Querying Hudi Tables

Conceptually, Hudi stores data physically once on DFS, while providing 3 different ways of querying, as explained before. Once the table is synced to the Hive metastore, it provides external Hive tables backed by Hudi’s custom inputformats. Once the proper hudi bundle has been installed, the table can be queried by popular query engines like Hive, Spark SQL, Spark Datasource API and Presto.

Specifically, following Hive tables are registered based off table name and table type configs passed during write.

If table name = hudi_trips and table type = COPY_ON_WRITE, then we get:

  • hudi_trips supports snapshot query and incremental query on the table backed by HoodieParquetInputFormat, exposing purely columnar data.

If table name = hudi_trips and table type = MERGE_ON_READ, then we get:

  • hudi_trips_rt supports snapshot query and incremental query (providing near-real time data) on the table backed by HoodieParquetRealtimeInputFormat, exposing merged view of base and log data.
  • hudi_trips_ro supports read optimized query on the table backed by HoodieParquetInputFormat, exposing purely columnar data stored in base files.

As discussed in the concepts section, the one key capability needed for incrementally processing, is obtaining a change stream/log from a table. Hudi tables can be queried incrementally, which means you can get ALL and ONLY the updated & new rows since a specified instant time. This, together with upserts, is particularly useful for building data pipelines where 1 or more source Hudi tables are incrementally queried (streams/facts), joined with other tables (tables/dimensions), to write out deltas to a target Hudi table. Incremental queries are realized by querying one of the tables above, with special configurations that indicates to query planning that only incremental data needs to be fetched out of the table.

Support Matrix

Following tables show whether a given query is supported on specific query engine.

Copy-On-Write tables

Query Engine Snapshot Queries Incremental Queries
Hive Y Y
Spark SQL Y Y
Spark Datasource Y Y
Presto Y N
Impala Y N

Note that Read Optimized queries are not applicable for COPY_ON_WRITE tables.

Merge-On-Read tables

Query Engine Snapshot Queries Incremental Queries Read Optimized Queries
Hive Y Y Y
Spark SQL Y Y Y
Spark Datasource N N Y
Presto N N Y
Impala N N N

In sections, below we will discuss specific setup to access different query types from different query engines.

Hive

In order for Hive to recognize Hudi tables and query correctly,

  • the HiveServer2 needs to be provided with the hudi-hadoop-mr-bundle-x.y.z-SNAPSHOT.jar in its aux jars path. This will ensure the input format classes with its dependencies are available for query planning & execution.
  • For MERGE_ON_READ tables, additionally the bundle needs to be put on the hadoop/hive installation across the cluster, so that queries can pick up the custom RecordReader as well.

In addition to setup above, for beeline cli access, the hive.input.format variable needs to be set to the fully qualified path name of the inputformat org.apache.hudi.hadoop.HoodieParquetInputFormat. For Tez, additionally the hive.tez.input.format needs to be set to org.apache.hadoop.hive.ql.io.HiveInputFormat. Then proceed to query the table like any other Hive table.

Incremental query

HiveIncrementalPuller allows incrementally extracting changes from large fact/dimension tables via HiveQL, combining the benefits of Hive (reliably process complex SQL queries) and incremental primitives (speed up querying tables incrementally instead of scanning fully). The tool uses Hive JDBC to run the hive query and saves its results in a temp table. that can later be upserted. Upsert utility (HoodieDeltaStreamer) has all the state it needs from the directory structure to know what should be the commit time on the target table. e.g: /app/incremental-hql/intermediate/{source_table_name}_temp/{last_commit_included}.The Delta Hive table registered will be of the form {tmpdb}.{source_table}_{last_commit_included}.

The following are the configuration options for HiveIncrementalPuller

Config Description Default
hiveUrl Hive Server 2 URL to connect to  
hiveUser Hive Server 2 Username  
hivePass Hive Server 2 Password  
queue YARN Queue name  
tmp Directory where the temporary delta data is stored in DFS. The directory structure will follow conventions. Please see the below section.  
extractSQLFile The SQL to execute on the source table to extract the data. The data extracted will be all the rows that changed since a particular point in time.  
sourceTable Source Table Name. Needed to set hive environment properties.  
sourceDb Source DB name. Needed to set hive environment properties.  
targetTable Target Table Name. Needed for the intermediate storage directory structure.  
targetDb Target table’s DB name.  
tmpdb The database to which the intermediate temp delta table will be created hoodie_temp
fromCommitTime This is the most important parameter. This is the point in time from which the changed records are queried from.  
maxCommits Number of commits to include in the query. Setting this to -1 will include all the commits from fromCommitTime. Setting this to a value > 0, will include records that ONLY changed in the specified number of commits after fromCommitTime. This may be needed if you need to catch up say 2 commits at a time. 3
help Utility Help  

Setting fromCommitTime=0 and maxCommits=-1 will fetch the entire source table and can be used to initiate backfills. If the target table is a Hudi table, then the utility can determine if the target table has no commits or is behind more than 24 hour (this is configurable), it will automatically use the backfill configuration, since applying the last 24 hours incrementally could take more time than doing a backfill. The current limitation of the tool is the lack of support for self-joining the same table in mixed mode (snapshot and incremental modes).

NOTE on Hive incremental queries that are executed using Fetch task: Since Fetch tasks invoke InputFormat.listStatus() per partition, Hoodie metadata can be listed in every such listStatus() call. In order to avoid this, it might be useful to disable fetch tasks using the hive session property for incremental queries: set hive.fetch.task.conversion=none; This would ensure Map Reduce execution is chosen for a Hive query, which combines partitions (comma separated) and calls InputFormat.listStatus() only once with all those partitions.

