestamp type with value 2016-12-29 09:54:00 will be written as timestamp 2016-12-29 09:54:00.0 in row-writer path, while it will be written as long value 1483023240000000 in non row-writer path. If enabled, then the timestamp value will be written in both the cases. Config Param: KEYGENERATOR_CONSISTENT_LOGICAL_TIMESTAMP_ENABLED Since Version: 0.10.1
hoodie.datasource.write.partitionpath.urlencode	false	Should we url encode the partition path value, before creating the folder structure. Config Param: URL_ENCODE_PARTITIONING
hoodie.datasource.write.reconcile.schema	false	This config controls how writer's schema will be selected based on the incoming batch's schema as well as existing table's one. When schema reconciliation is DISABLED, incoming batch's schema will be picked as a writer-schema (therefore updating table's schema). When schema reconciliation is ENABLED, writer-schema will be picked such that table's schema (after txn) is either kept the same or extended, meaning that we'll always prefer the schema that either adds new columns or stays the same. This enables us, to always extend the table's schema during evolution and never lose the data (when, for ex, existing column is being dropped in a new batch) Config Param: RECONCILE_SCHEMA
hoodie.datasource.write.row.writer.enable	true	When set to true, will perform write operations directly using the spark native Row representation, avoiding any additional conversion costs. Config Param: ENABLE_ROW_WRITER Since Version: 0.9.0
hoodie.datasource.write.streaming.checkpoint.identifier	default_single_writer	A stream identifier used for HUDI to fetch the right checkpoint(batch id to be more specific) corresponding this writer. Please note that keep the identifier an unique value for different writer if under multi-writer scenario. If the value is not set, will only keep the checkpoint info in the memory. This could introduce the potential issue that the job is restart(batch id is lost) while spark checkpoint write fails, causing spark will retry and rewrite the data. Config Param: STREAMING_CHECKPOINT_IDENTIFIER Since Version: 0.13.0
hoodie.datasource.write.streaming.disable.compaction	false	By default for MOR table, async compaction is enabled with spark streaming sink. By setting this config to true, we can disable it and the expectation is that, users will schedule and execute compaction in a different process/job altogether. Some users may wish to run it separately to manage resources across table services and regular ingestion pipeline and so this could be preferred on such cases. Config Param: STREAMING_DISABLE_COMPACTION Since Version: 0.14.0
hoodie.datasource.write.streaming.ignore.failed.batch	false	Config to indicate whether to ignore any non exception error (e.g. writestatus error) within a streaming microbatch. Turning this on, could hide the write status errors while the spark checkpoint moves ahead.So, would recommend users to use this with caution. Config Param: STREAMING_IGNORE_FAILED_BATCH Since Version: 0.9.0
hoodie.datasource.write.streaming.retry.count	3	Config to indicate how many times streaming job should retry for a failed micro batch. Config Param: STREAMING_RETRY_CNT Since Version: 0.9.0
hoodie.datasource.write.streaming.retry.interval.ms	2000	Config to indicate how long (by millisecond) before a retry should issued for failed microbatch Config Param: STREAMING_RETRY_INTERVAL_MS Since Version: 0.9.0
hoodie.meta.sync.client.tool.class	org.apache.hudi.hive.HiveSyncTool	Sync tool class name used to sync to metastore. Defaults to Hive. Config Param: META_SYNC_CLIENT_TOOL_CLASS_NAME Since Version: 0.9.0
hoodie.spark.sql.insert.into.operation	insert	Sql write operation to use with INSERT_INTO spark sql command. This comes with 3 possible values, bulk_insert, insert and upsert. "bulk_insert" is generally meant for initial loads and is known to be performant compared to insert. "insert" is the default value for this config and does small file handling in addition to bulk_insert, but will ensure to retain duplicates if ingested. If you may use INSERT_INTO for mutable dataset, then you may have to set this config value to "upsert". With upsert, Hudi will merge multiple versions of the record identified by record key configuration into one final record. Config Param: SPARK_SQL_INSERT_INTO_OPERATION Since Version: 0.14.0
hoodie.spark.sql.merge.into.partial.updates	true	Whether to write partial updates to the data blocks containing updates in MOR tables with Spark SQL MERGE INTO statement. The data blocks containing partial updates have a schema with a subset of fields compared to the full schema of the table. Config Param: ENABLE_MERGE_INTO_PARTIAL_UPDATES Since Version: 1.0.0
hoodie.spark.sql.optimized.writes.enable	true	Controls whether spark sql prepped update and delete are enabled. Config Param: SPARK_SQL_OPTIMIZED_WRITES Since Version: 0.14.0
hoodie.sql.bulk.insert.enable	false	When set to true, the sql insert statement will use bulk insert. This config is deprecated as of 0.14.0. Please use hoodie.spark.sql.insert.into.operation instead. Config Param: SQL_ENABLE_BULK_INSERT
hoodie.sql.insert.mode	upsert	Insert mode when insert data to pk-table. The optional modes are: upsert, strict and non-strict.For upsert mode, insert statement do the upsert operation for the pk-table which will update the duplicate record.For strict mode, insert statement will keep the primary key uniqueness constraint which do not allow duplicate record.While for non-strict mode, hudi just do the insert operation for the pk-table. This config is deprecated as of 0.14.0. Please use hoodie.spark.sql.insert.into.operation and hoodie.datasource.insert.dup.policy as you see fit. Config Param: SQL_INSERT_MODE
hoodie.streamer.source.kafka.value.deserializer.class	io.confluent.kafka.serializers.KafkaAvroDeserializer	This class is used by kafka client to deserialize the records Config Param: KAFKA_AVRO_VALUE_DESERIALIZER_CLASS Since Version: 0.9.0
hoodie.write.set.null.for.missing.columns	false	When a nullable column is missing from incoming batch during a write operation, the write operation will fail schema compatibility check. Set this option to true will make the missing column be filled with null values to successfully complete the write operation. Config Param: SET_NULL_FOR_MISSING_COLUMNS Since Version: 0.14.1

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PreCommit Validator Configurations

The following set of configurations help validate new data before commits.

Advanced Configs

Config Name	Default	Description
hoodie.precommit.validators		Comma separated list of class names that can be invoked to validate commit Config Param: VALIDATOR_CLASS_NAMES
hoodie.precommit.validators.equality.sql.queries		Spark SQL queries to run on table before committing new data to validate state before and after commit. Multiple queries separated by ';' delimiter are supported. Example: "select count(*) from <TABLE_NAME> Note <TABLE_NAME> is replaced by table state before and after commit. Config Param: EQUALITY_SQL_QUERIES
hoodie.precommit.validators.failure.policy	FAIL	Policy for handling pre-commit validation failures. FAIL (default): validation failures block the commit with an exception. WARN_LOG: validation failures emit a warning log but allow the commit to proceed. Useful for monitoring data quality without impacting write availability. Config Param: VALIDATION_FAILURE_POLICY Since Version: 1.2.0
hoodie.precommit.validators.inequality.sql.queries		Spark SQL queries to run on table before committing new data to validate state before and after commit.Multiple queries separated by ';' delimiter are supported.Example query: 'select count(*) from <TABLE_NAME> where col=null'Note <TABLE_NAME> variable is expected to be present in query. Config Param: INEQUALITY_SQL_QUERIES
hoodie.precommit.validators.single.value.sql.queries		Spark SQL queries to run on table before committing new data to validate state after commit.Multiple queries separated by ';' delimiter are supported.Expected result is included as part of query separated by '#'. Example query: 'query1#result1:query2#result2'Note <TABLE_NAME> variable is expected to be present in query. Config Param: SINGLE_VALUE_SQL_QUERIES
hoodie.precommit.validators.streaming.offset.tolerance.percentage	0.0	Tolerance percentage for streaming offset validation (used by org.apache.hudi.client.validator.StreamingOffsetValidator and org.apache.hudi.sink.validator.FlinkKafkaOffsetValidator). The validator compares the offset difference (expected records from source) with actual records written. If the deviation exceeds this percentage, the commit is rejected or warned depending on the validation failure policy. For upsert workloads with deduplication, set a higher tolerance. Default is 0.0 (strict mode, exact match required). Config Param: STREAMING_OFFSET_TOLERANCE_PERCENTAGE Since Version: 1.2.0

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Flink Sql Configs

These configs control the Hudi Flink SQL source/sink connectors, providing ability to define record keys, pick out the write operation, specify how to merge records, enable/disable asynchronous compaction or choosing query type to read.

Flink Options

Flink jobs using the SQL can be configured through the options in WITH clause. The actual datasource level configs are listed below.

Basic Configs

Config Name	Default	Description
hoodie.database.name	(N/A)	Database name to register to Hive metastore Config Param: DATABASE_NAME
hoodie.datasource.write.recordkey.field	(N/A)	Record key field. Value to be used as the recordKey component of HoodieKey. Actual value will be obtained by invoking .toString() on the field value. Nested fields can be specified using the dot notation eg: a.b.c Config Param: RECORD_KEY_FIELD
hoodie.table.name	(N/A)	Table name to register to Hive metastore Config Param: TABLE_NAME
ordering.fields	(N/A)	Comma separated list of fields used in records merging. When two records have the same key value, we will pick the one with the largest value for the ordering field, determined by Object.compareTo(..). For multiple fields if first key comparison is same, second key comparison is made and so on. Config precombine.field is now deprecated, please use ordering.fields instead. Config Param: ORDERING_FIELDS
path	(N/A)	Base path for the target hoodie table. The path would be created if it does not exist, otherwise a Hoodie table expects to be initialized successfully Config Param: PATH
read.commits.limit	(N/A)	The maximum number of commits allowed to read in each instant check, if it is streaming read, the avg read instants number per-second would be 'read.commits.limit'/'read.streaming.check-interval', by default no limit Config Param: READ_COMMITS_LIMIT
read.end-commit	(N/A)	End commit instant for reading, the commit time format should be 'yyyyMMddHHmmss' Config Param: READ_END_COMMIT
read.start-commit	(N/A)	Start commit instant for reading, the commit time format should be 'yyyyMMddHHmmss', by default reading from the latest instant for streaming read Config Param: READ_START_COMMIT
archive.max_commits	50	Max number of commits to keep before archiving older commits into a sequential log, default 50 Config Param: ARCHIVE_MAX_COMMITS
archive.min_commits	40	Min number of commits to keep before archiving older commits into a sequential log, default 40 Config Param: ARCHIVE_MIN_COMMITS
cdc.enabled	false	When enable, persist the change data if necessary, and can be queried as a CDC query mode Config Param: CDC_ENABLED
cdc.supplemental.logging.mode	DATA_BEFORE_AFTER	Setting 'op_key_only' persists the 'op' and the record key only, setting 'data_before' persists the additional 'before' image, and setting 'data_before_after' persists the additional 'before' and 'after' images. Config Param: SUPPLEMENTAL_LOGGING_MODE
changelog.enabled	false	Whether to keep all the intermediate changes, we try to keep all the changes of a record when enabled: 1). The sink accept the UPDATE_BEFORE message; 2). The source try to emit every changes of a record. The semantics is best effort because the compaction job would finally merge all changes of a record into one. default false to have UPSERT semantics Config Param: CHANGELOG_ENABLED
clean.async.enabled	true	Whether to cleanup the old commits immediately on new commits, enabled by default Config Param: CLEAN_ASYNC_ENABLED
clean.retain_commits	30	Number of commits to retain. So data will be retained for num_of_commits * time_between_commits (scheduled). This also directly translates into how much you can incrementally pull on this table, default 30 Config Param: CLEAN_RETAIN_COMMITS
clustering.async.enabled	false	Async Clustering, default false Config Param: CLUSTERING_ASYNC_ENABLED
clustering.plan.strategy.small.file.limit	600	Files smaller than the size specified here are candidates for clustering, default 600 MB Config Param: CLUSTERING_PLAN_STRATEGY_SMALL_FILE_LIMIT
clustering.plan.strategy.target.file.max.bytes	1073741824	Each group can produce 'N' (CLUSTERING_MAX_GROUP_SIZE/CLUSTERING_TARGET_FILE_SIZE) output file groups, default 1 GB Config Param: CLUSTERING_PLAN_STRATEGY_TARGET_FILE_MAX_BYTES
compaction.async.enabled	true	Async Compaction, enabled by default for MOR Config Param: COMPACTION_ASYNC_ENABLED
compaction.delta_commits	5	Max delta commits needed to trigger compaction, default 5 commits Config Param: COMPACTION_DELTA_COMMITS
hive_sync.enabled	false	Asynchronously sync Hive meta to HMS, default false Config Param: HIVE_SYNC_ENABLED
hive_sync.jdbc_url	jdbc:hive2://localhost:10000	Jdbc URL for hive sync, default 'jdbc:hive2://localhost:10000' Config Param: HIVE_SYNC_JDBC_URL
hive_sync.metastore.uris		Metastore uris for hive sync, default '' Config Param: HIVE_SYNC_METASTORE_URIS
hive_sync.mode	HMS	Mode to choose for Hive ops. Valid values are hms, jdbc and hiveql, default 'hms' Config Param: HIVE_SYNC_MODE
hoodie.datasource.query.type	snapshot	Decides how data files need to be read, in 1) Snapshot mode (obtain latest view, based on row & columnar data); 2) incremental mode (new data since an instantTime); 3) Read Optimized mode (obtain latest view, based on columnar data) .Default: snapshot Config Param: QUERY_TYPE
hoodie.datasource.write.hive_style_partitioning	false	Whether to use Hive style partitioning. If set true, the names of partition folders follow <partition_column_name>=<partition_value> format. By default false (the names of partition folders are only partition values) Config Param: HIVE_STYLE_PARTITIONING
hoodie.datasource.write.partitionpath.field		Partition path field. Value to be used at the partitionPath component of HoodieKey. Actual value obtained by invoking .toString(), default '' Config Param: PARTITION_PATH_FIELD
index.type	FLINK_STATE	Index type of Flink write job, default is using state backed index. Config Param: INDEX_TYPE
lookup.async	false	Whether to enable async lookup join. Config Param: LOOKUP_ASYNC
lookup.async-thread-number	16	The thread number for lookup async. Config Param: LOOKUP_ASYNC_THREAD_NUMBER
lookup.join.cache.ttl	PT1H	The cache TTL (e.g. 10min) for the build table in lookup join. Config Param: LOOKUP_JOIN_CACHE_TTL
lookup.join.cache.type	heap	The storage backend for the lookup join cache. Possible values: 'heap' (default) stores all dimension-table rows in JVM heap memory (may cause OutOfMemoryError for large tables); 'rocksdb' stores rows off-heap in an embedded RocksDB instance on local disk, which avoids OOM at the cost of additional serialization overhead. Config Param: LOOKUP_JOIN_CACHE_TYPE
lookup.join.rocksdb.path	/var/folders/60/wk8qzx310fd32b2dp7mhzvdc0000gn/T//hudi-lookup-rocksdb	Local directory path for storing RocksDB data when 'lookup.join.cache.type' is set to 'rocksdb'. Each task manager will create a unique subdirectory under this path. The directory is cleaned up when the lookup function is closed. Config Param: LOOKUP_JOIN_ROCKSDB_PATH
metadata.compaction.async.enabled	true	Whether to enable async compaction for metadata table,if true, the compaction for metadata table will be performed in the compaction pipeline, default enabled. Config Param: METADATA_COMPACTION_ASYNC_ENABLED
metadata.compaction.delta_commits	10	Max delta commits for metadata table to trigger compaction, default 10 Config Param: METADATA_COMPACTION_DELTA_COMMITS
metadata.enabled	true	Enable the internal metadata table which serves table metadata like level file listings, default enabled Config Param: METADATA_ENABLED
read.source-v2.enabled	false	Whether to use Flink FLIP27 new source to consume data files. Config Param: READ_SOURCE_V2_ENABLED
read.splits.limit	2147483647	The maximum number of splits allowed to read in each instant check, if it is streaming read, the avg read splits number per-second would be 'read.splits.limit'/'read.streaming.check-interval', by default no limit Config Param: READ_SPLITS_LIMIT
read.streaming.enabled	false	Whether to read as streaming source, default false Config Param: READ_AS_STREAMING
read.streaming.skip_insertoverwrite	false	Whether to skip insert overwrite instants to avoid reading base files of insert overwrite operations for streaming read. In streaming scenarios, insert overwrite is usually used to repair data, here you can control the visibility of downstream streaming read. Config Param: READ_STREAMING_SKIP_INSERT_OVERWRITE
table.type	COPY_ON_WRITE	Type of table to write. COPY_ON_WRITE (or) MERGE_ON_READ Config Param: TABLE_TYPE
write.insert.partitioner.class.name		Insert partitioner to use aiming to re-balance records and reducing small file number in the scenario of multi-level partitioning. For example dt/hour/eventIDCurrently support org.apache.hudi.sink.partitioner.GroupedInsertPartitioner Config Param: INSERT_PARTITIONER_CLASS_NAME
write.insert.partitioner.default_parallelism_per_partition	30	The parallelism to use in each partition when using GroupedInsertPartitioner. Config Param: DEFAULT_PARALLELISM_PER_PARTITION
write.operation	upsert	The write operation, that this write should do Config Param: OPERATION
write.parquet.max.file.size	120	Target size for parquet files produced by Hudi write phases. For DFS, this needs to be aligned with the underlying filesystem block size for optimal performance. Config Param: WRITE_PARQUET_MAX_FILE_SIZE

Advanced Configs

Config Name	Default	Description
clustering.tasks	(N/A)	Parallelism of tasks that do actual clustering, default same as the write task parallelism Config Param: CLUSTERING_TASKS
compaction.tasks	(N/A)	Parallelism of tasks that do actual compaction, default same as the write task parallelism Config Param: COMPACTION_TASKS
hive_sync.conf.dir	(N/A)	The hive configuration directory, where the hive-site.xml lies in, the file should be put on the client machine Config Param: HIVE_SYNC_CONF_DIR
hive_sync.serde_properties	(N/A)	Serde properties to hive table, the data format is k1=v1 k2=v2 Config Param: HIVE_SYNC_TABLE_SERDE_PROPERTIES
hive_sync.table_properties	(N/A)	Additional properties to store with table, the data format is k1=v1 k2=v2 Config Param: HIVE_SYNC_TABLE_PROPERTIES
hoodie.bucket.index.partition.expressions	(N/A)	Users can use this parameter to specify expression and the corresponding bucket numbers (separated by commas).Multiple rules are separated by semicolons like hoodie.bucket.index.partition.expressions=expression1,bucket-number1;expression2,bucket-number2 Config Param: BUCKET_INDEX_PARTITION_EXPRESSIONS
hoodie.datasource.write.keygenerator.class	(N/A)	Key generator class, that implements will extract the key out of incoming record Config Param: KEYGEN_CLASS_NAME
hoodie.table.partition_extractor_class	(N/A)	Partition value extractor class helps extract the partition value from partition paths Config Param: PARTITION_VALUE_EXTRACTOR
hoodie.write.record.merge.mode	(N/A)	Using EVENT_TIME_ORDERING to merge records by ordering value,using COMMIT_TIME_ORDERING to merge records by commit time,and using CUSTOM to merge records by the user specified logic. Config Param: RECORD_MERGE_MODE
index.write.tasks	(N/A)	Parallelism of tasks that do the index writing, default is the parallelism of the execution environment Config Param: INDEX_WRITE_TASKS
kafka.offset.trace.dc	(N/A)	Data center for checkpoint offset lookup Config Param: DC
kafka.offset.trace.env	(N/A)	Environment for checkpoint offset lookup Config Param: ENV
kafka.offset.trace.hadoop.user	(N/A)	Hadoop user for checkpoint offset lookup Config Param: HADOOP_USER
kafka.offset.trace.job.name	(N/A)	Flink job name for checkpoint offset lookup Config Param: JOB_NAME
kafka.offset.trace.source.cluster	(N/A)	Source Kafka cluster name Config Param: SOURCE_KAFKA_CLUSTER
kafka.offset.trace.target.cluster	(N/A)	Target Kafka cluster name Config Param: TARGET_KAFKA_CLUSTER
kafka.offset.trace.topic.id	(N/A)	Topic ID for checkpoint offset requests Config Param: TOPIC_ID
kafka.offset.trace.topic.name	(N/A)	Kafka topic name for storing topic metadata along with offsets Config Param: KAFKA_TOPIC_NAME
read.tasks	(N/A)	Parallelism of tasks that do actual read, default is the parallelism of the execution environment Config Param: READ_TASKS
source.avro-schema	(N/A)	Source avro schema string, the parsed schema is used for deserialization Config Param: SOURCE_AVRO_SCHEMA
source.avro-schema.path	(N/A)	Source avro schema file path, the parsed schema is used for deserialization Config Param: SOURCE_AVRO_SCHEMA_PATH
write.bucket_assign.tasks	(N/A)	Parallelism of tasks that do bucket assign, default same as the write task parallelism Config Param: BUCKET_ASSIGN_TASKS
write.buffer.sort.keys	(N/A)	Sort keys concatenated by comma for buffer sort in append write function. Data is sorted within the buffer configured by number of records or buffer size. The order of entire written file is not guaranteed. Config Param: WRITE_BUFFER_SORT_KEYS
write.index_bootstrap.tasks	(N/A)	Parallelism of tasks that do index bootstrap, default same as the write task parallelism Config Param: INDEX_BOOTSTRAP_TASKS
write.partition.format	(N/A)	Partition path format, only valid when 'write.datetime.partitioning' is true, default is: 1) 'yyyyMMddHH' for timestamp(3) WITHOUT TIME ZONE, LONG, FLOAT, DOUBLE, DECIMAL; 2) 'yyyyMMdd' for DATE and INT. Config Param: PARTITION_FORMAT
write.tasks	(N/A)	Parallelism of tasks that do actual write, default is the parallelism of the execution environment Config Param: WRITE_TASKS
clean.policy	KEEP_LATEST_COMMITS	Clean policy to manage the Hudi table. Available option: KEEP_LATEST_COMMITS, KEEP_LATEST_FILE_VERSIONS, KEEP_LATEST_BY_HOURS.Default is KEEP_LATEST_COMMITS. Config Param: CLEAN_POLICY
clean.retain_file_versions	5	Number of file versions to retain. default 5 Config Param: CLEAN_RETAIN_FILE_VERSIONS
clean.retain_hours	24	Number of hours for which commits need to be retained. This config provides a more flexible option ascompared to number of commits retained for cleaning service. Setting this property ensures all the files, but the latest in a file group, corresponding to commits with commit times older than the configured number of hours to be retained are cleaned. Config Param: CLEAN_RETAIN_HOURS
clustering.delta_commits	4	Max delta commits needed to trigger clustering, default 4 commits Config Param: CLUSTERING_DELTA_COMMITS
clustering.plan.partition.filter.mode	NONE	Partition filter mode used in the creation of clustering plan. Available values are - NONE: do not filter table partition and thus the clustering plan will include all partitions that have clustering candidate.RECENT_DAYS: keep a continuous range of partitions, worked together with configs 'clustering.plan.strategy.daybased.lookback.partitions' and 'clustering.plan.strategy.daybased.skipfromlatest.partitions.SELECTED_PARTITIONS: keep partitions that are in the specified range ['clustering.plan.strategy.cluster.begin.partition', 'clustering.plan.strategy.cluster.end.partition'].DAY_ROLLING: clustering partitions on a rolling basis by the hour to avoid clustering all partitions each time, which strategy sorts the partitions asc and chooses the partition of which index is divided by 24 and the remainder is equal to the current hour. Config Param: CLUSTERING_PLAN_PARTITION_FILTER_MODE_NAME
clustering.plan.strategy.class	org.apache.hudi.client.clustering.plan.strategy.FlinkSizeBasedClusteringPlanStrategy	Config to provide a strategy class (subclass of ClusteringPlanStrategy) to create clustering plan i.e select what file groups are being clustered. Default strategy, looks at the last N (determined by clustering.plan.strategy.daybased.lookback.partitions) day based partitions picks the small file slices within those partitions. Config Param: CLUSTERING_PLAN_STRATEGY_CLASS
clustering.plan.strategy.cluster.begin.partition		Begin partition used to filter partition (inclusive) Config Param: CLUSTERING_PLAN_STRATEGY_CLUSTER_BEGIN_PARTITION
clustering.plan.strategy.cluster.end.partition		End partition used to filter partition (inclusive) Config Param: CLUSTERING_PLAN_STRATEGY_CLUSTER_END_PARTITION
clustering.plan.strategy.daybased.lookback.partitions	2	Number of partitions to list to create ClusteringPlan, default is 2 Config Param: CLUSTERING_TARGET_PARTITIONS
clustering.plan.strategy.daybased.skipfromlatest.partitions	0	Number of partitions to skip from latest when choosing partitions to create ClusteringPlan Config Param: CLUSTERING_PLAN_STRATEGY_SKIP_PARTITIONS_FROM_LATEST
clustering.plan.strategy.max.num.groups	30	Maximum number of groups to create as part of ClusteringPlan. Increasing groups will increase parallelism, default is 30 Config Param: CLUSTERING_MAX_NUM_GROUPS
clustering.plan.strategy.partition.regex.pattern		Filter clustering partitions that matched regex pattern Config Param: CLUSTERING_PLAN_STRATEGY_PARTITION_REGEX_PATTERN
clustering.plan.strategy.partition.selected		Partitions to run clustering Config Param: CLUSTERING_PLAN_STRATEGY_PARTITION_SELECTED
clustering.plan.strategy.sort.columns		Columns to sort the data by when clustering Config Param: CLUSTERING_SORT_COLUMNS
clustering.schedule.enabled	false	Schedule the cluster plan, default false Config Param: CLUSTERING_SCHEDULE_ENABLED
compaction.delta_seconds	3600	Max delta seconds time needed to trigger compaction, default 1 hour Config Param: COMPACTION_DELTA_SECONDS
compaction.max_memory	100	Max memory in MB for compaction spillable map, default 100MB Config Param: COMPACTION_MAX_MEMORY
compaction.operation.execute.async.enabled	true	Whether the compaction operation should be executed asynchronously on compact operator, default enabled. Config Param: COMPACTION_OPERATION_EXECUTE_ASYNC_ENABLED
compaction.schedule.enabled	true	Schedule the compaction plan, enabled by default for MOR Config Param: COMPACTION_SCHEDULE_ENABLED
compaction.target_io	512000	Target IO in MB for per compaction (both read and write), default 500 GB Config Param: COMPACTION_TARGET_IO
compaction.timeout.seconds	1200	Max timeout time in seconds for online compaction to rollback, default 20 minutes Config Param: COMPACTION_TIMEOUT_SECONDS
compaction.trigger.strategy	num_commits	Strategy to trigger compaction, options are 'num_commits': trigger compaction when there are at least N delta commits after last completed compaction; 'num_commits_after_last_request': trigger compaction when there are at least N delta commits after last completed/requested compaction; 'time_elapsed': trigger compaction when time elapsed > N seconds since last compaction; 'num_and_time': trigger compaction when both NUM_COMMITS and TIME_ELAPSED are satisfied; 'num_or_time': trigger compaction when NUM_COMMITS or TIME_ELAPSED is satisfied. Default is 'num_commits' Config Param: COMPACTION_TRIGGER_STRATEGY
hive_sync.assume_date_partitioning	false	Assume partitioning is yyyy/mm/dd, default false Config Param: HIVE_SYNC_ASSUME_DATE_PARTITION
hive_sync.auto_create_db	true	Auto create hive database if it does not exists, default true Config Param: HIVE_SYNC_AUTO_CREATE_DB
hive_sync.db	default	Database name for hive sync, default 'default' Config Param: HIVE_SYNC_DB
hive_sync.file_format	PARQUET	File format for hive sync, default 'PARQUET' Config Param: HIVE_SYNC_FILE_FORMAT
hive_sync.ignore_exceptions	false	Ignore exceptions during hive synchronization, default false Config Param: HIVE_SYNC_IGNORE_EXCEPTIONS
hive_sync.partition_extractor_class	org.apache.hudi.hive.MultiPartKeysValueExtractor	Tool to extract the partition value from HDFS path, default 'MultiPartKeysValueExtractor' Config Param: HIVE_SYNC_PARTITION_EXTRACTOR_CLASS_NAME
hive_sync.partition_fields		Partition fields for hive sync, default '' Config Param: HIVE_SYNC_PARTITION_FIELDS
hive_sync.password	hive	Password for hive sync, default 'hive' Config Param: HIVE_SYNC_PASSWORD
hive_sync.skip_ro_suffix	false	Skip the _ro suffix for Read optimized table when registering, default false Config Param: HIVE_SYNC_SKIP_RO_SUFFIX
hive_sync.support_timestamp	true	INT64 with original type TIMESTAMP_MICROS is converted to hive timestamp type. Disabled by default for backward compatibility. Config Param: HIVE_SYNC_SUPPORT_TIMESTAMP
hive_sync.table	unknown	Table name for hive sync, default 'unknown' Config Param: HIVE_SYNC_TABLE
hive_sync.table.strategy	ALL	Hive table synchronization strategy. Available option: RO, RT, ALL. Config Param: HIVE_SYNC_TABLE_STRATEGY
hive_sync.use_jdbc	true	Use JDBC when hive synchronization is enabled, default true Config Param: HIVE_SYNC_USE_JDBC
hive_sync.username	hive	Username for hive sync, default 'hive' Config Param: HIVE_SYNC_USERNAME
hoodie.bucket.index.hash.field		Index key field. Value to be used as hashing to find the bucket ID. Should be a subset of or equal to the recordKey fields. Actual value will be obtained by invoking .toString() on the field value. Nested fields can be specified using the dot notation eg: a.b.c Config Param: INDEX_KEY_FIELD
hoodie.bucket.index.num.buckets	4	Hudi bucket number per partition. Only affected if using Hudi bucket index. Config Param: BUCKET_INDEX_NUM_BUCKETS
hoodie.bucket.index.partition.rule.type	regex	Rule parser for expressions when using partition level bucket index, default regex. Config Param: BUCKET_INDEX_PARTITION_RULE
hoodie.datasource.merge.type	payload_combine	For Snapshot query on merge on read table. Use this key to define how the payloads are merged, in 1) skip_merge: read the base file records plus the log file records without merging; 2) payload_combine: read the base file records first, for each record in base file, checks whether the key is in the log file records (combines the two records with same key for base and log file records), then read the left log file records Config Param: MERGE_TYPE
hoodie.datasource.write.keygenerator.type	SIMPLE	Key generator type, that implements will extract the key out of incoming record. Note This is being actively worked on. Please use hoodie.datasource.write.keygenerator.class instead. Config Param: KEYGEN_TYPE
hoodie.datasource.write.partitionpath.urlencode	false	Whether to encode the partition path url, default false Config Param: URL_ENCODE_PARTITIONING
hoodie.index.bucket.engine	SIMPLE	Type of bucket index engine. Available options: [SIMPLE
hoodie.table.format	native	Table format produced by this writer. Config Param: WRITE_TABLE_FORMAT
hoodie.write.table.version	9	Table version produced by this writer. Config Param: WRITE_TABLE_VERSION
index.bootstrap.enabled	false	Whether to bootstrap the index state from existing hoodie table, default false Config Param: INDEX_BOOTSTRAP_ENABLED
index.bootstrap.rocksdb.path	/var/folders/60/wk8qzx310fd32b2dp7mhzvdc0000gn/T/	Local directory path for RocksDB when bootstrap is enabled for record level index type.Each task manager creates a unique subdirectory under this path. Config Param: INDEX_BOOTSTRAP_ROCKSDB_PATH
index.global.enabled	true	Whether to update index for the old partition path if same key record with different partition path came in, default true Config Param: INDEX_GLOBAL_ENABLED
index.partition.regex	.*	Whether to load partitions in state if partition path matching， default * Config Param: INDEX_PARTITION_REGEX
index.rli.cache.concurrent.partitions.num	2	Expected number of partitions whose partitioned RLI caches are updated concurrently. Used to infer the initial memory size for each partition cache as INDEX_RLI_CACHE_SIZE / concurrency when historical cache usage is unavailable. Config Param: INDEX_RLI_CACHE_CONCURRENT_PARTITIONS_NUM
index.rli.cache.size	256	Maximum memory allocated for the record level index cache per bucket-assign task. The memory size of each individual cache within a checkpoint interval is dynamically calculated based on the average memory size of caches for historical checkpoints. Config Param: INDEX_RLI_CACHE_SIZE
index.rli.lookup.minibatch.size	1000	The maximum number of input records can be buffered for miniBatch during record index lookup. MiniBatch is an optimization to buffer input records to reduce the number of individual index lookups, which can significantly improve performance compared to processing each record individually. Default value is 1000, which is also the minimum value for the minibatch size, when the configured size is less than 1000, the default value will be used. Config Param: INDEX_RLI_LOOKUP_MINIBATCH_SIZE
index.rli.write.buffer.size	100	Maximum memory in MB for the buffer of index record writing operator, when the threshold hits, it flushes the index data to avoid OOM, default 100MB. Config Param: INDEX_RLI_WRITE_BUFFER_SIZE
index.state.ttl	0.0	Index state ttl in days, default stores the index permanently Config Param: INDEX_STATE_TTL
kafka.offset.trace.caller.service.name	ingestion-rt	Caller service name for checkpoint service RPC headers Config Param: CALLER_SERVICE_NAME
kafka.offset.trace.checkpoint.service	athena-job-manager	Checkpoint service name for offset lookup Config Param: ATHENA_SERVICE
kafka.offset.trace.service.name	ingestion-rt	Service name for checkpoint offset lookup Config Param: SERVICE_NAME
kafka.offset.trace.service.tier	DEFAULT	Service tier for checkpoint offset lookup (e.g., DEFAULT, CRITICAL) Config Param: SERVICE_TIER
metadata.compaction.schedule.enabled	true	Schedule the compaction plan for metadata table, enabled by default. Config Param: METADATA_COMPACTION_SCHEDULE_ENABLED
partition.default_name	HIVE_DEFAULT_PARTITION	The default partition name in case the dynamic partition column value is null/empty string Config Param: PARTITION_DEFAULT_NAME
payload.class	org.apache.hudi.common.model.EventTimeAvroPayload	Payload class used. Override this, if you like to roll your own merge logic, when upserting/inserting. This will render any value set for the option in-effective Config Param: PAYLOAD_CLASS_NAME
read.cdc.from.changelog	true	Whether to consume the delta changes only from the cdc changelog files. When CDC is enabled, i). for COW table, the changelog is generated on each file update; ii). for MOR table, the changelog is generated on compaction. By default, always read from the changelog file, once it is disabled, the reader would infer the changes based on the file slice dependencies. Config Param: READ_CDC_FROM_CHANGELOG
read.data.skipping.enabled	false	Enables data-skipping allowing queries to leverage indexes to reduce the search space by skipping over files Config Param: READ_DATA_SKIPPING_ENABLED
read.data.skipping.rli.keys.max.num	8	Record Level index statistics will be read from metadata table (MDT) for data skipping optimization, and currently the index statistics are collected by a single process. This config is used to constrain the maximum number of hoodie keys that can be read from MDT without sacrificing any performance. If the number of hoodie keys from query predicate is greater than the maximum value, the query will fallback to skip the record level index filtering. E.g., given query: SELECT * FROM T WHERE uuid IN (1,2,3,4,5,6,7,8,9), the number of hoodie keys is 9, and the maximum value is 8, so the source will not perform record level index filtering. Config Param: READ_DATA_SKIPPING_RLI_KEYS_MAX_NUM
read.streaming.check-interval	60	Check interval for streaming read of SECOND, default 1 minute Config Param: READ_STREAMING_CHECK_INTERVAL
read.streaming.skip_clustering	true	Whether to skip clustering instants to avoid reading base files of clustering operations for streaming read to improve read performance. Config Param: READ_STREAMING_SKIP_CLUSTERING
read.streaming.skip_compaction	true	Whether to skip compaction instants and avoid reading compacted base files for streaming read to improve read performance. This option can be used to avoid reading duplicates when changelog mode is enabled, it is a solution to keep data integrity Config Param: READ_STREAMING_SKIP_COMPACT
read.utc-timezone	true	Use UTC timezone or local timezone to the conversion between epoch time and LocalDateTime. Hive 0.x/1.x/2.x use local timezone. But Hive 3.x use UTC timezone, by default true Config Param: READ_UTC_TIMEZONE
record.merger.impls	org.apache.hudi.client.model.EventTimeFlinkRecordMerger	List of HoodieMerger implementations constituting Hudi's merging strategy -- based on the engine used. These merger impls will filter by record.merger.strategy. Hudi will pick most efficient implementation to perform merging/combining of the records (during update, reading MOR table, etc) Config Param: RECORD_MERGER_IMPLS
record.merger.strategy	eeb8d96f-b1e4-49fd-bbf8-28ac514178e5	Id of merger strategy. Hudi will pick HoodieRecordMerger implementations in record.merger.impls which has the same merger strategy id Config Param: RECORD_MERGER_STRATEGY_ID
write.batch.size	256.0	Batch buffer size in MB to flush data into the underneath filesystem, default 256MB Config Param: WRITE_BATCH_SIZE
write.buffer.disruptor.ring.size	16384	Ring buffer size for Disruptor (must be power of 2), default 16384 Config Param: WRITE_BUFFER_DISRUPTOR_RING_SIZE
write.buffer.disruptor.wait.strategy	BLOCKING_WAIT	Wait strategy for Disruptor: BLOCKING_WAIT (default), SLEEPING_WAIT, YIELDING_WAIT, BUSY_SPIN_WAIT Config Param: WRITE_BUFFER_DISRUPTOR_WAIT_STRATEGY
write.buffer.memory.type	ON_HEAP	The memory type used for the write buffer. Supported values are ON_HEAP (default) and MANAGED. ON_HEAP uses JVM heap memory, while MANAGED uses Flink managed memory which is accounted for in the task manager's memory budget and helps avoid OOM errors in containerized environments. Config Param: WRITE_BUFFER_MEMORY_TYPE
write.buffer.size	1000	Record count threshold for flushing the sort buffer in append write function. When buffer reaches this count, it is sorted and written. The order of entire written file is not guaranteed. Config Param: WRITE_BUFFER_SIZE
write.buffer.sort.continuous.drain.size	1	Number of records to drain each time the max capacity is reached when using continuous sorting. Default value of 1 provides smooth, incremental draining. Can be increased for batching if needed (e.g., 10, 100). Larger values reduce drain frequency but may cause latency spikes. Config Param: WRITE_BUFFER_SORT_CONTINUOUS_DRAIN_SIZE
write.buffer.sort.enabled	false	Deprecated: use write.buffer.type=DISRUPTOR instead. Whether to enable buffer sort within append write function. The order of entire written file is not guaranteed. Config Param: WRITE_BUFFER_SORT_ENABLED
write.buffer.type	NONE	Buffer type for append write function: NONE (no buffer sort, default), BOUNDED_IN_MEMORY (double buffer with async write), DISRUPTOR (ring buffer with async write, recommended for better throughput), CONTINUOUS_SORT (TreeMap-based continuous sorting with incremental draining) Config Param: WRITE_BUFFER_TYPE
write.bulk_insert.shuffle_input	true	Whether to shuffle the inputs by specific fields for bulk insert tasks, default true Config Param: WRITE_BULK_INSERT_SHUFFLE_INPUT
write.bulk_insert.sort_input	true	Whether to sort the inputs by specific fields for bulk insert tasks, default true Config Param: WRITE_BULK_INSERT_SORT_INPUT
write.bulk_insert.sort_input.by_record_key	false	Whether to sort the inputs by record keys for bulk insert tasks, default false Config Param: WRITE_BULK_INSERT_SORT_INPUT_BY_RECORD_KEY
write.client.id		Unique identifier used to distinguish different writer pipelines for concurrent mode Config Param: WRITE_CLIENT_ID
write.commit.ack.timeout	300000	Timeout limit for a writer task after it finishes a checkpoint and waits for the instant commit success, only for internal use. Config Param: WRITE_COMMIT_ACK_TIMEOUT
write.extra.metadata.enabled	false	If enabled, the checkpoint Id will also be written to hudi metadata. Config Param: WRITE_EXTRA_METADATA_ENABLED
write.fail.fast	false	Flag to indicate whether to fail job immediately when an error record is detected. Currently, this option is only applied to Flink append write functions. Config Param: WRITE_FAIL_FAST
write.ignore.failed	false	Flag to indicate whether to ignore any non exception error (e.g. writestatus error). within a checkpoint batch. By default false. Turning this on, could hide the write status errors while the flink checkpoint moves ahead. So, would recommend users to use this with caution. Config Param: IGNORE_FAILED
write.incremental.job.graph.generation	false	Flag saying whether the incremental job graph generation is enabled. Config Param: WRITE_INCREMENTAL_JOB_GRAPH_GENERATION
write.insert.cluster	false	Whether to merge small files for insert mode, if true, the write throughput will decrease because the read/write of existing small file, only valid for COW table, default false Config Param: INSERT_CLUSTER
write.log.max.size	1024	Maximum size allowed in MB for a log file before it is rolled over to the next version, default 1GB Config Param: WRITE_LOG_MAX_SIZE
write.log_block.size	128	Max log block size in MB for log file, default 128MB Config Param: WRITE_LOG_BLOCK_SIZE
write.memory.segment.page.size	32768	Page size for memory segment used for write buffer. Config Param: WRITE_MEMORY_SEGMENT_PAGE_SIZE
write.merge.max_memory	100	Max memory in MB for merge, default 100MB Config Param: WRITE_MERGE_MAX_MEMORY
write.parquet.block.size	120	Parquet RowGroup size. It's recommended to make this large enough that scan costs can be amortized by packing enough column values into a single row group. Config Param: WRITE_PARQUET_BLOCK_SIZE
write.parquet.page.size	1	Parquet page size. Page is the unit of read within a parquet file. Within a block, pages are compressed separately. Config Param: WRITE_PARQUET_PAGE_SIZE
write.partition.overwrite.mode	STATIC	When INSERT OVERWRITE a partitioned data source table, we currently support 2 modes: static and dynamic. Static mode deletes all the partitions that match the partition specification(e.g. PARTITION(a=1,b)) in the INSERT statement, before overwriting. Dynamic mode doesn't delete partitions ahead, and only overwrite those partitions that have data written into it at runtime. By default we use static mode to keep the same behavior of previous version. Config Param: WRITE_PARTITION_OVERWRITE_MODE
write.precombine	false	Flag to indicate whether to drop duplicates before insert/upsert. By default these cases will accept duplicates, to gain extra performance: 1) insert operation; 2) upsert for MOR table, the MOR table deduplicate on reading Config Param: PRE_COMBINE
write.rate.limit	0	Write record rate limit per second to prevent traffic jitter and improve stability, default 0 (no limit) Config Param: WRITE_RATE_LIMIT
write.retry.interval.ms	2000	Flag to indicate how long (by millisecond) before a retry should issued for failed checkpoint batch. By default 2000 and it will be doubled by every retry Config Param: RETRY_INTERVAL_MS
write.retry.times	3	Flag to indicate how many times streaming job should retry for a failed checkpoint batch. By default 3 Config Param: RETRY_TIMES
write.sort.memory	128	Sort memory in MB, default 128MB Config Param: WRITE_SORT_MEMORY
write.task.max.size	1024.0	Maximum memory in MB for a write task, when the threshold hits, it flushes the max size data bucket to avoid OOM, default 1GB Config Param: WRITE_TASK_MAX_SIZE
write.utc-timezone	true	Use UTC timezone or local timezone to the conversion between epoch time and LocalDateTime. Default value is utc timezone for forward compatibility. Config Param: WRITE_UTC_TIMEZONE

