Batch Data Ingestion In Practice

In practice, we need to run Pinot data ingestion as a pipeline or a scheduled job.

Assuming pinot-distribution is already built, inside examples directory, you could find several sample table layouts.

Table Layout

Usually each table deserves its own directory, like airlineStats.

Inside the table directory, rawdata is created to put all the input data.

Typically, for data events with timestamp, we partition those data and store them into a daily folder. E.g. a typically layout would follow this pattern: rawdata/%yyyy%/%mm%/%dd%/[daily_input_files].

/var/pinot/airlineStats/rawdata/2014/01/01/airlineStats_data_2014-01-01.avro
/var/pinot/airlineStats/rawdata/2014/01/02/airlineStats_data_2014-01-02.avro
/var/pinot/airlineStats/rawdata/2014/01/03/airlineStats_data_2014-01-03.avro
/var/pinot/airlineStats/rawdata/2014/01/04/airlineStats_data_2014-01-04.avro
/var/pinot/airlineStats/rawdata/2014/01/05/airlineStats_data_2014-01-05.avro
/var/pinot/airlineStats/rawdata/2014/01/06/airlineStats_data_2014-01-06.avro
/var/pinot/airlineStats/rawdata/2014/01/07/airlineStats_data_2014-01-07.avro
/var/pinot/airlineStats/rawdata/2014/01/08/airlineStats_data_2014-01-08.avro
/var/pinot/airlineStats/rawdata/2014/01/09/airlineStats_data_2014-01-09.avro
/var/pinot/airlineStats/rawdata/2014/01/10/airlineStats_data_2014-01-10.avro
/var/pinot/airlineStats/rawdata/2014/01/11/airlineStats_data_2014-01-11.avro
/var/pinot/airlineStats/rawdata/2014/01/12/airlineStats_data_2014-01-12.avro
/var/pinot/airlineStats/rawdata/2014/01/13/airlineStats_data_2014-01-13.avro
/var/pinot/airlineStats/rawdata/2014/01/14/airlineStats_data_2014-01-14.avro
/var/pinot/airlineStats/rawdata/2014/01/15/airlineStats_data_2014-01-15.avro
/var/pinot/airlineStats/rawdata/2014/01/16/airlineStats_data_2014-01-16.avro
/var/pinot/airlineStats/rawdata/2014/01/17/airlineStats_data_2014-01-17.avro
/var/pinot/airlineStats/rawdata/2014/01/18/airlineStats_data_2014-01-18.avro
/var/pinot/airlineStats/rawdata/2014/01/19/airlineStats_data_2014-01-19.avro
/var/pinot/airlineStats/rawdata/2014/01/20/airlineStats_data_2014-01-20.avro
/var/pinot/airlineStats/rawdata/2014/01/21/airlineStats_data_2014-01-21.avro
/var/pinot/airlineStats/rawdata/2014/01/22/airlineStats_data_2014-01-22.avro
/var/pinot/airlineStats/rawdata/2014/01/23/airlineStats_data_2014-01-23.avro
/var/pinot/airlineStats/rawdata/2014/01/24/airlineStats_data_2014-01-24.avro
/var/pinot/airlineStats/rawdata/2014/01/25/airlineStats_data_2014-01-25.avro
/var/pinot/airlineStats/rawdata/2014/01/26/airlineStats_data_2014-01-26.avro
/var/pinot/airlineStats/rawdata/2014/01/27/airlineStats_data_2014-01-27.avro
/var/pinot/airlineStats/rawdata/2014/01/28/airlineStats_data_2014-01-28.avro
/var/pinot/airlineStats/rawdata/2014/01/29/airlineStats_data_2014-01-29.avro
/var/pinot/airlineStats/rawdata/2014/01/30/airlineStats_data_2014-01-30.avro
/var/pinot/airlineStats/rawdata/2014/01/31/airlineStats_data_2014-01-31.avro

Configuring batch ingestion job

Create a batch ingestion job spec file to describe how to ingest the data.