Spark SQL

Once the Hudi tables have been registered to the Hive metastore, it can be queried using the Spark-Hive integration. It supports all query types across both Hudi table types, relying on the custom Hudi input formats again like Hive. Typically notebook users and spark-shell users leverage spark sql for querying Hudi tables. Please add hudi-spark-bundle as described above via –jars or –packages.

By default, Spark SQL will try to use its own parquet reader instead of Hive SerDe when reading from Hive metastore parquet tables. However, for MERGE_ON_READ tables which has both parquet and avro data, this default setting needs to be turned off using set spark.sql.hive.convertMetastoreParquet=false. This will force Spark to fallback to using the Hive Serde to read the data (planning/executions is still Spark).

$ spark-shell --driver-class-path /etc/hive/conf  --packages org.apache.hudi:hudi-spark-bundle_2.11:0.5.1-incubating,org.apache.spark:spark-avro_2.11:2.4.4 --conf spark.sql.hive.convertMetastoreParquet=false --num-executors 10 --driver-memory 7g --executor-memory 2g  --master yarn-client

scala> sqlContext.sql("select count(*) from hudi_trips_mor_rt where datestr = '2016-10-02'").show()
scala> sqlContext.sql("select count(*) from hudi_trips_mor_rt where datestr = '2016-10-02'").show()

For COPY_ON_WRITE tables, either Hive SerDe can be used by turning off spark.sql.hive.convertMetastoreParquet=false as described above or Spark’s built in support can be leveraged. If using spark’s built in support, additionally a path filter needs to be pushed into sparkContext as follows. This method retains Spark built-in optimizations for reading parquet files like vectorized reading on Hudi Hive tables.

spark.sparkContext.hadoopConfiguration.setClass("mapreduce.input.pathFilter.class", classOf[org.apache.hudi.hadoop.HoodieROTablePathFilter], classOf[org.apache.hadoop.fs.PathFilter]);

Spark Datasource

The Spark Datasource API is a popular way of authoring Spark ETL pipelines. Hudi COPY_ON_WRITE tables can be queried via Spark datasource similar to how standard datasources work (e.g: spark.read.parquet). Both snapshot querying and incremental querying are supported here. Typically spark jobs require adding --jars <path to jar>/hudi-spark-bundle_2.11-<hudi version>.jar to classpath of drivers and executors. Alternatively, hudi-spark-bundle can also fetched via the --packages options (e.g: --packages org.apache.hudi:hudi-spark-bundle_2.11:0.5.1-incubating).

Incremental query

Of special interest to spark pipelines, is Hudi’s ability to support incremental queries, like below. A sample incremental query, that will obtain all records written since beginInstantTime, looks like below. Thanks to Hudi’s support for record level change streams, these incremental pipelines often offer 10x efficiency over batch counterparts, by only processing the changed records. The following snippet shows how to obtain all records changed after beginInstantTime and run some SQL on them.

 Dataset<Row> hudiIncQueryDF = spark.read()
     .format("org.apache.hudi")
     .option(DataSourceReadOptions.QUERY_TYPE_OPT_KEY(), DataSourceReadOptions.QUERY_TYPE_INCREMENTAL_OPT_VAL())
     .option(DataSourceReadOptions.BEGIN_INSTANTTIME_OPT_KEY(), <beginInstantTime>)
     .load(tablePath); // For incremental query, pass in the root/base path of table
     
hudiIncQueryDF.createOrReplaceTempView("hudi_trips_incremental")
spark.sql("select `_hoodie_commit_time`, fare, begin_lon, begin_lat, ts from  hudi_trips_incremental where fare > 20.0").show()

For examples, refer to Setup spark-shell in quickstart. Please refer to configurations section, to view all datasource options.

Additionally, HoodieReadClient offers the following functionality using Hudi’s implicit indexing.

API Description
read(keys) Read out the data corresponding to the keys as a DataFrame, using Hudi’s own index for faster lookup
filterExists() Filter out already existing records from the provided RDD[HoodieRecord]. Useful for de-duplication
checkExists(keys) Check if the provided keys exist in a Hudi table

Presto

Presto is a popular query engine, providing interactive query performance. Presto currently supports snapshot queries on COPY_ON_WRITE and read optimized queries on MERGE_ON_READ Hudi tables. This requires the hudi-presto-bundle jar to be placed into <presto_install>/plugin/hive-hadoop2/, across the installation.

Impala (Not Officially Released)

Snapshot Query

Impala is able to query Hudi Copy-on-write table as an EXTERNAL TABLE on HDFS.

To create a Hudi read optimized table on Impala:

CREATE EXTERNAL TABLE database.table_name
LIKE PARQUET '/path/to/load/xxx.parquet'
STORED AS HUDIPARQUET
LOCATION '/path/to/load';

Impala is able to take advantage of the physical partition structure to improve the query performance. To create a partitioned table, the folder should follow the naming convention like year=2020/month=1. Impala use = to separate partition name and partition value.
To create a partitioned Hudi read optimized table on Impala:

CREATE EXTERNAL TABLE database.table_name
LIKE PARQUET '/path/to/load/xxx.parquet'
PARTITION BY (year int, month int, day int)
STORED AS HUDIPARQUET
LOCATION '/path/to/load';
ALTER TABLE database.table_name RECOVER PARTITIONS;

After Hudi made a new commit, refresh the Impala table to get the latest results.

REFRESH database.table_name
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