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Batch ingestion of data into Apache Pinot using Apache Spark.

Spark

Pinot supports Apache Spark (2.x and 3.x) as a processor to create and push segment files to the database. Pinot distribution is bundled with the Spark code to process your files and convert and upload them to Pinot.

To set up Spark, do one of the following:

  • Use the Spark-Pinot Connector. For more information, see the ReadMe.
  • Follow the instructions below.

You can follow the wiki to build Pinot from source. The resulting JAR file can be found in pinot/target/pinot-all-${PINOT_VERSION}-jar-with-dependencies.jar

If you do build Pinot from Source, you should consider opting into using the build-shaded-jar jar profile with -Pbuild-shaded-jar. While Pinot does not bundle spark into its jar, it does bundle certain hadoop libraries.

Next, you need to change the execution config in the job spec to the following:

# executionFrameworkSpec: Defines ingestion jobs to be running.
executionFrameworkSpec:

  # name: execution framework name
  name: 'spark'

  # segmentGenerationJobRunnerClassName: class name implements org.apache.pinot.spi.ingestion.batch.runner.IngestionJobRunner interface.
  segmentGenerationJobRunnerClassName: 'org.apache.pinot.plugin.ingestion.batch.spark.SparkSegmentGenerationJobRunner'

  # segmentTarPushJobRunnerClassName: class name implements org.apache.pinot.spi.ingestion.batch.runner.IngestionJobRunner interface.
  segmentTarPushJobRunnerClassName: 'org.apache.pinot.plugin.ingestion.batch.spark.SparkSegmentTarPushJobRunner'

  # segmentUriPushJobRunnerClassName: class name implements org.apache.pinot.spi.ingestion.batch.runner.IngestionJobRunner interface.
  segmentUriPushJobRunnerClassName: 'org.apache.pinot.plugin.ingestion.batch.spark.SparkSegmentUriPushJobRunner'

  #segmentMetadataPushJobRunnerClassName: class name implements org.apache.pinot.spi.ingestion.batch.runner.IngestionJobRunner interface
  segmentMetadataPushJobRunnerClassName: 'org.apache.pinot.plugin.ingestion.batch.spark.SparkSegmentMetadataPushJobRunner'

  # extraConfigs: extra configs for execution framework.
  extraConfigs:

    # stagingDir is used in distributed filesystem to host all the segments then move this directory entirely to output directory.
    stagingDir: your/local/dir/staging

To run Spark ingestion, you need the following jars in your classpath

  • pinot-batch-ingestion-spark plugin jar - available in plugins-external directory in the package
  • pinot-all jar - available in lib directory in the package

These jars can be specified using spark.driver.extraClassPath or any other option.

spark.driver.extraClassPath =>
pinot-batch-ingestion-spark-${PINOT_VERSION}-shaded.jar:pinot-all-${PINOT_VERSION}-jar-with-dependencies.jar

For loading any other plugins that you want to use, use:

spark.driver.extraJavaOptions =>
-Dplugins.dir=${PINOT_DISTRIBUTION_DIR}/plugins

The complete spark-submit command should look like this:

export PINOT_VERSION=0.10.0
export PINOT_DISTRIBUTION_DIR=/path/to/apache-pinot-${PINOT_VERSION}-bin

spark-submit //
--class org.apache.pinot.tools.admin.command.LaunchDataIngestionJobCommand //
--master local --deploy-mode client //
--conf "spark.driver.extraJavaOptions=-Dplugins.dir=${PINOT_DISTRIBUTION_DIR}/plugins" //
--conf "spark.driver.extraClassPath=${PINOT_DISTRIBUTION_DIR}/plugins-external/pinot-batch-ingestion/pinot-batch-ingestion-spark-2.4/pinot-batch-ingestion-spark-2.4-${PINOT_VERSION}-shaded.jar:${PINOT_DISTRIBUTION_DIR}/lib/pinot-all-${PINOT_VERSION}-jar-with-dependencies.jar" //
-conf "spark.executor.extraClassPath=${PINOT_DISTRIBUTION_DIR}/plugins-external/pinot-batch-ingestion/pinot-batch-ingestion-spark-2.4/pinot-batch-ingestion-spark-2.4-${PINOT_VERSION}-shaded.jar:${PINOT_DISTRIBUTION_DIR}/lib/pinot-all-${PINOT_VERSION}-jar-with-dependencies.jar" //
local://${PINOT_DISTRIBUTION_DIR}/lib/pinot-all-${PINOT_VERSION}-jar-with-dependencies.jar -jobSpecFile /path/to/spark_job_spec.yaml

Ensure environment variables PINOT_ROOT_DIR and PINOT_VERSION are set properly.

Note: You should change the master to yarn and deploy-mode to cluster for production environments.