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Write Client Configs

Internally, the Hudi datasource uses a RDD based HoodieWriteClient API to actually perform writes to storage. These configs provide deep control over lower level aspects like file sizing, compression, parallelism, compaction, write schema, cleaning etc. Although Hudi provides sane defaults, from time-time these configs may need to be tweaked to optimize for specific workloads.

Common Configurations

The following set of configurations are common across Hudi.

Basic Configs

Config Name	Default	Description
hoodie.base.path	(N/A)	Base path on lake storage, under which all the table data is stored. Always prefix it explicitly with the storage scheme (e.g hdfs://, s3:// etc). Hudi stores all the main meta-data about commits, savepoints, cleaning audit logs etc in .hoodie directory under this base path directory. Config Param: BASE_PATH

Advanced Configs

Config Name	Default	Description
as.of.instant	(N/A)	The query instant for time travel. Without specified this option, we query the latest snapshot. Config Param: TIMESTAMP_AS_OF
hoodie.memory.compaction.max.size	(N/A)	Maximum amount of memory used in bytes for compaction operations in bytes , before spilling to local storage. Config Param: MAX_MEMORY_FOR_COMPACTION
hoodie.common.diskmap.compression.enabled	true	Turn on compression for BITCASK disk map used by the External Spillable Map Config Param: DISK_MAP_BITCASK_COMPRESSION_ENABLED
hoodie.common.spillable.diskmap.type	BITCASK	When handling input data that cannot be held in memory, to merge with a file on storage, a spillable diskmap is employed. By default, we use a persistent hashmap based loosely on bitcask, that offers O(1) inserts, lookups. Change this to ROCKS_DB to prefer using rocksDB, for handling the spill. Config Param: SPILLABLE_DISK_MAP_TYPE
hoodie.datasource.write.reconcile.schema	false	This config controls how writer's schema will be selected based on the incoming batch's schema as well as existing table's one. When schema reconciliation is DISABLED, incoming batch's schema will be picked as a writer-schema (therefore updating table's schema). When schema reconciliation is ENABLED, writer-schema will be picked such that table's schema (after txn) is either kept the same or extended, meaning that we'll always prefer the schema that either adds new columns or stays the same. This enables us, to always extend the table's schema during evolution and never lose the data (when, for ex, existing column is being dropped in a new batch) Config Param: RECONCILE_SCHEMA
hoodie.file.index.cache.spillable.mem	524288000	Amount of memory to be used in bytes for holding cachedAllInputFileSlices in org.apache.hudi.BaseHoodieTableFileIndex. Config Param: HOODIE_FILE_INDEX_SPILLABLE_MEMORY Since Version: 1.1.0
hoodie.file.index.cache.use.spillable.map	false	Property to enable spillable map for caching input file slices in org.apache.hudi.BaseHoodieTableFileIndex Config Param: HOODIE_FILE_INDEX_USE_SPILLABLE_MAP Since Version: 1.1.0
hoodie.fs.atomic_creation.support		This config is used to specify the file system which supports atomic file creation . atomic means that an operation either succeeds and has an effect or has fails and has no effect; now this feature is used by FileSystemLockProvider to guaranteeing that only one writer can create the lock file at a time. since some FS does not support atomic file creation (eg: S3), we decide the FileSystemLockProvider only support HDFS,local FS and View FS as default. if you want to use FileSystemLockProvider with other FS, you can set this config with the FS scheme, eg: fs1,fs2 Config Param: HOODIE_FS_ATOMIC_CREATION_SUPPORT Since Version: 0.14.0
hoodie.memory.dfs.buffer.max.size	16777216	Property to control the max memory in bytes for dfs input stream buffer size Config Param: MAX_DFS_STREAM_BUFFER_SIZE
hoodie.read.timeline.holes.resolution.policy	FAIL	When doing incremental queries, there could be hollow commits (requested or inflight commits that are not the latest) that are produced by concurrent writers and could lead to potential data loss. This config allows users to have different ways of handling this situation. The valid values are [FAIL, BLOCK, USE_TRANSITION_TIME]: Use FAIL to throw an exception when hollow commit is detected. This is helpful when hollow commits are not expected. Use BLOCK to block processing commits from going beyond the hollow ones. This fits the case where waiting for hollow commits to finish is acceptable. Use USE_TRANSITION_TIME (experimental) to query commits in range by state transition time (completion time), instead of commit time (start time). Using this mode will result in begin.instanttime and end.instanttime using stateTransitionTime instead of the instant's commit time. Config Param: INCREMENTAL_READ_HANDLE_HOLLOW_COMMIT Since Version: 0.14.0
hoodie.schema.on.read.enable	false	Enables support for Schema Evolution feature Config Param: SCHEMA_EVOLUTION_ENABLE
hoodie.write.set.null.for.missing.columns	false	When a nullable column is missing from incoming batch during a write operation, the write operation will fail schema compatibility check. Set this option to true will make the missing column be filled with null values to successfully complete the write operation. Config Param: SET_NULL_FOR_MISSING_COLUMNS Since Version: 0.14.1

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Memory Configurations

Controls memory usage for compaction and merges, performed internally by Hudi.

Advanced Configs

Config Name	Default	Description
hoodie.memory.compaction.max.size	(N/A)	Maximum amount of memory used in bytes for compaction operations in bytes , before spilling to local storage. Config Param: MAX_MEMORY_FOR_COMPACTION
hoodie.memory.spillable.map.path	(N/A)	Default file path for spillable map Config Param: SPILLABLE_MAP_BASE_PATH
hoodie.memory.compaction.fraction	0.6	HoodieCompactedLogScanner reads logblocks, converts records to HoodieRecords and then merges these log blocks and records. At any point, the number of entries in a log block can be less than or equal to the number of entries in the corresponding parquet file. This can lead to OOM in the Scanner. Hence, a spillable map helps alleviate the memory pressure. Use this config to set the max allowable inMemory footprint of the spillable map Config Param: MAX_MEMORY_FRACTION_FOR_COMPACTION
hoodie.memory.dfs.buffer.max.size	16777216	Property to control the max memory in bytes for dfs input stream buffer size Config Param: MAX_DFS_STREAM_BUFFER_SIZE
hoodie.memory.merge.fraction	0.6	This fraction is multiplied with the user memory fraction (1 - spark.memory.fraction) to get a final fraction of heap space to use during merge Config Param: MAX_MEMORY_FRACTION_FOR_MERGE
hoodie.memory.merge.max.size	1073741824	Maximum amount of memory used in bytes for merge operations, before spilling to local storage. Config Param: MAX_MEMORY_FOR_MERGE
hoodie.memory.writestatus.failure.fraction	0.1	Property to control how what fraction of the failed record, exceptions we report back to driver. Default is 10%. If set to 100%, with lot of failures, this can cause memory pressure, cause OOMs and mask actual data errors. Config Param: WRITESTATUS_FAILURE_FRACTION

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Metadata Configs

Configurations used by the Hudi Metadata Table. This table maintains the metadata about a given Hudi table (e.g file listings) to avoid overhead of accessing cloud storage, during queries.

Basic Configs

Config Name	Default	Description
hoodie.index.name	(N/A)	Name of the expression index. This is also used for the partition name in the metadata table. Config Param: SECONDARY_INDEX_NAME Since Version: 1.0.0
hoodie.index.name	(N/A)	Name of the expression index. This is also used for the partition name in the metadata table. Config Param: EXPRESSION_INDEX_NAME Since Version: 1.0.0
hoodie.metadata.index.drop	(N/A)	Drop the specified index. The value should be the name of the index to delete. You can check index names using SHOW INDEXES command. The index name either starts with or matches exactly can be one of the following: files, column_stats, bloom_filters, record_index, expr_index_, secondary_index_, partition_stats, files Config Param: DROP_METADATA_INDEX Since Version: 1.0.1
hoodie.expression.index.type	COLUMN_STATS	Type of the expression index. Default is column_stats if there are no functions and expressions in the command. Valid options could be BITMAP, COLUMN_STATS, LUCENE, etc. If index_type is not provided, and there are functions or expressions in the command then a expression index using column stats will be created. Config Param: EXPRESSION_INDEX_TYPE Since Version: 1.0.0
hoodie.metadata.enable	true	Enable the internal metadata table which serves table metadata like level file listings Config Param: ENABLE Since Version: 0.7.0
hoodie.metadata.index.bloom.filter.enable	false	Enable indexing bloom filters of user data files under metadata table. When enabled, metadata table will have a partition to store the bloom filter index and will be used during the index lookups. Config Param: ENABLE_METADATA_INDEX_BLOOM_FILTER Since Version: 0.11.0
hoodie.metadata.index.column.stats.enable	false	Enable indexing column ranges of user data files under metadata table key lookups. When enabled, metadata table will have a partition to store the column ranges and will be used for pruning files during the index lookups. For the Spark engine, this config defaults to true (enabled), overriding the base default of false. For Flink and Java engines, this remains false by default. Config Param: ENABLE_METADATA_INDEX_COLUMN_STATS Since Version: 0.11.0
hoodie.metadata.index.expression.enable	false	Enable expression index within the metadata table. When this configuration property is enabled (true), the Hudi writer automatically keeps all expression indexes consistent with the data table. When disabled (false), all expression indexes are deleted. Note that individual expression index can only be created through a CREATE INDEX and deleted through a DROP INDEX statement in Spark SQL. Config Param: EXPRESSION_INDEX_ENABLE_PROP Since Version: 1.0.0
hoodie.metadata.index.secondary.enable	true	Enable secondary index within the metadata table. When this configuration property is enabled (true), the Hudi writer automatically keeps all secondary indexes consistent with the data table. When disabled (false), all secondary indexes are deleted. Note that individual secondary index can only be created through a CREATE INDEX and deleted through a DROP INDEX statement in Spark SQL. Config Param: SECONDARY_INDEX_ENABLE_PROP Since Version: 1.0.0

Advanced Configs

Config Name	Default	Description
hoodie.metadata.index.bloom.filter.column.list	(N/A)	Comma-separated list of columns for which bloom filter index will be built. If not set, only record key will be indexed. Config Param: BLOOM_FILTER_INDEX_FOR_COLUMNS Since Version: 0.11.0
hoodie.metadata.index.column.stats.column.list	(N/A)	Comma-separated list of columns for which column stats index will be built. If not set, all columns will be indexed. For nested fields within ARRAY types, use: field.list.element (e.g., items.list.element or items.list.element.price). For nested fields within MAP types, use: field.key_value.key for keys or field.key_value.value for values (e.g., metadata.key_value.key, metadata.key_value.value, or metadata.key_value.value.nested_field). Config Param: COLUMN_STATS_INDEX_FOR_COLUMNS Since Version: 0.11.0
hoodie.metadata.index.column.stats.processing.mode.override	(N/A)	By default Column Stats Index is automatically determining whether it should be read and processed either'in-memory' (w/in executing process) or using Spark (on a cluster), based on some factors like the size of the Index and how many columns are read. This config allows to override this behavior. Config Param: COLUMN_STATS_INDEX_PROCESSING_MODE_OVERRIDE Since Version: 0.12.0
hoodie.metadata.index.expression.column	(N/A)	Column for which expression index will be built. Config Param: EXPRESSION_INDEX_COLUMN Since Version: 1.0.1
hoodie.metadata.index.expression.options	(N/A)	Options for the expression index, e.g. "expr='from_unixtime', format='yyyy-MM-dd'" Config Param: EXPRESSION_INDEX_OPTIONS Since Version: 1.0.1
hoodie.metadata.index.secondary.column	(N/A)	Column for which secondary index will be built. Config Param: SECONDARY_INDEX_COLUMN Since Version: 1.0.1
_hoodie.metadata.ignore.spurious.deletes	true	There are cases when extra files are requested to be deleted from metadata table which are never added before. This config determines how to handle such spurious deletes Config Param: IGNORE_SPURIOUS_DELETES Since Version: 0.10.0
hoodie.file.listing.parallelism	200	Parallelism to use, when listing the table on lake storage. Config Param: FILE_LISTING_PARALLELISM_VALUE Since Version: 0.7.0
hoodie.metadata.auto.delete.partitions	true	When enabled (default), metadata table partitions (indexes) that are disabled in the write config will be automatically deleted. When disabled, users must explicitly drop metadata partitions using the hudi-cli or DROP INDEX command. Disabling automatic deletion is recommended for production datasets with multiple writers to avoid accidental deletion of large metadata indexes due to misconfiguration. Config Param: AUTO_DELETE_PARTITIONS Since Version: 1.2.0
hoodie.metadata.auto.initialize	true	Initializes the metadata table by reading from the file system when the table is first created. Enabled by default. Warning: This should only be disabled when manually constructing the metadata table outside of typical Hudi writer flows. Config Param: AUTO_INITIALIZE Since Version: 0.14.0
hoodie.metadata.bloom.filter.dynamic.max.entries	100000	The threshold for the maximum number of keys to record in a dynamic bloom filter row for the files in the metadata table. Only applies if the filter type (hoodie.metadata.bloom.filter.type ) is BloomFilterTypeCode.DYNAMIC_V0. Config Param: BLOOM_FILTER_DYNAMIC_MAX_ENTRIES Since Version: 1.1.0
hoodie.metadata.bloom.filter.enable	false	Whether to use bloom filter in the files for lookup in the metadata table. Config Param: BLOOM_FILTER_ENABLE Since Version: 1.1.0
hoodie.metadata.bloom.filter.fpp	0.000000001	Expected probability a false positive in a bloom filter for the files in the metadata table. This is used to calculate how many bits should be assigned for the bloom filter and the number of hash functions. This is usually set very low (default: 0.000000001), we like to tradeoff disk space for lower false positives. If the number of entries added to bloom filter exceeds the configured value (hoodie.metadata.bloom.num_entries), then this fpp may not be honored. Config Param: BLOOM_FILTER_FPP Since Version: 1.1.0
hoodie.metadata.bloom.filter.num.entries	10000	This is the number of entries stored in a bloom filter for the files in the metadata table. The rationale for the default: 10000 is chosen to be a good tradeoff between false positive rate and storage size. Warning: Setting this very low generates a lot of false positives and the metadata table reading has to scan a lot more files than it has to and setting this to a very high number increases the size every base file linearly (roughly 4KB for every 50000 entries). This config is also used with DYNAMIC bloom filter which determines the initial size for the bloom. Config Param: BLOOM_FILTER_NUM_ENTRIES Since Version: 1.1.0
hoodie.metadata.bloom.filter.type	DYNAMIC_V0	Bloom filter type for the files in the metadata table org.apache.hudi.common.bloom.BloomFilterTypeCode: Filter type used by Bloom filter. SIMPLE: Bloom filter that is based on the configured size. DYNAMIC_V0(default): Bloom filter that is auto sized based on number of keys. Config Param: BLOOM_FILTER_TYPE Since Version: 1.1.0
hoodie.metadata.compact.max.delta.commits	10	Controls how often the metadata table is compacted. Config Param: COMPACT_NUM_DELTA_COMMITS Since Version: 0.7.0
hoodie.metadata.compact.max.delta.seconds	7200	Number of elapsed seconds after the last compaction, before scheduling a new one (for metadata table). This config takes effect only for the compaction triggering strategy based on the elapsed time, i.e., TIME_ELAPSED, NUM_AND_TIME, and NUM_OR_TIME. Config Param: COMPACT_TIME_DELTA_SECONDS Since Version: 1.2.0
hoodie.metadata.compact.trigger.strategy	NUM_COMMITS	Controls how compaction scheduling is triggered for metadata table,by time or num delta commits or combination of both. Config Param: COMPACT_TRIGGER_STRATEGY Since Version: 1.2.0
hoodie.metadata.derive.from.datatable.clean.policy	true	This config determines whether the cleaner policy should use data table's cleaner policy. Config Param: DERIVE_FROM_DATA_TABLE_CLEAN_POLICY Since Version: 1.2.0
hoodie.metadata.dir.filter.regex		Directories matching this regex, will be filtered out when initializing metadata table from lake storage for the first time. Config Param: DIR_FILTER_REGEX Since Version: 0.7.0
hoodie.metadata.file.cache.max.size.mb	50	Max size in MB below which metadata file (HFile) will be downloaded and cached entirely for the HFileReader. Config Param: METADATA_FILE_CACHE_MAX_SIZE_MB Since Version: 1.1.0
hoodie.metadata.global.record.level.index.enable	false	Create the HUDI Record Index within the Metadata Table Config Param: GLOBAL_RECORD_LEVEL_INDEX_ENABLE_PROP Since Version: 0.14.0
hoodie.metadata.global.record.level.index.max.filegroup.count	10000	Maximum number of file groups to use for Record Index. Config Param: GLOBAL_RECORD_LEVEL_INDEX_MAX_FILE_GROUP_COUNT_PROP Since Version: 0.14.0
hoodie.metadata.global.record.level.index.min.filegroup.count	10	Minimum number of file groups to use for Record Index. Config Param: GLOBAL_RECORD_LEVEL_INDEX_MIN_FILE_GROUP_COUNT_PROP Since Version: 0.14.0
hoodie.metadata.index.async	false	Enable asynchronous indexing of metadata table. Config Param: ASYNC_INDEX_ENABLE Since Version: 0.11.0
hoodie.metadata.index.bloom.filter.file.group.count	4	Metadata bloom filter index partition file group count. This controls the size of the base and log files and read parallelism in the bloom filter index partition. The recommendation is to size the file group count such that the base files are under 1GB. Config Param: METADATA_INDEX_BLOOM_FILTER_FILE_GROUP_COUNT Since Version: 0.11.0
hoodie.metadata.index.bloom.filter.parallelism	200	Parallelism to use for generating bloom filter index in metadata table. Config Param: BLOOM_FILTER_INDEX_PARALLELISM Since Version: 0.11.0
hoodie.metadata.index.check.timeout.seconds	900	After the async indexer has finished indexing upto the base instant, it will ensure that all inflight writers reliably write index updates as well. If this timeout expires, then the indexer will abort itself safely. Config Param: METADATA_INDEX_CHECK_TIMEOUT_SECONDS Since Version: 0.11.0
hoodie.metadata.index.column.stats.file.group.count	2	Metadata column stats partition file group count. This controls the size of the base and log files and read parallelism in the column stats index partition. The recommendation is to size the file group count such that the base files are under 1GB. Config Param: METADATA_INDEX_COLUMN_STATS_FILE_GROUP_COUNT Since Version: 0.11.0
hoodie.metadata.index.column.stats.inMemory.projection.threshold	100000	When reading Column Stats Index, if the size of the expected resulting projection is below the in-memory threshold (counted by the # of rows), it will be attempted to be loaded "in-memory" (ie not using the execution engine like Spark, Flink, etc). If the value is above the threshold execution engine will be used to compose the projection. Config Param: COLUMN_STATS_INDEX_IN_MEMORY_PROJECTION_THRESHOLD Since Version: 0.12.0
hoodie.metadata.index.column.stats.max.columns.to.index	32	Maximum number of columns to generate column stats for. If the config hoodie.metadata.index.column.stats.column.list is set, this config will be ignored. If the config hoodie.metadata.index.column.stats.column.list is not set, the column stats of the first n columns (n defined by this config) in the table schema are generated. Config Param: COLUMN_STATS_INDEX_MAX_COLUMNS Since Version: 1.0.0
hoodie.metadata.index.column.stats.parallelism	200	Parallelism to use, when generating column stats index. Config Param: COLUMN_STATS_INDEX_PARALLELISM Since Version: 0.11.0
hoodie.metadata.index.expression.file.group.count	2	Metadata expression index partition file group count. Config Param: EXPRESSION_INDEX_FILE_GROUP_COUNT Since Version: 1.0.0
hoodie.metadata.index.expression.parallelism	200	Parallelism to use, when generating expression index. Config Param: EXPRESSION_INDEX_PARALLELISM Since Version: 1.0.0
hoodie.metadata.index.partition.stats.file.group.count	1	Metadata partition stats file group count. This controls the size of the base and log files and read parallelism in the partition stats index. Config Param: METADATA_INDEX_PARTITION_STATS_FILE_GROUP_COUNT Since Version: 1.0.0
hoodie.metadata.index.partition.stats.parallelism	200	Parallelism to use, when generating partition stats index. Config Param: PARTITION_STATS_INDEX_PARALLELISM Since Version: 1.0.0
hoodie.metadata.index.secondary.parallelism	200	Parallelism to use, when generating secondary index. Config Param: SECONDARY_INDEX_PARALLELISM Since Version: 1.0.0
hoodie.metadata.log.compaction.blocks.threshold	5	Controls the criteria to log compacted files groups in metadata table. Config Param: LOG_COMPACT_BLOCKS_THRESHOLD Since Version: 0.14.0
hoodie.metadata.log.compaction.enable	false	This configs enables logcompaction for the metadata table. Config Param: ENABLE_LOG_COMPACTION_ON_METADATA_TABLE Since Version: 0.14.0
hoodie.metadata.max.deltacommits.when_pending	1000	When there is a pending instant in data table, this config limits the allowed number of deltacommits in metadata table to prevent the metadata table's timeline from growing unboundedly as compaction won't be triggered due to the pending data table instant. Config Param: METADATA_MAX_NUM_DELTACOMMITS_WHEN_PENDING Since Version: 0.14.0
hoodie.metadata.max.init.parallelism	100000	Maximum parallelism to use when initializing Record Index. Config Param: RECORD_INDEX_MAX_PARALLELISM Since Version: 0.14.0
hoodie.metadata.max.logfile.size	2147483648	Maximum size in bytes of a single log file. Larger log files can contain larger log blocks thereby reducing the number of blocks to search for keys Config Param: MAX_LOG_FILE_SIZE_BYTES_PROP Since Version: 0.14.0
hoodie.metadata.max.reader.buffer.size	10485760	Max memory to use for the reader buffer while merging log blocks Config Param: MAX_READER_BUFFER_SIZE_PROP Since Version: 0.14.0
hoodie.metadata.max.reader.memory	1073741824	Max memory to use for the reader to read from metadata Config Param: MAX_READER_MEMORY_PROP Since Version: 0.14.0
hoodie.metadata.metrics.enable	false	Enable publishing of metrics around metadata table. Config Param: METRICS_ENABLE Since Version: 0.7.0
hoodie.metadata.optimized.log.blocks.scan.enable	false	Optimized log blocks scanner that addresses all the multi-writer use-cases while appending to log files. It also differentiates original blocks written by ingestion writers and compacted blocks written by log compaction. Config Param: ENABLE_OPTIMIZED_LOG_BLOCKS_SCAN Since Version: 0.13.0
hoodie.metadata.range.repartition.random.seed	42	Random seed used for sampling during range-based repartitioning. This ensures reproducible results across runs. Config Param: RANGE_REPARTITION_RANDOM_SEED Since Version: 1.1.0
hoodie.metadata.range.repartition.sampling.fraction	0.01	Sampling fraction used for range-based repartitioning during metadata table lookups. This controls the accuracy vs performance trade-off for key distribution sampling. Config Param: RANGE_REPARTITION_SAMPLING_FRACTION Since Version: 1.1.0
hoodie.metadata.range.repartition.target.records.per.partition	10000	Target number of records per partition during range-based repartitioning. This helps control the size of each partition for optimal processing. Config Param: RANGE_REPARTITION_TARGET_RECORDS_PER_PARTITION Since Version: 1.1.0
hoodie.metadata.record.index.enable.validation.on.initialization	false	Enable validation of record index after initialization by comparing the expected record count with the actual record count stored in the metadata table. This validation runs in a distributed manner using the compute engine. Disabled by default as it adds overhead to the initialization process. Config Param: RECORD_INDEX_INITIALIZATION_VALIDATION_ENABLE Since Version: 1.1.0
hoodie.metadata.record.index.growth.factor	2.0	The current number of records are multiplied by this number when estimating the number of file groups to create automatically. This helps account for growth in the number of records in the dataset. Config Param: RECORD_INDEX_GROWTH_FACTOR_PROP Since Version: 0.14.0
hoodie.metadata.record.index.max.filegroup.size	1073741824	Maximum size in bytes of a single file group. Large file group takes longer to compact. Config Param: RECORD_INDEX_MAX_FILE_GROUP_SIZE_BYTES_PROP Since Version: 0.14.0
hoodie.metadata.record.level.index.defer.init	false	When enabled, defers RLI initialization to 2nd commit for a fresh table. This should help with determining the file group count dynamically for Record Index (global and non-global RLI) Config Param: DEFER_RLI_INIT_FOR_FRESH_TABLE Since Version: 1.2.0
hoodie.metadata.record.level.index.enable	false	Create the HUDI Record Index within the Metadata Table for a partitioned dataset where a pair of partition path and record key is unique across the entire table Config Param: RECORD_LEVEL_INDEX_ENABLE_PROP Since Version: 1.1.0
hoodie.metadata.record.level.index.max.filegroup.count	10	Maximum number of file groups to use for Partitioned Record Index. Config Param: RECORD_LEVEL_INDEX_MAX_FILE_GROUP_COUNT_PROP Since Version: 1.1.0
hoodie.metadata.record.level.index.min.filegroup.count	1	Minimum number of file groups to use for Partitioned Record Index. Config Param: RECORD_LEVEL_INDEX_MIN_FILE_GROUP_COUNT_PROP Since Version: 1.1.0
hoodie.metadata.record.preparation.parallelism	0	when set to positive number, metadata table record preparation stages honor the set value for number of tasks. If not, number of write status's from data table writes will be used for metadata table record preparation Config Param: RECORD_PREPARATION_PARALLELISM Since Version: 1.1.0
hoodie.metadata.repartition.default.partitions	200	Default number of partitions to use when repartitioning is needed. This provides a reasonable level of parallelism for metadata table operations. Config Param: REPARTITION_DEFAULT_PARTITIONS Since Version: 1.1.0
hoodie.metadata.repartition.min.partitions.threshold	100	Minimum number of partitions threshold below which repartitioning is triggered. When the number of partitions is below this threshold, data will be repartitioned for better parallelism. Config Param: REPARTITION_MIN_PARTITIONS_THRESHOLD Since Version: 1.1.0
hoodie.metadata.spillable.map.path		Path on local storage to use, when keys read from metadata are held in a spillable map. Config Param: SPILLABLE_MAP_DIR_PROP Since Version: 0.14.0
hoodie.metadata.streaming.write.datatable.write.statuses.coalesce.divisor	5000	When streaming writes to metadata table is enabled via hoodie.metadata.streaming.write.enabled, the data table write statuses are unioned with metadata table write statuses before triggering the entire write dag. The data table write statuses will be coalesce down to the number of write statuses divided by the specified divisor to avoid triggering thousands of no-op tasks for the data table writes which have their status cached. Config Param: STREAMING_WRITE_DATATABLE_WRITE_STATUSES_COALESCE_DIVISOR Since Version: 1.1.0
hoodie.metadata.streaming.write.enabled	false	Whether to enable streaming writes to metadata table or not. With streaming writes, we execute writes to both data table and metadata table in streaming manner rather than two disjoint writes. By default streaming writes to metadata table is enabled for SPARK engine for incremental operations and disabled for all other cases. Config Param: STREAMING_WRITE_ENABLED Since Version: 1.1.0
hoodie.metadata.table.service.manager.actions		Comma-separated list of table service actions on the metadata table that should be delegated to the table service manager. Currently supported actions are: compaction, logcompaction. Config Param: TABLE_SERVICE_MANAGER_ACTIONS
hoodie.metadata.table.service.manager.enabled	false	If true, delegate specified table service actions on the metadata table to the table service manager instead of executing them inline. This prevents the current writer from executing compaction/logcompaction on the metadata table, allowing a separate async pipeline to handle them. Config Param: TABLE_SERVICE_MANAGER_ENABLED
hoodie.metadata.write.concurrency.mode	SINGLE_WRITER	Change this to OPTIMISTIC_CONCURRENCY_CONTROL when MDT operations are being performed from an external concurrent writer (such as a table service platform) so that appropriate locks are taken. Config Param: METADATA_WRITE_CONCURRENCY_MODE
hoodie.metadata.write.fail.on.table.service.failures	true	when set to true, it fails the job on metadata table's table services operation failure Config Param: FAIL_ON_TABLE_SERVICE_FAILURES Since Version: 1.2.0

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Metaserver Configs

Configurations used by the Hudi Metaserver.

Advanced Configs

Config Name	Default	Description
hoodie.database.name	(N/A)	Database name. If different databases have the same table name during incremental query, we can set it to limit the table name under a specific database Config Param: DATABASE_NAME Since Version: 0.13.0
hoodie.table.name	(N/A)	Table name that will be used for registering with Hive. Needs to be same across runs. Config Param: TABLE_NAME Since Version: 0.13.0
hoodie.metaserver.connect.retries	3	Number of retries while opening a connection to metaserver Config Param: METASERVER_CONNECTION_RETRIES Since Version: 0.13.0
hoodie.metaserver.connect.retry.delay	1	Number of seconds for the client to wait between consecutive connection attempts Config Param: METASERVER_CONNECTION_RETRY_DELAY Since Version: 0.13.0
hoodie.metaserver.enabled	false	Enable Hudi metaserver for storing Hudi tables' metadata. Config Param: METASERVER_ENABLE Since Version: 0.13.0
hoodie.metaserver.uris	thrift://localhost:9090	Metaserver server uris Config Param: METASERVER_URLS Since Version: 0.13.0

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Storage Configs

Configurations that control aspects around writing, sizing, reading base and log files.

