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Version: 0.11.1

Query Engine Setup

Spark

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.

If your Spark environment does not have the Hudi jars installed, add --jars <path to jar>/hudi-spark-bundle_2.11-<hudi version>.jar to the 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_2.11:0.5.3).

PrestoDB

PrestoDB is a popular query engine, providing interactive query performance. PrestoDB currently supports snapshot querying on COPY_ON_WRITE tables. Both snapshot and read optimized queries are supported on MERGE_ON_READ Hudi tables. Since PrestoDB-Hudi integration has evolved over time, the installation instructions for PrestoDB would vary based on versions. Please check the below table for query types supported and installation instructions for different versions of PrestoDB.

PrestoDB VersionInstallation descriptionQuery types supported
< 0.233Requires the hudi-presto-bundle jar to be placed into <presto_install>/plugin/hive-hadoop2/, across the installation.Snapshot querying on COW tables. Read optimized querying on MOR tables.
>= 0.233No action needed. Hudi (0.5.1-incubating) is a compile time dependency.Snapshot querying on COW tables. Read optimized querying on MOR tables.
>= 0.240No action needed. Hudi 0.5.3 version is a compile time dependency.Snapshot querying on both COW and MOR tables
note

We upgraded Hudi version from 0.5.3 to 0.9.0 in Presto 0.265 but that introduced a breaking dependency change in another presto module. See this issue for more details. Since then, we have fixed the hudi-presto-bundle in version 0.10.1. Now, we need to upgrade Hudi in Presto again. This is being tracked by HUDI-3010. Our suggestion is to avoid upgrading Presto until the issue is fixed. However, if this is not an option, then the workaround is to download the hudi-presto-bundle jar from our maven repo and place it in <presto_install>/plugin/hive-hadoop2/.

Presto Environment

  1. Configure Presto according to the Presto configuration document.
  2. Configure hive catalog in /presto-server-0.2xxx/etc/catalog/hive.properties as follows:
connector.name=hive-hadoop2
hive.metastore.uri=thrift://xxx.xxx.xxx.xxx:9083
hive.config.resources=.../hadoop-2.x/etc/hadoop/core-site.xml,.../hadoop-2.x/etc/hadoop/hdfs-site.xml

Query

Beginning query by connecting hive metastore with presto client. The presto client connection command is as follows:

# The presto client connection command
./presto --server xxx.xxx.xxx.xxx:9999 --catalog hive --schema default

Trino

note

Trino (formerly PrestoSQL) was forked off of PrestoDB a few years ago. Hudi supports 'Snapshot' queries for Copy-On-Write tables and 'Read Optimized' queries for Merge-On-Read tables. This is through the initial input format based integration in PrestoDB (pre forking). This approach has known performance limitations with very large tables, which has been since fixed on PrestoDB. We are working on replicating the same fixes on Trino as well.

To query Hudi tables on Trino, please place the hudi-trino-bundle jar into the Hive connector installation <trino_install>/plugin/hive-hadoop2.

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.