{% hint style="info" %} We have stopped including spark-core dependency in our jars post 0.10.0 release. Users can try 0.11.0-SNAPSHOT and later versions of pinot-batch-ingestion-spark in case of any runtime issues. You can either build from source or download latest master build jars. {% endhint %}

Running in Cluster Mode on YARN

If you want to run the spark job in cluster mode on YARN/EMR cluster, the following needs to be done -

  • Build Pinot from source with option -DuseProvidedHadoop
  • Copy Pinot binaries to S3, HDFS or any other distributed storage that is accessible from all nodes.
  • Copy Ingestion spec YAML file to S3, HDFS or any other distributed storage. Mention this path as part of --files argument in the command
  • Add --jars options that contain the s3/hdfs paths to all the required plugin and pinot-all jar
  • Point classPath to spark working directory. Generally, just specifying the jar names without any paths works. Same should be done for main jar as well as the spec YAML file

Example

spark-submit //
--class org.apache.pinot.tools.admin.command.LaunchDataIngestionJobCommand //
--master yarn --deploy-mode cluster //
--conf "spark.driver.extraJavaOptions=-Dplugins.dir=${PINOT_DISTRIBUTION_DIR}/plugins" //
--conf "spark.driver.extraClassPath=pinot-batch-ingestion-spark-2.4/pinot-batch-ingestion-spark-2.4-${PINOT_VERSION}-shaded.jar:pinot-all-${PINOT_VERSION}-jar-with-dependencies.jar" //
--conf "spark.executor.extraClassPath=pinot-batch-ingestion-spark-2.4/pinot-batch-ingestion-spark-2.4-${PINOT_VERSION}-shaded.jar:pinot-all-${PINOT_VERSION}-jar-with-dependencies.jar" //
--jars "${PINOT_DISTRIBUTION_DIR}/plugins-external/pinot-batch-ingestion/pinot-batch-ingestion-spark-2.4/pinot-batch-ingestion-spark-2.4-${PINOT_VERSION}-shaded.jar,${PINOT_DISTRIBUTION_DIR}/lib/pinot-all-${PINOT_VERSION}-jar-with-dependencies.jar"
--files s3://path/to/spark_job_spec.yaml
local://pinot-all-${PINOT_VERSION}-jar-with-dependencies.jar -jobSpecFile spark_job_spec.yaml

{% hint style="success" %} For Spark 3.x, replace pinot-batch-ingestion-spark-2.4 with pinot-batch-ingestion-spark-3.2 in all places in the commands.

Also, ensure the classpath in ingestion spec is changed from org.apache.pinot.plugin.ingestion.batch.spark.
to
org.apache.pinot.plugin.ingestion.batch.spark3. {% endhint %}

FAQ

Q - I am getting the following exception - Class has been compiled by a more recent version of the Java Runtime (class file version 55.0), this version of the Java Runtime only recognizes class file versions up to 52.0

Since 0.8.0 release, Pinot binaries are compiled with JDK 11. If you are using Spark along with Hadoop 2.7+, you need to use the Java8 version of Pinot. Currently, you need to build jdk 8 version from source.

Q - I am not able to find pinot-batch-ingestion-spark jar.

For Pinot version prior to 0.10.0, the spark plugin is located in plugin dir of binary distribution. For 0.10.0 and later, it is located in pinot-external dir.

Q - Spark is not able to find the jars leading to java.nio.file.NoSuchFileException

This means the classpath for spark job has not been configured properly. If you are running spark in a distributed environment such as Yarn or k8s, make sure both spark.driver.classpath and spark.executor.classpath are set. Also, the jars in driver.classpath should be added to --jars argument in spark-submit so that spark can distribute those jars to all the nodes in your cluster. You also need to take provide appropriate scheme with the file path when running the jar. In this doc, we have used local:\\ but it can be different depending on your cluster setup.

Q - Spark job failing while pushing the segments.

It can be because of misconfigured controllerURI in job spec yaml file. If the controllerURI is correct, make sure it is accessible from all the nodes of your YARN or k8s cluster.

Q - My data gets overwritten during ingestion.

Set segmentPushType to APPEND in the tableConfig.

If already set to APPEND, this is likely due to a missing timeColumnName in your table config. If you can't provide a time column, use our segment name generation configs in ingestion spec. Generally using inputFile segment name generator should fix your issue.

Q - I am getting java.lang.RuntimeException: java.io.IOException: Failed to create directory: pinot-plugins-dir-0/plugins/*

Removing -Dplugins.dir=${PINOT_DISTRIBUTION_DIR}/plugins from spark.driver.extraJavaOptions should fix this. As long as plugins are mentioned in classpath and jars argument it should not be an issue.

Q - Getting Class not found: exception

Check if extraClassPath arguments contain all the plugin jars for both driver and executors. Also, all the plugin jars are mentioned in the --jars argument. If both of these are correct, check if the extraClassPath contains local filesystem classpaths and not s3 or hdfs or any other distributed file system classpaths.