Basic Configs

Config Name	Default	Description
hoodie.hfile.writes.allow.duplicates	false	When bootstrapping RI, if the main dataset contains duplicates then it will fail the bootstrap job. TO avoid the failure and bootstrap the RI with dups this config can be set to true. One thing to note is that, there is no deterministic way to specify which among these records will be ingested into RI. Config Param: HFILE_WRITER_TO_ALLOW_DUPLICATES
hoodie.parquet.compression.codec	gzip	Compression Codec for parquet files Config Param: PARQUET_COMPRESSION_CODEC_NAME
hoodie.parquet.max.file.size	125829120	Target size in bytes for parquet files produced by Hudi write phases. For DFS, this needs to be aligned with the underlying filesystem block size for optimal performance. Config Param: PARQUET_MAX_FILE_SIZE

Advanced Configs

Config Name	Default	Description
hoodie.logfile.data.block.format	(N/A)	Format of the data block within delta logs. Following formats are currently supported "avro", "hfile", "parquet" Config Param: LOGFILE_DATA_BLOCK_FORMAT
hoodie.parquet.write.config.injector.class	(N/A)	Config injector implementation for HoodieParquetConfigInjector class, for users willing to inject some custom configs to parquet writers Config Param: HOODIE_PARQUET_CONFIG_INJECTOR_CLASS Since Version: 1.2.0
hoodie.parquet.writelegacyformat.enabled	(N/A)	Sets spark.sql.parquet.writeLegacyFormat. If true, data will be written in a way of Spark 1.4 and earlier. For example, decimal values will be written in Parquet's fixed-length byte array format which other systems such as Apache Hive and Apache Impala use. If false, the newer format in Parquet will be used. For example, decimals will be written in int-based format. Config Param: PARQUET_WRITE_LEGACY_FORMAT_ENABLED
hoodie.avro.write.support.class	org.apache.hudi.avro.HoodieAvroWriteSupport	Provided write support class should extend HoodieAvroWriteSupport class and it is loaded at runtime. This is only required when trying to override the existing write context. Config Param: HOODIE_AVRO_WRITE_SUPPORT_CLASS Since Version: 0.14.0
hoodie.bloom.index.filter.dynamic.max.entries	100000	The threshold for the maximum number of keys to record in a dynamic Bloom filter row. Only applies if filter type is BloomFilterTypeCode.DYNAMIC_V0. Config Param: BLOOM_FILTER_DYNAMIC_MAX_ENTRIES
hoodie.bloom.index.filter.type	DYNAMIC_V0	org.apache.hudi.common.bloom.BloomFilterTypeCode: Filter type used by Bloom filter. SIMPLE: Bloom filter that is based on the configured size. DYNAMIC_V0(default): Bloom filter that is auto sized based on number of keys. Config Param: BLOOM_FILTER_TYPE
hoodie.hfile.block.size	1048576	Lower values increase the size in bytes of metadata tracked within HFile, but can offer potentially faster lookup times. Config Param: HFILE_BLOCK_SIZE
hoodie.hfile.compression.algorithm	GZ	Compression codec to use for hfile base files. Config Param: HFILE_COMPRESSION_ALGORITHM_NAME
hoodie.hfile.max.file.size	125829120	Target file size in bytes for HFile base files. Config Param: HFILE_MAX_FILE_SIZE
hoodie.index.bloom.fpp	0.000000001	Only applies if index type is BLOOM. Error rate allowed given the number of entries. This is used to calculate how many bits should be assigned for the bloom filter and the number of hash functions. This is usually set very low (default: 0.000000001), we like to tradeoff disk space for lower false positives. If the number of entries added to bloom filter exceeds the configured value (hoodie.index.bloom.num_entries), then this fpp may not be honored. Config Param: BLOOM_FILTER_FPP_VALUE
hoodie.index.bloom.num_entries	60000	Only applies if index type is BLOOM. This is the number of entries to be stored in the bloom filter. The rationale for the default: Assume the maxParquetFileSize is 128MB and averageRecordSize is 1kb and hence we approx a total of 130K records in a file. The default (60000) is roughly half of this approximation. Warning: Setting this very low, will generate a lot of false positives and index lookup will have to scan a lot more files than it has to and setting this to a very high number will increase the size every base file linearly (roughly 4KB for every 50000 entries). This config is also used with DYNAMIC bloom filter which determines the initial size for the bloom. Config Param: BLOOM_FILTER_NUM_ENTRIES_VALUE
hoodie.io.factory.class	org.apache.hudi.io.storage.hadoop.HoodieHadoopIOFactory	The fully-qualified class name of the factory class to return readers and writers of files used by Hudi. The provided class should implement org.apache.hudi.io.storage.HoodieIOFactory. Config Param: HOODIE_IO_FACTORY_CLASS Since Version: 0.15.0
hoodie.lance.max.file.size	125829120	Target file size in bytes for Lance base files. Config Param: LANCE_MAX_FILE_SIZE
hoodie.lance.read.allocator.size.bytes	268435456	Maximum size in bytes of the Arrow child allocator used by the Lance reader for the per-read data buffers. Raise this for files with very wide rows or large blob columns. Config Param: LANCE_READ_ALLOCATOR_SIZE_BYTES
hoodie.lance.read.metadata.allocator.size.bytes	8388608	Maximum size in bytes of the Arrow child allocator used by the Lance reader for footer/metadata operations (schema, bloom filter). Independent of the data allocator since metadata allocations are small and short-lived. Config Param: LANCE_READ_METADATA_ALLOCATOR_SIZE_BYTES
hoodie.lance.write.allocator.size.bytes	268435456	Maximum size in bytes of the Arrow child allocator used by the Lance writer for buffering in-flight batch data. Must be large enough that the Arrow BaseLargeVariableWidthVector's power-of-2 doubling growth never requests a buffer exceeding this cap, otherwise writes fail with OutOfMemoryException. The default of 256MB clears the 128MB doubling step with headroom; pair with hoodie.lance.write.flush.byte.watermark to bound in-flight memory regardless of blob size or row count. Config Param: LANCE_WRITE_ALLOCATOR_SIZE_BYTES
hoodie.lance.write.flush.byte.watermark	100663296	Byte-size watermark on the Lance writer's in-flight Arrow buffers; the writer flushes the current batch when the sum of FieldVector buffer sizes reaches this value, in addition to the row-count batch threshold. Keeps the data buffer below the next power-of-2 doubling step so reallocation cannot exceed hoodie.lance.write.allocator.size.bytes. Default is roughly 3/8 of the allocator size, leaving room for offset/validity buffers. Config Param: LANCE_WRITE_FLUSH_BYTE_WATERMARK
hoodie.logfile.data.block.max.size	268435456	LogFile Data block max size in bytes. This is the maximum size allowed for a single data block to be appended to a log file. This helps to make sure the data appended to the log file is broken up into sizable blocks to prevent from OOM errors. This size should be greater than the JVM memory. Config Param: LOGFILE_DATA_BLOCK_MAX_SIZE
hoodie.logfile.max.size	1073741824	LogFile max size in bytes. This is the maximum size allowed for a log file before it is rolled over to the next version. Config Param: LOGFILE_MAX_SIZE
hoodie.logfile.to.parquet.compression.ratio	0.35	Expected additional compression as records move from log files to parquet. Used for merge_on_read table to send inserts into log files & control the size of compacted parquet file.When encoding log blocks in parquet format, increase this value for a more accurate estimation Config Param: LOGFILE_TO_PARQUET_COMPRESSION_RATIO_FRACTION
hoodie.orc.block.size	125829120	ORC block size, recommended to be aligned with the target file size. Config Param: ORC_BLOCK_SIZE
hoodie.orc.compression.codec	ZLIB	Compression codec to use for ORC base files. Config Param: ORC_COMPRESSION_CODEC_NAME
hoodie.orc.max.file.size	125829120	Target file size in bytes for ORC base files. Config Param: ORC_FILE_MAX_SIZE
hoodie.orc.stripe.size	67108864	Size of the memory buffer in bytes for writing Config Param: ORC_STRIPE_SIZE
hoodie.parquet.block.size	125829120	Parquet RowGroup size in bytes. It's recommended to make this large enough that scan costs can be amortized by packing enough column values into a single row group. Config Param: PARQUET_BLOCK_SIZE
hoodie.parquet.bloom.filter.enabled	true	Control whether to write bloom filter or not. Default true. We can set to false in non bloom index cases for CPU resource saving. Config Param: PARQUET_WITH_BLOOM_FILTER_ENABLED Since Version: 0.15.0
hoodie.parquet.compression.ratio	0.1	Expected compression of parquet data used by Hudi, when it tries to size new parquet files. Increase this value, if bulk_insert is producing smaller than expected sized files Config Param: PARQUET_COMPRESSION_RATIO_FRACTION
hoodie.parquet.dictionary.enabled	true	Whether to use dictionary encoding Config Param: PARQUET_DICTIONARY_ENABLED
hoodie.parquet.field_id.write.enabled	true	Would only be effective with Spark 3.3+. Sets spark.sql.parquet.fieldId.write.enabled. If enabled, Spark will write out parquet native field ids that are stored inside StructField's metadata as parquet.field.id to parquet files. Config Param: PARQUET_FIELD_ID_WRITE_ENABLED Since Version: 0.12.0
hoodie.parquet.flink.rowdata.write.support.class	org.apache.hudi.io.storage.row.HoodieRowDataParquetWriteSupport	Provided write support class should extend HoodieRowDataParquetWriteSupport class and it is loaded at runtime. This is only required when trying to override the existing write support. Config Param: HOODIE_PARQUET_FLINK_ROW_DATA_WRITE_SUPPORT_CLASS Since Version: 1.1.0
hoodie.parquet.outputtimestamptype	TIMESTAMP_MICROS	Sets spark.sql.parquet.outputTimestampType. Parquet timestamp type to use when Spark writes data to Parquet files. Config Param: PARQUET_OUTPUT_TIMESTAMP_TYPE
hoodie.parquet.page.size	1048576	Parquet page size in bytes. Page is the unit of read within a parquet file. Within a block, pages are compressed separately. Config Param: PARQUET_PAGE_SIZE
hoodie.parquet.spark.row.write.support.class	org.apache.hudi.io.storage.row.HoodieRowParquetWriteSupport	Provided write support class should extend HoodieRowParquetWriteSupport class and it is loaded at runtime. This is only required when trying to override the existing write context when hoodie.datasource.write.row.writer.enable=true. Config Param: HOODIE_PARQUET_SPARK_ROW_WRITE_SUPPORT_CLASS Since Version: 0.15.0
hoodie.parquet.write.utc-timezone.enabled	true	Use UTC timezone or local timezone to the conversion between epoch time and LocalDateTime. Default value is utc timezone for forward compatibility. Config Param: WRITE_UTC_TIMEZONE Since Version: 1.1.0
hoodie.storage.class	org.apache.hudi.storage.hadoop.HoodieHadoopStorage	The fully-qualified class name of the HoodieStorage implementation class to instantiate. The provided class should implement org.apache.hudi.storage.HoodieStorage Config Param: HOODIE_STORAGE_CLASS Since Version: 0.15.0

---

Consistency Guard Configurations

The consistency guard related config options, to help talk to eventually consistent object storage.(Tip: S3 is NOT eventually consistent anymore!)

Advanced Configs

Config Name	Default	Description
_hoodie.optimistic.consistency.guard.enable	false	Enable consistency guard, which optimistically assumes consistency is achieved after a certain time period. Config Param: OPTIMISTIC_CONSISTENCY_GUARD_ENABLE Since Version: 0.6.0
hoodie.consistency.check.enabled	false	Enabled to handle S3 eventual consistency issue. This property is no longer required since S3 is now strongly consistent. Will be removed in the future releases. Config Param: ENABLE Since Version: 0.5.0 Deprecated since: 0.7.0
hoodie.consistency.check.initial_interval_ms	400	Amount of time (in ms) to wait, before checking for consistency after an operation on storage. Config Param: INITIAL_CHECK_INTERVAL_MS Since Version: 0.5.0 Deprecated since: 0.7.0
hoodie.consistency.check.max_checks	6	Maximum number of consistency checks to perform, with exponential backoff. Config Param: MAX_CHECKS Since Version: 0.5.0 Deprecated since: 0.7.0
hoodie.consistency.check.max_interval_ms	20000	Maximum amount of time (in ms), to wait for consistency checking. Config Param: MAX_CHECK_INTERVAL_MS Since Version: 0.5.0 Deprecated since: 0.7.0
hoodie.optimistic.consistency.guard.sleep_time_ms	500	Amount of time (in ms), to wait after which we assume storage is consistent. Config Param: OPTIMISTIC_CONSISTENCY_GUARD_SLEEP_TIME_MS Since Version: 0.6.0

---

FileSystem Guard Configurations

The filesystem retry related config options, to help deal with runtime exception like list/get/put/delete performance issues.

Advanced Configs

Config Name	Default	Description
hoodie.filesystem.operation.retry.enable	false	Enabled to handle list/get/delete etc file system performance issue. Config Param: FILESYSTEM_RETRY_ENABLE Since Version: 0.11.0
hoodie.filesystem.operation.retry.exceptions		The class name of the Exception that needs to be retried, separated by commas. Default is empty which means retry all the IOException and RuntimeException from FileSystem Config Param: RETRY_EXCEPTIONS Since Version: 0.11.0
hoodie.filesystem.operation.retry.initial_interval_ms	100	Amount of time (in ms) to wait, before retry to do operations on storage. Config Param: INITIAL_RETRY_INTERVAL_MS Since Version: 0.11.0
hoodie.filesystem.operation.retry.max_interval_ms	2000	Maximum amount of time (in ms), to wait for next retry. Config Param: MAX_RETRY_INTERVAL_MS Since Version: 0.11.0
hoodie.filesystem.operation.retry.max_numbers	4	Maximum number of retry actions to perform, with exponential backoff. Config Param: MAX_RETRY_NUMBERS Since Version: 0.11.0

---

File System View Storage Configurations

Configurations that control how file metadata is stored by Hudi, for transaction processing and queries.

Advanced Configs

Config Name	Default	Description
hoodie.filesystem.remote.backup.view.enable	true	Config to control whether backup needs to be configured if clients were not able to reach timeline service. Config Param: REMOTE_BACKUP_VIEW_ENABLE
hoodie.filesystem.view.incr.timeline.sync.enable	false	Controls whether or not, the file system view is incrementally updated as new actions are performed on the timeline. Config Param: INCREMENTAL_TIMELINE_SYNC_ENABLE
hoodie.filesystem.view.remote.host	localhost	We expect this to be rarely hand configured. Config Param: REMOTE_HOST_NAME
hoodie.filesystem.view.remote.port	26754	Port to serve file system view queries, when remote. We expect this to be rarely hand configured. Config Param: REMOTE_PORT_NUM
hoodie.filesystem.view.remote.retry.enable	false	Whether to enable API request retry for remote file system view. Config Param: REMOTE_RETRY_ENABLE Since Version: 0.12.1
hoodie.filesystem.view.remote.retry.exceptions		The class name of the Exception that needs to be retried, separated by commas. Default is empty which means retry all the IOException and RuntimeException from Remote Request. Config Param: RETRY_EXCEPTIONS Since Version: 0.12.1
hoodie.filesystem.view.remote.retry.initial_interval_ms	100	Amount of time (in ms) to wait, before retry to do operations on storage. Config Param: REMOTE_INITIAL_RETRY_INTERVAL_MS Since Version: 0.12.1
hoodie.filesystem.view.remote.retry.max_interval_ms	2000	Maximum amount of time (in ms), to wait for next retry. Config Param: REMOTE_MAX_RETRY_INTERVAL_MS Since Version: 0.12.1
hoodie.filesystem.view.remote.retry.max_numbers	3	Maximum number of retry for API requests against a remote file system view. e.g timeline server. Config Param: REMOTE_MAX_RETRY_NUMBERS Since Version: 0.12.1
hoodie.filesystem.view.remote.timeout.secs	300	Timeout in seconds, to wait for API requests against a remote file system view. e.g timeline server. Config Param: REMOTE_TIMEOUT_SECS
hoodie.filesystem.view.rocksdb.base.path	/tmp/hoodie_timeline_rocksdb	Path on local storage to use, when storing file system view in embedded kv store/rocksdb. Config Param: ROCKSDB_BASE_PATH
hoodie.filesystem.view.secondary.type	MEMORY	Specifies the secondary form of storage for file system view, if the primary (e.g timeline server) is unavailable. Config Param: SECONDARY_VIEW_TYPE
hoodie.filesystem.view.spillable.bootstrap.base.file.mem.fraction	0.05	Fraction of the file system view memory, to be used for holding mapping to bootstrap base files. Config Param: BOOTSTRAP_BASE_FILE_MEM_FRACTION
hoodie.filesystem.view.spillable.clustering.mem.fraction	0.02	Fraction of the file system view memory, to be used for holding clustering related metadata. Config Param: SPILLABLE_CLUSTERING_MEM_FRACTION
hoodie.filesystem.view.spillable.compaction.mem.fraction	0.1	Fraction of the file system view memory, to be used for holding compaction related metadata. Config Param: SPILLABLE_COMPACTION_MEM_FRACTION
hoodie.filesystem.view.spillable.dir	/tmp/	Path on local storage to use, when file system view is held in a spillable map. Config Param: SPILLABLE_DIR
hoodie.filesystem.view.spillable.log.compaction.mem.fraction	0.02	Fraction of the file system view memory, to be used for holding log compaction related metadata. Config Param: SPILLABLE_LOG_COMPACTION_MEM_FRACTION Since Version: 0.13.0
hoodie.filesystem.view.spillable.mem	104857600	Amount of memory to be used in bytes for holding file system view, before spilling to disk. Config Param: SPILLABLE_MEMORY
hoodie.filesystem.view.spillable.replaced.mem.fraction	0.05	Fraction of the file system view memory, to be used for holding replace commit related metadata. Config Param: SPILLABLE_REPLACED_MEM_FRACTION
hoodie.filesystem.view.type	MEMORY	File system view provides APIs for viewing the files on the underlying lake storage, as file groups and file slices. This config controls how such a view is held. Options include MEMORY,SPILLABLE_DISK,EMBEDDED_KV_STORE,REMOTE_ONLY,REMOTE_FIRST which provide different trade offs for memory usage and API request performance. Config Param: VIEW_TYPE

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Archival Configs

Configurations that control archival.

Basic Configs

Config Name	Default	Description
hoodie.keep.max.commits	30	Archiving service moves older entries from timeline into an archived log after each write, to keep the metadata overhead constant, even as the table size grows. This config controls the maximum number of instants to retain in the active timeline. Config Param: MAX_COMMITS_TO_KEEP
hoodie.keep.min.commits	20	Similar to hoodie.keep.max.commits, but controls the minimum number of instants to retain in the active timeline. Config Param: MIN_COMMITS_TO_KEEP

Advanced Configs

Config Name	Default	Description
hoodie.archive.async	false	Only applies when hoodie.archive.automatic is turned on. When turned on runs archiver async with writing, which can speed up overall write performance. Config Param: ASYNC_ARCHIVE Since Version: 0.11.0
hoodie.archive.automatic	true	When enabled, the archival table service is invoked immediately after each commit, to archive commits if we cross a maximum value of commits. It's recommended to enable this, to ensure number of active commits is bounded. Config Param: AUTO_ARCHIVE
hoodie.archive.beyond.savepoint	false	If enabled, archival will proceed beyond savepoint, skipping savepoint commits. If disabled, archival will stop at the earliest savepoint commit. Config Param: ARCHIVE_BEYOND_SAVEPOINT Since Version: 0.12.0
hoodie.archive.block.on.latest.clean.ectr	false	If enabled, archival will not archive commits beyond the Earliest Commit To Retain (ECTR) from the last completed clean. ECTR represents the oldest commit whose data files are still needed by the table and have not yet been cleaned up. Blocking archival at this point ensures that timeline metadata is not removed for commits whose data files still exist on storage, preventing inconsistencies between the timeline and the actual data. Config Param: BLOCK_ARCHIVAL_ON_LATEST_CLEAN_ECTR Since Version: 1.2.0
hoodie.archive.delete.parallelism	100	When performing archival operation, Hudi needs to delete the files of the archived instants in the active timeline in .hoodie folder. The file deletion also happens after merging small archived files into larger ones if enabled. This config limits the Spark parallelism for deleting files in both cases, i.e., parallelism of deleting files does not go above the configured value and the parallelism is the number of files to delete if smaller than the configured value. If you see that the file deletion in archival operation is slow because of the limited parallelism, you can increase this to tune the performance. Config Param: DELETE_ARCHIVED_INSTANT_PARALLELISM_VALUE
hoodie.commits.archival.batch	10	Archiving of instants is batched in best-effort manner, to pack more instants into a single archive log. This config controls such archival batch size. Config Param: COMMITS_ARCHIVAL_BATCH_SIZE
hoodie.timeline.compaction.batch.size	10	The number of small files to compact at once. Config Param: TIMELINE_COMPACTION_BATCH_SIZE
hoodie.timeline.compaction.target.file.max.bytes	1048576000	Max size (in bytes) for each archived timeline file. Config Param: TIMELINE_COMPACTION_TARGET_FILE_MAX_BYTES
hoodie.timeline.manifest.retained.versions	3	Number of timeline manifest versions to retain. Config Param: TIMELINE_MANIFEST_RETAINED_VERSIONS

---

Bootstrap Configs

Configurations that control how you want to bootstrap your existing tables for the first time into hudi. The bootstrap operation can flexibly avoid copying data over before you can use Hudi and support running the existing writers and new hudi writers in parallel, to validate the migration.

Basic Configs

Config Name	Default	Description
hoodie.bootstrap.base.path	(N/A)	Base path of the dataset that needs to be bootstrapped as a Hudi table Config Param: BASE_PATH Since Version: 0.6.0

Advanced Configs

Config Name	Default	Description
hoodie.bootstrap.data.queries.only	false	Improves query performance, but queries cannot use hudi metadata fields Config Param: DATA_QUERIES_ONLY Since Version: 0.14.0
hoodie.bootstrap.full.input.provider	org.apache.hudi.bootstrap.SparkParquetBootstrapDataProvider	Class to use for reading the bootstrap dataset partitions/files, for Bootstrap mode FULL_RECORD Config Param: FULL_BOOTSTRAP_INPUT_PROVIDER_CLASS_NAME Since Version: 0.6.0
hoodie.bootstrap.index.class	org.apache.hudi.common.bootstrap.index.hfile.HFileBootstrapIndex	Implementation to use, for mapping a skeleton base file to a bootstrap base file. Config Param: INDEX_CLASS_NAME Since Version: 0.6.0
hoodie.bootstrap.mode.selector	org.apache.hudi.client.bootstrap.selector.MetadataOnlyBootstrapModeSelector	Selects the mode in which each file/partition in the bootstrapped dataset gets bootstrapped Config Param: MODE_SELECTOR_CLASS_NAME Since Version: 0.6.0
hoodie.bootstrap.mode.selector.regex	.*	Matches each bootstrap dataset partition against this regex and applies the mode below to it. Config Param: PARTITION_SELECTOR_REGEX_PATTERN Since Version: 0.6.0
hoodie.bootstrap.mode.selector.regex.mode	METADATA_ONLY	org.apache.hudi.client.bootstrap.BootstrapMode: Bootstrap mode for importing an existing table into Hudi FULL_RECORD: In this mode, the full record data is copied into hudi and metadata columns are added. A full record bootstrap is functionally equivalent to a bulk-insert. After a full record bootstrap, Hudi will function properly even if the original table is modified or deleted. METADATA_ONLY(default): In this mode, the full record data is not copied into Hudi therefore it avoids full cost of rewriting the dataset. Instead, 'skeleton' files containing just the corresponding metadata columns are added to the Hudi table. Hudi relies on the data in the original table and will face data-loss or corruption if files in the original table location are deleted or modified. Config Param: PARTITION_SELECTOR_REGEX_MODE Since Version: 0.6.0
hoodie.bootstrap.parallelism	1500	For metadata-only bootstrap, Hudi parallelizes the operation so that each table partition is handled by one Spark task. This config limits the number of parallelism. We pick the configured parallelism if the number of table partitions is larger than this configured value. The parallelism is assigned to the number of table partitions if it is smaller than the configured value. For full-record bootstrap, i.e., BULK_INSERT operation of the records, this configured value is passed as the BULK_INSERT shuffle parallelism (hoodie.bulkinsert.shuffle.parallelism), determining the BULK_INSERT write behavior. If you see that the bootstrap is slow due to the limited parallelism, you can increase this. Config Param: PARALLELISM_VALUE Since Version: 0.6.0
hoodie.bootstrap.partitionpath.translator.class	org.apache.hudi.client.bootstrap.translator.IdentityBootstrapPartitionPathTranslator	Translates the partition paths from the bootstrapped data into how is laid out as a Hudi table. Config Param: PARTITION_PATH_TRANSLATOR_CLASS_NAME Since Version: 0.6.0

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Clean Configs

Cleaning (reclamation of older/unused file groups/slices).

Basic Configs

Config Name	Default	Description
hoodie.clean.async.enabled	false	Only applies when hoodie.clean.automatic is turned on. When turned on runs cleaner async with writing, which can speed up overall write performance. Config Param: ASYNC_CLEAN
hoodie.clean.commits.retained	10	When KEEP_LATEST_COMMITS cleaning policy is used, the number of commits to retain, without cleaning. This will be retained for num_of_commits * time_between_commits (scheduled). This also directly translates into how much data retention the table supports for incremental queries. Config Param: CLEANER_COMMITS_RETAINED
hoodie.prewrite.cleaner.policy	NONE	org.apache.hudi.common.model.HoodiePreWriteCleanerPolicy: If set, force attempting clean and/or failed writes rollback before starting a new ingestion write commit. This should only be set to ensure that data files do not build up on DFS if an ingestion writer is perpetually failing before completing a CLEAN. NONE(default): No pre-write clean or rollback. Default behavior. CLEAN: Force a CLEAN table service call before starting the write (also performs rollback of failed writes). ROLLBACK_FAILED_WRITES: Only perform rollback of failed writes before starting the write. Config Param: PREWRITE_CLEANER_POLICY Since Version: 1.2.0

Advanced Configs

Config Name	Default	Description
hoodie.clean.partition.filter.regex	(N/A)	When incremental clean is disabled, this regex can be used to filter the partitions to be cleaned. Only partitions matching this regex pattern will be cleaned. This can be useful for very large tables to avoid OOM issues during cleaning. If both this config and hoodie.clean.partition.filter.selected are set, the selected partitions take precedence. Config Param: CLEAN_PARTITION_FILTER_REGEX Since Version: 1.2.0
hoodie.clean.partition.filter.selected	(N/A)	When incremental clean is disabled, this comma-separated list of partitions can be used to filter the partitions to be cleaned. Only the specified partitions will be cleaned. This can be useful for very large tables to avoid OOM issues during cleaning. If both this config and hoodie.clean.partition.filter.regex are set, the selected partitions take precedence. Config Param: CLEAN_PARTITION_FILTER_SELECTED Since Version: 1.2.0
hoodie.clean.automatic	true	When enabled, the cleaner table service is invoked immediately after each commit, to delete older file slices. It's recommended to enable this, to ensure metadata and data storage growth is bounded. Config Param: AUTO_CLEAN
hoodie.clean.delete.bootstrap.base.file	false	When set to true, cleaner also deletes the bootstrap base file when it's skeleton base file is cleaned. Turn this to true, if you want to ensure the bootstrap dataset storage is reclaimed over time, as the table receives updates/deletes. Another reason to turn this on, would be to ensure data residing in bootstrap base files are also physically deleted, to comply with data privacy enforcement processes. Config Param: CLEANER_BOOTSTRAP_BASE_FILE_ENABLE
hoodie.clean.failed.writes.policy	EAGER	org.apache.hudi.common.model.HoodieFailedWritesCleaningPolicy: Policy that controls how to clean up failed writes. Hudi will delete any files written by failed writes to re-claim space. EAGER(default): Clean failed writes inline after every write operation. LAZY: Clean failed writes lazily after heartbeat timeout when the cleaning service runs. This policy is required when multi-writers are enabled. NEVER: Never clean failed writes. Config Param: FAILED_WRITES_CLEANER_POLICY
hoodie.clean.fileversions.retained	3	When KEEP_LATEST_FILE_VERSIONS cleaning policy is used, the minimum number of file slices to retain in each file group, during cleaning. Config Param: CLEANER_FILE_VERSIONS_RETAINED
hoodie.clean.hours.retained	24	When KEEP_LATEST_BY_HOURS cleaning policy is used, the number of hours for which commits need to be retained. This config provides a more flexible option as compared to number of commits retained for cleaning service. Setting this property ensures all the files, but the latest in a file group, corresponding to commits with commit times older than the configured number of hours to be retained are cleaned. Config Param: CLEANER_HOURS_RETAINED
hoodie.clean.incremental.enabled	true	When enabled, the plans for each cleaner service run is computed incrementally off the events in the timeline, since the last cleaner run. This is much more efficient than obtaining listings for the full table for each planning (even with a metadata table). Config Param: CLEANER_INCREMENTAL_MODE_ENABLE
hoodie.clean.max.commits.to.clean	9223372036854775807	Maximum number of commits to clean in one clean commit. Applicable only when the clean policy is based on KEEP_LATEST_COMMITS or KEEP_LATEST_HOURS Config Param: MAX_COMMITS_TO_CLEAN
hoodie.clean.multiple.enabled	false	Allows scheduling/executing multiple cleans by enabling this config. If users prefer to strictly ensure clean requests should be mutually exclusive, .i.e. a 2nd clean will not be scheduled if another clean is not yet completed to avoid repeat cleaning of same files, they might want to disable this config. Config Param: ALLOW_MULTIPLE_CLEANS Since Version: 0.11.0 Deprecated since: 0.15.0
hoodie.clean.optimize.using.local.engine.context	true	Optimizes clean planning by using local engine context (driver-only) for metadata tables and non-partitioned datasets. This allows handling OOM errors during clean planning by scaling only driver memory instead of all executor memory. Some datasets with large record_index partitions can cause OOM errors during file listing in clean planning. By using local engine context, file listing is performed on the driver, allowing targeted memory scaling. When enabled, both non-partitioned datasets and metadata tables use the driver for scheduling cleans. Config Param: CLEAN_OPTIMIZE_USING_LOCAL_ENGINE_CONTEXT Since Version: 1.2.0
hoodie.clean.parallelism	200	This config controls the behavior of both the cleaning plan and cleaning execution. Deriving the cleaning plan is parallelized at the table partition level, i.e., each table partition is processed by one Spark task to figure out the files to clean. The cleaner picks the configured parallelism if the number of table partitions is larger than this configured value. The parallelism is assigned to the number of table partitions if it is smaller than the configured value. The clean execution, i.e., the file deletion, is parallelized at file level, which is the unit of Spark task distribution. Similarly, the actual parallelism cannot exceed the configured value if the number of files is larger. If cleaning plan or execution is slow due to limited parallelism, you can increase this to tune the performance.. Config Param: CLEANER_PARALLELISM_VALUE
hoodie.clean.policy	KEEP_LATEST_COMMITS	org.apache.hudi.common.model.HoodieCleaningPolicy: Cleaning policy to be used. The cleaner service deletes older file slices files to re-claim space. Long running query plans may often refer to older file slices and will break if those are cleaned, before the query has had a chance to run. So, it is good to make sure that the data is retained for more than the maximum query execution time. By default, the cleaning policy is determined based on one of the following configs explicitly set by the user (at most one of them can be set; otherwise, KEEP_LATEST_COMMITS cleaning policy is used). KEEP_LATEST_FILE_VERSIONS: keeps the last N versions of the file slices written; used when "hoodie.clean.fileversions.retained" is explicitly set only. KEEP_LATEST_COMMITS(default): keeps the file slices written by the last N commits; used when "hoodie.clean.commits.retained" is explicitly set only. KEEP_LATEST_BY_HOURS: keeps the file slices written in the last N hours based on the commit time; used when "hoodie.clean.hours.retained" is explicitly set only. Config Param: CLEANER_POLICY
hoodie.clean.trigger.max.commits	1	Number of commits after the last clean operation, before scheduling of a new clean is attempted. Config Param: CLEAN_TRIGGER_MAX_COMMITS
hoodie.clean.trigger.strategy	NUM_COMMITS	org.apache.hudi.table.action.clean.CleaningTriggerStrategy: Controls when cleaning is scheduled. NUM_COMMITS(default): Trigger the cleaning service every N commits, determined by hoodie.clean.trigger.max.commits. Config Param: CLEAN_TRIGGER_STRATEGY
hoodie.write.empty.clean.interval.hours	-1	In some cases empty clean commit needs to be created to ensure the clean planner does not look through entire dataset if there are no clean plans. This is possible for append-only dataset. Also, for these datasets we cannot ignore clean completely since in the future there could be upsert or replace operations. By creating empty clean commit, earliest_commit_to_retain value will be updated so that now clean planner can only check for partitions that are modified after the last empty clean's earliest_commit_toRetain value thereby optimizing the clean planning Config Param: INTERVAL_TO_CREATE_EMPTY_CLEAN_HOURS

---

Clustering Configs

Configurations that control the clustering table service in hudi, which optimizes the storage layout for better query performance by sorting and sizing data files.