Below is an example (also located at examples/batch/airlineStats/ingestionJobSpec.yaml)

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

  # name: execution framework name
  name: 'standalone'

  # segmentGenerationJobRunnerClassName: class name implements org.apache.pinot.spi.batch.ingestion.runner.SegmentGenerationJobRunner interface.
  segmentGenerationJobRunnerClassName: 'org.apache.pinot.plugin.ingestion.batch.standalone.SegmentGenerationJobRunner'

  # segmentTarPushJobRunnerClassName: class name implements org.apache.pinot.spi.batch.ingestion.runner.SegmentTarPushJobRunner interface.
  segmentTarPushJobRunnerClassName: 'org.apache.pinot.plugin.ingestion.batch.standalone.SegmentTarPushJobRunner'

  # segmentUriPushJobRunnerClassName: class name implements org.apache.pinot.spi.batch.ingestion.runner.SegmentUriPushJobRunner interface.
  segmentUriPushJobRunnerClassName: 'org.apache.pinot.plugin.ingestion.batch.standalone.SegmentUriPushJobRunner'

# jobType: Pinot ingestion job type.
# Supported job types are:
#   'SegmentCreation'
#   'SegmentTarPush'
#   'SegmentUriPush'
#   'SegmentCreationAndTarPush'
#   'SegmentCreationAndUriPush'
jobType: SegmentCreationAndTarPush

# inputDirURI: Root directory of input data, expected to have scheme configured in PinotFS.
inputDirURI: 'examples/batch/airlineStats/rawdata'

# includeFileNamePattern: include file name pattern, supported glob pattern.
# Sample usage:
#   'glob:*.avro' will include all avro files just under the inputDirURI, not sub directories;
#   'glob:**\/*.avro' will include all the avro files under inputDirURI recursively.
includeFileNamePattern: 'glob:**/*.avro'

# excludeFileNamePattern: exclude file name pattern, supported glob pattern.
# Sample usage:
#   'glob:*.avro' will exclude all avro files just under the inputDirURI, not sub directories;
#   'glob:**\/*.avro' will exclude all the avro files under inputDirURI recursively.
# _excludeFileNamePattern: ''

# outputDirURI: Root directory of output segments, expected to have scheme configured in PinotFS.
outputDirURI: 'examples/batch/airlineStats/segments'

# overwriteOutput: Overwrite output segments if existed.
overwriteOutput: true

# Create a separated metadata only tar gz file to reduce the data transfer of segment metadata push job.
createMetadataTarGz: true

# Job parallelism for segment creation
segmentCreationJobParallelism: 4

# pinotFSSpecs: defines all related Pinot file systems.
pinotFSSpecs:

  - # scheme: used to identify a PinotFS.
    # E.g. local, hdfs, dbfs, etc
    scheme: file

    # className: Class name used to create the PinotFS instance.
    # E.g.
    #   org.apache.pinot.spi.filesystem.LocalPinotFS is used for local filesystem
    #   org.apache.pinot.plugin.filesystem.AzurePinotFS is used for Azure Data Lake
    #   org.apache.pinot.plugin.filesystem.HadoopPinotFS is used for HDFS
    className: org.apache.pinot.spi.filesystem.LocalPinotFS

# recordReaderSpec: defines all record reader
recordReaderSpec:

  # dataFormat: Record data format, e.g. 'avro', 'parquet', 'orc', 'csv', 'json', 'thrift' etc.
  dataFormat: 'avro'

  # className: Corresponding RecordReader class name.
  # E.g.
  #   org.apache.pinot.plugin.inputformat.avro.AvroRecordReader
  #   org.apache.pinot.plugin.inputformat.csv.CSVRecordReader
  #   org.apache.pinot.plugin.inputformat.parquet.ParquetRecordReader
  #   org.apache.pinot.plugin.inputformat.json.JSONRecordReader
  #   org.apache.pinot.plugin.inputformat.orc.ORCRecordReader
  #   org.apache.pinot.plugin.inputformat.thrift.ThriftRecordReader
  className: 'org.apache.pinot.plugin.inputformat.avro.AvroRecordReader'

