Querying Data

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This page is no longer maintained. Please refer to Hudi SQL DDL, SQL DML, SQL Queries and Procedures for the latest documentation.

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, Flink, Trino and PrestoDB.

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

Spark Datasource

The Spark Datasource API is a popular way of authoring Spark ETL pipelines. Hudi tables can be queried via the Spark datasource with a simple spark.read.parquet. See the Spark Quick Start for more examples of Spark datasource reading queries.

Setup

If your Spark environment does not have the Hudi jars installed, add hudi-spark-bundle jar to the classpath of drivers and executors using --jars option. Alternatively, hudi-spark-bundle can also fetched via the --packages options (e.g: --packages org.apache.hudi:hudi-spark3.5-bundle_2.12:1.0.0).

Snapshot query

Retrieve the data table at the present point in time.

val hudiSnapshotQueryDF = spark
     .read
     .format("hudi")
     .option("hoodie.datasource.query.type", "snapshot")
     .load(tablePath) 

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("hoodie.datasource.query.type", "incremental"())
     .option("hoodie.datasource.read.begin.instanttime", <beginInstantTime>)
     .option(DataSourceReadOptions.INCR_PATH_GLOB_OPT_KEY(), "/year=2020/month=*/day=*") // Optional, use glob pattern if querying certain partitions
     .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 Incremental Queries in the Spark 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

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 (HoodieStreamer) 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).