Basic Configs

Config Name	Default	Description
hoodie.clustering.async.enabled	false	Enable running of clustering service, asynchronously as inserts happen on the table. Config Param: ASYNC_CLUSTERING_ENABLE Since Version: 0.7.0
hoodie.clustering.inline	false	Turn on inline clustering - clustering will be run after each write operation is complete Config Param: INLINE_CLUSTERING Since Version: 0.7.0
hoodie.clustering.plan.generation.use.local.engine.context	false	When enabled, uses a local engine context (e.g., driver-side in Spark) instead of the distributed engine context to compute clustering groups for each partition during clustering plan generation. By default this is disabled, meaning the distributed engine context is used (e.g., with Spark, each partition's clustering groups are computed in a separate Spark task). Enable this for cases where there are guaranteed to only be a few partitions with many files in the clustering plan, and it would be more resource-efficient to compute locally on the driver rather than allocate executor resources. Config Param: PLAN_GENERATION_USE_LOCAL_ENGINE_CONTEXT Since Version: 1.2.0
hoodie.clustering.plan.strategy.small.file.limit	314572800	Files smaller than the size in bytes specified here are candidates for clustering Config Param: PLAN_STRATEGY_SMALL_FILE_LIMIT Since Version: 0.7.0
hoodie.clustering.plan.strategy.target.file.max.bytes	1073741824	Each group can produce 'N' (CLUSTERING_MAX_GROUP_SIZE/CLUSTERING_TARGET_FILE_SIZE) output file groups Config Param: PLAN_STRATEGY_TARGET_FILE_MAX_BYTES Since Version: 0.7.0

Advanced Configs

Config Name	Default	Description
hoodie.clustering.plan.strategy.cluster.begin.partition	(N/A)	Begin partition used to filter partition (inclusive), only effective when the filter mode 'hoodie.clustering.plan.partition.filter.mode' is SELECTED_PARTITIONS Config Param: PARTITION_FILTER_BEGIN_PARTITION Since Version: 0.11.0
hoodie.clustering.plan.strategy.cluster.end.partition	(N/A)	End partition used to filter partition (inclusive), only effective when the filter mode 'hoodie.clustering.plan.partition.filter.mode' is SELECTED_PARTITIONS Config Param: PARTITION_FILTER_END_PARTITION Since Version: 0.11.0
hoodie.clustering.plan.strategy.earliest.commit.to.cluster	(N/A)	Earliest commit time (exclusive) to start clustering from. Only commits after this time will be considered for commit-based clustering plan strategy. Config Param: PLAN_STRATEGY_EARLIEST_COMMIT_TO_CLUSTER Since Version: 0.14.0
hoodie.clustering.plan.strategy.partition.regex.pattern	(N/A)	Filter clustering partitions that matched regex pattern Config Param: PARTITION_REGEX_PATTERN Since Version: 0.11.0
hoodie.clustering.plan.strategy.partition.selected	(N/A)	Partitions to run clustering Config Param: PARTITION_SELECTED Since Version: 0.11.0
hoodie.clustering.plan.strategy.sort.columns	(N/A)	Columns to sort the data by when clustering Config Param: PLAN_STRATEGY_SORT_COLUMNS Since Version: 0.7.0
hoodie.clustering.async.max.commits	4	Config to control frequency of async clustering Config Param: ASYNC_CLUSTERING_MAX_COMMITS Since Version: 0.9.0
hoodie.clustering.enable.expirations	false	When enabled, rollback of failed writes (under LAZY cleaning policy) will also attempt to rollback clustering replacecommit instants whose heartbeat has expired. Clustering jobs will start a heartbeat before scheduling a plan, so that other writers can detect stale/failed clustering attempts. Note that the same client must be used to schedule, execute, and commit the clustering instant. And a clustering plan cannot be re-attempted Config Param: ENABLE_EXPIRATIONS
hoodie.clustering.execution.strategy.class	org.apache.hudi.client.clustering.run.strategy.SparkSortAndSizeExecutionStrategy	Config to provide a strategy class (subclass of RunClusteringStrategy) to define how the clustering plan is executed. By default, we sort the file groups in th plan by the specified columns, while meeting the configured target file sizes. Config Param: EXECUTION_STRATEGY_CLASS_NAME Since Version: 0.7.0
hoodie.clustering.expiration.threshold.mins	60	When hoodie.clustering.enable.expirations is enabled, a clustering instant will not be considered expired unless its instant creation time is at least this many minutes old. This serves as a guardrail to avoid unnecessary work in rolling back clustering instants that other writers are already attempting to roll back. Config Param: EXPIRATION_THRESHOLD_MINS
hoodie.clustering.group.read.parallelism	20	Maximum number of parallelism when Spark read records from clustering group. Config Param: CLUSTERING_GROUP_READ_PARALLELISM Since Version: 1.0.0
hoodie.clustering.inline.max.commits	4	Config to control frequency of clustering planning Config Param: INLINE_CLUSTERING_MAX_COMMITS Since Version: 0.7.0
hoodie.clustering.max.parallelism	15	Maximum number of parallelism jobs submitted in clustering operation. If the resource is sufficient(Like Spark engine has enough idle executors), increasing this value will let the clustering job run faster, while it will give additional pressure to the execution engines to manage more concurrent running jobs. Config Param: CLUSTERING_MAX_PARALLELISM Since Version: 0.14.0
hoodie.clustering.plan.partition.filter.mode	NONE	org.apache.hudi.table.action.cluster.ClusteringPlanPartitionFilterMode: Partition filter mode used in the creation of clustering plan. NONE(default): Do not filter partitions. The clustering plan will include all partitions that have clustering candidates. RECENT_DAYS: This filter assumes that your data is partitioned by date. The clustering plan will only include partitions from K days ago to N days ago, where K >= N. K is determined by hoodie.clustering.plan.strategy.daybased.lookback.partitions and N is determined by hoodie.clustering.plan.strategy.daybased.skipfromlatest.partitions. SELECTED_PARTITIONS: The clustering plan will include only partition paths with names that sort within the inclusive range [hoodie.clustering.plan.strategy.cluster.begin.partition, hoodie.clustering.plan.strategy.cluster.end.partition]. DAY_ROLLING: To determine the partitions in the clustering plan, the eligible partitions will be sorted in ascending order. Each partition will have an index i in that list. The clustering plan will only contain partitions such that i mod 24 = H, where H is the current hour of the day (from 0 to 23). Config Param: PLAN_PARTITION_FILTER_MODE_NAME Since Version: 0.11.0
hoodie.clustering.plan.strategy.binary.copy.schema.evolution.enable	false	Enable schema evolution support for binary file stitching during clustering. When enabled, allows clustering of files with different but compatible schemas (e.g., files with added columns). When disabled (default), only files with identical schemas will be clustered together, providing better performance but requiring schema consistency across all files in a clustering group. Config Param: FILE_STITCHING_BINARY_COPY_SCHEMA_EVOLUTION_ENABLE Since Version: 1.1.0
hoodie.clustering.plan.strategy.class	org.apache.hudi.client.clustering.plan.strategy.SparkSizeBasedClusteringPlanStrategy	Config to provide a strategy class (subclass of ClusteringPlanStrategy) to create clustering plan i.e select what file groups are being clustered. Default strategy, looks at the clustering small file size limit (determined by hoodie.clustering.plan.strategy.small.file.limit) to pick the small file slices within partitions for clustering. Config Param: PLAN_STRATEGY_CLASS_NAME Since Version: 0.7.0
hoodie.clustering.plan.strategy.daybased.lookback.partitions	2	Number of partitions to list to create ClusteringPlan Config Param: DAYBASED_LOOKBACK_PARTITIONS Since Version: 0.7.0
hoodie.clustering.plan.strategy.daybased.skipfromlatest.partitions	0	Number of partitions to skip from latest when choosing partitions to create ClusteringPlan Config Param: PLAN_STRATEGY_SKIP_PARTITIONS_FROM_LATEST Since Version: 0.9.0
hoodie.clustering.plan.strategy.file.slices.sort.by	SIZE	Comma-separated list of fields to sort file slices by when packing files together within a partition to create clustering groups. Available fields: INSTANT_TIME (sort by commit time ascending, so that older data files are clustered first), SIZE (sort by file size descending). For example, 'INSTANT_TIME,SIZE' sorts by commit time first then by size. Default 'SIZE' sorts by file size only. Config Param: PLAN_STRATEGY_FILE_SLICES_SORT_BY Since Version: 1.2.0
hoodie.clustering.plan.strategy.max.bytes.per.group	2147483648	Each clustering operation can create multiple output file groups. Total amount of data processed by clustering operation is defined by below two properties (CLUSTERING_MAX_BYTES_PER_GROUP * CLUSTERING_MAX_NUM_GROUPS). Max amount of data to be included in one group Config Param: PLAN_STRATEGY_MAX_BYTES_PER_OUTPUT_FILEGROUP Since Version: 0.7.0
hoodie.clustering.plan.strategy.max.num.groups	30	Maximum number of groups to create as part of ClusteringPlan. Increasing groups will increase parallelism Config Param: PLAN_STRATEGY_MAX_GROUPS Since Version: 0.7.0
hoodie.clustering.plan.strategy.single.group.clustering.enabled	true	Whether to generate clustering plan when there is only one file group involved, by default true Config Param: PLAN_STRATEGY_SINGLE_GROUP_CLUSTERING_ENABLED Since Version: 0.14.0
hoodie.clustering.rollback.pending.replacecommit.on.conflict	false	If updates are allowed to file groups pending clustering, then set this config to rollback failed or pending clustering instants. Pending clustering will be rolled back ONLY IF there is conflict between incoming upsert and filegroup to be clustered. Please exercise caution while setting this config, especially when clustering is done very frequently. This could lead to race condition in rare scenarios, for example, when the clustering completes after instants are fetched but before rollback completed. Config Param: ROLLBACK_PENDING_CLUSTERING_ON_CONFLICT Since Version: 0.10.0
hoodie.clustering.schedule.inline	false	When set to true, clustering service will be attempted for inline scheduling after each write. Users have to ensure they have a separate job to run async clustering(execution) for the one scheduled by this writer. Users can choose to set both hoodie.clustering.inline and hoodie.clustering.schedule.inline to false and have both scheduling and execution triggered by any async process, on which case hoodie.clustering.async.enabled is expected to be set to true. But if hoodie.clustering.inline is set to false, and hoodie.clustering.schedule.inline is set to true, regular writers will schedule clustering inline, but users are expected to trigger async job for execution. If hoodie.clustering.inline is set to true, regular writers will do both scheduling and execution inline for clustering Config Param: SCHEDULE_INLINE_CLUSTERING
hoodie.clustering.updates.strategy		Determines how to handle updates, deletes to file groups that are under clustering. Default strategy just rejects the update Config Param: UPDATES_STRATEGY Since Version: 0.7.0
hoodie.layout.optimize.build.curve.sample.size	200000	Determines target sample size used by the Boundary-based Interleaved Index method of building space-filling curve. Larger sample size entails better layout optimization outcomes, at the expense of higher memory footprint. Config Param: LAYOUT_OPTIMIZE_BUILD_CURVE_SAMPLE_SIZE Since Version: 0.10.0
hoodie.layout.optimize.curve.build.method	DIRECT	org.apache.hudi.config.HoodieClusteringConfig$SpatialCurveCompositionStrategyType: This configuration only has effect if hoodie.layout.optimize.strategy is set to either "z-order" or "hilbert" (i.e. leveraging space-filling curves). This configuration controls the type of a strategy to use for building the space-filling curves, tackling specifically how the Strings are ordered based on the curve. Since we truncate the String to 8 bytes for ordering, there are two issues: (1) it can lead to poor aggregation effect, (2) the truncation of String longer than 8 bytes loses the precision, if the Strings are different but the 8-byte prefix is the same. The boundary-based interleaved index method ("SAMPLE") has better generalization, solving the two problems above, but is slower than direct method ("DIRECT"). User should benchmark the write and query performance before tweaking this in production, if this is actually a problem. Please refer to RFC-28 for more details. DIRECT(default): This strategy builds the spatial curve in full, filling in all of the individual points corresponding to each individual record, which requires less compute. SAMPLE: This strategy leverages boundary-base interleaved index method (described in more details in Amazon DynamoDB blog https://aws.amazon.com/cn/blogs/database/tag/z-order/) and produces a better layout compared to DIRECT strategy. It requires more compute and is slower. Config Param: LAYOUT_OPTIMIZE_SPATIAL_CURVE_BUILD_METHOD Since Version: 0.10.0
hoodie.layout.optimize.data.skipping.enable	true	Enable data skipping by collecting statistics once layout optimization is complete. Config Param: LAYOUT_OPTIMIZE_DATA_SKIPPING_ENABLE Since Version: 0.10.0 Deprecated since: 0.11.0
hoodie.layout.optimize.enable	false	This setting has no effect. Please refer to clustering configuration, as well as LAYOUT_OPTIMIZE_STRATEGY config to enable advanced record layout optimization strategies Config Param: LAYOUT_OPTIMIZE_ENABLE Since Version: 0.10.0 Deprecated since: 0.11.0
hoodie.layout.optimize.strategy	LINEAR	org.apache.hudi.config.HoodieClusteringConfig$LayoutOptimizationStrategy: Determines ordering strategy for records layout optimization. LINEAR(default): Orders records lexicographically ZORDER: Orders records along Z-order spatial-curve. HILBERT: Orders records along Hilbert's spatial-curve. Config Param: LAYOUT_OPTIMIZE_STRATEGY Since Version: 0.10.0

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Compaction Configs

Configurations that control compaction (merging of log files onto a new base files).

Basic Configs

Config Name	Default	Description
hoodie.compact.inline	false	When set to true, compaction service is triggered after each write. While being simpler operationally, this adds extra latency on the write path. Config Param: INLINE_COMPACT
hoodie.compact.inline.max.delta.commits	5	Number of delta commits after the last compaction, before scheduling of a new compaction is attempted. This config takes effect only for the compaction triggering strategy based on the number of commits, i.e., NUM_COMMITS, NUM_COMMITS_AFTER_LAST_REQUEST, NUM_AND_TIME, and NUM_OR_TIME. Config Param: INLINE_COMPACT_NUM_DELTA_COMMITS

Advanced Configs

Config Name	Default	Description
hoodie.compaction.partition.path.regex	(N/A)	Used to specify the partition path regex for compaction. Only partitions that match the regex will be compacted. Only be used when configure PartitionRegexBasedCompactionStrategy. Config Param: COMPACTION_SPECIFY_PARTITION_PATH_REGEX
hoodie.compact.inline.max.delta.seconds	3600	Number of elapsed seconds after the last compaction, before scheduling a new one. This config takes effect only for the compaction triggering strategy based on the elapsed time, i.e., TIME_ELAPSED, NUM_AND_TIME, and NUM_OR_TIME. Config Param: INLINE_COMPACT_TIME_DELTA_SECONDS
hoodie.compact.inline.trigger.strategy	NUM_COMMITS	org.apache.hudi.table.action.compact.CompactionTriggerStrategy: Controls when compaction is scheduled. NUM_COMMITS(default): triggers compaction when there are at least N delta commits after last completed compaction. NUM_COMMITS_AFTER_LAST_REQUEST: triggers compaction when there are at least N delta commits after last completed or requested compaction. TIME_ELAPSED: triggers compaction after N seconds since last compaction. NUM_AND_TIME: triggers compaction when both there are at least N delta commits and N seconds elapsed (both must be satisfied) after last completed compaction. NUM_OR_TIME: triggers compaction when both there are at least N delta commits or N seconds elapsed (either condition is satisfied) after last completed compaction. Config Param: INLINE_COMPACT_TRIGGER_STRATEGY
hoodie.compact.schedule.inline	false	When set to true, compaction service will be attempted for inline scheduling after each write. Users have to ensure they have a separate job to run async compaction(execution) for the one scheduled by this writer. Users can choose to set both hoodie.compact.inline and hoodie.compact.schedule.inline to false and have both scheduling and execution triggered by any async process. But if hoodie.compact.inline is set to false, and hoodie.compact.schedule.inline is set to true, regular writers will schedule compaction inline, but users are expected to trigger async job for execution. If hoodie.compact.inline is set to true, regular writers will do both scheduling and execution inline for compaction Config Param: SCHEDULE_INLINE_COMPACT
hoodie.compaction.daybased.target.partitions	10	Used by org.apache.hudi.io.compact.strategy.DayBasedCompactionStrategy to denote the number of latest partitions to compact during a compaction run. Config Param: TARGET_PARTITIONS_PER_DAYBASED_COMPACTION
hoodie.compaction.logfile.num.threshold	0	Only if the log file num is greater than the threshold, the file group will be compacted. Config Param: COMPACTION_LOG_FILE_NUM_THRESHOLD Since Version: 0.13.0
hoodie.compaction.logfile.size.threshold	0	Only if the log file size is greater than the threshold in bytes, the file group will be compacted. Config Param: COMPACTION_LOG_FILE_SIZE_THRESHOLD
hoodie.compaction.plan.generator	org.apache.hudi.table.action.compact.plan.generators.HoodieCompactionPlanGenerator	Compaction plan generator for data files. Override with a custom plan generator if there's a need to use extraMetadata in the compaction plan for optimizations, ignore otherwise Config Param: COMPACTION_PLAN_GENERATOR
hoodie.compaction.strategy	org.apache.hudi.table.action.compact.strategy.LogFileSizeBasedCompactionStrategy	Compaction strategy decides which file groups are picked up for compaction during each compaction run. By default. Hudi picks the log file with most accumulated unmerged data. The strategy can be composed with multiple strategies by concatenating the class names with ','. Config Param: COMPACTION_STRATEGY
hoodie.compaction.target.io	512000	Amount of MBs to spend during compaction run for the LogFileSizeBasedCompactionStrategy. This value helps bound ingestion latency while compaction is run inline mode. Config Param: TARGET_IO_PER_COMPACTION_IN_MB
hoodie.copyonwrite.insert.auto.split	true	Config to control whether we control insert split sizes automatically based on average record sizes. It's recommended to keep this turned on, since hand tuning is otherwise extremely cumbersome. Config Param: COPY_ON_WRITE_AUTO_SPLIT_INSERTS
hoodie.copyonwrite.insert.split.size	500000	Number of inserts assigned for each partition/bucket for writing. We based the default on writing out 100MB files, with at least 1kb records (100K records per file), and over provision to 500K. As long as auto-tuning of splits is turned on, this only affects the first write, where there is no history to learn record sizes from. Config Param: COPY_ON_WRITE_INSERT_SPLIT_SIZE
hoodie.copyonwrite.record.size.estimate	1024	The average record size. If not explicitly specified, hudi will compute the record size estimate compute dynamically based on commit metadata. This is critical in computing the insert parallelism and bin-packing inserts into small files. Config Param: COPY_ON_WRITE_RECORD_SIZE_ESTIMATE
hoodie.log.compaction.blocks.threshold	5	Log compaction can be scheduled if the no. of log blocks crosses this threshold value. This is effective only when log compaction is enabled via hoodie.log.compaction.inline Config Param: LOG_COMPACTION_BLOCKS_THRESHOLD Since Version: 0.13.0
hoodie.log.compaction.enable	false	By enabling log compaction through this config, log compaction will also get enabled for the metadata table. Config Param: ENABLE_LOG_COMPACTION Since Version: 0.14.0
hoodie.log.compaction.inline	false	When set to true, logcompaction service is triggered after each write. While being simpler operationally, this adds extra latency on the write path. Config Param: INLINE_LOG_COMPACT Since Version: 0.13.0
hoodie.parquet.small.file.limit	104857600	During upsert operation, we opportunistically expand existing small files on storage, instead of writing new files, to keep number of files to an optimum. This config sets the file size limit below which a file on storage becomes a candidate to be selected as such a small file. By default, treat any file <= 100MB as a small file. Also note that if this set <= 0, will not try to get small files and directly write new files Config Param: PARQUET_SMALL_FILE_LIMIT
hoodie.record.size.estimation.threshold	1.0	We use the previous commits' metadata to calculate the estimated record size and use it to bin pack records into partitions. If the previous commit is too small to make an accurate estimation, Hudi will search commits in the reverse order, until we find a commit that has totalBytesWritten larger than (PARQUET_SMALL_FILE_LIMIT_BYTES * this_threshold) Config Param: RECORD_SIZE_ESTIMATION_THRESHOLD

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Error table Configs

Configurations that are required for Error table configs

Basic Configs

Config Name	Default	Description
hoodie.errortable.base.path	(N/A)	Base path for error table under which all error records would be stored. Config Param: ERROR_TABLE_BASE_PATH
hoodie.errortable.target.table.name	(N/A)	Table name to be used for the error table Config Param: ERROR_TARGET_TABLE
hoodie.errortable.write.class	(N/A)	Class which handles the error table writes. This config is used to configure a custom implementation for Error Table Writer. Specify the full class name of the custom error table writer as a value for this config Config Param: ERROR_TABLE_WRITE_CLASS
hoodie.errortable.enable	false	Config to enable error table. If the config is enabled, all the records with processing error in DeltaStreamer are transferred to error table. Config Param: ERROR_TABLE_ENABLED
hoodie.errortable.insert.shuffle.parallelism	200	Config to set insert shuffle parallelism. The config is similar to hoodie.insert.shuffle.parallelism config but applies to the error table. Config Param: ERROR_TABLE_INSERT_PARALLELISM_VALUE
hoodie.errortable.source.rdd.persist	false	Enabling this config, persists the sourceRDD to disk which helps in faster processing of data table + error table write DAG Config Param: ERROR_TABLE_PERSIST_SOURCE_RDD
hoodie.errortable.upsert.shuffle.parallelism	200	Config to set upsert shuffle parallelism. The config is similar to hoodie.upsert.shuffle.parallelism config but applies to the error table. Config Param: ERROR_TABLE_UPSERT_PARALLELISM_VALUE
hoodie.errortable.validate.recordcreation.enable	true	Records that fail to be created due to keygeneration failure or other issues will be sent to the Error Table Config Param: ERROR_ENABLE_VALIDATE_RECORD_CREATION Since Version: 0.15.0
hoodie.errortable.validate.targetschema.enable	false	Records with schema mismatch with Target Schema are sent to Error Table. Config Param: ERROR_ENABLE_VALIDATE_TARGET_SCHEMA
hoodie.errortable.write.failure.strategy	ROLLBACK_COMMIT	The config specifies the failure strategy if error table write fails. Use one of - [ROLLBACK_COMMIT (Rollback the corresponding base table write commit for which the error events were triggered) , LOG_ERROR (Error is logged but the base table write succeeds) ] Config Param: ERROR_TABLE_WRITE_FAILURE_STRATEGY
hoodie.errortable.write.union.enable	false	Enable error table union with data table when writing for improved commit performance. By default it is disabled meaning data table and error table writes are sequential Config Param: ENABLE_ERROR_TABLE_WRITE_UNIFICATION

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Layout Configs

Configurations that control storage layout and data distribution, which defines how the files are organized within a table.

Advanced Configs

Config Name	Default	Description
hoodie.storage.layout.partitioner.class	(N/A)	Partitioner class, it is used to distribute data in a specific way. Config Param: LAYOUT_PARTITIONER_CLASS_NAME
hoodie.storage.layout.type	DEFAULT	org.apache.hudi.table.storage.HoodieStorageLayout$LayoutType: Determines how the files are organized within a table. DEFAULT(default): Each file group contains records of a certain set of keys, without particular grouping criteria. BUCKET: Each file group contains records of a set of keys which map to a certain range of hash values, so that using the hash function can easily identify the file group a record belongs to, based on the record key. Config Param: LAYOUT_TYPE

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PreWrite Validator Configurations

The following set of configurations help validate data before writes.

Advanced Configs

Config Name	Default	Description
hoodie.prewrite.validators		Comma separated list of class names that can be invoked to validate before write operations Config Param: VALIDATOR_CLASS_NAMES

---

TTL management Configs

Data ttl management

Advanced Configs

Config Name	Default	Description
hoodie.partition.ttl.strategy.class	(N/A)	Config to provide a strategy class (subclass of PartitionTTLStrategy) to get the expired partitions Config Param: PARTITION_TTL_STRATEGY_CLASS_NAME Since Version: 1.0.0
hoodie.partition.ttl.strategy.partition.selected	(N/A)	Partitions to manage ttl Config Param: PARTITION_SELECTED Since Version: 1.0.0
hoodie.partition.ttl.inline	false	When enabled, the partition ttl management service is invoked immediately after each commit, to delete exipired partitions Config Param: INLINE_PARTITION_TTL Since Version: 1.0.0
hoodie.partition.ttl.management.strategy.type	KEEP_BY_TIME	Partition ttl management strategy type to determine the strategy class Config Param: PARTITION_TTL_STRATEGY_TYPE Since Version: 1.0.0
hoodie.partition.ttl.strategy.days.retain	-1	Partition ttl management KEEP_BY_TIME strategy days retain Config Param: DAYS_RETAIN Since Version: 1.0.0
hoodie.partition.ttl.strategy.max.delete.partitions	1000	max partitions to delete in partition ttl management Config Param: MAX_PARTITION_TO_DELETE Since Version: 1.0.0

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Write Configurations

Configurations that control write behavior on Hudi tables. These can be directly passed down from even higher level frameworks (e.g Spark datasources, Flink sink) and utilities (e.g Hudi Streamer).

Basic Configs

Config Name	Default	Description
hoodie.base.path	(N/A)	Base path on lake storage, under which all the table data is stored. Always prefix it explicitly with the storage scheme (e.g hdfs://, s3:// etc). Hudi stores all the main meta-data about commits, savepoints, cleaning audit logs etc in .hoodie directory under this base path directory. Config Param: BASE_PATH
hoodie.datasource.write.precombine.field	(N/A)	Comma separated list of fields used in preCombining before actual write. When two records have the same key value, we will pick the one with the largest value for the precombine field, determined by Object.compareTo(..). For multiple fields if first key comparison is same, second key comparison is made and so on. This config is used for combining records within the same batch and also for merging using event time merge mode Config Param: PRECOMBINE_FIELD_NAME
hoodie.table.name	(N/A)	Table name that will be used for registering with metastores like HMS. Needs to be same across runs. Config Param: TBL_NAME
hoodie.write.record.merge.mode	(N/A)	org.apache.hudi.common.config.RecordMergeMode: Determines the logic of merging updates COMMIT_TIME_ORDERING: Using transaction time to merge records, i.e., the record from later transaction overwrites the earlier record with the same key. EVENT_TIME_ORDERING: Using event time as the ordering to merge records, i.e., the record with the larger event time overwrites the record with the smaller event time on the same key, regardless of transaction time. The event time or ordering fields need to be specified by the user. CUSTOM: Using custom merging logic specified by the user. Config Param: RECORD_MERGE_MODE Since Version: 1.0.0
hoodie.block.writes.on.speculative.execution	true	When enabled (default), throws an exception if Spark speculative execution is enabled during the client creation. This prevents potential data corruption due to duplicate writes by speculative executors. Set to false only if you understand the risks of running with Spark speculative execution enabled. Config Param: BLOCK_WRITES_ON_SPECULATIVE_EXECUTION Since Version: 0.14.0
hoodie.fail.job.on.duplicate.data.file.detection	false	If config is enabled, entire job is failed on invalid file detection Config Param: FAIL_JOB_ON_DUPLICATE_DATA_FILE_DETECTION
hoodie.write.auto.upgrade	true	If enabled, writers automatically migrate the table to the specified write table version if the current table version is lower. Config Param: AUTO_UPGRADE_VERSION Since Version: 1.0.0
hoodie.write.can.ignore.post.commit.failures	false	When this config is true, any failures in post-commit operations are ignored and do not kill the application. Config Param: CAN_IGNORE_POST_COMMIT_FAILURES
hoodie.write.concurrency.mode	SINGLE_WRITER	org.apache.hudi.common.model.WriteConcurrencyMode: Concurrency modes for write operations. SINGLE_WRITER(default): Only one active writer to the table. Maximizes throughput. OPTIMISTIC_CONCURRENCY_CONTROL: Multiple writers can operate on the table with lazy conflict resolution using locks. This means that only one writer succeeds if multiple writers write to the same file group. NON_BLOCKING_CONCURRENCY_CONTROL: Multiple writers can operate on the table with non-blocking conflict resolution. The writers can write into the same file group with the conflicts resolved automatically by the query reader and the compactor. Config Param: WRITE_CONCURRENCY_MODE
hoodie.write.ignore.failed	true	Flag to indicate whether to ignore any non exception error (e.g. write status error).By default true for backward compatibility. Config Param: IGNORE_FAILED Since Version:
hoodie.write.table.version	9	The table version this writer is storing the table in. This should match the current table version. Config Param: WRITE_TABLE_VERSION Since Version: 1.0.0