# tableSpec: defines table name and where to fetch corresponding table config and table schema.
tableSpec:

  # tableName: Table name
  tableName: 'airlineStats'

  # schemaURI: defines where to read the table schema, supports PinotFS or HTTP.
  # E.g.
  #   hdfs://path/to/table_schema.json
  #   http://localhost:9000/tables/myTable/schema
  schemaURI: 'http://localhost:9000/tables/airlineStats/schema'

  # tableConfigURI: defines where to reade the table config.
  # Supports using PinotFS or HTTP.
  # E.g.
  #   hdfs://path/to/table_config.json
  #   http://localhost:9000/tables/myTable
  # Note that the API to read Pinot table config directly from pinot controller contains a JSON wrapper.
  # The real table config is the object under the field 'OFFLINE'.
  tableConfigURI: 'http://localhost:9000/tables/airlineStats'

# segmentNameGeneratorSpec: defines how to init a SegmentNameGenerator.
segmentNameGeneratorSpec:

  # type: Current supported type is 'simple' and 'normalizedDate'.
  type: normalizedDate

  # configs: Configs to init SegmentNameGenerator.
  configs:
    segment.name.prefix: 'airlineStats_batch'
    exclude.sequence.id: true

# pinotClusterSpecs: defines the Pinot Cluster Access Point.
pinotClusterSpecs:
  - # controllerURI: used to fetch table/schema information and data push.
    # E.g. http://localhost:9000
    controllerURI: 'http://localhost:9000'

# pushJobSpec: defines segment push job related configuration.
pushJobSpec:

  # Job parallelism for segment push
  pushParallelism: 4

  # pushAttempts: number of attempts for push job, default is 1, which means no retry.
  pushAttempts: 2

  # pushRetryIntervalMillis: retry wait Ms, default to 1 second.
  pushRetryIntervalMillis: 1000
  
  # Applicable for URI and METADATA push types.
  #If true, and if segment was not already in the deep store, move it to deep store.
  copyToDeepStoreForMetadataPush: false
  
  # Prefer using segment metadata tar gz file to push segment if exists.
  preferMetadataTarGz: true

Executing the job

Below command will create example table into Pinot cluster.

bin/pinot-admin.sh AddTable  -schemaFile examples/batch/airlineStats/airlineStats_schema.json -tableConfigFile examples/batch/airlineStats/airlineStats_offline_table_config.json -exec

Below command will kick off the ingestion job to generate Pinot segments and push them into the cluster.

bin/pinot-admin.sh LaunchDataIngestionJob -jobSpecFile examples/batch/airlineStats/ingestionJobSpec.yaml

After job finished, segments are stored in examples/batch/airlineStats/segments following same layout of input directory layout.

/var/pinot/airlineStats/segments/2014/01/01/airlineStats_batch_2014-01-01_2014-01-01.tar.gz
/var/pinot/airlineStats/segments/2014/01/02/airlineStats_batch_2014-01-02_2014-01-02.tar.gz
/var/pinot/airlineStats/segments/2014/01/03/airlineStats_batch_2014-01-03_2014-01-03.tar.gz
/var/pinot/airlineStats/segments/2014/01/04/airlineStats_batch_2014-01-04_2014-01-04.tar.gz
/var/pinot/airlineStats/segments/2014/01/05/airlineStats_batch_2014-01-05_2014-01-05.tar.gz
/var/pinot/airlineStats/segments/2014/01/06/airlineStats_batch_2014-01-06_2014-01-06.tar.gz
/var/pinot/airlineStats/segments/2014/01/07/airlineStats_batch_2014-01-07_2014-01-07.tar.gz
/var/pinot/airlineStats/segments/2014/01/08/airlineStats_batch_2014-01-08_2014-01-08.tar.gz
/var/pinot/airlineStats/segments/2014/01/09/airlineStats_batch_2014-01-09_2014-01-09.tar.gz
/var/pinot/airlineStats/segments/2014/01/10/airlineStats_batch_2014-01-10_2014-01-10.tar.gz
/var/pinot/airlineStats/segments/2014/01/11/airlineStats_batch_2014-01-11_2014-01-11.tar.gz
/var/pinot/airlineStats/segments/2014/01/12/airlineStats_batch_2014-01-12_2014-01-12.tar.gz
/var/pinot/airlineStats/segments/2014/01/13/airlineStats_batch_2014-01-13_2014-01-13.tar.gz
/var/pinot/airlineStats/segments/2014/01/14/airlineStats_batch_2014-01-14_2014-01-14.tar.gz
/var/pinot/airlineStats/segments/2014/01/15/airlineStats_batch_2014-01-15_2014-01-15.tar.gz
/var/pinot/airlineStats/segments/2014/01/16/airlineStats_batch_2014-01-16_2014-01-16.tar.gz
/var/pinot/airlineStats/segments/2014/01/17/airlineStats_batch_2014-01-17_2014-01-17.tar.gz
/var/pinot/airlineStats/segments/2014/01/18/airlineStats_batch_2014-01-18_2014-01-18.tar.gz
/var/pinot/airlineStats/segments/2014/01/19/airlineStats_batch_2014-01-19_2014-01-19.tar.gz
/var/pinot/airlineStats/segments/2014/01/20/airlineStats_batch_2014-01-20_2014-01-20.tar.gz
/var/pinot/airlineStats/segments/2014/01/21/airlineStats_batch_2014-01-21_2014-01-21.tar.gz
/var/pinot/airlineStats/segments/2014/01/22/airlineStats_batch_2014-01-22_2014-01-22.tar.gz
/var/pinot/airlineStats/segments/2014/01/23/airlineStats_batch_2014-01-23_2014-01-23.tar.gz
/var/pinot/airlineStats/segments/2014/01/24/airlineStats_batch_2014-01-24_2014-01-24.tar.gz
/var/pinot/airlineStats/segments/2014/01/25/airlineStats_batch_2014-01-25_2014-01-25.tar.gz
/var/pinot/airlineStats/segments/2014/01/26/airlineStats_batch_2014-01-26_2014-01-26.tar.gz
/var/pinot/airlineStats/segments/2014/01/27/airlineStats_batch_2014-01-27_2014-01-27.tar.gz
/var/pinot/airlineStats/segments/2014/01/28/airlineStats_batch_2014-01-28_2014-01-28.tar.gz
/var/pinot/airlineStats/segments/2014/01/29/airlineStats_batch_2014-01-29_2014-01-29.tar.gz
/var/pinot/airlineStats/segments/2014/01/30/airlineStats_batch_2014-01-30_2014-01-30.tar.gz
/var/pinot/airlineStats/segments/2014/01/31/airlineStats_batch_2014-01-31_2014-01-31.tar.gz

Executing the job using Spark

Below example is running in a spark local mode. You can download spark distribution and start it by running:

wget https://downloads.apache.org/spark/spark-2.4.6/spark-2.4.6-bin-hadoop2.7.tgz
tar xvf spark-2.4.6-bin-hadoop2.7.tgz
cd spark-2.4.6-bin-hadoop2.7
./bin/spark-shell --master 'local[2]'

Build latest Pinot Distribution following this .

Below command shows how to use spark-submit command to submit a spark job using pinot-all-${PINOT_VERSION}-jar-with-dependencies.jar.

Sample Spark ingestion job spec yaml, (also located at examples/batch/airlineStats/sparkIngestionJobSpec.yaml):

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

  # name: execution framework name
  name: 'spark'

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

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

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

  # 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: examples/batch/airlineStats/staging

# jobType: Pinot ingestion job type.
# Supported job types are:
#   'SegmentCreation'
#   'SegmentTarPush'
#   'SegmentUriPush'
#   'SegmentCreationAndTarPush'
#   'SegmentCreationAndUriPush'
jobType: SegmentCreationAndTarPush

# inputDirURI: Root directory of input data, expected to have scheme configured in PinotFS.
inputDirURI: 'examples/batch/airlineStats/rawdata'

# includeFileNamePattern: include file name pattern, supported glob pattern.
# Sample usage:
#   'glob:*.avro' will include all avro files just under the inputDirURI, not sub directories;
#   'glob:**\/*.avro' will include all the avro files under inputDirURI recursively.
includeFileNamePattern: 'glob:**/*.avro'