Advanced Configs

Config Name	Default	Description
hoodie.avro.schema	(N/A)	Schema string representing the current write schema of the table. Hudi passes this to implementations of HoodieRecordPayload to convert incoming records to avro. This is also used as the write schema evolving records during an update. Config Param: AVRO_SCHEMA_STRING
hoodie.bulkinsert.user.defined.partitioner.class	(N/A)	If specified, this class will be used to re-partition records before they are bulk inserted. This can be used to sort, pack, cluster data optimally for common query patterns. For now we support a build-in user defined bulkinsert partitioner org.apache.hudi.execution.bulkinsert.RDDCustomColumnsSortPartitioner which can does sorting based on specified column values set by hoodie.bulkinsert.user.defined.partitioner.sort.columns Config Param: BULKINSERT_USER_DEFINED_PARTITIONER_CLASS_NAME
hoodie.bulkinsert.user.defined.partitioner.sort.columns	(N/A)	Columns to sort the data by when use org.apache.hudi.execution.bulkinsert.RDDCustomColumnsSortPartitioner as user defined partitioner during bulk_insert. For example 'column1,column2' Config Param: BULKINSERT_USER_DEFINED_PARTITIONER_SORT_COLUMNS
hoodie.datasource.write.keygenerator.class	(N/A)	Key generator class, that implements org.apache.hudi.keygen.KeyGenerator extract a key out of incoming records. Config Param: KEYGENERATOR_CLASS_NAME
hoodie.datasource.write.payload.class	(N/A)	Payload class used. Override this, if you like to roll your own merge logic, when upserting/inserting. This will render any value set for PRECOMBINE_FIELD_OPT_VAL in-effective Config Param: WRITE_PAYLOAD_CLASS_NAME
hoodie.internal.schema	(N/A)	Schema string representing the latest schema of the table. Hudi passes this to implementations of evolution of schema Config Param: INTERNAL_SCHEMA_STRING
hoodie.write.record.merge.custom.implementation.classes	(N/A)	List of HoodieMerger implementations constituting Hudi's merging strategy -- based on the engine used. These record merge impls will filter by hoodie.write.record.merge.strategy.idHudi will pick most efficient implementation to perform merging/combining of the records (during update, reading MOR table, etc) Config Param: RECORD_MERGE_IMPL_CLASSES Since Version: 0.13.0
hoodie.write.record.merge.strategy.id	(N/A)	ID of record merge strategy. Hudi will pick HoodieRecordMerger implementations in hoodie.write.record.merge.custom.implementation.classes which has the same merge strategy id Config Param: RECORD_MERGE_STRATEGY_ID Since Version: 0.13.0
hoodie.write.schema	(N/A)	Config allowing to override writer's schema. This might be necessary in cases when writer's schema derived from the incoming dataset might actually be different from the schema we actually want to use when writing. This, for ex, could be the case for'partial-update' use-cases (like MERGE INTO Spark SQL statement for ex) where only a projection of the incoming dataset might be used to update the records in the existing table, prompting us to override the writer's schema Config Param: WRITE_SCHEMA_OVERRIDE
_.hoodie.allow.multi.write.on.same.instant	false	Config Param: ALLOW_MULTI_WRITE_ON_SAME_INSTANT_ENABLE
_hoodie.record.size.estimator.max.commits	5	The maximum number of commits that will be read to estimate the avg record size. This makes sure we parse a limited number of commit metadata, as parsing the entire active timeline can be expensive and unnecessary. Config Param: RECORD_SIZE_ESTIMATOR_MAX_COMMITS Since Version: 1.0.0
hoodie.allow.empty.commit	true	Whether to allow generation of empty commits, even if no data was written in the commit. It's useful in cases where extra metadata needs to be published regardless e.g tracking source offsets when ingesting data Config Param: ALLOW_EMPTY_COMMIT
hoodie.allow.operation.metadata.field	false	Whether to include '_hoodie_operation' in the metadata fields. Once enabled, all the changes of a record are persisted to the delta log directly without merge Config Param: ALLOW_OPERATION_METADATA_FIELD Since Version: 0.9.0
hoodie.auto.adjust.lock.configs	false	Auto adjust lock configurations when metadata table is enabled and for async table services. Config Param: AUTO_ADJUST_LOCK_CONFIGS Since Version: 0.11.0
hoodie.avro.schema.external.transformation	false	When enabled, records in older schema are rewritten into newer schema during upsert,delete and background compaction,clustering operations. Config Param: AVRO_EXTERNAL_SCHEMA_TRANSFORMATION_ENABLE
hoodie.avro.schema.validate	false	Validate the schema used for the write against the latest schema, for backwards compatibility. Config Param: AVRO_SCHEMA_VALIDATE_ENABLE
hoodie.base.file.format	PARQUET	File format to store all the base file data. org.apache.hudi.common.model.HoodieFileFormat: Hoodie file formats. PARQUET(default): Apache Parquet is an open source, column-oriented data file format designed for efficient data storage and retrieval. It provides efficient data compression and encoding schemes with enhanced performance to handle complex data in bulk. HFILE: (internal config) File format for metadata table. A file of sorted key/value pairs. Both keys and values are byte arrays. ORC: The Optimized Row Columnar (ORC) file format provides a highly efficient way to store Hive data. It was designed to overcome limitations of the other Hive file formats. Using ORC files improves performance when Hive is reading, writing, and processing data. Config Param: BASE_FILE_FORMAT
hoodie.bulkinsert.shuffle.parallelism	0	For large initial imports using bulk_insert operation, controls the parallelism to use for sort modes or custom partitioning done before writing records to the table. Before 0.13.0 release, if users do not configure it, Hudi would use 200 as the default shuffle parallelism. From 0.13.0 onwards Hudi by default automatically uses the parallelism deduced by Spark based on the source data or the parallelism based on the logical plan for row writer. If the shuffle parallelism is explicitly configured by the user, the user-configured parallelism is used in defining the actual parallelism. If you observe small files from the bulk insert operation, we suggest configuring this shuffle parallelism explicitly, so that the parallelism is around total_input_data_size/120MB. Config Param: BULKINSERT_PARALLELISM_VALUE
hoodie.bulkinsert.sort.mode	NONE	org.apache.hudi.execution.bulkinsert.BulkInsertSortMode: Modes for sorting records during bulk insert. NONE(default): No sorting. Fastest and matches spark.write.parquet() in number of files and overhead. GLOBAL_SORT: This ensures best file sizes, with lowest memory overhead at cost of sorting. PARTITION_SORT: Strikes a balance by only sorting within a Spark RDD partition, still keeping the memory overhead of writing low. File sizing is not as good as GLOBAL_SORT. PARTITION_PATH_REPARTITION: This ensures that the data for a single physical partition in the table is written by the same Spark executor. This should only be used when input data is evenly distributed across different partition paths. If data is skewed (most records are intended for a handful of partition paths among all) then this can cause an imbalance among Spark executors. PARTITION_PATH_REPARTITION_AND_SORT: This ensures that the data for a single physical partition in the table is written by the same Spark executor. This should only be used when input data is evenly distributed across different partition paths. Compared to PARTITION_PATH_REPARTITION, this sort mode does an additional step of sorting the records based on the partition path within a single Spark partition, given that data for multiple physical partitions can be sent to the same Spark partition and executor. If data is skewed (most records are intended for a handful of partition paths among all) then this can cause an imbalance among Spark executors. Config Param: BULK_INSERT_SORT_MODE
hoodie.bulkinsert.sort.suffix.record_key	false	When using user defined sort columns there can be possibility of skew because spark's RangePartitioner used in sort can reduce the number of outputSparkPartitionsif the sampled dataset has a low cardinality on the provided sort columns. This can cause an increase in commit durations as we are not leveraging the original parallelism.Enabling this config suffixes the record key at the end to avoid skew.This config is used by RowCustomColumnsSortPartitioner, RDDCustomColumnsSortPartitioner and JavaCustomColumnsSortPartitioner Config Param: BULKINSERT_SUFFIX_RECORD_KEY_SORT_COLUMNS Since Version: 1.0.0
hoodie.cdc.file.group.iterator.memory.spill.bytes	104857600	Amount of memory in bytes to be used in bytes for CDCFileGroupIterator holding data in-memory, before spilling to disk. Config Param: CDC_FILE_GROUP_ITERATOR_MEMORY_SPILL_BYTES Since Version: 1.0.1
hoodie.client.heartbeat.interval_in_ms	60000	Writers perform heartbeats to indicate liveness. Controls how often (in ms), such heartbeats are registered to lake storage. Config Param: CLIENT_HEARTBEAT_INTERVAL_IN_MS
hoodie.client.heartbeat.tolerable.misses	2	Number of heartbeat misses, before a writer is deemed not alive and all pending writes are aborted. Config Param: CLIENT_HEARTBEAT_NUM_TOLERABLE_MISSES
hoodie.client.init.callback.classes		Fully-qualified class names of the Hudi client init callbacks to run at the initialization of the Hudi client. The class names are separated by ,. The class must be a subclass of org.apache.hudi.callback.HoodieClientInitCallback.By default, no Hudi client init callback is executed. Config Param: CLIENT_INIT_CALLBACK_CLASS_NAMES Since Version: 0.14.0
hoodie.clustering.fail.on.pending.ingestion.during.conflict.resolution	false	Only applicable when "hoodie.write.concurrency.mode" is set to OCC or NBCC and the conflict resolution strategy ("hoodie.write.conflict.resolution.strategy") is set to PreferWriterConflictResolutionStrategy. When enabled, proactively prevents clustering from committing if there are any ongoing ingestion writes that have not transitioned from requested to inflight yet and have an active heartbeat, since ingestion may be targeting the same files and should have precedence. Config Param: CLUSTERING_BLOCK_FOR_PENDING_INGESTION
hoodie.combine.before.delete	true	During delete operations, controls whether we should combine deletes (and potentially also upserts) before writing to storage. Config Param: COMBINE_BEFORE_DELETE
hoodie.combine.before.insert	false	When inserted records share same key, controls whether they should be first combined (i.e de-duplicated) before writing to storage. Config Param: COMBINE_BEFORE_INSERT
hoodie.combine.before.upsert	true	When upserted records share same key, controls whether they should be first combined (i.e de-duplicated) before writing to storage. This should be turned off only if you are absolutely certain that there are no duplicates incoming, otherwise it can lead to duplicate keys and violate the uniqueness guarantees. Config Param: COMBINE_BEFORE_UPSERT
hoodie.compact.merge.handle.class	org.apache.hudi.io.FileGroupReaderBasedMergeHandle	Merge handle class for compaction Config Param: COMPACT_MERGE_HANDLE_CLASS_NAME Since Version: 1.1.0
hoodie.consistency.check.initial_interval_ms	2000	Initial time between successive attempts to ensure written data's metadata is consistent on storage. Grows with exponential backoff after the initial value. Config Param: INITIAL_CONSISTENCY_CHECK_INTERVAL_MS
hoodie.consistency.check.max_checks	7	Maximum number of checks, for consistency of written data. Config Param: MAX_CONSISTENCY_CHECKS
hoodie.consistency.check.max_interval_ms	300000	Max time to wait between successive attempts at performing consistency checks Config Param: MAX_CONSISTENCY_CHECK_INTERVAL_MS
hoodie.datasource.write.keygenerator.type	SIMPLE	Note This is being actively worked on. Please use hoodie.datasource.write.keygenerator.class instead. org.apache.hudi.keygen.constant.KeyGeneratorType: Key generator type, indicating the key generator class to use, that implements org.apache.hudi.keygen.KeyGenerator. SIMPLE(default): Simple key generator, which takes names of fields to be used for recordKey and partitionPath as configs. SIMPLE_AVRO: Simple key generator, which takes names of fields to be used for recordKey and partitionPath as configs. COMPLEX: Complex key generator, which takes names of fields to be used for recordKey and partitionPath as configs. COMPLEX_AVRO: Complex key generator, which takes names of fields to be used for recordKey and partitionPath as configs. TIMESTAMP: Timestamp-based key generator, that relies on timestamps for partitioning field. Still picks record key by name. TIMESTAMP_AVRO: Timestamp-based key generator, that relies on timestamps for partitioning field. Still picks record key by name. CUSTOM: This is a generic implementation type of KeyGenerator where users can configure record key as a single field or a combination of fields. Similarly partition path can be configured to have multiple fields or only one field. This KeyGenerator expects value for prop "hoodie.datasource.write.partitionpath.field" in a specific format. For example: properties.put("hoodie.datasource.write.partitionpath.field", "field1:PartitionKeyType1,field2:PartitionKeyType2"). CUSTOM_AVRO: This is a generic implementation type of KeyGenerator where users can configure record key as a single field or a combination of fields. Similarly partition path can be configured to have multiple fields or only one field. This KeyGenerator expects value for prop "hoodie.datasource.write.partitionpath.field" in a specific format. For example: properties.put("hoodie.datasource.write.partitionpath.field", "field1:PartitionKeyType1,field2:PartitionKeyType2"). NON_PARTITION: Simple Key generator for non-partitioned tables. NON_PARTITION_AVRO: Simple Key generator for non-partitioned tables. GLOBAL_DELETE: Key generator for deletes using global indices. GLOBAL_DELETE_AVRO: Key generator for deletes using global indices. AUTO_RECORD: Automatic record key generation. AUTO_RECORD_AVRO: Automatic record key generation. HOODIE_TABLE_METADATA: Custom key generator for the Hudi table metadata. SPARK_SQL: Custom spark-sql specific KeyGenerator overriding behavior handling TimestampType partition values. SPARK_SQL_UUID: A KeyGenerator which use the uuid as the record key. SPARK_SQL_MERGE_INTO: Meant to be used internally for the spark sql MERGE INTO command. USER_PROVIDED: A KeyGenerator specified from the configuration. Config Param: KEYGENERATOR_TYPE
hoodie.datasource.write.schema.allow.auto.evolution.column.drop	false	Controls whether table's schema is allowed to automatically evolve when incoming batch's schema can have any of the columns dropped. By default, Hudi will not allow this kind of (auto) schema evolution. Set this config to true to allow table's schema to be updated automatically when columns are dropped from the new incoming batch. Config Param: SCHEMA_ALLOW_AUTO_EVOLUTION_COLUMN_DROP Since Version: 0.13.0
hoodie.delete.shuffle.parallelism	0	Parallelism used for delete operation. Delete operations also performs shuffles, similar to upsert operation. Before 0.13.0 release, if users do not configure it, Hudi would use 200 as the default shuffle parallelism. From 0.13.0 onwards Hudi by default automatically uses the parallelism deduced by Spark based on the source data. If the shuffle parallelism is explicitly configured by the user, the user-configured parallelism is used in defining the actual parallelism. Config Param: DELETE_PARALLELISM_VALUE
hoodie.embed.timeline.server	true	When true, spins up an instance of the timeline server (meta server that serves cached file listings, statistics),running on each writer's driver process, accepting requests during the write from executors. Config Param: EMBEDDED_TIMELINE_SERVER_ENABLE
hoodie.embed.timeline.server.async	false	Controls whether or not, the requests to the timeline server are processed in asynchronous fashion, potentially improving throughput. Config Param: EMBEDDED_TIMELINE_SERVER_USE_ASYNC_ENABLE
hoodie.embed.timeline.server.gzip	true	Controls whether gzip compression is used, for large responses from the timeline server, to improve latency. Config Param: EMBEDDED_TIMELINE_SERVER_COMPRESS_ENABLE
hoodie.embed.timeline.server.port	0	Port at which the timeline server listens for requests. When running embedded in each writer, it picks a free port and communicates to all the executors. This should rarely be changed. Config Param: EMBEDDED_TIMELINE_SERVER_PORT_NUM
hoodie.embed.timeline.server.reuse.enabled	false	Controls whether the timeline server instance should be cached and reused across the tablesto avoid startup costs and server overhead. This should only be used if you are running multiple writers in the same JVM. Config Param: EMBEDDED_TIMELINE_SERVER_REUSE_ENABLED
hoodie.embed.timeline.server.threads	-1	Number of threads to serve requests in the timeline server. By default, auto configured based on the number of underlying cores. Config Param: EMBEDDED_TIMELINE_NUM_SERVER_THREADS
hoodie.fail.on.timeline.archiving	true	Timeline archiving removes older instants from the timeline, after each write operation, to minimize metadata overhead. Controls whether or not, the write should be failed as well, if such archiving fails. Config Param: FAIL_ON_TIMELINE_ARCHIVING_ENABLE
hoodie.fail.writes.on.inline.table.service.exception	true	Table services such as compaction and clustering can fail and prevent syncing to the metaclient. Set this to true to fail writes when table services fail Config Param: FAIL_ON_INLINE_TABLE_SERVICE_EXCEPTION Since Version: 0.13.0
hoodie.fileid.prefix.provider.class	org.apache.hudi.table.RandomFileIdPrefixProvider	File Id Prefix provider class, that implements org.apache.hudi.fileid.FileIdPrefixProvider Config Param: FILEID_PREFIX_PROVIDER_CLASS Since Version: 0.10.0
hoodie.finalize.write.parallelism	200	Parallelism for the write finalization internal operation, which involves removing any partially written files from lake storage, before committing the write. Reduce this value, if the high number of tasks incur delays for smaller tables or low latency writes. Config Param: FINALIZE_WRITE_PARALLELISM_VALUE
hoodie.insert.shuffle.parallelism	0	Parallelism for inserting records into the table. Inserts can shuffle data before writing to tune file sizes and optimize the storage layout. Before 0.13.0 release, if users do not configure it, Hudi would use 200 as the default shuffle parallelism. From 0.13.0 onwards Hudi by default automatically uses the parallelism deduced by Spark based on the source data. If the shuffle parallelism is explicitly configured by the user, the user-configured parallelism is used in defining the actual parallelism. If you observe small files from the insert operation, we suggest configuring this shuffle parallelism explicitly, so that the parallelism is around total_input_data_size/120MB. Config Param: INSERT_PARALLELISM_VALUE
hoodie.markers.delete.parallelism	100	Determines the parallelism for deleting marker files, which are used to track all files (valid or invalid/partial) written during a write operation. Increase this value if delays are observed, with large batch writes. Config Param: MARKERS_DELETE_PARALLELISM_VALUE
hoodie.markers.timeline_server_based.batch.interval_ms	50	The batch interval in milliseconds for marker creation batch processing Config Param: MARKERS_TIMELINE_SERVER_BASED_BATCH_INTERVAL_MS Since Version: 0.9.0
hoodie.markers.timeline_server_based.batch.num_threads	20	Number of threads to use for batch processing marker creation requests at the timeline server Config Param: MARKERS_TIMELINE_SERVER_BASED_BATCH_NUM_THREADS Since Version: 0.9.0
hoodie.merge.allow.duplicate.on.inserts	true	When enabled, we allow duplicate keys even if inserts are routed to merge with an existing file (for ensuring file sizing). This is only relevant for insert operation, since upsert, delete operations will ensure unique key constraints are maintained. Config Param: MERGE_ALLOW_DUPLICATE_ON_INSERTS_ENABLE
hoodie.merge.data.validation.enabled	false	When enabled, data validation checks are performed during merges to ensure expected number of records after merge operation. Config Param: MERGE_DATA_VALIDATION_CHECK_ENABLE
hoodie.merge.small.file.group.candidates.limit	1	Limits number of file groups, whose base file satisfies small-file limit, to consider for appending records during upsert operation. Only applicable to MOR tables Config Param: MERGE_SMALL_FILE_GROUP_CANDIDATES_LIMIT
hoodie.record.size.estimator.average.metadata.size	0	The approximate metadata size in bytes to subtract from the file size when estimating the record size. Config Param: RECORD_SIZE_ESTIMATOR_AVERAGE_METADATA_SIZE Since Version: 1.0.0
hoodie.record.size.estimator.class	org.apache.hudi.estimator.AverageRecordSizeEstimator	Class that estimates the size of records written by implementing org.apache.hudi.estimator.RecordSizeEstimator. Default implementation is org.apache.hudi.estimator.AverageRecordSizeEstimator Config Param: RECORD_SIZE_ESTIMATOR_CLASS_NAME Since Version: 1.0.0
hoodie.release.resource.on.completion.enable	true	Control to enable release all persist rdds when the spark job finish. Config Param: RELEASE_RESOURCE_ENABLE Since Version: 0.11.0
hoodie.rollback.avoid.duplicate.plan	false	When enabled in multi-writer mode, before scheduling a new rollback plan, the writer reloads the timeline under lock to check if another writer already scheduled one for the same failed commit. This avoids duplicate rollback instants and uses heartbeats to ensure only one writer executes the rollback at a time. Config Param: ROLLBACK_AVOID_DUPLICATE_PLAN
hoodie.rollback.instant.backup.dir	.rollback_backup	Path where instants being rolled back are copied. If not absolute path then a directory relative to .hoodie folder is created. Config Param: ROLLBACK_INSTANT_BACKUP_DIRECTORY
hoodie.rollback.instant.backup.enabled	false	Backup instants removed during rollback and restore (useful for debugging) Config Param: ROLLBACK_INSTANT_BACKUP_ENABLED
hoodie.rollback.parallelism	100	This config controls the parallelism for rollback of commits. Rollbacks perform deletion of files or logging delete blocks to file groups on storage in parallel. The configure value limits the parallelism so that the number of Spark tasks do not exceed the value. If rollback is slow due to the limited parallelism, you can increase this to tune the performance. Config Param: ROLLBACK_PARALLELISM_VALUE
hoodie.rollback.using.markers	true	Enables a more efficient mechanism for rollbacks based on the marker files generated during the writes. Turned on by default. Config Param: ROLLBACK_USING_MARKERS_ENABLE
hoodie.sensitive.config.keys	ssl,tls,sasl,auth,credentials	Comma separated list of filters for sensitive config keys. Hudi Streamer will not print any configuration which contains the configured filter. For example with a configured filter ssl, value for config ssl.trustore.location would be masked. Config Param: SENSITIVE_CONFIG_KEYS_FILTER Since Version: 0.14.0
hoodie.skip.default.partition.validation	false	When table is upgraded from pre 0.12 to 0.12, we check for "default" partition and fail if found one. Users are expected to rewrite the data in those partitions. Enabling this config will bypass this validation Config Param: SKIP_DEFAULT_PARTITION_VALIDATION Since Version: 0.12.0
hoodie.table.services.enabled	true	Master control to disable all table services including archive, clean, compact, cluster, etc. Config Param: TABLE_SERVICES_ENABLED Since Version: 0.11.0
hoodie.table.services.incremental.enabled	true	Whether to enable incremental table service. So far Clustering and Compaction support incremental processing. Config Param: INCREMENTAL_TABLE_SERVICE_ENABLED Since Version: 1.0.0
hoodie.timeline.layout.version	2	Controls the layout of the timeline. Version 0 relied on renames, Version 1 (default) models the timeline as an immutable log relying only on atomic writes for object storage. Config Param: TIMELINE_LAYOUT_VERSION_NUM Since Version: 0.5.1
hoodie.upsert.shuffle.parallelism	0	Parallelism to use for upsert operation on the table. Upserts can shuffle data to perform index lookups, file sizing, bin packing records optimally into file groups. Before 0.13.0 release, if users do not configure it, Hudi would use 200 as the default shuffle parallelism. From 0.13.0 onwards Hudi by default automatically uses the parallelism deduced by Spark based on the source data. If the shuffle parallelism is explicitly configured by the user, the user-configured parallelism is used in defining the actual parallelism. If you observe small files from the upsert operation, we suggest configuring this shuffle parallelism explicitly, so that the parallelism is around total_input_data_size/120MB. Config Param: UPSERT_PARALLELISM_VALUE
hoodie.write.application.id	Unknown	Application identifier (e.g. Spark application id) used to populate lock metadata so lock holders can be identified. Config Param: APPLICATION_ID
hoodie.write.buffer.limit.bytes	4194304	Size of in-memory buffer used for parallelizing network reads and lake storage writes. Config Param: WRITE_BUFFER_LIMIT_BYTES_VALUE
hoodie.write.buffer.record.cache.limit	131072	Maximum queue size of in-memory buffer for parallelizing network reads and lake storage writes. Config Param: WRITE_BUFFER_RECORD_CACHE_LIMIT Since Version: 0.15.0
hoodie.write.buffer.record.sampling.rate	64	Sampling rate of in-memory buffer used to estimate object size. Higher value lead to lower CPU usage. Config Param: WRITE_BUFFER_RECORD_SAMPLING_RATE Since Version: 0.15.0
hoodie.write.complex.keygen.new.encoding	false	This config only takes effect for writing table version 8 and below. If set to false, the record key field name is encoded and prepended in the case where a single record key field is used in the complex key generator, i.e., record keys stored in _hoodie_record_key meta field is in the format of <field_name>:<field_value>, which conforms to the behavior in 0.14.0 release and older. If set to true, the record key field name is not encoded under the same case in the complex key generator, i.e., record keys stored in _hoodie_record_key meta field is in the format of <field_value>, which conforms to the behavior in 0.14.1, 0.15.0, 1.0.0, 1.0.1, 1.0.2 releases. Config Param: COMPLEX_KEYGEN_NEW_ENCODING Since Version: 1.1.0
hoodie.write.complex.keygen.validation.enable	true	This config only takes effect for writing table version 8 and below, upgrade or downgrade. If set to true, the writer enables the validation on whether the table uses the complex key generator with a single record key field, which can be affected by a breaking change in 0.14.1, 0.15.0, 1.0.0, 1.0.1, 1.0.2 releases, causing key encoding change and potential duplicates in the table. The validation fails the pipeline if the table meets the condition for the user to take proper action. The user can turn this validation off by setting the config to false, after evaluating the table and situation and doing table repair if needed. Config Param: ENABLE_COMPLEX_KEYGEN_VALIDATION Since Version: 1.1.0
hoodie.write.concat.handle.class	org.apache.hudi.io.HoodieConcatHandle	The merge handle class to use to concat the records from a base file with an iterator of incoming records. Config Param: CONCAT_HANDLE_CLASS_NAME Since Version: 1.1.0
hoodie.write.concurrency.async.conflict.detector.initial_delay_ms	0	Used for timeline-server-based markers with AsyncTimelineServerBasedDetectionStrategy. The time in milliseconds to delay the first execution of async marker-based conflict detection. Config Param: ASYNC_CONFLICT_DETECTOR_INITIAL_DELAY_MS Since Version: 0.13.0
hoodie.write.concurrency.async.conflict.detector.period_ms	30000	Used for timeline-server-based markers with AsyncTimelineServerBasedDetectionStrategy. The period in milliseconds between successive executions of async marker-based conflict detection. Config Param: ASYNC_CONFLICT_DETECTOR_PERIOD_MS Since Version: 0.13.0
hoodie.write.concurrency.early.conflict.check.commit.conflict	false	Whether to enable commit conflict checking or not during early conflict detection. Config Param: EARLY_CONFLICT_DETECTION_CHECK_COMMIT_CONFLICT Since Version: 0.13.0
hoodie.write.concurrency.early.conflict.detection.enable	false	Whether to enable early conflict detection based on markers. It eagerly detects writing conflict before create markers and fails fast if a conflict is detected, to release cluster compute resources as soon as possible. Config Param: EARLY_CONFLICT_DETECTION_ENABLE Since Version: 0.13.0
hoodie.write.concurrency.early.conflict.detection.strategy		The class name of the early conflict detection strategy to use. This should be a subclass of org.apache.hudi.common.conflict.detection.EarlyConflictDetectionStrategy. Config Param: EARLY_CONFLICT_DETECTION_STRATEGY_CLASS_NAME Since Version: 0.13.0
hoodie.write.concurrency.schema.conflict.resolution.enable	true	If turned on, we detect and abort incompatible concurrent schema evolution. Config Param: ENABLE_SCHEMA_CONFLICT_RESOLUTION Since Version: 1.1.0
hoodie.write.executor.disruptor.buffer.limit.bytes	1024	The size of the Disruptor Executor ring buffer, must be power of 2 Config Param: WRITE_EXECUTOR_DISRUPTOR_BUFFER_LIMIT_BYTES Since Version: 0.13.0
hoodie.write.executor.disruptor.wait.strategy	BLOCKING_WAIT	org.apache.hudi.common.util.queue.DisruptorWaitStrategyType: Strategy employed for making Disruptor Executor wait on a cursor. BLOCKING_WAIT(default): The slowest of the available wait strategies. However, it is the most conservative with the respect to CPU usage and will give the most consistent behaviour across the widest variety of deployment options. SLEEPING_WAIT: Like the BLOCKING_WAIT strategy, it attempts to be conservative with CPU usage by using a simple busy wait loop. The difference is that the SLEEPING_WAIT strategy uses a call to LockSupport.parkNanos(1) in the middle of the loop. On a typical Linux system this will pause the thread for around 60µs. YIELDING_WAIT: The YIELDING_WAIT strategy is one of two wait strategy that can be used in low-latency systems. It is designed for cases where there is an opportunity to burn CPU cycles with the goal of improving latency. The YIELDING_WAIT strategy will busy spin, waiting for the sequence to increment to the appropriate value. Inside the body of the loop Thread#yield() will be called allowing other queued threads to run. This is the recommended wait strategy when you need very high performance, and the number of EventHandler threads is lower than the total number of logical cores, such as when hyper-threading is enabled. BUSY_SPIN_WAIT: The BUSY_SPIN_WAIT strategy is the highest performing wait strategy. Like the YIELDING_WAIT strategy, it can be used in low-latency systems, but puts the highest constraints on the deployment environment. Config Param: WRITE_EXECUTOR_DISRUPTOR_WAIT_STRATEGY Since Version: 0.13.0
hoodie.write.executor.type	SIMPLE	org.apache.hudi.common.util.queue.ExecutorType: Types of executor that implements org.apache.hudi.common.util.queue.HoodieExecutor. The executor orchestrates concurrent producers and consumers communicating through a message queue. BOUNDED_IN_MEMORY: Executor which orchestrates concurrent producers and consumers communicating through a bounded in-memory message queue using LinkedBlockingQueue. This queue will use extra lock to balance producers and consumers. DISRUPTOR: Executor which orchestrates concurrent producers and consumers communicating through disruptor as a lock free message queue to gain better writing performance. Although DisruptorExecutor is still an experimental feature. SIMPLE(default): Executor with no inner message queue and no inner lock. Consuming and writing records from iterator directly. The advantage is that there is no need for additional memory and cpu resources due to lock or multithreading. The disadvantage is that the executor is a single-write-single-read model, cannot support functions such as speed limit and can not de-couple the network read (shuffle read) and network write (writing objects/files to storage) anymore. Config Param: WRITE_EXECUTOR_TYPE Since Version: 0.13.0
hoodie.write.markers.type	TIMELINE_SERVER_BASED	org.apache.hudi.common.table.marker.MarkerType: Marker type indicating how markers are stored in the file system, used for identifying the files written and cleaning up files not committed which should be deleted. DIRECT: Individual marker file corresponding to each data file is directly created by the writer. TIMELINE_SERVER_BASED(default): Marker operations are all handled at the timeline service which serves as a proxy. New marker entries are batch processed and stored in a limited number of underlying files for efficiency. If HDFS is used or timeline server is disabled, DIRECT markers are used as fallback even if this is configured. This configuration does not take effect for Spark structured streaming; DIRECT markers are always used. Config Param: MARKERS_TYPE Since Version: 0.9.0
hoodie.write.merge.handle.class	org.apache.hudi.io.FileGroupReaderBasedMergeHandle	The merge handle class that implements interface HoodieMergeHandle to merge the records from a base file with an iterator of incoming records or a map of updates and deletes from log files at a file group level. Config Param: MERGE_HANDLE_CLASS_NAME Since Version: 1.1.0
hoodie.write.merge.handle.fallback	true	When using a custom Hoodie Merge Handle Implementation controlled by the config hoodie.write.merge.handle.class or when using a custom Hoodie Concat Handle Implementation controlled by the config hoodie.write.concat.handle.class, enabling this config results in fallback to the default implementations if instantiation of the custom implementation fails Config Param: MERGE_HANDLE_PERFORM_FALLBACK Since Version: 1.1.0
hoodie.write.num.retries.on.conflict.failures	0	Maximum number of times to retry a batch on conflict failure. Config Param: NUM_RETRIES_ON_CONFLICT_FAILURES Since Version: 0.14.0
hoodie.write.partial.update.schema		Avro schema of the partial updates. This is automatically set by the Hudi write client and user is not expected to manually change the value. Config Param: WRITE_PARTIAL_UPDATE_SCHEMA Since Version: 1.0.0
hoodie.write.record.positions	true	Whether to write record positions to the block header for data blocks containing updates and delete blocks. The record positions can be used to improve the performance of merging records from base and log files. Config Param: WRITE_RECORD_POSITIONS Since Version: 1.0.0
hoodie.write.rolling.metadata.keys		Comma-separated list of extra metadata keys that should be automatically carried forward to every new commit. These keys will be read from recent commit metadata and included in new commits, ensuring they remain accessible without walking the timeline or worrying about archival. This is useful for tracking checkpoint information (e.g., Kafka offsets, Flink checkpoints) or any metadata that needs to persist across commits. New values override old ones. Only applies to data table commits. Config Param: ROLLING_METADATA_KEYS Since Version: 1.2.0
hoodie.write.rolling.metadata.timeline.lookback.commits	10	Maximum number of completed commits to walk back in the timeline when searching for rolling metadata keys. If a rolling metadata key is not found in the latest commit, the system will walk back up to this many commits to find the most recent value. This ensures rolling metadata is preserved even if some commits don't update all keys. Higher values provide more resilience but may impact performance. Only applies when hoodie.write.rolling.metadata.keys is configured. Config Param: ROLLING_METADATA_TIMELINE_LOOKBACK_COMMITS Since Version: 1.2.0
hoodie.write.status.storage.level	MEMORY_AND_DISK_SER	Write status objects hold metadata about a write (stats, errors), that is not yet committed to storage. This controls the how that information is cached for inspection by clients. We rarely expect this to be changed. Config Param: WRITE_STATUS_STORAGE_LEVEL_VALUE
hoodie.write.tagged.record.storage.level	MEMORY_AND_DISK_SER	Determine what level of persistence is used to cache write RDDs. Refer to org.apache.spark.storage.StorageLevel for different values Config Param: TAGGED_RECORD_STORAGE_LEVEL_VALUE
hoodie.write.track.event.time.watermark	false	Records event time watermark metadata in commit metadata when enabled Config Param: TRACK_EVENT_TIME_WATERMARK Since Version: 1.1.0
hoodie.writestatus.class	org.apache.hudi.client.WriteStatus	Subclass of org.apache.hudi.client.WriteStatus to be used to collect information about a write. Can be overridden to collection additional metrics/statistics about the data if needed. Config Param: WRITE_STATUS_CLASS_NAME

---

Commit Callback Configs

Configurations controlling callback behavior into HTTP endpoints, to push notifications on commits on hudi tables.

Write commit callback configs

Advanced Configs

Config Name	Default	Description
hoodie.write.commit.callback.http.custom.headers	(N/A)	Http callback custom headers. Format: HeaderName1:HeaderValue1;HeaderName2:HeaderValue2 Config Param: CALLBACK_HTTP_CUSTOM_HEADERS Since Version: 0.15.0
hoodie.write.commit.callback.http.url	(N/A)	Callback host to be sent along with callback messages Config Param: CALLBACK_HTTP_URL Since Version: 0.6.0
hoodie.write.commit.callback.class	org.apache.hudi.callback.impl.HoodieWriteCommitHttpCallback	Full path of callback class and must be a subclass of HoodieWriteCommitCallback class, org.apache.hudi.callback.impl.HoodieWriteCommitHttpCallback by default Config Param: CALLBACK_CLASS_NAME Since Version: 0.6.0
hoodie.write.commit.callback.http.api.key	hudi_write_commit_http_callback	Http callback API key. hudi_write_commit_http_callback by default Config Param: CALLBACK_HTTP_API_KEY_VALUE Since Version: 0.6.0
hoodie.write.commit.callback.http.timeout.seconds	30	Callback timeout in seconds. Config Param: CALLBACK_HTTP_TIMEOUT_IN_SECONDS Since Version: 0.6.0
hoodie.write.commit.callback.on	false	Turn commit callback on/off. off by default. Config Param: TURN_CALLBACK_ON Since Version: 0.6.0

---

Write commit Kafka callback configs

Controls notifications sent to Kafka, on events happening to a hudi table.

Advanced Configs

Config Name	Default	Description
hoodie.write.commit.callback.kafka.bootstrap.servers	(N/A)	Bootstrap servers of kafka cluster, to be used for publishing commit metadata. Config Param: BOOTSTRAP_SERVERS Since Version: 0.7.0
hoodie.write.commit.callback.kafka.partition	(N/A)	It may be desirable to serialize all changes into a single Kafka partition for providing strict ordering. By default, Kafka messages are keyed by table name, which guarantees ordering at the table level, but not globally (or when new partitions are added) Config Param: PARTITION Since Version: 0.7.0
hoodie.write.commit.callback.kafka.topic	(N/A)	Kafka topic name to publish timeline activity into. Config Param: TOPIC Since Version: 0.7.0
hoodie.write.commit.callback.kafka.acks	all	kafka acks level, all by default to ensure strong durability. Config Param: ACKS Since Version: 0.7.0
hoodie.write.commit.callback.kafka.retries	3	Times to retry the produce. 3 by default Config Param: RETRIES Since Version: 0.7.0

---

Write commit pulsar callback configs

Controls notifications sent to pulsar, on events happening to a hudi table.

Advanced Configs

Config Name	Default	Description
hoodie.write.commit.callback.pulsar.broker.service.url	(N/A)	Server's url of pulsar cluster, to be used for publishing commit metadata. Config Param: BROKER_SERVICE_URL Since Version: 0.11.0
hoodie.write.commit.callback.pulsar.topic	(N/A)	pulsar topic name to publish timeline activity into. Config Param: TOPIC Since Version: 0.11.0
hoodie.write.commit.callback.pulsar.connection-timeout	10s	Duration of waiting for a connection to a broker to be established. Config Param: CONNECTION_TIMEOUT Since Version: 0.11.0
hoodie.write.commit.callback.pulsar.keepalive-interval	30s	Duration of keeping alive interval for each client broker connection. Config Param: KEEPALIVE_INTERVAL Since Version: 0.11.0
hoodie.write.commit.callback.pulsar.operation-timeout	30s	Duration of waiting for completing an operation. Config Param: OPERATION_TIMEOUT Since Version: 0.11.0
hoodie.write.commit.callback.pulsar.producer.block-if-queue-full	true	When the queue is full, the method is blocked instead of an exception is thrown. Config Param: PRODUCER_BLOCK_QUEUE_FULL Since Version: 0.11.0
hoodie.write.commit.callback.pulsar.producer.pending-queue-size	1000	The maximum size of a queue holding pending messages. Config Param: PRODUCER_PENDING_QUEUE_SIZE Since Version: 0.11.0
hoodie.write.commit.callback.pulsar.producer.pending-total-size	50000	The maximum number of pending messages across partitions. Config Param: PRODUCER_PENDING_SIZE Since Version: 0.11.0
hoodie.write.commit.callback.pulsar.producer.route-mode	RoundRobinPartition	Message routing logic for producers on partitioned topics. Config Param: PRODUCER_ROUTE_MODE Since Version: 0.11.0
hoodie.write.commit.callback.pulsar.producer.send-timeout	30s	The timeout in each sending to pulsar. Config Param: PRODUCER_SEND_TIMEOUT Since Version: 0.11.0
hoodie.write.commit.callback.pulsar.request-timeout	60s	Duration of waiting for completing a request. Config Param: REQUEST_TIMEOUT Since Version: 0.11.0

---

Lock Configs

Configurations that control locking mechanisms required for concurrency control between writers to a Hudi table. Concurrency between Hudi's own table services are auto managed internally.

Common Lock Configurations

Basic Configs

Config Name	Default	Description
hoodie.write.lock.heartbeat_interval_ms	60000	Heartbeat interval in ms, to send a heartbeat to indicate that hive client holding locks. Config Param: LOCK_HEARTBEAT_INTERVAL_MS Since Version: 0.15.0

Advanced Configs

Config Name	Default	Description
hoodie.write.lock.filesystem.path	(N/A)	For DFS based lock providers, path to store the locks under. use Table's meta path as default Config Param: FILESYSTEM_LOCK_PATH Since Version: 0.8.0
hoodie.write.lock.hivemetastore.database	(N/A)	For Hive based lock provider, the Hive database to acquire lock against Config Param: HIVE_DATABASE_NAME Since Version: 0.8.0
hoodie.write.lock.hivemetastore.table	(N/A)	For Hive based lock provider, the Hive table to acquire lock against Config Param: HIVE_TABLE_NAME Since Version: 0.8.0
hoodie.write.lock.hivemetastore.uris	(N/A)	For Hive based lock provider, the Hive metastore URI to acquire locks against. Config Param: HIVE_METASTORE_URI Since Version: 0.8.0
hoodie.write.lock.provider	(N/A)	Lock provider class name, user can provide their own implementation of LockProvider which should be subclass of org.apache.hudi.common.lock.LockProvider Config Param: LOCK_PROVIDER_CLASS_NAME Since Version: 0.8.0
hoodie.write.lock.zookeeper.base_path	(N/A)	The base path on Zookeeper under which to create lock related ZNodes. This should be same for all concurrent writers to the same table Config Param: ZK_BASE_PATH Since Version: 0.8.0
hoodie.write.lock.zookeeper.port	(N/A)	Zookeeper port to connect to. Config Param: ZK_PORT Since Version: 0.8.0
hoodie.write.lock.zookeeper.url	(N/A)	Zookeeper URL to connect to. Config Param: ZK_CONNECT_URL Since Version: 0.8.0
hoodie.write.lock.client.num_retries	50	Maximum number of times to retry to acquire lock additionally from the lock manager. Config Param: LOCK_ACQUIRE_CLIENT_NUM_RETRIES Since Version: 0.8.0
hoodie.write.lock.client.wait_time_ms_between_retry	5000	Amount of time to wait between retries on the lock provider by the lock manager Config Param: LOCK_ACQUIRE_CLIENT_RETRY_WAIT_TIME_IN_MILLIS Since Version: 0.8.0
hoodie.write.lock.conflict.resolution.strategy	org.apache.hudi.client.transaction.SimpleConcurrentFileWritesConflictResolutionStrategy	Lock provider class name, this should be subclass of org.apache.hudi.client.transaction.ConflictResolutionStrategy Config Param: WRITE_CONFLICT_RESOLUTION_STRATEGY_CLASS_NAME Since Version: 0.8.0
hoodie.write.lock.filesystem.expire	0	For DFS based lock providers, expire time in minutes, must be a non-negative number, default means no expire Config Param: FILESYSTEM_LOCK_EXPIRE Since Version: 0.12.0
hoodie.write.lock.max_wait_time_ms_between_retry	16000	Maximum amount of time to wait between retries by lock provider client. This bounds the maximum delay from the exponential backoff. Currently used by ZK based lock provider only. Config Param: LOCK_ACQUIRE_RETRY_MAX_WAIT_TIME_IN_MILLIS Since Version: 0.8.0
hoodie.write.lock.num_retries	15	Maximum number of times to retry lock acquire, at each lock provider Config Param: LOCK_ACQUIRE_NUM_RETRIES Since Version: 0.8.0
hoodie.write.lock.wait_time_ms	60000	Timeout in ms, to wait on an individual lock acquire() call, at the lock provider. Config Param: LOCK_ACQUIRE_WAIT_TIMEOUT_MS Since Version: 0.8.0
hoodie.write.lock.wait_time_ms_between_retry	1000	Initial amount of time to wait between retries to acquire locks, subsequent retries will exponentially backoff. Config Param: LOCK_ACQUIRE_RETRY_WAIT_TIME_IN_MILLIS Since Version: 0.8.0
hoodie.write.lock.zookeeper.connection_timeout_ms	15000	Timeout in ms, to wait for establishing connection with Zookeeper. Config Param: ZK_CONNECTION_TIMEOUT_MS Since Version: 0.8.0
hoodie.write.lock.zookeeper.lock_key		Key name under base_path at which to create a ZNode and acquire lock. Final path on zk will look like base_path/lock_key. If this parameter is not set, we would set it as the table name Config Param: ZK_LOCK_KEY Since Version: 0.8.0
hoodie.write.lock.zookeeper.session_timeout_ms	60000	Timeout in ms, to wait after losing connection to ZooKeeper, before the session is expired Config Param: ZK_SESSION_TIMEOUT_MS Since Version: 0.8.0

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Azure based Locks Configurations

Configs that control Azure Blob/ADLS based locking mechanisms required for concurrency control between writers to a Hudi table.

Advanced Configs

Config Name	Default	Description
hoodie.write.lock.azure.client.id	(N/A)	For Azure based lock provider, Azure AD application (client) ID used together with 'hoodie.write.lock.azure.client.tenant.id' and 'hoodie.write.lock.azure.client.secret' to authenticate via service principal (ClientSecretCredential). All three must be set for this auth mode to activate. Config Param: AZURE_CLIENT_ID Since Version: 1.2.0
hoodie.write.lock.azure.client.secret	(N/A)	For Azure based lock provider, Azure AD client secret used together with 'hoodie.write.lock.azure.client.tenant.id' and 'hoodie.write.lock.azure.client.id' to authenticate via service principal (ClientSecretCredential). All three must be set for this auth mode to activate. Config Param: AZURE_CLIENT_SECRET Since Version: 1.2.0
hoodie.write.lock.azure.client.tenant.id	(N/A)	For Azure based lock provider, Azure AD tenant ID used together with 'hoodie.write.lock.azure.client.id' and 'hoodie.write.lock.azure.client.secret' to authenticate via service principal (ClientSecretCredential). All three must be set for this auth mode to activate. Config Param: AZURE_CLIENT_TENANT_ID Since Version: 1.2.0
hoodie.write.lock.azure.connection.string	(N/A)	For Azure based lock provider, optional Azure Storage connection string used for authenticating BlobServiceClient. Config Param: AZURE_CONNECTION_STRING Since Version: 1.2.0
hoodie.write.lock.azure.managed.identity.client.id	(N/A)	For Azure based lock provider, client ID of a user-assigned managed identity to authenticate BlobServiceClient with ManagedIdentityCredential. Config Param: AZURE_MANAGED_IDENTITY_CLIENT_ID Since Version: 1.2.0
hoodie.write.lock.azure.sas.token	(N/A)	For Azure based lock provider, optional SAS token used for authenticating BlobServiceClient when connection string is not provided. SAS token is not recommended for production use by Azure. Config Param: AZURE_SAS_TOKEN Since Version: 1.2.0

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DynamoDB based Locks Configurations

Configs that control DynamoDB based locking mechanisms required for concurrency control between writers to a Hudi table. Concurrency between Hudi's own table services are auto managed internally.

Advanced Configs

Config Name	Default	Description
hoodie.write.lock.dynamodb.endpoint_url	(N/A)	For DynamoDB based lock provider, the url endpoint used for Amazon DynamoDB service. Useful for development with a local dynamodb instance. Config Param: DYNAMODB_ENDPOINT_URL Since Version: 0.10.1
hoodie.write.lock.dynamodb.billing_mode	PAY_PER_REQUEST	For DynamoDB based lock provider, by default it is PAY_PER_REQUEST mode. Alternative is PROVISIONED. Config Param: DYNAMODB_LOCK_BILLING_MODE Since Version: 0.10.0
hoodie.write.lock.dynamodb.partition_key		For DynamoDB based lock provider, the partition key for the DynamoDB lock table. Each Hudi dataset should has it's unique key so concurrent writers could refer to the same partition key. By default we use the Hudi table name specified to be the partition key Config Param: DYNAMODB_LOCK_PARTITION_KEY Since Version: 0.10.0
hoodie.write.lock.dynamodb.read_capacity	20	For DynamoDB based lock provider, read capacity units when using PROVISIONED billing mode Config Param: DYNAMODB_LOCK_READ_CAPACITY Since Version: 0.10.0
hoodie.write.lock.dynamodb.region	us-east-1	For DynamoDB based lock provider, the region used in endpoint for Amazon DynamoDB service. Would try to first get it from AWS_REGION environment variable. If not find, by default use us-east-1 Config Param: DYNAMODB_LOCK_REGION Since Version: 0.10.0
hoodie.write.lock.dynamodb.table	hudi_locks	For DynamoDB based lock provider, the name of the DynamoDB table acting as lock table Config Param: DYNAMODB_LOCK_TABLE_NAME Since Version: 0.10.0
hoodie.write.lock.dynamodb.table_creation_timeout	120000	For DynamoDB based lock provider, the maximum number of milliseconds to wait for creating DynamoDB table Config Param: DYNAMODB_LOCK_TABLE_CREATION_TIMEOUT Since Version: 0.10.0
hoodie.write.lock.dynamodb.write_capacity	10	For DynamoDB based lock provider, write capacity units when using PROVISIONED billing mode Config Param: DYNAMODB_LOCK_WRITE_CAPACITY Since Version: 0.10.0
hoodie.write.lock.wait_time_ms	60000	Lock Acquire Wait Timeout in milliseconds Config Param: LOCK_ACQUIRE_WAIT_TIMEOUT_MS_PROP_KEY Since Version: 0.10.0

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Key Generator Configs

Hudi maintains keys (record key + partition path) for uniquely identifying a particular record. These configs allow developers to setup the Key generator class that extracts these out of incoming records.

Key Generator Options

Basic Configs

Config Name	Default	Description
hoodie.datasource.write.partitionpath.field	(N/A)	Partition path field. Value to be used at the partitionPath component of HoodieKey. Actual value obtained by invoking .toString() Config Param: PARTITIONPATH_FIELD_NAME
hoodie.datasource.write.recordkey.field	(N/A)	Record key field. Value to be used as the recordKey component of HoodieKey. Actual value will be obtained by invoking .toString() on the field value. Nested fields can be specified using the dot notation eg: a.b.c Config Param: RECORDKEY_FIELD_NAME
hoodie.datasource.write.secondarykey.column	(N/A)	Columns that constitute the secondary key component. Actual value will be obtained by invoking .toString() on the field value. Nested fields can be specified using the dot notation eg: a.b.c Config Param: SECONDARYKEY_COLUMN_NAME
hoodie.datasource.write.hive_style_partitioning	false	Flag to indicate whether to use Hive style partitioning. If set true, the names of partition folders follow <partition_column_name>=<partition_value> format. By default false (the names of partition folders are only partition values) Config Param: HIVE_STYLE_PARTITIONING_ENABLE

Advanced Configs

Config Name	Default	Description
hoodie.datasource.write.keygenerator.consistent.logical.timestamp.enabled	false	When set to true, consistent value will be generated for a logical timestamp type column, like timestamp-millis and timestamp-micros, irrespective of whether row-writer is enabled. Disabled by default so as not to break the pipeline that deploy either fully row-writer path or non row-writer path. For example, if it is kept disabled then record key of timestamp type with value 2016-12-29 09:54:00 will be written as timestamp 2016-12-29 09:54:00.0 in row-writer path, while it will be written as long value 1483023240000000 in non row-writer path. If enabled, then the timestamp value will be written in both the cases. Config Param: KEYGENERATOR_CONSISTENT_LOGICAL_TIMESTAMP_ENABLED Since Version: 0.10.1
hoodie.datasource.write.partitionpath.urlencode	false	Should we url encode the partition path value, before creating the folder structure. Config Param: URL_ENCODE_PARTITIONING
hoodie.datasource.write.slash.separated.date.partitioning	false	Flag to indicate whether to use slash separated date partitioning. If set to true, date partition values in yyyy-MM-dd format will be transformed to yyyy/MM/dd directory structure. By default false. Cannot be used together with hive-style partitioning. Config Param: SLASH_SEPARATED_DATE_PARTITIONING

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Timestamp-based key generator configs

Configs used for TimestampBasedKeyGenerator which relies on timestamps for the partition field. The field values are interpreted as timestamps and not just converted to string while generating partition path value for records. Record key is same as before where it is chosen by field name.