# excludeFileNamePattern: exclude file name pattern, supported glob pattern.
# Sample usage:
#   'glob:*.avro' will exclude all avro files just under the inputDirURI, not sub directories;
#   'glob:**\/*.avro' will exclude all the avro files under inputDirURI recursively.
# excludeFileNamePattern: ''

# outputDirURI: Root directory of output segments, expected to have scheme configured in PinotFS.
outputDirURI: 'examples/batch/airlineStats/segments'

# overwriteOutput: Overwrite output segments if existed.
overwriteOutput: true

# pinotFSSpecs: defines all related Pinot file systems.
pinotFSSpecs:

  - # scheme: used to identify a PinotFS.
    # E.g. local, hdfs, dbfs, etc
    scheme: file

    # className: Class name used to create the PinotFS instance.
    # E.g.
    #   org.apache.pinot.spi.filesystem.LocalPinotFS is used for local filesystem
    #   org.apache.pinot.plugin.filesystem.AzurePinotFS is used for Azure Data Lake
    #   org.apache.pinot.plugin.filesystem.HadoopPinotFS is used for HDFS
    className: org.apache.pinot.plugin.filesystem.HadoopPinotFS

# recordReaderSpec: defines all record reader
recordReaderSpec:

  # dataFormat: Record data format, e.g. 'avro', 'parquet', 'orc', 'csv', 'json', 'thrift' etc.
  dataFormat: 'avro'

  # className: Corresponding RecordReader class name.
  # E.g.
  #   org.apache.pinot.plugin.inputformat.avro.AvroRecordReader
  #   org.apache.pinot.plugin.inputformat.csv.CSVRecordReader
  #   org.apache.pinot.plugin.inputformat.parquet.ParquetRecordReader
  #   org.apache.pinot.plugin.inputformat.json.JSONRecordReader
  #   org.apache.pinot.plugin.inputformat.orc.ORCRecordReader
  #   org.apache.pinot.plugin.inputformat.thrift.ThriftRecordReader
  className: 'org.apache.pinot.plugin.inputformat.avro.AvroRecordReader'

# tableSpec: defines table name and where to fetch corresponding table config and table schema.
tableSpec:

  # tableName: Table name
  tableName: 'airlineStats'

  # schemaURI: defines where to read the table schema, supports PinotFS or HTTP.
  # E.g.
  #   hdfs://path/to/table_schema.json
  #   http://localhost:9000/tables/myTable/schema
  schemaURI: 'http://localhost:9000/tables/airlineStats/schema'

  # tableConfigURI: defines where to reade the table config.
  # Supports using PinotFS or HTTP.
  # E.g.
  #   hdfs://path/to/table_config.json
  #   http://localhost:9000/tables/myTable
  # Note that the API to read Pinot table config directly from pinot controller contains a JSON wrapper.
  # The real table config is the object under the field 'OFFLINE'.
  tableConfigURI: 'http://localhost:9000/tables/airlineStats'

# segmentNameGeneratorSpec: defines how to init a SegmentNameGenerator.
segmentNameGeneratorSpec:

  # type: Current supported type is 'simple' and 'normalizedDate'.
  type: normalizedDate

  # configs: Configs to init SegmentNameGenerator.
  configs:
    segment.name.prefix: 'airlineStats_batch'
    exclude.sequence.id: true

# pinotClusterSpecs: defines the Pinot Cluster Access Point.
pinotClusterSpecs:
  - # controllerURI: used to fetch table/schema information and data push.
    # E.g. http://localhost:9000
    controllerURI: 'http://localhost:9000'

# pushJobSpec: defines segment push job related configuration.
pushJobSpec:

  # pushParallelism: push job parallelism, default is 1.
  pushParallelism: 2

  # pushAttempts: number of attempts for push job, default is 1, which means no retry.
  pushAttempts: 2

  # pushRetryIntervalMillis: retry wait Ms, default to 1 second.
  pushRetryIntervalMillis: 1000

Ensure parameter PINOT_ROOT_DIR and PINOT_VERSION are set properly.

Ensure you set

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

Or put all the required plugins jars to CLASSPATH, then set -Dplugins.dir=${CLASSPATH}

spark.driver.extraClassPath =>
pinot-all-${PINOT_VERSION}-jar-with-depdencies.jar

export PINOT_VERSION=0.10.0-SNAPSHOT
export PINOT_DISTRIBUTION_DIR=${PINOT_ROOT_DIR}/build/
cd ${PINOT_DISTRIBUTION_DIR}
${SPARK_HOME}/bin/spark-submit \
  --class org.apache.pinot.tools.admin.command.LaunchDataIngestionJobCommand \
  --master "local[2]" \
  --deploy-mode client \
  --conf "spark.driver.extraJavaOptions=-Dplugins.dir=${PINOT_DISTRIBUTION_DIR}/plugins -Dlog4j2.configurationFile=${PINOT_DISTRIBUTION_DIR}/conf/pinot-ingestion-job-log4j2.xml" \
  --conf "spark.driver.extraClassPath=${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 ${PINOT_DISTRIBUTION_DIR}/examples/batch/airlineStats/sparkIngestionJobSpec.yaml

Executing the job using Hadoop

Below command shows how to use Hadoop jar command to run a Hadoop job using pinot-all-${PINOT_VERSION}-jar-with-dependencies.jar.

Sample Hadoop ingestion job spec yaml(also located at examples/batch/airlineStats/hadoopIngestionJobSpec.yaml):

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

  # name: execution framework name
  name: 'hadoop'

  # segmentGenerationJobRunnerClassName: class name implements org.apache.pinot.spi.batch.ingestion.runner.SegmentGenerationJobRunner interface.
  segmentGenerationJobRunnerClassName: 'org.apache.pinot.plugin.ingestion.batch.hadoop.HadoopSegmentGenerationJobRunner'

  # segmentTarPushJobRunnerClassName: class name implements org.apache.pinot.spi.batch.ingestion.runner.SegmentTarPushJobRunner interface.
  segmentTarPushJobRunnerClassName: 'org.apache.pinot.plugin.ingestion.batch.hadoop.HadoopSegmentTarPushJobRunner'

  # segmentUriPushJobRunnerClassName: class name implements org.apache.pinot.spi.batch.ingestion.runner.SegmentUriPushJobRunner interface.
  segmentUriPushJobRunnerClassName: 'org.apache.pinot.plugin.ingestion.batch.hadoop.HadoopSegmentUriPushJobRunner'

  # 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: examples/batch/airlineStats/staging

# jobType: Pinot ingestion job type.
# Supported job types are:
#   'SegmentCreation'
#   'SegmentTarPush'
#   'SegmentUriPush'
#   'SegmentCreationAndTarPush'
#   'SegmentCreationAndUriPush'
jobType: SegmentCreationAndTarPush

# inputDirURI: Root directory of input data, expected to have scheme configured in PinotFS.
inputDirURI: 'examples/batch/airlineStats/rawdata'

# includeFileNamePattern: include file name pattern, supported glob pattern.
# Sample usage:
#   'glob:*.avro' will include all avro files just under the inputDirURI, not sub directories;
#   'glob:**\/*.avro' will include all the avro files under inputDirURI recursively.
includeFileNamePattern: 'glob:**/*.avro'

# excludeFileNamePattern: exclude file name pattern, supported glob pattern.
# Sample usage:
#   'glob:*.avro' will exclude all avro files just under the inputDirURI, not sub directories;
#   'glob:**\/*.avro' will exclude all the avro files under inputDirURI recursively.
# _excludeFileNamePattern: ''

# outputDirURI: Root directory of output segments, expected to have scheme configured in PinotFS.
outputDirURI: 'examples/batch/airlineStats/segments'

# overwriteOutput: Overwrite output segments if existed.
overwriteOutput: true

# pinotFSSpecs: defines all related Pinot file systems.
pinotFSSpecs:

  - # scheme: used to identify a PinotFS.
    # E.g. local, hdfs, dbfs, etc
    scheme: file