Advanced Configs

Config Name	Default	Description
hoodie.keygen.timebased.timestamp.type	(N/A)	Timestamp type of the field, which should be one of the timestamp types supported: UNIX_TIMESTAMP, DATE_STRING, MIXED, EPOCHMILLISECONDS, EPOCHMICROSECONDS, SCALAR. Config Param: TIMESTAMP_TYPE_FIELD
hoodie.keygen.datetime.parser.class	org.apache.hudi.keygen.parser.HoodieDateTimeParser	Date time parser class name. Config Param: DATE_TIME_PARSER
hoodie.keygen.timebased.input.dateformat		Input date format such as yyyy-MM-dd'T'HH:mm:ss.SSSZ. Config Param: TIMESTAMP_INPUT_DATE_FORMAT
hoodie.keygen.timebased.input.dateformat.list.delimiter.regex	,	The delimiter for allowed input date format list, usually ,. Config Param: TIMESTAMP_INPUT_DATE_FORMAT_LIST_DELIMITER_REGEX
hoodie.keygen.timebased.input.timezone	UTC	Timezone of the input timestamp, such as UTC. Config Param: TIMESTAMP_INPUT_TIMEZONE_FORMAT
hoodie.keygen.timebased.output.dateformat		Output date format such as yyyy-MM-dd'T'HH:mm:ss.SSSZ. Config Param: TIMESTAMP_OUTPUT_DATE_FORMAT
hoodie.keygen.timebased.output.timezone	UTC	Timezone of the output timestamp, such as UTC. Config Param: TIMESTAMP_OUTPUT_TIMEZONE_FORMAT
hoodie.keygen.timebased.timestamp.scalar.time.unit	SECONDS	When timestamp type SCALAR is used, this specifies the time unit, with allowed unit specified by TimeUnit enums (NANOSECONDS, MICROSECONDS, MILLISECONDS, SECONDS, MINUTES, HOURS, DAYS). Config Param: INPUT_TIME_UNIT
hoodie.keygen.timebased.timezone	UTC	Timezone of both input and output timestamp if they are the same, such as UTC. Please use hoodie.keygen.timebased.input.timezone and hoodie.keygen.timebased.output.timezone instead if the input and output timezones are different. Config Param: TIMESTAMP_TIMEZONE_FORMAT

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Index Configs

Configurations that control indexing behavior, which tags incoming records as either inserts or updates to older records.

Common Index Configs

Basic Configs

Config Name	Default	Description
hoodie.expression.index.function	(N/A)	Function to be used for building the expression index. Config Param: INDEX_FUNCTION Since Version: 1.0.0
hoodie.index.name	(N/A)	Name of the expression index. This is also used for the partition name in the metadata table. Config Param: INDEX_NAME Since Version: 1.0.0
hoodie.table.checksum	(N/A)	Index definition checksum is used to guard against partial writes in HDFS. It is added as the last entry in index.properties and then used to validate while reading table config. Config Param: INDEX_DEFINITION_CHECKSUM Since Version: 1.0.0
hoodie.expression.index.type	COLUMN_STATS	Type of the expression index. Default is column_stats if there are no functions and expressions in the command. Valid options could be BITMAP, COLUMN_STATS, LUCENE, etc. If index_type is not provided, and there are functions or expressions in the command then a expression index using column stats will be created. Config Param: INDEX_TYPE Since Version: 1.0.0

Advanced Configs

Config Name	Default	Description
hoodie.expression.index.range.metadata.storage.level	MEMORY_AND_DISK_SER	Determine what level of persistence is used to cache range metadata RDDs created to compute expression index. Refer to org.apache.spark.storage.StorageLevel for different values Config Param: EXPRESSION_INDEX_RANGE_METADATA_STORAGE_LEVEL_VALUE Since Version: 1.2.0

---

Common Index Configs

Basic Configs

Config Name	Default	Description
hoodie.index.type	(N/A)	org.apache.hudi.index.HoodieIndex$IndexType: Determines how input records are indexed, i.e., looked up based on the key for the location in the existing table. Default is SIMPLE on Spark engine, and INMEMORY on Flink and Java engines. INMEMORY: Uses in-memory hashmap in Spark and Java engine and Flink in-memory state in Flink for indexing. BLOOM: Employs bloom filters built out of the record keys, optionally also pruning candidate files using record key ranges. Key uniqueness is enforced inside partitions. GLOBAL_BLOOM: Employs bloom filters built out of the record keys, optionally also pruning candidate files using record key ranges. Key uniqueness is enforced across all partitions in the table. SIMPLE: Performs a lean join of the incoming update/delete records against keys extracted from the table on storage.Key uniqueness is enforced inside partitions. GLOBAL_SIMPLE: Performs a lean join of the incoming update/delete records against keys extracted from the table on storage.Key uniqueness is enforced across all partitions in the table. BUCKET: locates the file group containing the record fast by using bucket hashing, particularly beneficial in large scale. Use hoodie.index.bucket.engine to choose bucket engine type, i.e., how buckets are generated. FLINK_STATE: Internal Config for indexing based on Flink state. RECORD_INDEX: Index which saves the record key to location mappings in the HUDI Metadata Table. Record index is a global index, enforcing key uniqueness across all partitions in the table. Supports sharding to achieve very high scale. For a table with keys that are only unique inside each partition, use RECORD_LEVEL_INDEX instead. This enum is deprecated. Use GLOBAL_RECORD_LEVEL_INDEX for global uniqueness of record keys or RECORD_LEVEL_INDEX for partition-level uniqueness of record keys. GLOBAL_RECORD_LEVEL_INDEX: Index which saves the record key to location mappings in the HUDI Metadata Table. Record index is a global index, enforcing key uniqueness across all partitions in the table. Supports sharding to achieve very high scale. For a table with keys that are only unique inside each partition, use RECORD_LEVEL_INDEX instead. RECORD_LEVEL_INDEX: Index which saves the record key to location mappings in the HUDI Metadata Table. Supports sharding to achieve very high scale. This is a non global index, where keys can be replicated across partitions, since a pair of partition path and record keys will uniquely map to a location using this index. If users expect record keys to be unique across all partitions, use GLOBAL_RECORD_LEVEL_INDEX instead. Config Param: INDEX_TYPE
hoodie.bucket.index.query.pruning	true	Control if table with bucket index use bucket query or not Config Param: BUCKET_QUERY_INDEX
hoodie.bucket.index.remote.partitioner.enable	false	Use Remote Partitioner using centralized allocation of partition IDs to do repartition based on bucket aiming to resolve data skew. Default local hash partitioner Config Param: BUCKET_PARTITIONER

Advanced Configs

Config Name	Default	Description
hoodie.bucket.index.hash.field	(N/A)	Index key. It is used to index the record and find its file group. If not set, use record key field as default Config Param: BUCKET_INDEX_HASH_FIELD
hoodie.bucket.index.max.num.buckets	(N/A)	Only applies if bucket index engine is consistent hashing. Determine the upper bound of the number of buckets in the hudi table. Bucket resizing cannot be done higher than this max limit. Config Param: BUCKET_INDEX_MAX_NUM_BUCKETS Since Version: 0.13.0
hoodie.bucket.index.min.num.buckets	(N/A)	Only applies if bucket index engine is consistent hashing. Determine the lower bound of the number of buckets in the hudi table. Bucket resizing cannot be done lower than this min limit. Config Param: BUCKET_INDEX_MIN_NUM_BUCKETS Since Version: 0.13.0
hoodie.bucket.index.partition.expressions	(N/A)	Users can use this parameter to specify expression and the corresponding bucket numbers (separated by commas).Multiple rules are separated by semicolons like hoodie.bucket.index.partition.expressions=expression1,bucket-number1;expression2,bucket-number2 Config Param: BUCKET_INDEX_PARTITION_EXPRESSIONS
hoodie.bloom.index.bucketized.checking	true	Only applies if index type is BLOOM. When true, bucketized bloom filtering is enabled. This reduces skew seen in sort based bloom index lookup Config Param: BLOOM_INDEX_BUCKETIZED_CHECKING
hoodie.bloom.index.bucketized.checking.enable.dynamic.parallelism	false	Only applies if index type is BLOOM and the bucketized bloom filtering is enabled. When true, the index parallelism is determined by the number of file groups to look up and the number of keys per bucket to split comparisons within a file group; otherwise, the index parallelism is limited by the input parallelism. PLEASE NOTE that if the bloom index parallelism (hoodie.bloom.index.parallelism) is configured, the bloom index parallelism takes effect instead of the input parallelism and always limits the number of buckets calculated based on the number of keys per bucket in the bucketized bloom filtering. Config Param: BLOOM_INDEX_BUCKETIZED_CHECKING_ENABLE_DYNAMIC_PARALLELISM Since Version: 1.1.0
hoodie.bloom.index.fileid.key.sorting.enable	false	Only applies if index type is BLOOM. When true, the global sorting based on the fileId and key is enabled during key lookup. This reduces skew in the key lookup in the bloom index. Config Param: BLOOM_INDEX_FILE_GROUP_ID_KEY_SORTING Since Version: 1.1.0
hoodie.bloom.index.input.storage.level	MEMORY_AND_DISK_SER	Only applies when #bloomIndexUseCaching is set. Determine what level of persistence is used to cache input RDDs. Refer to org.apache.spark.storage.StorageLevel for different values Config Param: BLOOM_INDEX_INPUT_STORAGE_LEVEL_VALUE
hoodie.bloom.index.keys.per.bucket	10000000	Only applies if bloomIndexBucketizedChecking is enabled and index type is bloom. This configuration controls the “bucket” size which tracks the number of record-key checks made against a single file and is the unit of work allocated to each partition performing bloom filter lookup. A higher value would amortize the fixed cost of reading a bloom filter to memory. Config Param: BLOOM_INDEX_KEYS_PER_BUCKET
hoodie.bloom.index.parallelism	0	Only applies if index type is BLOOM. This is the amount of parallelism for index lookup, which involves a shuffle. By default, this is auto computed based on input workload characteristics. If the parallelism is explicitly configured by the user, the user-configured value is used in defining the actual parallelism. If the indexing stage is slow due to the limited parallelism, you can increase this to tune the performance. Config Param: BLOOM_INDEX_PARALLELISM
hoodie.bloom.index.prune.by.ranges	true	Only applies if index type is BLOOM. When true, range information from files to leveraged speed up index lookups. Particularly helpful, if the key has a monotonously increasing prefix, such as timestamp. If the record key is completely random, it is better to turn this off, since range pruning will only add extra overhead to the index lookup. Config Param: BLOOM_INDEX_PRUNE_BY_RANGES
hoodie.bloom.index.update.partition.path	true	Only applies if index type is GLOBAL_BLOOM. When set to true, an update including the partition path of a record that already exists will result in inserting the incoming record into the new partition and deleting the original record in the old partition. When set to false, the original record will only be updated in the old partition Config Param: BLOOM_INDEX_UPDATE_PARTITION_PATH_ENABLE
hoodie.bloom.index.use.caching	true	Only applies if index type is BLOOM.When true, the input RDD will cached to speed up index lookup by reducing IO for computing parallelism or affected partitions Config Param: BLOOM_INDEX_USE_CACHING
hoodie.bloom.index.use.metadata	false	Only applies if index type is BLOOM.When true, the index lookup uses bloom filters and column stats from metadata table when available to speed up the process. Config Param: BLOOM_INDEX_USE_METADATA Since Version: 0.11.0
hoodie.bloom.index.use.treebased.filter	true	Only applies if index type is BLOOM. When true, interval tree based file pruning optimization is enabled. This mode speeds-up file-pruning based on key ranges when compared with the brute-force mode Config Param: BLOOM_INDEX_TREE_BASED_FILTER
hoodie.bucket.index.merge.threshold	0.2	Control if buckets should be merged when using consistent hashing bucket indexSpecifically, if a file slice size is smaller than hoodie.xxxx.max.file.size * threshold, then it will be consideredas a merge candidate. Config Param: BUCKET_MERGE_THRESHOLD Since Version: 0.13.0
hoodie.bucket.index.num.buckets	256	Only applies if index type is BUCKET. Determine the number of buckets in the hudi table, and each partition is divided to N buckets. Config Param: BUCKET_INDEX_NUM_BUCKETS
hoodie.bucket.index.partition.rule.type	regex	Rule parser for expressions when using partition level bucket index, default regex. Config Param: BUCKET_INDEX_PARTITION_RULE_TYPE
hoodie.bucket.index.split.threshold	2.0	Control if the bucket should be split when using consistent hashing bucket index.Specifically, if a file slice size reaches hoodie.xxxx.max.file.size * threshold, then split will be carried out. Config Param: BUCKET_SPLIT_THRESHOLD Since Version: 0.13.0
hoodie.global.index.reconcile.parallelism	60	Only applies if index type is GLOBAL_BLOOM or GLOBAL_SIMPLE. This controls the parallelism for deduplication during indexing where more than 1 record could be tagged due to partition update. Config Param: GLOBAL_INDEX_RECONCILE_PARALLELISM
hoodie.global.simple.index.parallelism	0	Only applies if index type is GLOBAL_SIMPLE. This limits the parallelism of fetching records from the base files of all table partitions. The index picks the configured parallelism if the number of base files is larger than this configured value; otherwise, the number of base files is used as the parallelism. If the indexing stage is slow due to the limited parallelism, you can increase this to tune the performance. Config Param: GLOBAL_SIMPLE_INDEX_PARALLELISM
hoodie.index.bucket.engine	SIMPLE	org.apache.hudi.index.HoodieIndex$BucketIndexEngineType: Determines the type of bucketing or hashing to use when hoodie.index.type is set to BUCKET. SIMPLE(default): Uses a fixed number of buckets for file groups which cannot shrink or expand. This works for both COW and MOR tables. CONSISTENT_HASHING: Supports dynamic number of buckets with bucket resizing to properly size each bucket. This solves potential data skew problem where one bucket can be significantly larger than others in SIMPLE engine type. This only works with MOR tables. Config Param: BUCKET_INDEX_ENGINE_TYPE Since Version: 0.11.0
hoodie.index.class		Full path of user-defined index class and must be a subclass of HoodieIndex class. It will take precedence over the hoodie.index.type configuration if specified Config Param: INDEX_CLASS_NAME
hoodie.record.index.input.storage.level	MEMORY_AND_DISK_SER	Only applies when #recordIndexUseCaching is set. Determine what level of persistence is used to cache input RDDs. Refer to org.apache.spark.storage.StorageLevel for different values Config Param: RECORD_INDEX_INPUT_STORAGE_LEVEL_VALUE Since Version: 0.14.0
hoodie.record.index.update.partition.path	false	Similar to Key: 'hoodie.bloom.index.update.partition.path' , default: true , isAdvanced: true , description: Only applies if index type is GLOBAL_BLOOM. When set to true, an update including the partition path of a record that already exists will result in inserting the incoming record into the new partition and deleting the original record in the old partition. When set to false, the original record will only be updated in the old partition since version: version is not defined deprecated after: version is not defined, but for record index. Config Param: RECORD_INDEX_UPDATE_PARTITION_PATH_ENABLE Since Version: 0.14.0
hoodie.record.index.use.caching	true	Only applies if index type is RECORD_INDEX.When true, the input RDD will be cached to speed up index lookup by reducing IO for computing parallelism or affected partitions Config Param: RECORD_INDEX_USE_CACHING Since Version: 0.14.0
hoodie.simple.index.input.storage.level	MEMORY_AND_DISK_SER	Only applies when #simpleIndexUseCaching is set. Determine what level of persistence is used to cache input RDDs. Refer to org.apache.spark.storage.StorageLevel for different values Config Param: SIMPLE_INDEX_INPUT_STORAGE_LEVEL_VALUE
hoodie.simple.index.parallelism	0	Only applies if index type is SIMPLE. This limits the parallelism of fetching records from the base files of affected partitions. By default, this is auto computed based on input workload characteristics. If the parallelism is explicitly configured by the user, the user-configured value is used in defining the actual parallelism. If the indexing stage is slow due to the limited parallelism, you can increase this to tune the performance. Config Param: SIMPLE_INDEX_PARALLELISM
hoodie.simple.index.update.partition.path	true	Similar to Key: 'hoodie.bloom.index.update.partition.path' , default: true , isAdvanced: true , description: Only applies if index type is GLOBAL_BLOOM. When set to true, an update including the partition path of a record that already exists will result in inserting the incoming record into the new partition and deleting the original record in the old partition. When set to false, the original record will only be updated in the old partition since version: version is not defined deprecated after: version is not defined, but for simple index. Config Param: SIMPLE_INDEX_UPDATE_PARTITION_PATH_ENABLE
hoodie.simple.index.use.caching	true	Only applies if index type is SIMPLE. When true, the incoming writes will cached to speed up index lookup by reducing IO for computing parallelism or affected partitions Config Param: SIMPLE_INDEX_USE_CACHING

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Reader Configs

Please fill in the description for Config Group Name: Reader Configs

Reader Configs

Configurations that control file group reading.

Advanced Configs

Config Name	Default	Description
hoodie.compaction.lazy.block.read	true	When merging the delta log files, this config helps to choose whether the log blocks should be read lazily or not. Choose true to use lazy block reading (low memory usage, but incurs seeks to each block header) or false for immediate block read (higher memory usage) Config Param: COMPACTION_LAZY_BLOCK_READ_ENABLE
hoodie.compaction.reverse.log.read	false	HoodieLogFormatReader reads a logfile in the forward direction starting from pos=0 to pos=file_length. If this config is set to true, the reader reads the logfile in reverse direction, from pos=file_length to pos=0 Config Param: COMPACTION_REVERSE_LOG_READ_ENABLE
hoodie.datasource.merge.type	payload_combine	For Snapshot query on merge on read table. Use this key to define how the payloads are merged, in 1) skip_merge: read the base file records plus the log file records without merging; 2) payload_combine: read the base file records first, for each record in base file, checks whether the key is in the log file records (combines the two records with same key for base and log file records), then read the left log file records Config Param: MERGE_TYPE
hoodie.file.group.reader.enabled	true	Use engine agnostic file group reader if enabled Config Param: FILE_GROUP_READER_ENABLED Since Version: 1.0.0
hoodie.hfile.block.cache.enabled	true	Enable HFile block-level caching for metadata files. This caches frequently accessed HFile blocks in memory to reduce I/O operations during metadata queries. Improves performance for workloads with repeated metadata access patterns. Config Param: HFILE_BLOCK_CACHE_ENABLED Since Version: 1.1.0
hoodie.hfile.block.cache.size	100	Maximum number of HFile blocks to cache in memory per metadata file reader. Higher values improve cache hit rates but consume more memory. Only effective when hfile.block.cache.enabled is true. Config Param: HFILE_BLOCK_CACHE_SIZE Since Version: 1.1.0
hoodie.hfile.block.cache.ttl.minutes	60	Time-to-live (TTL) in minutes for cached HFile blocks. Blocks are evicted from the cache after this duration to prevent memory leaks. Only effective when hfile.block.cache.enabled is true. Config Param: HFILE_BLOCK_CACHE_TTL_MINUTES Since Version: 1.1.0
hoodie.merge.use.record.positions	true	Whether to use positions in the block header for data blocks containing updates and delete blocks for merging. Config Param: MERGE_USE_RECORD_POSITIONS Since Version: 1.0.0
hoodie.read.blob.inline.mode	DESCRIPTOR	How Hudi interprets INLINE BLOB values on read. DESCRIPTOR (default) returns an OUT_OF_LINE-shaped reference pointing at the backing Lance file with the INLINE payload's position and size, so callers can skip the byte content read. CONTENT returns the raw inline bytes directly in the data field on every read. Config Param: BLOB_INLINE_READ_MODE Since Version: 1.2.0

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Metastore and Catalog Sync Configs

Configurations used by the Hudi to sync metadata to external metastores and catalogs.

Common Metadata Sync Configs

Basic Configs

Config Name	Default	Description
hoodie.datasource.meta.sync.enable	false	Enable Syncing the Hudi Table with an external meta store or data catalog. Config Param: META_SYNC_ENABLED

Advanced Configs

Config Name	Default	Description
hoodie.datasource.hive_sync.base_file_format	PARQUET	Base file format for the sync. Config Param: META_SYNC_BASE_FILE_FORMAT
hoodie.datasource.hive_sync.database	default	The name of the destination database that we should sync the hudi table to. Config Param: META_SYNC_DATABASE_NAME
hoodie.datasource.hive_sync.partition_extractor_class	org.apache.hudi.hive.MultiPartKeysValueExtractor	Class which implements PartitionValueExtractor to extract the partition values, default is inferred based on partition configuration. Config Param: META_SYNC_PARTITION_EXTRACTOR_CLASS
hoodie.datasource.hive_sync.partition_fields		Field in the table to use for determining hive partition columns. Config Param: META_SYNC_PARTITION_FIELDS
hoodie.datasource.hive_sync.table	unknown	The name of the destination table that we should sync the hudi table to. Config Param: META_SYNC_TABLE_NAME
hoodie.datasource.meta.sync.base.path		Base path of the hoodie table to sync Config Param: META_SYNC_BASE_PATH
hoodie.datasource.meta_sync.condition.sync	false	If true, only sync on conditions like schema change or partition change. Config Param: META_SYNC_CONDITIONAL_SYNC
hoodie.meta.sync.decode_partition	false	If true, meta sync will url-decode the partition path, as it is deemed as url-encoded. Default to false. Config Param: META_SYNC_DECODE_PARTITION
hoodie.meta.sync.incremental	true	Whether to incrementally sync the partitions to the metastore, i.e., only added, changed, and deleted partitions based on the commit metadata. If set to false, the meta sync executes a full partition sync operation when partitions are lost. Config Param: META_SYNC_INCREMENTAL Since Version: 0.14.0
hoodie.meta.sync.metadata_file_listing	true	Enable the internal metadata table for file listing for syncing with metastores Config Param: META_SYNC_USE_FILE_LISTING_FROM_METADATA
hoodie.meta.sync.no_partition_metadata	false	If true, the partition metadata will not be synced to the metastore. This is useful when the partition metadata is large, and the partition info can be obtained from Hudi's internal metadata table. Note, Key: 'hoodie.metadata.enable' , default: true , isAdvanced: false , description: Enable the internal metadata table which serves table metadata like level file listings since version: 0.7.0 deprecated after: version is not defined must be set to true. Config Param: META_SYNC_NO_PARTITION_METADATA Since Version: 1.0.0
hoodie.meta.sync.sync_snapshot_with_table_name	true	sync meta info to origin table if enable Config Param: META_SYNC_SNAPSHOT_WITH_TABLE_NAME Since Version: 0.14.0
hoodie.meta.sync.touch.partitions.enabled	false	If true, TOUCH partition events will be emitted during meta sync. TOUCH events indicate partitions that exist in both storage and metastore, no schema or location change, but the partition has received data. Config Param: META_SYNC_TOUCH_PARTITIONS_ENABLED Since Version: 1.2.0
hoodie.meta_sync.spark.version		The spark version used when syncing with a metastore. Config Param: META_SYNC_SPARK_VERSION

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Glue catalog sync based client Configurations

Configs that control Glue catalog sync based client.

Basic Configs

Config Name	Default	Description
hoodie.datasource.meta.sync.glue.partition_index_fields		Specify the partitions fields to index on aws glue. Separate the fields by semicolon. By default, when the feature is enabled, all the partition will be indexed. You can create up to three indexes, separate them by comma. Eg: col1;col2;col3,col2,col3 Config Param: META_SYNC_PARTITION_INDEX_FIELDS Since Version: 0.15.0
hoodie.datasource.meta.sync.glue.partition_index_fields.enable	false	Enable aws glue partition index feature, to speedup partition based query pattern Config Param: META_SYNC_PARTITION_INDEX_FIELDS_ENABLE Since Version: 0.15.0

Advanced Configs

Config Name	Default	Description
hoodie.datasource.meta.sync.glue.catalogId	(N/A)	The catalogId needs to be populated for syncing hoodie tables in a different AWS account Config Param: GLUE_CATALOG_ID Since Version: 1.1.0
hoodie.datasource.meta.sync.glue.resource_tags	(N/A)	Tags to be applied to AWS Glue databases and tables during sync. Format: key1:value1,key2:value2 Config Param: GLUE_SYNC_RESOURCE_TAGS Since Version: 1.1.0
hoodie.datasource.meta.sync.glue.all_partitions_read_parallelism	1	Parallelism for listing all partitions(first time sync). Should be in interval [1, 10]. Config Param: ALL_PARTITIONS_READ_PARALLELISM Since Version: 0.15.0
hoodie.datasource.meta.sync.glue.changed_partitions_read_parallelism	1	Parallelism for listing changed partitions(second and subsequent syncs). Config Param: CHANGED_PARTITIONS_READ_PARALLELISM Since Version: 0.15.0
hoodie.datasource.meta.sync.glue.database_name		The name of the destination database that we should sync the hudi table to. Config Param: GLUE_SYNC_DATABASE_NAME Since Version: 1.1.0
hoodie.datasource.meta.sync.glue.metadata_file_listing	false	Makes athena use the metadata table to list partitions and files. Currently it won't benefit from other features such stats indexes Config Param: GLUE_METADATA_FILE_LISTING Since Version: 0.14.0
hoodie.datasource.meta.sync.glue.partition_change_parallelism	1	Parallelism for change operations - such as create/update/delete. Config Param: PARTITION_CHANGE_PARALLELISM Since Version: 0.15.0
hoodie.datasource.meta.sync.glue.recreate_table_on_error	false	Glue sync may fail if the Glue table exists with partitions differing from the Hoodie table or if schema evolution is not supported by Glue.Enabling this configuration will drop and create the table to match the Hoodie config Config Param: RECREATE_GLUE_TABLE_ON_ERROR Since Version: 0.14.0
hoodie.datasource.meta.sync.glue.skip_table_archive	true	Glue catalog sync based client will skip archiving the table version if this config is set to true Config Param: GLUE_SKIP_TABLE_ARCHIVE Since Version: 0.14.0
hoodie.datasource.meta.sync.glue.table_name		The name of the destination table that we should sync the hudi table to. Config Param: GLUE_SYNC_TABLE_NAME Since Version: 1.1.0

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BigQuery Sync Configs

Configurations used by the Hudi to sync metadata to Google BigQuery.

Basic Configs

Config Name	Default	Description
hoodie.datasource.meta.sync.enable	false	Enable Syncing the Hudi Table with an external meta store or data catalog. Config Param: META_SYNC_ENABLED

Advanced Configs

Config Name	Default	Description
hoodie.gcp.bigquery.sync.big_lake_connection_id	(N/A)	The Big Lake connection ID to use Config Param: BIGQUERY_SYNC_BIG_LAKE_CONNECTION_ID Since Version: 0.14.1
hoodie.gcp.bigquery.sync.billing.project.id	(N/A)	Name of the billing project id in BigQuery. By default it uses the configuration from hoodie.gcp.bigquery.sync.project_id if this configuration is not set. This can only be used with manifest file based approach Config Param: BIGQUERY_SYNC_BILLING_PROJECT_ID Since Version: 1.0.0
hoodie.gcp.bigquery.sync.dataset_location	(N/A)	Location of the target dataset in BigQuery Config Param: BIGQUERY_SYNC_DATASET_LOCATION
hoodie.gcp.bigquery.sync.project_id	(N/A)	Name of the target project in BigQuery Config Param: BIGQUERY_SYNC_PROJECT_ID
hoodie.gcp.bigquery.sync.source_uri	(N/A)	Name of the source uri gcs path of the table Config Param: BIGQUERY_SYNC_SOURCE_URI
hoodie.gcp.bigquery.sync.source_uri_prefix	(N/A)	Name of the source uri gcs path prefix of the table Config Param: BIGQUERY_SYNC_SOURCE_URI_PREFIX
hoodie.datasource.hive_sync.base_file_format	PARQUET	Base file format for the sync. Config Param: META_SYNC_BASE_FILE_FORMAT
hoodie.datasource.hive_sync.database	default	The name of the destination database that we should sync the hudi table to. Config Param: META_SYNC_DATABASE_NAME
hoodie.datasource.hive_sync.partition_extractor_class	org.apache.hudi.hive.MultiPartKeysValueExtractor	Class which implements PartitionValueExtractor to extract the partition values, default is inferred based on partition configuration. Config Param: META_SYNC_PARTITION_EXTRACTOR_CLASS
hoodie.datasource.hive_sync.partition_fields		Field in the table to use for determining hive partition columns. Config Param: META_SYNC_PARTITION_FIELDS
hoodie.datasource.hive_sync.table	unknown	The name of the destination table that we should sync the hudi table to. Config Param: META_SYNC_TABLE_NAME
hoodie.datasource.meta.sync.base.path		Base path of the hoodie table to sync Config Param: META_SYNC_BASE_PATH
hoodie.datasource.meta_sync.condition.sync	false	If true, only sync on conditions like schema change or partition change. Config Param: META_SYNC_CONDITIONAL_SYNC
hoodie.gcp.bigquery.sync.dataset_name		Name of the target dataset in BigQuery Config Param: BIGQUERY_SYNC_DATASET_NAME
hoodie.gcp.bigquery.sync.partition_fields		Comma-delimited partition fields. Default to non-partitioned. Config Param: BIGQUERY_SYNC_PARTITION_FIELDS
hoodie.gcp.bigquery.sync.require_partition_filter	false	If true, configure table to require a partition filter to be specified when querying the table Config Param: BIGQUERY_SYNC_REQUIRE_PARTITION_FILTER Since Version: 0.14.1
hoodie.gcp.bigquery.sync.table_name		Name of the target table in BigQuery Config Param: BIGQUERY_SYNC_TABLE_NAME
hoodie.gcp.bigquery.sync.use_bq_manifest_file	false	If true, generate a manifest file with data file absolute paths and use BigQuery manifest file support to directly create one external table over the Hudi table. If false (default), generate a manifest file with data file names and create two external tables and one view in BigQuery. Query the view for the same results as querying the Hudi table Config Param: BIGQUERY_SYNC_USE_BQ_MANIFEST_FILE Since Version: 0.14.0
hoodie.gcp.bigquery.sync.use_file_listing_from_metadata	true	Fetch file listing from Hudi's metadata Config Param: BIGQUERY_SYNC_USE_FILE_LISTING_FROM_METADATA
hoodie.meta.sync.decode_partition	false	If true, meta sync will url-decode the partition path, as it is deemed as url-encoded. Default to false. Config Param: META_SYNC_DECODE_PARTITION
hoodie.meta.sync.incremental	true	Whether to incrementally sync the partitions to the metastore, i.e., only added, changed, and deleted partitions based on the commit metadata. If set to false, the meta sync executes a full partition sync operation when partitions are lost. Config Param: META_SYNC_INCREMENTAL Since Version: 0.14.0
hoodie.meta.sync.metadata_file_listing	true	Enable the internal metadata table for file listing for syncing with metastores Config Param: META_SYNC_USE_FILE_LISTING_FROM_METADATA
hoodie.meta.sync.no_partition_metadata	false	If true, the partition metadata will not be synced to the metastore. This is useful when the partition metadata is large, and the partition info can be obtained from Hudi's internal metadata table. Note, Key: 'hoodie.metadata.enable' , default: true , isAdvanced: false , description: Enable the internal metadata table which serves table metadata like level file listings since version: 0.7.0 deprecated after: version is not defined must be set to true. Config Param: META_SYNC_NO_PARTITION_METADATA Since Version: 1.0.0
hoodie.meta.sync.sync_snapshot_with_table_name	true	sync meta info to origin table if enable Config Param: META_SYNC_SNAPSHOT_WITH_TABLE_NAME Since Version: 0.14.0
hoodie.meta.sync.touch.partitions.enabled	false	If true, TOUCH partition events will be emitted during meta sync. TOUCH events indicate partitions that exist in both storage and metastore, no schema or location change, but the partition has received data. Config Param: META_SYNC_TOUCH_PARTITIONS_ENABLED Since Version: 1.2.0
hoodie.meta_sync.spark.version		The spark version used when syncing with a metastore. Config Param: META_SYNC_SPARK_VERSION

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Hive Sync Configs

Configurations used by the Hudi to sync metadata to Hive Metastore.

Basic Configs

Config Name	Default	Description
hoodie.datasource.hive_sync.mode	(N/A)	Mode to choose for Hive ops. Valid values are hms, jdbc and hiveql. Config Param: HIVE_SYNC_MODE
hoodie.datasource.hive_sync.enable	false	When set to true, register/sync the table to Apache Hive metastore. Config Param: HIVE_SYNC_ENABLED
hoodie.datasource.hive_sync.jdbcurl	jdbc:hive2://localhost:10000	Hive metastore url Config Param: HIVE_URL
hoodie.datasource.hive_sync.metastore.uris	thrift://localhost:9083	Hive metastore url Config Param: METASTORE_URIS
hoodie.datasource.meta.sync.enable	false	Enable Syncing the Hudi Table with an external meta store or data catalog. Config Param: META_SYNC_ENABLED

Advanced Configs

Config Name	Default	Description
hoodie.datasource.hive_sync.serde_properties	(N/A)	Serde properties to hive table. Config Param: HIVE_TABLE_SERDE_PROPERTIES
hoodie.datasource.hive_sync.table_properties	(N/A)	Additional properties to store with table. Config Param: HIVE_TABLE_PROPERTIES
hoodie.datasource.hive_sync.auto_create_database	true	Auto create hive database if does not exists Config Param: HIVE_AUTO_CREATE_DATABASE
hoodie.datasource.hive_sync.base_file_format	PARQUET	Base file format for the sync. Config Param: META_SYNC_BASE_FILE_FORMAT
hoodie.datasource.hive_sync.batch_num	1000	The number of partitions one batch when synchronous partitions to hive. Config Param: HIVE_BATCH_SYNC_PARTITION_NUM
hoodie.datasource.hive_sync.bucket_sync	false	Whether sync hive metastore bucket specification when using bucket index.The specification is 'CLUSTERED BY (trace_id) SORTED BY (trace_id ASC) INTO 65536 BUCKETS' Config Param: HIVE_SYNC_BUCKET_SYNC
hoodie.datasource.hive_sync.bucket_sync_spec		The hive metastore bucket specification when using bucket index.The specification is 'CLUSTERED BY (trace_id) SORTED BY (trace_id ASC) INTO 65536 BUCKETS' Config Param: HIVE_SYNC_BUCKET_SYNC_SPEC
hoodie.datasource.hive_sync.create_managed_table	false	Whether to sync the table as managed table. Config Param: HIVE_CREATE_MANAGED_TABLE
hoodie.datasource.hive_sync.database	default	The name of the destination database that we should sync the hudi table to. Config Param: META_SYNC_DATABASE_NAME
hoodie.datasource.hive_sync.filter_pushdown_enabled	false	Whether to enable push down partitions by filter Config Param: HIVE_SYNC_FILTER_PUSHDOWN_ENABLED
hoodie.datasource.hive_sync.filter_pushdown_max_size	1000	Max size limit to push down partition filters, if the estimate push down filters exceed this size, will directly try to fetch all partitions between the min/max.In case of glue metastore, this value should be reduced because it has a filter length limit. Config Param: HIVE_SYNC_FILTER_PUSHDOWN_MAX_SIZE
hoodie.datasource.hive_sync.ignore_exceptions	false	Ignore exceptions when syncing with Hive. Config Param: HIVE_IGNORE_EXCEPTIONS
hoodie.datasource.hive_sync.omit_metadata_fields	false	Whether to omit the hoodie metadata fields in the target table. Config Param: HIVE_SYNC_OMIT_METADATA_FIELDS Since Version: 0.13.0
hoodie.datasource.hive_sync.partition_extractor_class	org.apache.hudi.hive.MultiPartKeysValueExtractor	Class which implements PartitionValueExtractor to extract the partition values, default is inferred based on partition configuration. Config Param: META_SYNC_PARTITION_EXTRACTOR_CLASS
hoodie.datasource.hive_sync.partition_fields		Field in the table to use for determining hive partition columns. Config Param: META_SYNC_PARTITION_FIELDS
hoodie.datasource.hive_sync.password	hive	hive password to use Config Param: HIVE_PASS
hoodie.datasource.hive_sync.recreate_table_on_error	false	Hive sync may fail if the Hive table exists with partitions differing from the Hoodie table or if schema evolution if not supported by Hive.Enabling this configuration will drop and create the table to match the Hoodie config Config Param: RECREATE_HIVE_TABLE_ON_ERROR Since Version: 0.14.0
hoodie.datasource.hive_sync.schema_string_length_thresh	4000	Config Param: HIVE_SYNC_SCHEMA_STRING_LENGTH_THRESHOLD
hoodie.datasource.hive_sync.skip_ro_suffix	false	Skip the _ro suffix for Read optimized table, when registering Config Param: HIVE_SKIP_RO_SUFFIX_FOR_READ_OPTIMIZED_TABLE
hoodie.datasource.hive_sync.support_timestamp	false	‘INT64’ with original type TIMESTAMP_MICROS is converted to hive ‘timestamp’ type. Disabled by default for backward compatibility. NOTE: On Spark entrypoints, this is defaulted to TRUE Config Param: HIVE_SUPPORT_TIMESTAMP_TYPE
hoodie.datasource.hive_sync.sync_as_datasource	true	Config Param: HIVE_SYNC_AS_DATA_SOURCE_TABLE
hoodie.datasource.hive_sync.sync_comment	false	Whether to sync the table column comments while syncing the table. Config Param: HIVE_SYNC_COMMENT
hoodie.datasource.hive_sync.table	unknown	The name of the destination table that we should sync the hudi table to. Config Param: META_SYNC_TABLE_NAME
hoodie.datasource.hive_sync.table.strategy	ALL	Hive table synchronization strategy. Available option: RO, RT, ALL. Config Param: HIVE_SYNC_TABLE_STRATEGY Since Version: 0.13.0
hoodie.datasource.hive_sync.use_jdbc	true	Use JDBC when hive synchronization is enabled Config Param: HIVE_USE_JDBC
hoodie.datasource.hive_sync.use_spark_catalog	false	Use Spark catalog backed IMetaStoreClient implementation for hive sync. Config Param: HIVE_SYNC_USE_SPARK_CATALOG
hoodie.datasource.hive_sync.username	hive	hive user name to use Config Param: HIVE_USER
hoodie.datasource.meta.sync.base.path		Base path of the hoodie table to sync Config Param: META_SYNC_BASE_PATH
hoodie.datasource.meta_sync.condition.sync	false	If true, only sync on conditions like schema change or partition change. Config Param: META_SYNC_CONDITIONAL_SYNC
hoodie.meta.sync.decode_partition	false	If true, meta sync will url-decode the partition path, as it is deemed as url-encoded. Default to false. Config Param: META_SYNC_DECODE_PARTITION
hoodie.meta.sync.incremental	true	Whether to incrementally sync the partitions to the metastore, i.e., only added, changed, and deleted partitions based on the commit metadata. If set to false, the meta sync executes a full partition sync operation when partitions are lost. Config Param: META_SYNC_INCREMENTAL Since Version: 0.14.0
hoodie.meta.sync.metadata_file_listing	true	Enable the internal metadata table for file listing for syncing with metastores Config Param: META_SYNC_USE_FILE_LISTING_FROM_METADATA
hoodie.meta.sync.no_partition_metadata	false	If true, the partition metadata will not be synced to the metastore. This is useful when the partition metadata is large, and the partition info can be obtained from Hudi's internal metadata table. Note, Key: 'hoodie.metadata.enable' , default: true , isAdvanced: false , description: Enable the internal metadata table which serves table metadata like level file listings since version: 0.7.0 deprecated after: version is not defined must be set to true. Config Param: META_SYNC_NO_PARTITION_METADATA Since Version: 1.0.0
hoodie.meta.sync.sync_snapshot_with_table_name	true	sync meta info to origin table if enable Config Param: META_SYNC_SNAPSHOT_WITH_TABLE_NAME Since Version: 0.14.0
hoodie.meta.sync.touch.partitions.enabled	false	If true, TOUCH partition events will be emitted during meta sync. TOUCH events indicate partitions that exist in both storage and metastore, no schema or location change, but the partition has received data. Config Param: META_SYNC_TOUCH_PARTITIONS_ENABLED Since Version: 1.2.0
hoodie.meta_sync.spark.version		The spark version used when syncing with a metastore. Config Param: META_SYNC_SPARK_VERSION

---

Global Hive Sync Configs

Global replication configurations used by the Hudi to sync metadata to Hive Metastore.

Basic Configs

Config Name	Default	Description
hoodie.datasource.hive_sync.mode	(N/A)	Mode to choose for Hive ops. Valid values are hms, jdbc and hiveql. Config Param: HIVE_SYNC_MODE
hoodie.datasource.hive_sync.enable	false	When set to true, register/sync the table to Apache Hive metastore. Config Param: HIVE_SYNC_ENABLED
hoodie.datasource.hive_sync.jdbcurl	jdbc:hive2://localhost:10000	Hive metastore url Config Param: HIVE_URL
hoodie.datasource.hive_sync.metastore.uris	thrift://localhost:9083	Hive metastore url Config Param: METASTORE_URIS
hoodie.datasource.meta.sync.enable	false	Enable Syncing the Hudi Table with an external meta store or data catalog. Config Param: META_SYNC_ENABLED

Advanced Configs

Config Name	Default	Description
hoodie.datasource.hive_sync.serde_properties	(N/A)	Serde properties to hive table. Config Param: HIVE_TABLE_SERDE_PROPERTIES
hoodie.datasource.hive_sync.table_properties	(N/A)	Additional properties to store with table. Config Param: HIVE_TABLE_PROPERTIES
hoodie.meta_sync.global.replicate.timestamp	(N/A)	Config Param: META_SYNC_GLOBAL_REPLICATE_TIMESTAMP
hoodie.datasource.hive_sync.auto_create_database	true	Auto create hive database if does not exists Config Param: HIVE_AUTO_CREATE_DATABASE
hoodie.datasource.hive_sync.base_file_format	PARQUET	Base file format for the sync. Config Param: META_SYNC_BASE_FILE_FORMAT
hoodie.datasource.hive_sync.batch_num	1000	The number of partitions one batch when synchronous partitions to hive. Config Param: HIVE_BATCH_SYNC_PARTITION_NUM
hoodie.datasource.hive_sync.bucket_sync	false	Whether sync hive metastore bucket specification when using bucket index.The specification is 'CLUSTERED BY (trace_id) SORTED BY (trace_id ASC) INTO 65536 BUCKETS' Config Param: HIVE_SYNC_BUCKET_SYNC
hoodie.datasource.hive_sync.bucket_sync_spec		The hive metastore bucket specification when using bucket index.The specification is 'CLUSTERED BY (trace_id) SORTED BY (trace_id ASC) INTO 65536 BUCKETS' Config Param: HIVE_SYNC_BUCKET_SYNC_SPEC
hoodie.datasource.hive_sync.create_managed_table	false	Whether to sync the table as managed table. Config Param: HIVE_CREATE_MANAGED_TABLE
hoodie.datasource.hive_sync.database	default	The name of the destination database that we should sync the hudi table to. Config Param: META_SYNC_DATABASE_NAME
hoodie.datasource.hive_sync.filter_pushdown_enabled	false	Whether to enable push down partitions by filter Config Param: HIVE_SYNC_FILTER_PUSHDOWN_ENABLED
hoodie.datasource.hive_sync.filter_pushdown_max_size	1000	Max size limit to push down partition filters, if the estimate push down filters exceed this size, will directly try to fetch all partitions between the min/max.In case of glue metastore, this value should be reduced because it has a filter length limit. Config Param: HIVE_SYNC_FILTER_PUSHDOWN_MAX_SIZE
hoodie.datasource.hive_sync.ignore_exceptions	false	Ignore exceptions when syncing with Hive. Config Param: HIVE_IGNORE_EXCEPTIONS
hoodie.datasource.hive_sync.omit_metadata_fields	false	Whether to omit the hoodie metadata fields in the target table. Config Param: HIVE_SYNC_OMIT_METADATA_FIELDS Since Version: 0.13.0
hoodie.datasource.hive_sync.partition_extractor_class	org.apache.hudi.hive.MultiPartKeysValueExtractor	Class which implements PartitionValueExtractor to extract the partition values, default is inferred based on partition configuration. Config Param: META_SYNC_PARTITION_EXTRACTOR_CLASS
hoodie.datasource.hive_sync.partition_fields		Field in the table to use for determining hive partition columns. Config Param: META_SYNC_PARTITION_FIELDS
hoodie.datasource.hive_sync.password	hive	hive password to use Config Param: HIVE_PASS
hoodie.datasource.hive_sync.recreate_table_on_error	false	Hive sync may fail if the Hive table exists with partitions differing from the Hoodie table or if schema evolution if not supported by Hive.Enabling this configuration will drop and create the table to match the Hoodie config Config Param: RECREATE_HIVE_TABLE_ON_ERROR Since Version: 0.14.0
hoodie.datasource.hive_sync.schema_string_length_thresh	4000	Config Param: HIVE_SYNC_SCHEMA_STRING_LENGTH_THRESHOLD
hoodie.datasource.hive_sync.skip_ro_suffix	false	Skip the _ro suffix for Read optimized table, when registering Config Param: HIVE_SKIP_RO_SUFFIX_FOR_READ_OPTIMIZED_TABLE
hoodie.datasource.hive_sync.support_timestamp	false	‘INT64’ with original type TIMESTAMP_MICROS is converted to hive ‘timestamp’ type. Disabled by default for backward compatibility. NOTE: On Spark entrypoints, this is defaulted to TRUE Config Param: HIVE_SUPPORT_TIMESTAMP_TYPE
hoodie.datasource.hive_sync.sync_as_datasource	true	Config Param: HIVE_SYNC_AS_DATA_SOURCE_TABLE
hoodie.datasource.hive_sync.sync_comment	false	Whether to sync the table column comments while syncing the table. Config Param: HIVE_SYNC_COMMENT
hoodie.datasource.hive_sync.table	unknown	The name of the destination table that we should sync the hudi table to. Config Param: META_SYNC_TABLE_NAME
hoodie.datasource.hive_sync.table.strategy	ALL	Hive table synchronization strategy. Available option: RO, RT, ALL. Config Param: HIVE_SYNC_TABLE_STRATEGY Since Version: 0.13.0
hoodie.datasource.hive_sync.use_jdbc	true	Use JDBC when hive synchronization is enabled Config Param: HIVE_USE_JDBC
hoodie.datasource.hive_sync.use_spark_catalog	false	Use Spark catalog backed IMetaStoreClient implementation for hive sync. Config Param: HIVE_SYNC_USE_SPARK_CATALOG
hoodie.datasource.hive_sync.username	hive	hive user name to use Config Param: HIVE_USER
hoodie.datasource.meta.sync.base.path		Base path of the hoodie table to sync Config Param: META_SYNC_BASE_PATH
hoodie.datasource.meta_sync.condition.sync	false	If true, only sync on conditions like schema change or partition change. Config Param: META_SYNC_CONDITIONAL_SYNC
hoodie.meta.sync.decode_partition	false	If true, meta sync will url-decode the partition path, as it is deemed as url-encoded. Default to false. Config Param: META_SYNC_DECODE_PARTITION
hoodie.meta.sync.incremental	true	Whether to incrementally sync the partitions to the metastore, i.e., only added, changed, and deleted partitions based on the commit metadata. If set to false, the meta sync executes a full partition sync operation when partitions are lost. Config Param: META_SYNC_INCREMENTAL Since Version: 0.14.0
hoodie.meta.sync.metadata_file_listing	true	Enable the internal metadata table for file listing for syncing with metastores Config Param: META_SYNC_USE_FILE_LISTING_FROM_METADATA
hoodie.meta.sync.no_partition_metadata	false	If true, the partition metadata will not be synced to the metastore. This is useful when the partition metadata is large, and the partition info can be obtained from Hudi's internal metadata table. Note, Key: 'hoodie.metadata.enable' , default: true , isAdvanced: false , description: Enable the internal metadata table which serves table metadata like level file listings since version: 0.7.0 deprecated after: version is not defined must be set to true. Config Param: META_SYNC_NO_PARTITION_METADATA Since Version: 1.0.0
hoodie.meta.sync.sync_snapshot_with_table_name	true	sync meta info to origin table if enable Config Param: META_SYNC_SNAPSHOT_WITH_TABLE_NAME Since Version: 0.14.0
hoodie.meta.sync.touch.partitions.enabled	false	If true, TOUCH partition events will be emitted during meta sync. TOUCH events indicate partitions that exist in both storage and metastore, no schema or location change, but the partition has received data. Config Param: META_SYNC_TOUCH_PARTITIONS_ENABLED Since Version: 1.2.0
hoodie.meta_sync.spark.version		The spark version used when syncing with a metastore. Config Param: META_SYNC_SPARK_VERSION

---

DataHub Sync Configs

Configurations used by the Hudi to sync metadata to DataHub.

Basic Configs

Config Name	Default	Description
hoodie.datasource.meta.sync.enable	false	Enable Syncing the Hudi Table with an external meta store or data catalog. Config Param: META_SYNC_ENABLED

Advanced Configs

Config Name	Default	Description
hoodie.datasource.hive_sync.serde_properties	(N/A)	Serde properties to hive table. Config Param: HIVE_TABLE_SERDE_PROPERTIES
hoodie.meta.sync.datahub.dataplatform_instance.name	(N/A)	String used to represent Hudi instance when emitting Container and Dataset entities with the corresponding DataPlatformInstance, only if given. Config Param: META_SYNC_DATAHUB_DATAPLATFORM_INSTANCE_NAME
hoodie.meta.sync.datahub.domain.identifier	(N/A)	Domain identifier for the dataset. When provided all datasets will be attached to the provided domain. Must be in urn form (e.g., urn:li:domain:_domain_id). Config Param: META_SYNC_DATAHUB_DOMAIN_IDENTIFIER
hoodie.meta.sync.datahub.emitter.server	(N/A)	Server URL of the DataHub instance. Config Param: META_SYNC_DATAHUB_EMITTER_SERVER
hoodie.meta.sync.datahub.emitter.supplier.class	(N/A)	Pluggable class to supply a DataHub REST emitter to connect to the DataHub instance. This overwrites other emitter configs. Config Param: META_SYNC_DATAHUB_EMITTER_SUPPLIER_CLASS
hoodie.meta.sync.datahub.emitter.token	(N/A)	Auth token to connect to the DataHub instance. Config Param: META_SYNC_DATAHUB_EMITTER_TOKEN
hoodie.meta.sync.datahub.tls.ca.cert.path	(N/A)	Path to the CA certificate file for TLS verification. Used when connecting to DataHub over HTTPS with custom CA certificates. Config Param: META_SYNC_DATAHUB_TLS_CA_CERT_PATH Since Version: 1.1.0
hoodie.meta.sync.datahub.tls.keystore.password	(N/A)	Password for the keystore file. Optional but recommended for security. If not provided, an empty password will be used. Config Param: META_SYNC_DATAHUB_TLS_KEYSTORE_PASSWORD Since Version: 1.1.0
hoodie.meta.sync.datahub.tls.keystore.path	(N/A)	Path to the keystore file for TLS client authentication. Used when connecting to DataHub over HTTPS with mutual TLS authentication. Config Param: META_SYNC_DATAHUB_TLS_KEYSTORE_PATH Since Version: 1.1.0
hoodie.meta.sync.datahub.tls.truststore.password	(N/A)	Password for the truststore file. Optional but recommended for security. If not provided, an empty password will be used. Config Param: META_SYNC_DATAHUB_TLS_TRUSTSTORE_PASSWORD Since Version: 1.1.0
hoodie.meta.sync.datahub.tls.truststore.path	(N/A)	Path to the truststore file for TLS server verification. Alternative to CA certificate file for trust management. Config Param: META_SYNC_DATAHUB_TLS_TRUSTSTORE_PATH Since Version: 1.1.0
hoodie.datasource.hive_sync.base_file_format	PARQUET	Base file format for the sync. Config Param: META_SYNC_BASE_FILE_FORMAT
hoodie.datasource.hive_sync.database	default	The name of the destination database that we should sync the hudi table to. Config Param: META_SYNC_DATABASE_NAME
hoodie.datasource.hive_sync.partition_extractor_class	org.apache.hudi.hive.MultiPartKeysValueExtractor	Class which implements PartitionValueExtractor to extract the partition values, default is inferred based on partition configuration. Config Param: META_SYNC_PARTITION_EXTRACTOR_CLASS
hoodie.datasource.hive_sync.partition_fields		Field in the table to use for determining hive partition columns. Config Param: META_SYNC_PARTITION_FIELDS
hoodie.datasource.hive_sync.schema_string_length_thresh	4000	Config Param: HIVE_SYNC_SCHEMA_STRING_LENGTH_THRESHOLD
hoodie.datasource.hive_sync.table	unknown	The name of the destination table that we should sync the hudi table to. Config Param: META_SYNC_TABLE_NAME
hoodie.datasource.meta.sync.base.path		Base path of the hoodie table to sync Config Param: META_SYNC_BASE_PATH
hoodie.datasource.meta_sync.condition.sync	false	If true, only sync on conditions like schema change or partition change. Config Param: META_SYNC_CONDITIONAL_SYNC
hoodie.meta.sync.datahub.database.name		The name of the destination database that we should sync the hudi table to. Config Param: META_SYNC_DATAHUB_DATABASE_NAME
hoodie.meta.sync.datahub.dataplatform.name	hudi	String used to represent Hudi when creating its corresponding DataPlatform entity within Datahub Config Param: META_SYNC_DATAHUB_DATAPLATFORM_NAME
hoodie.meta.sync.datahub.dataset.env	DEV	Environment to use when pushing entities to Datahub Config Param: META_SYNC_DATAHUB_DATASET_ENV
hoodie.meta.sync.datahub.dataset.identifier.class	org.apache.hudi.sync.datahub.config.HoodieDataHubDatasetIdentifier	Pluggable class to help provide info to identify a DataHub Dataset. Config Param: META_SYNC_DATAHUB_DATASET_IDENTIFIER_CLASS
hoodie.meta.sync.datahub.sync.suppress.exceptions	true	Suppress exceptions during DataHub sync. This is true by default to ensure that when running inline with other jobs, the sync does not fail the job. Config Param: META_SYNC_DATAHUB_SYNC_SUPPRESS_EXCEPTIONS
hoodie.meta.sync.datahub.table.name		The name of the destination table that we should sync the hudi table to. Config Param: META_SYNC_DATAHUB_TABLE_NAME
hoodie.meta.sync.decode_partition	false	If true, meta sync will url-decode the partition path, as it is deemed as url-encoded. Default to false. Config Param: META_SYNC_DECODE_PARTITION
hoodie.meta.sync.incremental	true	Whether to incrementally sync the partitions to the metastore, i.e., only added, changed, and deleted partitions based on the commit metadata. If set to false, the meta sync executes a full partition sync operation when partitions are lost. Config Param: META_SYNC_INCREMENTAL Since Version: 0.14.0
hoodie.meta.sync.metadata_file_listing	true	Enable the internal metadata table for file listing for syncing with metastores Config Param: META_SYNC_USE_FILE_LISTING_FROM_METADATA
hoodie.meta.sync.no_partition_metadata	false	If true, the partition metadata will not be synced to the metastore. This is useful when the partition metadata is large, and the partition info can be obtained from Hudi's internal metadata table. Note, Key: 'hoodie.metadata.enable' , default: true , isAdvanced: false , description: Enable the internal metadata table which serves table metadata like level file listings since version: 0.7.0 deprecated after: version is not defined must be set to true. Config Param: META_SYNC_NO_PARTITION_METADATA Since Version: 1.0.0
hoodie.meta.sync.sync_snapshot_with_table_name	true	sync meta info to origin table if enable Config Param: META_SYNC_SNAPSHOT_WITH_TABLE_NAME Since Version: 0.14.0
hoodie.meta.sync.touch.partitions.enabled	false	If true, TOUCH partition events will be emitted during meta sync. TOUCH events indicate partitions that exist in both storage and metastore, no schema or location change, but the partition has received data. Config Param: META_SYNC_TOUCH_PARTITIONS_ENABLED Since Version: 1.2.0
hoodie.meta_sync.spark.version		The spark version used when syncing with a metastore. Config Param: META_SYNC_SPARK_VERSION

---

Metrics Configs

These set of configs are used to enable monitoring and reporting of key Hudi stats and metrics.

Metrics Configurations for Amazon CloudWatch

Enables reporting on Hudi metrics using Amazon CloudWatch. Hudi publishes metrics on every commit, clean, rollback etc.

Advanced Configs

Config Name	Default	Description
hoodie.metrics.cloudwatch.maxDatumsPerRequest	20	Max number of Datums per request Config Param: MAX_DATUMS_PER_REQUEST Since Version: 0.10.0
hoodie.metrics.cloudwatch.metric.prefix		Metric prefix of reporter Config Param: METRIC_PREFIX Since Version: 0.10.0
hoodie.metrics.cloudwatch.namespace	Hudi	Namespace of reporter Config Param: METRIC_NAMESPACE Since Version: 0.10.0
hoodie.metrics.cloudwatch.report.period.seconds	60	Reporting interval in seconds Config Param: REPORT_PERIOD_SECONDS Since Version: 0.10.0

---

Metrics Configurations

Enables reporting on Hudi metrics. Hudi publishes metrics on every commit, clean, rollback etc. The following sections list the supported reporters.

Basic Configs

Config Name	Default	Description
hoodie.metrics.on	false	Turn on/off metrics reporting. off by default. Config Param: TURN_METRICS_ON Since Version: 0.5.0
hoodie.metrics.reporter.type	GRAPHITE	Type of metrics reporter. Config Param: METRICS_REPORTER_TYPE_VALUE Since Version: 0.5.0
hoodie.metricscompaction.log.blocks.on	false	Turn on/off metrics reporting for log blocks with compaction commit. off by default. Config Param: TURN_METRICS_COMPACTION_LOG_BLOCKS_ON Since Version: 0.14.0

Advanced Configs

Config Name	Default	Description
hoodie.metrics.executor.enable	(N/A)	Config Param: EXECUTOR_METRICS_ENABLE Since Version: 0.7.0
hoodie.metrics.configs.properties		Comma separated list of config file paths for metric exporter configs Config Param: METRICS_REPORTER_FILE_BASED_CONFIGS_PATH Since Version: 0.14.0
hoodie.metrics.lock.enable	false	Enable metrics for locking infra. Useful when operating in multiwriter mode Config Param: LOCK_METRICS_ENABLE Since Version: 0.13.0
hoodie.metrics.reporter.class		Config Param: METRICS_REPORTER_CLASS_NAME Since Version: 0.6.0
hoodie.metrics.reporter.metricsname.prefix		The prefix given to the metrics names. Config Param: METRICS_REPORTER_PREFIX Since Version: 0.11.0

---

Metrics Configurations for Datadog reporter

Enables reporting on Hudi metrics using the Datadog reporter type. Hudi publishes metrics on every commit, clean, rollback etc.

Advanced Configs

Config Name	Default	Description
hoodie.metrics.datadog.api.key	(N/A)	Datadog API key Config Param: API_KEY Since Version: 0.6.0
hoodie.metrics.datadog.api.key.supplier	(N/A)	Datadog API key supplier to supply the API key at runtime. This will take effect if hoodie.metrics.datadog.api.key is not set. Config Param: API_KEY_SUPPLIER Since Version: 0.6.0
hoodie.metrics.datadog.api.site	(N/A)	Datadog API site: EU or US Config Param: API_SITE_VALUE Since Version: 0.6.0
hoodie.metrics.datadog.metric.host	(N/A)	Datadog metric host to be sent along with metrics data. Config Param: METRIC_HOST_NAME Since Version: 0.6.0
hoodie.metrics.datadog.metric.prefix	(N/A)	Datadog metric prefix to be prepended to each metric name with a dot as delimiter. For example, if it is set to foo, foo. will be prepended. Config Param: METRIC_PREFIX_VALUE Since Version: 0.6.0
hoodie.metrics.datadog.metric.tags	(N/A)	Datadog metric tags (comma-delimited) to be sent along with metrics data. Config Param: METRIC_TAG_VALUES Since Version: 0.6.0
hoodie.metrics.datadog.api.key.skip.validation	false	Before sending metrics via Datadog API, whether to skip validating Datadog API key or not. Default to false. Config Param: API_KEY_SKIP_VALIDATION Since Version: 0.6.0
hoodie.metrics.datadog.api.timeout.seconds	3	Datadog API timeout in seconds. Default to 3. Config Param: API_TIMEOUT_IN_SECONDS Since Version: 0.6.0
hoodie.metrics.datadog.report.period.seconds	30	Datadog reporting period in seconds. Default to 30. Config Param: REPORT_PERIOD_IN_SECONDS Since Version: 0.6.0

---

Metrics Configurations for Graphite

Enables reporting on Hudi metrics using Graphite. Hudi publishes metrics on every commit, clean, rollback etc.

Advanced Configs

Config Name	Default	Description
hoodie.metrics.graphite.metric.prefix	(N/A)	Standard prefix applied to all metrics. This helps to add datacenter, environment information for e.g Config Param: GRAPHITE_METRIC_PREFIX_VALUE Since Version: 0.5.1
hoodie.metrics.graphite.host	localhost	Graphite host to connect to. Config Param: GRAPHITE_SERVER_HOST_NAME Since Version: 0.5.0
hoodie.metrics.graphite.port	4756	Graphite port to connect to. Config Param: GRAPHITE_SERVER_PORT_NUM Since Version: 0.5.0
hoodie.metrics.graphite.report.period.seconds	30	Graphite reporting period in seconds. Default to 30. Config Param: GRAPHITE_REPORT_PERIOD_IN_SECONDS Since Version: 0.10.0

---

Metrics Configurations for Jmx

Enables reporting on Hudi metrics using Jmx. Hudi publishes metrics on every commit, clean, rollback etc.

Advanced Configs

Config Name	Default	Description
hoodie.metrics.jmx.host	localhost	Jmx host to connect to Config Param: JMX_HOST_NAME Since Version: 0.5.1
hoodie.metrics.jmx.port	9889	Jmx port to connect to Config Param: JMX_PORT_NUM Since Version: 0.5.1

---

Metrics Configurations for M3

Enables reporting on Hudi metrics using M3. Hudi publishes metrics on every commit, clean, rollback etc.

Basic Configs

Config Name	Default	Description
hoodie.metrics.m3.env	production	M3 tag to label the environment (defaults to 'production'), applied to all metrics. Config Param: M3_ENV Since Version: 0.15.0
hoodie.metrics.m3.host	localhost	M3 host to connect to. Config Param: M3_SERVER_HOST_NAME Since Version: 0.15.0
hoodie.metrics.m3.port	9052	M3 port to connect to. Config Param: M3_SERVER_PORT_NUM Since Version: 0.15.0
hoodie.metrics.m3.service	hoodie	M3 tag to label the service name (defaults to 'hoodie'), applied to all metrics. Config Param: M3_SERVICE Since Version: 0.15.0
hoodie.metrics.m3.tags		Optional M3 tags applied to all metrics. Config Param: M3_TAGS Since Version: 0.15.0

---

Metrics Configurations for Prometheus

Enables reporting on Hudi metrics using Prometheus. Hudi publishes metrics on every commit, clean, rollback etc.

Advanced Configs

Config Name	Default	Description
hoodie.metrics.prometheus.port	9090	Port for prometheus server. Config Param: PROMETHEUS_PORT_NUM Since Version: 0.6.0
hoodie.metrics.pushgateway.delete.on.shutdown	true	Delete the pushgateway info or not when job shutdown, true by default. Config Param: PUSHGATEWAY_DELETE_ON_SHUTDOWN_ENABLE Since Version: 0.6.0
hoodie.metrics.pushgateway.host	localhost	Hostname of the prometheus push gateway. Config Param: PUSHGATEWAY_HOST_NAME Since Version: 0.6.0
hoodie.metrics.pushgateway.job.name		Name of the push gateway job. Config Param: PUSHGATEWAY_JOBNAME Since Version: 0.6.0
hoodie.metrics.pushgateway.port	9091	Port for the push gateway. Config Param: PUSHGATEWAY_PORT_NUM Since Version: 0.6.0
hoodie.metrics.pushgateway.random.job.name.suffix	true	Whether the pushgateway name need a random suffix , default true. Config Param: PUSHGATEWAY_RANDOM_JOBNAME_SUFFIX Since Version: 0.6.0
hoodie.metrics.pushgateway.report.labels		Label for the metrics emitted to the Pushgateway. Labels can be specified with key:value pairs separated by commas Config Param: PUSHGATEWAY_LABELS Since Version: 0.14.0
hoodie.metrics.pushgateway.report.period.seconds	30	Reporting interval in seconds. Config Param: PUSHGATEWAY_REPORT_PERIOD_IN_SECONDS Since Version: 0.6.0

---

Record Payload Config

This is the lowest level of customization offered by Hudi. Record payloads define how to produce new values to upsert based on incoming new record and stored old record. Hudi provides default implementations such as OverwriteWithLatestAvroPayload which simply update table with the latest/last-written record. This can be overridden to a custom class extending HoodieRecordPayload class, on both datasource and WriteClient levels.

Payload Configurations

Payload related configs, that can be leveraged to control merges based on specific business fields in the data.

Advanced Configs

Config Name	Default	Description
hoodie.payload.event.time.field	(N/A)	Table column/field name to derive timestamp associated with the records. This canbe useful for e.g, determining the freshness of the table. Config Param: EVENT_TIME_FIELD
hoodie.payload.ordering.field	(N/A)	Table column/field name to order records that have the same key, before merging and writing to storage. Config Param: ORDERING_FIELDS
hoodie.compaction.payload.class	org.apache.hudi.common.model.DefaultHoodieRecordPayload	This needs to be same as class used during insert/upserts. Just like writing, compaction also uses the record payload class to merge records in the log against each other, merge again with the base file and produce the final record to be written after compaction. Config Param: PAYLOAD_CLASS_NAME

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Kafka Connect Configs

These set of configs are used for Kafka Connect Sink Connector for writing Hudi Tables

Kafka Sink Connect Configurations

Configurations for Kafka Connect Sink Connector for Hudi.

Basic Configs

Config Name	Default	Description
bootstrap.servers	localhost:9092	The bootstrap servers for the Kafka Cluster. Config Param: KAFKA_BOOTSTRAP_SERVERS

Advanced Configs

Config Name	Default	Description
hadoop.conf.dir	(N/A)	The Hadoop configuration directory. Config Param: HADOOP_CONF_DIR
hadoop.home	(N/A)	The Hadoop home directory. Config Param: HADOOP_HOME
hoodie.kafka.allow.commit.on.errors	true	Commit even when some records failed to be written Config Param: ALLOW_COMMIT_ON_ERRORS
hoodie.kafka.commit.interval.secs	60	The interval at which Hudi will commit the records written to the files, making them consumable on the read-side. Config Param: COMMIT_INTERVAL_SECS
hoodie.kafka.compaction.async.enable	true	Controls whether async compaction should be turned on for MOR table writing. Config Param: ASYNC_COMPACT_ENABLE
hoodie.kafka.control.topic	hudi-control-topic	Kafka topic name used by the Hudi Sink Connector for sending and receiving control messages. Not used for data records. Config Param: CONTROL_TOPIC_NAME
hoodie.kafka.coordinator.write.timeout.secs	300	The timeout after sending an END_COMMIT until when the coordinator will wait for the write statuses from all the partitionsto ignore the current commit and start a new commit. Config Param: COORDINATOR_WRITE_TIMEOUT_SECS
hoodie.meta.sync.classes	org.apache.hudi.hive.HiveSyncTool	Meta sync client tool, using comma to separate multi tools Config Param: META_SYNC_CLASSES
hoodie.meta.sync.enable	false	Enable Meta Sync such as Hive Config Param: META_SYNC_ENABLE
hoodie.schemaprovider.class	org.apache.hudi.schema.FilebasedSchemaProvider	subclass of org.apache.hudi.schema.SchemaProvider to attach schemas to input & target table data, built in options: org.apache.hudi.schema.FilebasedSchemaProvider. Config Param: SCHEMA_PROVIDER_CLASS

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Amazon Web Services Configs

Configurations specific to Amazon Web Services.

Amazon Web Services Configs

Amazon Web Services configurations to access resources like Amazon DynamoDB (for locks), Amazon CloudWatch (metrics) and Amazon Glue (metadata).

Advanced Configs

Config Name	Default	Description
hoodie.aws.access.key	(N/A)	AWS access key id Config Param: AWS_ACCESS_KEY Since Version: 0.10.0
hoodie.aws.glue.endpoint	(N/A)	Aws glue endpoint Config Param: AWS_GLUE_ENDPOINT Since Version: 0.15.0
hoodie.aws.glue.region	(N/A)	Aws glue endpoint Config Param: AWS_GLUE_REGION Since Version: 0.15.0
hoodie.aws.role.arn	(N/A)	AWS Role ARN to assume Config Param: AWS_ASSUME_ROLE_ARN Since Version: 0.15.0
hoodie.aws.role.external.id	(N/A)	External ID use when assuming the AWS Role Config Param: AWS_ASSUME_ROLE_EXTERNAL_ID Since Version: 0.15.0
hoodie.aws.secret.key	(N/A)	AWS secret key Config Param: AWS_SECRET_KEY Since Version: 0.10.0
hoodie.aws.session.token	(N/A)	AWS session token Config Param: AWS_SESSION_TOKEN Since Version: 0.10.0
hoodie.aws.role.session.name	hoodie	Session name to use when assuming the AWS Role Config Param: AWS_ASSUME_ROLE_SESSION_NAME Since Version: 0.15.0

---

Hudi Streamer Configs

These set of configs are used for Hudi Streamer utility which provides the way to ingest from different sources such as DFS or Kafka.

Hudi Streamer Configs

Basic Configs

Config Name	Default	Description
hoodie.streamer.source.kafka.topic	(N/A)	Kafka topic name. The config is specific to HoodieMultiTableStreamer Config Param: KAFKA_TOPIC

Advanced Configs

Config Name	Default	Description
hoodie.streamer.checkpoint.provider.path	(N/A)	The path for providing the checkpoints. Config Param: CHECKPOINT_PROVIDER_PATH
hoodie.streamer.ingestion.tablesToBeIngested	(N/A)	Comma separated names of tables to be ingested in the format <database>.<table>, for example db1.table1,db1.table2 Config Param: TABLES_TO_BE_INGESTED
hoodie.streamer.transformer.class	(N/A)	Names of transformer classes to apply. The config is specific to HoodieMultiTableStreamer. Config Param: TRANSFORMER_CLASS Since Version: 0.14.0
hoodie.streamer.checkpoint.force.skip	false	Config to force to skip saving checkpoint in the commit metadata.It is typically used in one-time backfill scenarios, where checkpoints are not to be persisted. Config Param: CHECKPOINT_FORCE_SKIP
hoodie.streamer.ingestion.targetBasePath		The path to which a particular table is ingested. The config is specific to HoodieMultiTableStreamer and overrides path determined using option --base-path-prefix for a table. This config is ignored for a single table streamer Config Param: TARGET_BASE_PATH
hoodie.streamer.row.throw.explicit.exceptions	false	When enabled, the dataframe generated from reading source data is wrapped with an exception handler to explicitly surface exceptions. Config Param: ROW_THROW_EXPLICIT_EXCEPTIONS Since Version: 0.15.0
hoodie.streamer.sample.writes.enabled	false	Set this to true to sample from the first batch of records and write to the auxiliary path, before writing to the table.The sampled records are used to calculate the average record size. The relevant write client will have hoodie.copyonwrite.record.size.estimate being overwritten by the calculated result. Config Param: SAMPLE_WRITES_ENABLED Since Version: 0.14.0
hoodie.streamer.sample.writes.size	5000	Number of records to sample from the first write. To improve the estimation's accuracy, for smaller or more compressable record size, set the sample size bigger. For bigger or less compressable record size, set smaller. Config Param: SAMPLE_WRITES_SIZE Since Version: 0.14.0
hoodie.streamer.schema.make.columns.nullable	false	When set to true, all columns in the incoming dataset schema are made nullable. This applies to every column in the incoming batch, not just newly added ones. Hudi's schema evolution supports widening nullability (non-nullable to nullable), so enabling this config on an existing table with non-nullable columns is safe - existing data retains its values while the schema is updated to allow nulls. This is useful for maintaining backwards compatibility when new columns are added via SQL queries. Config Param: SCHEMA_MAKE_COLUMNS_NULLABLE
hoodie.streamer.source.kafka.append.offsets	false	When enabled, appends kafka offset info like source offset(_hoodie_kafka_source_offset), partition (_hoodie_kafka_source_partition) and timestamp (_hoodie_kafka_source_timestamp) to the records. By default its disabled and no kafka offsets are added Config Param: KAFKA_APPEND_OFFSETS
hoodie.streamer.source.sanitize.invalid.char.mask	__	Defines the character sequence that replaces invalid characters in schema field names if hoodie.streamer.source.sanitize.invalid.schema.field.names is enabled. Config Param: SCHEMA_FIELD_NAME_INVALID_CHAR_MASK
hoodie.streamer.source.sanitize.invalid.schema.field.names	false	Sanitizes names of invalid schema fields both in the data read from source and also in the schema Replaces invalid characters with hoodie.streamer.source.sanitize.invalid.char.mask. Invalid characters are by goes by avro naming convention (https://avro.apache.org/docs/current/spec.html#names). Config Param: SANITIZE_SCHEMA_FIELD_NAMES

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Hudi Streamer SQL Transformer Configs

Configurations controlling the behavior of SQL transformer in Hudi Streamer.

Basic Configs

Config Name	Default	Description
hoodie.streamer.transformer.sql	(N/A)	SQL Query to be executed during write Config Param: TRANSFORMER_SQL
hoodie.streamer.transformer.sql.file	(N/A)	File with a SQL script to be executed during write Config Param: TRANSFORMER_SQL_FILE

---

Hudi Streamer Source Configs

Configurations controlling the behavior of reading source data.

Cloud Source Configs

Configs that are common during ingestion across different cloud stores

Advanced Configs

Config Name	Default	Description
hoodie.streamer.source.cloud.data.datasource.options	(N/A)	A JSON string passed to the Spark DataFrameReader while loading the dataset. Example: hoodie.streamer.gcp.spark.datasource.options={"header":"true","encoding":"UTF-8"} Config Param: SPARK_DATASOURCE_OPTIONS
hoodie.streamer.source.cloud.data.ignore.relpath.prefix	(N/A)	Ignore objects in the bucket whose relative path starts this prefix Config Param: IGNORE_RELATIVE_PATH_PREFIX
hoodie.streamer.source.cloud.data.ignore.relpath.substring	(N/A)	Ignore objects in the bucket whose relative path contains this substring Config Param: IGNORE_RELATIVE_PATH_SUBSTR
hoodie.streamer.source.cloud.data.partition.fields.from.path	(N/A)	A comma delimited list of path-based partition fields in the source file structure. Config Param: PATH_BASED_PARTITION_FIELDS Since Version: 0.14.0
hoodie.streamer.source.cloud.data.partition.max.size	(N/A)	specify this value in bytes, to coalesce partitions of source dataset not greater than specified limit Config Param: SOURCE_MAX_BYTES_PER_PARTITION Since Version: 0.14.1
hoodie.streamer.source.cloud.data.select.file.extension	(N/A)	Only match files with this extension. By default, this is the same as hoodie.streamer.source.hoodieincr.file.format Config Param: CLOUD_DATAFILE_EXTENSION
hoodie.streamer.source.cloud.data.select.relative.path.regex	(N/A)	Only selects objects in the bucket whose relative path matches this regex. For example: When hoodie.streamer.source.cloud.data.select.relpath.prefix is set to /path/prefix, and the hoodie.streamer.source.cloud.data.select.relative.path.regex is regex/files[0-9]+, only files located in the /path/prefix/regex directory that match the pattern (e.g., file1, file2, etc.) will be ingested. If hoodie.streamer.source.cloud.data.select.relpath.prefix is not set, the ingestion process will look for files matching /regex/files[0-9]+ in the source bucket. Config Param: SELECT_RELATIVE_PATH_REGEX Since Version: 1.0.0
hoodie.streamer.source.cloud.data.select.relpath.prefix	(N/A)	Only selects objects in the bucket whose relative path starts with this prefix Config Param: SELECT_RELATIVE_PATH_PREFIX
hoodie.streamer.source.cloud.data.check.file.exists	false	If true, checks whether file exists before attempting to pull it Config Param: ENABLE_EXISTS_CHECK
hoodie.streamer.source.cloud.data.datafile.format	parquet	Format of the data file. By default, this will be the same as hoodie.streamer.source.hoodieincr.file.format Config Param: DATAFILE_FORMAT
hoodie.streamer.source.cloud.data.merge.schema.enable	true	For Parquet and ORC data files in S3/GCS incremental ingestion, merge schemas across all files in each read (Spark mergeSchema). Default true so mixed-schema batches during initial ingest/bootstrap produce a valid unified schema. Set false to restore prior behavior. hoodie.streamer.source.cloud.data.datasource.options is applied after this flag and can override mergeSchema. Note: the per-read mergeSchema option is honored by Spark's native Parquet reader and by Spark's native ORC reader (Spark 3.0+, default ORC impl since Spark 2.4). On older runtimes the option is silently ignored. Config Param: CLOUD_INCREMENTAL_MERGE_SCHEMA Since Version: 1.2.0
hoodie.streamer.source.cloud.data.reader.coalesce.aliases	true	Boolean value to allow coalesce alias columns with actual columns while reading from source Config Param: SPARK_DATASOURCE_READER_COALESCE_ALIAS_COLUMNS Since Version: 1.0.0
hoodie.streamer.source.cloud.data.reader.comma.separated.path.format	false	Boolean value for specifying path format in load args of spark.read.format("..").load("a.xml,b.xml,c.xml"). Set true if path format needs to be comma separated string value; false if it's passed as array of strings likespark.read.format("..").load(new String[]{a.xml,b.xml,c.xml}) Config Param: SPARK_DATASOURCE_READER_COMMA_SEPARATED_PATH_FORMAT Since Version: 0.14.1
hoodie.streamer.source.cloud.meta.ack	true	Whether to acknowledge Metadata messages during Cloud Ingestion or not. This is useful during dev and testing. In Prod this should always be true. In case of Cloud Pubsub, not acknowledging means Pubsub will keep redelivering the same messages. Config Param: ACK_MESSAGES
hoodie.streamer.source.cloud.meta.batch.size	10	Number of metadata messages to pull in one API call to the cloud events queue. Multiple API calls with this batch size are sent to cloud events queue, until we consume hoodie.streamer.source.cloud.meta.max.num.messages.per.syncfrom the queue or hoodie.streamer.source.cloud.meta.max.fetch.time.per.sync.ms amount of time has passed or queue is empty. Config Param: BATCH_SIZE_CONF
hoodie.streamer.source.cloud.meta.events.per.partition	10000	Number of metadata events to be grouped into a single partition while creating dataframe from metadata events. Config Param: META_EVENTS_PER_PARTITION Since Version: 1.2.0
hoodie.streamer.source.cloud.meta.max.fetch.time.per.sync.secs	60	Max time in secs to consume hoodie.streamer.source.cloud.meta.max.num.messages.per.sync messages from cloud queue. Cloud event queues like SQS, PubSub can return empty responses even when messages are available the queue, this config ensures we don't wait forever to consume MAX_MESSAGES_CONF messages, but time out and move on further. Config Param: MAX_FETCH_TIME_PER_SYNC_SECS Since Version: 0.14.1
hoodie.streamer.source.cloud.meta.max.num.messages.per.sync	1000	Maximum number of messages to consume per sync round. Multiple rounds of hoodie.streamer.source.cloud.meta.batch.size could be invoked to reach max messages as configured by this config Config Param: MAX_NUM_MESSAGES_PER_SYNC Since Version: 0.14.1

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DFS Path Selector Configs

Configurations controlling the behavior of path selector for DFS source in Hudi Streamer.

Basic Configs

Config Name	Default	Description
hoodie.streamer.source.dfs.root	(N/A)	Root path of the source on DFS Config Param: ROOT_INPUT_PATH

Advanced Configs

Config Name	Default	Description
hoodie.streamer.source.input.selector	(N/A)	Source input selector Config Param: SOURCE_INPUT_SELECTOR

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Date Partition Path Selector Configs

Configurations controlling the behavior of date partition path selector for DFS source in Hudi Streamer.

Advanced Configs

Config Name	Default	Description
hoodie.streamer.source.dfs.datepartitioned.selector.currentdate	(N/A)	Current date. Config Param: CURRENT_DATE
hoodie.streamer.source.dfs.datepartitioned.date.format	yyyy-MM-dd	Date format. Config Param: DATE_FORMAT
hoodie.streamer.source.dfs.datepartitioned.selector.depth	0	Depth of the files to scan. 0 implies no (date) partition. Config Param: DATE_PARTITION_DEPTH
hoodie.streamer.source.dfs.datepartitioned.selector.lookback.days	2	The maximum look-back days for scanning. Config Param: LOOKBACK_DAYS
hoodie.streamer.source.dfs.datepartitioned.selector.parallelism	20	Parallelism for listing partitions. Config Param: PARTITIONS_LIST_PARALLELISM

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GCS Events Source Configs

Configurations controlling the behavior of GCS Events Source in Hudi Streamer.

Advanced Configs

Config Name	Default	Description
hoodie.streamer.source.gcs.project.id	(N/A)	The GCP Project Id where the Pubsub Subscription to ingest from resides. Needed to connect to the Pubsub subscription Config Param: GOOGLE_PROJECT_ID
hoodie.streamer.source.gcs.subscription.id	(N/A)	The GCP Pubsub subscription id for the GCS Notifications. Needed to connect to the Pubsub subscription Config Param: PUBSUB_SUBSCRIPTION_ID

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Hive Incremental Pulling Source Configs

Configurations controlling the behavior of incremental pulling from a Hive table as a source in Hudi Streamer.

Advanced Configs

Config Name	Default	Description
hoodie.streamer.source.incrpull.root	(N/A)	The root path of Hive incremental pulling source. Config Param: ROOT_INPUT_PATH

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Hudi Incremental Source Configs

Configurations controlling the behavior of incremental pulling from a Hudi table as a source in Hudi Streamer.

Basic Configs

Config Name	Default	Description
hoodie.streamer.source.hoodieincr.path	(N/A)	Base-path for the source Hudi table Config Param: HOODIE_SRC_BASE_PATH

Advanced Configs

Config Name	Default	Description
hoodie.streamer.source.hoodieincr.data.datasource.options	(N/A)	A comma-separated list of Hudi options that can be passed to the spark dataframe reader of a hudi table, eg: hoodie.metadata.enable=true,hoodie.enable.data.skipping=true. Used only for incremental source. Config Param: HOODIE_INCREMENTAL_SPARK_DATASOURCE_OPTIONS Since Version: 0.15.0
hoodie.streamer.source.hoodieincr.missing.checkpoint.strategy	(N/A)	Allows Hudi Streamer to decide the instant to consume from when checkpoint is not set. Possible values: [IncrSourceHelper.MissingCheckpointStrategy.READ_LATEST(description=Read from latest commit in hoodie source table), IncrSourceHelper.MissingCheckpointStrategy.READ_UPTO_LATEST_COMMIT(description=Read everything upto latest commit)] Config Param: MISSING_CHECKPOINT_STRATEGY
hoodie.streamer.source.hoodieincr.partition.extractor.class	(N/A)	PartitionValueExtractor class to extract partition fields from _hoodie_partition_path Config Param: HOODIE_SRC_PARTITION_EXTRACTORCLASS
hoodie.streamer.source.hoodieincr.partition.fields	(N/A)	Specifies partition fields that needs to be added to source table after parsing _hoodie_partition_path. Config Param: HOODIE_SRC_PARTITION_FIELDS
hoodie.streamer.source.hoodieincr.drop.all.meta.fields.from.source	false	Drops all meta fields from the source hudi table while ingesting into sink hudi table. Config Param: HOODIE_DROP_ALL_META_FIELDS_FROM_SOURCE
hoodie.streamer.source.hoodieincr.file.format	parquet	This config is passed to the reader while loading dataset. Default value is parquet. Config Param: SOURCE_FILE_FORMAT
hoodie.streamer.source.hoodieincr.num_instants	5	Max number of instants whose changes can be incrementally fetched Config Param: NUM_INSTANTS_PER_FETCH
hoodie.streamer.source.hoodieincr.read_latest_on_missing_ckpt	false	If true, allows Hudi Streamer to incrementally fetch from latest committed instant when checkpoint is not provided. This config is deprecated. Please refer to hoodie.streamer.source.hoodieincr.missing.checkpoint.strategy Config Param: READ_LATEST_INSTANT_ON_MISSING_CKPT

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JDBC Source Configs

Configurations controlling the behavior of JDBC source in Hudi Streamer.

Advanced Configs

Config Name	Default	Description
hoodie.streamer.jdbc.driver.class	(N/A)	Driver class used for JDBC connection Config Param: DRIVER_CLASS
hoodie.streamer.jdbc.extra.options.	(N/A)	Used to set any extra options the user specifies for jdbc Config Param: EXTRA_OPTIONS
hoodie.streamer.jdbc.incr.fallback.to.full.fetch	(N/A)	If set true, makes incremental fetch to fallback to full fetch in case of any error Config Param: FALLBACK_TO_FULL_FETCH
hoodie.streamer.jdbc.incr.pull	(N/A)	Will the JDBC source do an incremental pull? Config Param: IS_INCREMENTAL
hoodie.streamer.jdbc.password	(N/A)	Password used for JDBC connection Config Param: PASSWORD
hoodie.streamer.jdbc.password.file	(N/A)	Base-path for the JDBC password file. Config Param: PASSWORD_FILE
hoodie.streamer.jdbc.storage.level	(N/A)	Used to control the persistence level. Default value: MEMORY_AND_DISK_SER Config Param: STORAGE_LEVEL
hoodie.streamer.jdbc.table.incr.column.name	(N/A)	If run in incremental mode, this field is to pull new data incrementally Config Param: INCREMENTAL_COLUMN
hoodie.streamer.jdbc.table.name	(N/A)	RDBMS table to pull Config Param: RDBMS_TABLE_NAME
hoodie.streamer.jdbc.url	(N/A)	JDBC url for the Hoodie datasource. Config Param: URL
hoodie.streamer.jdbc.user	(N/A)	Username used for JDBC connection Config Param: USER

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Json Kafka Post Processor Configs

Configurations controlling the post processor of Json Kafka Source in Hudi Streamer.

Advanced Configs

Config Name	Default	Description
hoodie.streamer.source.json.kafka.post.processor.maxwell.database.regex	(N/A)	Database name regex Config Param: DATABASE_NAME_REGEX
hoodie.streamer.source.json.kafka.post.processor.maxwell.table.regex	(N/A)	Table name regex Config Param: TABLE_NAME_REGEX
hoodie.streamer.source.json.kafka.processor.class	(N/A)	Json kafka source post processor class name, post process data after consuming fromsource and before giving it to Hudi Streamer. Config Param: JSON_KAFKA_PROCESSOR_CLASS
hoodie.streamer.source.json.kafka.post.processor.maxwell.ordering.fields.format	yyyy-MM-dd HH:mm:ss	When the preCombine filed is in DATE_STRING format, use should tell hoodiewhat format it is. 'yyyy-MM-dd HH:mm:ss' by default Config Param: ORDERING_FIELDS_FORMAT
hoodie.streamer.source.json.kafka.post.processor.maxwell.ordering.fields.type	DATE_STRING	Data type of the ordering field. could be NON_TIMESTAMP, DATE_STRING,UNIX_TIMESTAMP or EPOCHMILLISECONDS. DATE_STRING by default Config Param: ORDERING_FIELDS_TYPE

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Kafka Source Configs

Configurations controlling the behavior of Kafka source in Hudi Streamer.

Basic Configs

Config Name	Default	Description
hoodie.streamer.source.kafka.topic	(N/A)	Kafka topic name. Config Param: KAFKA_TOPIC_NAME
hoodie.streamer.source.kafka.proto.value.deserializer.class	org.apache.kafka.common.serialization.ByteArrayDeserializer	Kafka Proto Payload Deserializer Class Config Param: KAFKA_PROTO_VALUE_DESERIALIZER_CLASS Since Version: 0.15.0

Advanced Configs

Config Name	Default	Description
hoodie.streamer.source.kafka.value.deserializer.schema	(N/A)	Schema to deserialize the records. Config Param: KAFKA_VALUE_DESERIALIZER_SCHEMA
auto.offset.reset	LATEST	Kafka consumer strategy for reading data. Config Param: KAFKA_AUTO_OFFSET_RESET
hoodie.streamer.kafka.source.maxEvents	5000000	Maximum number of records obtained in each batch. Config Param: MAX_EVENTS_FROM_KAFKA_SOURCE
hoodie.streamer.source.kafka.checkpoint.type	string	Kafka checkpoint type. Value must be one of the following: string, timestamp, single_offset. Default type is string. For type string, checkpoint should be provided as: topicName,0:offset0,1:offset1,2:offset2. For type timestamp, checkpoint should be provided as long value of desired timestamp. For type single_offset, we assume that topic consists of a single partition, so checkpoint should be provided as long value of desired offset. Config Param: KAFKA_CHECKPOINT_TYPE
hoodie.streamer.source.kafka.enable.commit.offset	false	Automatically submits offset to kafka. Config Param: ENABLE_KAFKA_COMMIT_OFFSET
hoodie.streamer.source.kafka.enable.failOnDataLoss	false	Fail when checkpoint goes out of bounds instead of seeking to earliest offsets. Config Param: ENABLE_FAIL_ON_DATA_LOSS
hoodie.streamer.source.kafka.fetch_partition.time.out	300000	Time out for fetching partitions. 5min by default Config Param: KAFKA_FETCH_PARTITION_TIME_OUT
hoodie.streamer.source.kafka.ignore.prefix.config.list		Comma-separated list of prefixes for config keys that should be dropped from the configs when passed to the Kafka consumer. Config Param: IGNORE_PREFIX_CONFIG_LIST Since Version: 1.1.0
hoodie.streamer.source.kafka.minPartitions	0	Desired minimum number of partitions to read from Kafka. By default, Hudi has a 1-1 mapping of topicPartitions to Hudi partitions consuming from Kafka. If set this option to a value greater than topicPartitions, Hudi will divvy up large Kafka partitions to smaller pieces. Please note that this configuration is like a hint: the number of input tasks will be approximately minPartitions. It can be less or more depending on rounding errors or Kafka partitions that didn't receive any new data. Config Param: KAFKA_SOURCE_MIN_PARTITIONS Since Version: 0.14.0
hoodie.streamer.source.kafka.offset.skip.buffer.minutes	-1	Time in minutes to move forward from retention time for reading offsets when the checkpoints are not available or out-of-bound to avoid potential OffsetOutOfRange exception, as it is possible that the earliest Kafka offsets may expire soon while the job is progressing. Config Param: OFFSET_SKIP_BUFFER_MINUTES Since Version: 1.1.0
hoodie.streamer.source.kafka.retry.exceptions		The class name of the Exception that needs to be retried, separated by commas. Default is empty which means retry all the IOException and RuntimeException from KafkaConsumer Config Param: RETRY_EXCEPTIONS Since Version: 1.1.0
hoodie.streamer.source.kafka.retry.initial_interval_ms	100	Amount of time (in ms) to wait, before retry to do operations on KafkaConsumer. Config Param: INITIAL_RETRY_INTERVAL_MS Since Version: 1.1.0
hoodie.streamer.source.kafka.retry.max_count	4	Maximum number of retry actions to perform, with exponential backoff. Config Param: MAX_RETRY_COUNT Since Version: 1.1.0
hoodie.streamer.source.kafka.retry.max_interval_ms	2000	Maximum amount of time (in ms), to wait for next retry. Config Param: MAX_RETRY_INTERVAL_MS Since Version: 1.1.0
hoodie.streamer.source.kafka.value.deserializer.class	io.confluent.kafka.serializers.KafkaAvroDeserializer	This class is used by kafka client to deserialize the records. Config Param: KAFKA_AVRO_VALUE_DESERIALIZER_CLASS Since Version: 0.9.0

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Kinesis Source Configs

Configurations controlling the behavior of Kinesis source in Hudi Streamer.

Basic Configs

Config Name	Default	Description
hoodie.streamer.source.kinesis.stream.name	(N/A)	Kinesis Data Streams stream name. Config Param: KINESIS_STREAM_NAME Since Version: 1.2.0

Advanced Configs

Config Name	Default	Description
hoodie.streamer.source.kinesis.access.key	(N/A)	AWS access key for Kinesis. Used when connecting to custom endpoints (e.g., LocalStack). If not set with endpoint, uses the default AWS credential chain. Config Param: KINESIS_ACCESS_KEY Since Version: 1.2.0
hoodie.streamer.source.kinesis.endpoint.url	(N/A)	Custom endpoint URL for Kinesis (e.g., for localstack). If not set, uses the default AWS endpoint for the region. Config Param: KINESIS_ENDPOINT_URL Since Version: 1.2.0
hoodie.streamer.source.kinesis.region	(N/A)	AWS region for the Kinesis stream (e.g., us-east-1). Config Param: KINESIS_REGION Since Version: 1.2.0
hoodie.streamer.source.kinesis.secret.key	(N/A)	AWS secret key for Kinesis. Used when connecting to custom endpoints (e.g., LocalStack). If not set with endpoint, uses the default AWS credential chain. Config Param: KINESIS_SECRET_KEY Since Version: 1.2.0
hoodie.streamer.source.kinesis.append.offsets	false	When enabled, appends Kinesis metadata (sequence number, shard id, arrival timestamp, partition key) to records. Config Param: KINESIS_APPEND_OFFSETS Since Version: 1.2.0
hoodie.streamer.source.kinesis.enable.deaggregation	true	When enabled, de-aggregates records produced by Kinesis Producer Library (KPL). Non-aggregated records pass through unchanged. Set to false if producers do not use KPL. Config Param: KINESIS_ENABLE_DEAGGREGATION Since Version: 1.2.0
hoodie.streamer.source.kinesis.fail.on.data.loss.enable	false	Fail when checkpoint references an expired shard which has not been fully consumed. Config Param: ENABLE_FAIL_ON_DATA_LOSS Since Version: 1.2.0
hoodie.streamer.source.kinesis.get.records.interval.ms	200	Minimum interval in ms between two GetRecords API calls per shard. Config Param: KINESIS_GET_RECORDS_INTERVAL_MS Since Version: 1.2.0
hoodie.streamer.source.kinesis.max.events	5000000	Maximum number of records obtained in each batch from Kinesis. Config Param: MAX_EVENTS_FROM_KINESIS_SOURCE Since Version: 1.2.0
hoodie.streamer.source.kinesis.max.records.per.request	10000	Maximum number of records to fetch per GetRecords API call. Kinesis limit is 10000. Config Param: KINESIS_MAX_RECORDS_PER_REQUEST Since Version: 1.2.0
hoodie.streamer.source.kinesis.partitions	0	Desired number of Spark partitions when reading from Kinesis. By default, Hudi has a 1-1 mapping of Kinesis shards to Spark partitions. If set to a value greater than 0, the result RDD will be repartitioned to increase/decrease downstream parallelism. Use 0 for 1-1 mapping. Config Param: KINESIS_SOURCE_MANUAL_PARTITIONS Since Version: 1.2.0
hoodie.streamer.source.kinesis.persist.fetch.rdd	true	When enabled, the fetch RDD is persisted (MEMORY_AND_DISK) so it can be used both for record RDD and checkpoint summaries without recomputation. When false, the fetch is computed twice (once for checkpoint, once when the record RDD is consumed), which can cause duplicate records to be written. Set to true for correct, duplicate-free ingestion. Config Param: KINESIS_PERSIST_FETCH_RDD Since Version: 1.2.0
hoodie.streamer.source.kinesis.retry.initial.interval.ms	1000	Initial backoff in ms when Kinesis returns ProvisionedThroughputExceededException. Backoff doubles each retry up to retry.max.interval.ms. Config Param: KINESIS_RETRY_INITIAL_INTERVAL_MS Since Version: 1.2.0
hoodie.streamer.source.kinesis.retry.max.interval.ms	10000	Maximum backoff in ms between retries for throughput exceeded. Config Param: KINESIS_RETRY_MAX_INTERVAL_MS Since Version: 1.2.0
hoodie.streamer.source.kinesis.retry.throttle.timeout.ms	600000	Maximum time in ms to keep retrying GetRecords calls after ProvisionedThroughputExceededException with no successful fetch. When exceeded, the read fails. Default: 600000 (10 minutes). Config Param: KINESIS_THROTTLE_TIMEOUT_MS Since Version: 1.2.0
hoodie.streamer.source.kinesis.starting.position	LATEST	Starting position when no checkpoint exists. EARLIEST or LATEST. Default: LATEST. Config Param: KINESIS_STARTING_POSITION Since Version: 1.2.0

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ORC DFS Source Configs

Configurations controlling the behavior of ORC DFS source in Hudi Streamer.

Advanced Configs

Config Name	Default	Description
hoodie.streamer.source.orc.dfs.merge.schema.enable	true	Whether to merge schema across ORC files within a single read. Defaults to true: heterogeneous-schema source files (e.g. during bootstrap or evolving producers) get a unioned schema instead of silently dropping columns that exist only in some files. Requires spark.sql.orc.impl=native (default since Spark 2.4); the option is silently ignored under spark.sql.orc.impl=hive. Config Param: ORC_DFS_MERGE_SCHEMA Since Version: 1.2.0

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Parquet DFS Source Configs

Configurations controlling the behavior of Parquet DFS source in Hudi Streamer.

Advanced Configs

Config Name	Default	Description
hoodie.streamer.source.parquet.dfs.merge.schema.enable	true	Whether to merge schema across parquet files within a single read. Defaults to true: heterogeneous-schema source files (e.g. during bootstrap or evolving producers) get a unioned schema instead of silently dropping columns that exist only in some files. Set to false to restore the previous reader behavior (single file's schema wins). Config Param: PARQUET_DFS_MERGE_SCHEMA Since Version: 0.15.0

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Pulsar Source Configs

Configurations controlling the behavior of Pulsar source in Hudi Streamer.

Basic Configs

Config Name	Default	Description
hoodie.streamer.source.pulsar.topic	(N/A)	Name of the target Pulsar topic to source data from Config Param: PULSAR_SOURCE_TOPIC_NAME
hoodie.streamer.source.pulsar.endpoint.admin.url	http://localhost:8080	URL of the target Pulsar endpoint (of the form 'pulsar://host:port' Config Param: PULSAR_SOURCE_ADMIN_ENDPOINT_URL
hoodie.streamer.source.pulsar.endpoint.service.url	pulsar://localhost:6650	URL of the target Pulsar endpoint (of the form 'pulsar://host:port' Config Param: PULSAR_SOURCE_SERVICE_ENDPOINT_URL

Advanced Configs

Config Name	Default	Description
hoodie.streamer.source.pulsar.maxRecords	5000000	Max number of records obtained in a single each batch Config Param: PULSAR_SOURCE_MAX_RECORDS_PER_BATCH_THRESHOLD
hoodie.streamer.source.pulsar.offset.autoResetStrategy	LATEST	Policy determining how offsets shall be automatically reset in case there's no checkpoint information present Config Param: PULSAR_SOURCE_OFFSET_AUTO_RESET_STRATEGY

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S3 Event-based Hudi Incremental Source Configs

Configurations controlling the behavior of incremental pulling from S3 events meta information from Hudi table as a source in Hudi Streamer.

Advanced Configs

Config Name	Default	Description
hoodie.streamer.source.s3incr.ignore.key.prefix	(N/A)	Control whether to ignore the s3 objects starting with this prefix Config Param: S3_IGNORE_KEY_PREFIX
hoodie.streamer.source.s3incr.ignore.key.substring	(N/A)	Control whether to ignore the s3 objects with this substring Config Param: S3_IGNORE_KEY_SUBSTRING
hoodie.streamer.source.s3incr.key.prefix	(N/A)	Control whether to filter the s3 objects starting with this prefix Config Param: S3_KEY_PREFIX
hoodie.streamer.source.s3incr.spark.datasource.options	(N/A)	Json string, passed to the reader while loading dataset. Example Hudi Streamer conf --hoodie-conf hoodie.streamer.source.s3incr.spark.datasource.options={"header":"true","encoding":"UTF-8"} Config Param: SPARK_DATASOURCE_OPTIONS
hoodie.streamer.source.s3incr.check.file.exists	false	Control whether we do existence check for files before consuming them Config Param: S3_INCR_ENABLE_EXISTS_CHECK
hoodie.streamer.source.s3incr.fs.prefix	s3	The file system prefix. Config Param: S3_FS_PREFIX

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S3 Source Configs

Configurations controlling the behavior of S3 source in Hudi Streamer.

Basic Configs

Config Name	Default	Description
hoodie.streamer.s3.source.queue.url	(N/A)	Queue url for cloud object events Config Param: S3_SOURCE_QUEUE_URL

Advanced Configs

Config Name	Default	Description
hoodie.streamer.s3.source.queue.region	(N/A)	Case-sensitive region name of the cloud provider for the queue. For example, "us-east-1". Config Param: S3_SOURCE_QUEUE_REGION
hoodie.streamer.s3.source.queue.fs	s3	File system corresponding to queue. For example, for AWS SQS it is s3/s3a. Config Param: S3_SOURCE_QUEUE_FS
hoodie.streamer.s3.source.queue.long.poll.wait	20	Long poll wait time in seconds, If set as 0 then client will fetch on short poll basis. Config Param: S3_QUEUE_LONG_POLL_WAIT
hoodie.streamer.s3.source.queue.max.messages.per.batch	5	Max messages for each batch of Hudi Streamer run. Source will process these maximum number of message at a time. Config Param: S3_SOURCE_QUEUE_MAX_MESSAGES_PER_BATCH
hoodie.streamer.s3.source.queue.max.messages.per.request	10	Max messages for each request Config Param: S3_SOURCE_QUEUE_MAX_MESSAGES_PER_REQUEST
hoodie.streamer.s3.source.queue.visibility.timeout	30	Visibility timeout for messages in queue. After we consume the message, queue will move the consumed messages to in-flight state, these messages can't be consumed again by source for this timeout period. Config Param: S3_SOURCE_QUEUE_VISIBILITY_TIMEOUT

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File-based SQL Source Configs

Configurations controlling the behavior of File-based SQL Source in Hudi Streamer.

Basic Configs

Config Name	Default	Description
hoodie.streamer.source.sql.file	(N/A)	SQL file path containing the SQL query to read source data. Config Param: SOURCE_SQL_FILE Since Version: 0.14.0

Advanced Configs

Config Name	Default	Description
hoodie.streamer.source.sql.checkpoint.emit	false	Whether to emit the current epoch as the streamer checkpoint. Config Param: EMIT_EPOCH_CHECKPOINT Since Version: 0.14.0

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SQL Source Configs

Configurations controlling the behavior of SQL source in Hudi Streamer.

Basic Configs

Config Name	Default	Description
hoodie.streamer.source.sql.sql.query	(N/A)	SQL query for fetching source data. Config Param: SOURCE_SQL

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Hudi Streamer Schema Provider Configs

Configurations that control the schema provider for Hudi Streamer.

Hudi Streamer Schema Provider Configs

Basic Configs

Config Name	Default	Description
hoodie.streamer.schemaprovider.registry.targetUrl	(N/A)	The schema of the target you are writing to e.g. https://foo:bar@schemaregistry.org Config Param: TARGET_SCHEMA_REGISTRY_URL
hoodie.streamer.schemaprovider.registry.url	(N/A)	The schema of the source you are reading from e.g. https://foo:bar@schemaregistry.org Config Param: SRC_SCHEMA_REGISTRY_URL

Advanced Configs

Config Name	Default	Description
hoodie.streamer.schemaprovider.registry.baseUrl	(N/A)	The base URL of the schema registry. Config Param: SCHEMA_REGISTRY_BASE_URL
hoodie.streamer.schemaprovider.registry.schemaconverter	(N/A)	The class name of the custom schema converter to use. Config Param: SCHEMA_CONVERTER
hoodie.streamer.schemaprovider.registry.sourceUrlSuffix	(N/A)	The source URL suffix. Config Param: SCHEMA_REGISTRY_SOURCE_URL_SUFFIX
hoodie.streamer.schemaprovider.registry.targetUrlSuffix	(N/A)	The target URL suffix. Config Param: SCHEMA_REGISTRY_TARGET_URL_SUFFIX
hoodie.streamer.schemaprovider.registry.urlSuffix	(N/A)	The suffix of the URL for the schema registry. Config Param: SCHEMA_REGISTRY_URL_SUFFIX

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File-based Schema Provider Configs

Configurations for file-based schema provider.

Basic Configs

Config Name	Default	Description
hoodie.streamer.schemaprovider.source.schema.file	(N/A)	The schema of the source you are reading from Config Param: SOURCE_SCHEMA_FILE
hoodie.streamer.schemaprovider.target.schema.file	(N/A)	The schema of the target you are writing to Config Param: TARGET_SCHEMA_FILE

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Hive Schema Provider Configs

Configurations for Hive schema provider.

Advanced Configs

Config Name	Default	Description
hoodie.streamer.schemaprovider.source.schema.hive.table	(N/A)	Hive table from where source schema can be fetched Config Param: SOURCE_SCHEMA_TABLE
hoodie.streamer.schemaprovider.target.schema.hive.table	(N/A)	Hive table from where target schema can be fetched Config Param: TARGET_SCHEMA_TABLE
hoodie.streamer.schemaprovider.source.schema.hive.database	default	Hive database from where source schema can be fetched Config Param: SOURCE_SCHEMA_DATABASE
hoodie.streamer.schemaprovider.target.schema.hive.database	default	Hive database from where target schema can be fetched Config Param: TARGET_SCHEMA_DATABASE

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JDBC-based Schema Provider Configs

Configurations for JDBC-based schema provider.

Advanced Configs

Config Name	Default	Description
hoodie.streamer.schemaprovider.source.schema.jdbc.connection.url	(N/A)	The JDBC URL to connect to. The source-specific connection properties may be specified in the URL. e.g., jdbc:postgresql://localhost/test?user=fred&password=secret Config Param: SOURCE_SCHEMA_JDBC_CONNECTION_URL
hoodie.streamer.schemaprovider.source.schema.jdbc.dbtable	(N/A)	The table with the schema to reference e.g. test_database.test1_table or test1_table Config Param: SOURCE_SCHEMA_JDBC_DBTABLE
hoodie.streamer.schemaprovider.source.schema.jdbc.driver.type	(N/A)	The class name of the JDBC driver to use to connect to this URL. e.g. org.h2.Driver Config Param: SOURCE_SCHEMA_JDBC_DRIVER_TYPE
hoodie.streamer.schemaprovider.source.schema.jdbc.nullable	(N/A)	If true, all the columns are nullable. Config Param: SOURCE_SCHEMA_JDBC_NULLABLE
hoodie.streamer.schemaprovider.source.schema.jdbc.password	(N/A)	Password for the connection e.g. secret Config Param: SOURCE_SCHEMA_JDBC_PASSWORD
hoodie.streamer.schemaprovider.source.schema.jdbc.timeout	(N/A)	The number of seconds the driver will wait for a Statement object to execute. Zero means there is no limit. In the write path, this option depends on how JDBC drivers implement the API setQueryTimeout, e.g., the h2 JDBC driver checks the timeout of each query instead of an entire JDBC batch. It defaults to 0. Config Param: SOURCE_SCHEMA_JDBC_TIMEOUT
hoodie.streamer.schemaprovider.source.schema.jdbc.username	(N/A)	Username for the connection e.g. fred Config Param: SOURCE_SCHEMA_JDBC_USERNAME

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JDBC-based Schema Provider Configs

Configurations for Proto schema provider.

Advanced Configs

Config Name	Default	Description
hoodie.streamer.schemaprovider.proto.class.name	(N/A)	The Protobuf Message class used as the source for the schema. Config Param: PROTO_SCHEMA_CLASS_NAME Since Version: 0.13.0
hoodie.streamer.schemaprovider.proto.flatten.wrappers	false	When set to true wrapped primitives like Int64Value are translated to a record with a single 'value' field. By default, the value is false and the wrapped primitives are treated as a nullable value Config Param: PROTO_SCHEMA_WRAPPED_PRIMITIVES_AS_RECORDS Since Version: 0.13.0
hoodie.streamer.schemaprovider.proto.max.recursion.depth	5	The max depth to unravel the Proto schema when translating into an Avro schema. Setting this depth allows the user to convert a schema that is recursive in proto into something that can be represented in their lake format like Parquet. After a given class has been seen N times within a single branch, the schema provider will create a record with a byte array to hold the remaining proto data and a string to hold the message descriptor's name for context. Config Param: PROTO_SCHEMA_MAX_RECURSION_DEPTH Since Version: 0.13.0
hoodie.streamer.schemaprovider.proto.timestamps.as.records	false	When set to true Timestamp fields are translated to a record with a seconds and nanos field. By default, the value is false and the timestamp is converted to a long with the timestamp-micros logical type Config Param: PROTO_SCHEMA_TIMESTAMPS_AS_RECORDS Since Version: 0.13.0

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Schema Post Processor Config Configs

Configurations for Schema Post Processor

Advanced Configs

Config Name	Default	Description
hoodie.streamer.schemaprovider.schema_post_processor	(N/A)	The class name of the schema post processor. Config Param: SCHEMA_POST_PROCESSOR
hoodie.streamer.schemaprovider.schema_post_processor.add.column.default	(N/A)	New column's default value Config Param: SCHEMA_POST_PROCESSOR_ADD_COLUMN_DEFAULT_PROP
hoodie.streamer.schemaprovider.schema_post_processor.add.column.doc	(N/A)	Docs about new column Config Param: SCHEMA_POST_PROCESSOR_ADD_COLUMN_DOC_PROP
hoodie.streamer.schemaprovider.schema_post_processor.add.column.name	(N/A)	New column's name Config Param: SCHEMA_POST_PROCESSOR_ADD_COLUMN_NAME_PROP
hoodie.streamer.schemaprovider.schema_post_processor.add.column.type	(N/A)	New column's type Config Param: SCHEMA_POST_PROCESSOR_ADD_COLUMN_TYPE_PROP
hoodie.streamer.schemaprovider.schema_post_processor.delete.columns	(N/A)	Columns to delete in the schema post processor. Config Param: DELETE_COLUMN_POST_PROCESSOR_COLUMN
hoodie.streamer.schemaprovider.schema_post_processor.add.column.nullable	true	New column's nullable Config Param: SCHEMA_POST_PROCESSOR_ADD_COLUMN_NULLABLE_PROP

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