    # className: Class name used to create the PinotFS instance.
    # E.g.
    #   org.apache.pinot.spi.filesystem.LocalPinotFS is used for local filesystem
    #   org.apache.pinot.plugin.filesystem.AzurePinotFS is used for Azure Data Lake
    #   org.apache.pinot.plugin.filesystem.HadoopPinotFS is used for HDFS
    className: org.apache.pinot.plugin.filesystem.HadoopPinotFS

# recordReaderSpec: defines all record reader
recordReaderSpec:

  # dataFormat: Record data format, e.g. 'avro', 'parquet', 'orc', 'csv', 'json', 'thrift' etc.
  dataFormat: 'avro'

  # className: Corresponding RecordReader class name.
  # E.g.
  #   org.apache.pinot.plugin.inputformat.avro.AvroRecordReader
  #   org.apache.pinot.plugin.inputformat.csv.CSVRecordReader
  #   org.apache.pinot.plugin.inputformat.parquet.ParquetRecordReader
  #   org.apache.pinot.plugin.inputformat.json.JSONRecordReader
  #   org.apache.pinot.plugin.inputformat.orc.ORCRecordReader
  #   org.apache.pinot.plugin.inputformat.thrift.ThriftRecordReader
  className: 'org.apache.pinot.plugin.inputformat.avro.AvroRecordReader'

# tableSpec: defines table name and where to fetch corresponding table config and table schema.
tableSpec:

  # tableName: Table name
  tableName: 'airlineStats'

  # schemaURI: defines where to read the table schema, supports PinotFS or HTTP.
  # E.g.
  #   hdfs://path/to/table_schema.json
  #   http://localhost:9000/tables/myTable/schema
  schemaURI: 'http://localhost:9000/tables/airlineStats/schema'

  # tableConfigURI: defines where to reade the table config.
  # Supports using PinotFS or HTTP.
  # E.g.
  #   hdfs://path/to/table_config.json
  #   http://localhost:9000/tables/myTable
  # Note that the API to read Pinot table config directly from pinot controller contains a JSON wrapper.
  # The real table config is the object under the field 'OFFLINE'.
  tableConfigURI: 'http://localhost:9000/tables/airlineStats'

# segmentNameGeneratorSpec: defines how to init a SegmentNameGenerator.
segmentNameGeneratorSpec:

  # type: Current supported type is 'simple' and 'normalizedDate'.
  type: normalizedDate

  # configs: Configs to init SegmentNameGenerator.
  configs:
    segment.name.prefix: 'airlineStats_batch'
    exclude.sequence.id: true

# pinotClusterSpecs: defines the Pinot Cluster Access Point.
pinotClusterSpecs:
  - # controllerURI: used to fetch table/schema information and data push.
    # E.g. http://localhost:9000
    controllerURI: 'http://localhost:9000'

# pushJobSpec: defines segment push job related configuration.
pushJobSpec:

  # pushParallelism: push job parallelism, default is 1.
  pushParallelism: 2

  # pushAttempts: number of attempts for push job, default is 1, which means no retry.
  pushAttempts: 2

  # pushRetryIntervalMillis: retry wait Ms, default to 1 second.
  pushRetryIntervalMillis: 1000

Ensure parameter PINOT_ROOT_DIR and PINOT_VERSION are set properly.

export PINOT_VERSION=0.10.0-SNAPSHOT
export PINOT_DISTRIBUTION_DIR=${PINOT_ROOT_DIR}/build/
export HADOOP_CLIENT_OPTS="-Dplugins.dir=${PINOT_DISTRIBUTION_DIR}/plugins -Dlog4j2.configurationFile=${PINOT_DISTRIBUTION_DIR}/conf/pinot-ingestion-job-log4j2.xml"
hadoop jar  \
        ${PINOT_DISTRIBUTION_DIR}/lib/pinot-all-${PINOT_VERSION}-jar-with-dependencies.jar \
        org.apache.pinot.tools.admin.command.LaunchDataIngestionJobCommand \
        -jobSpecFile ${PINOT_DISTRIBUTION_DIR}/examples/batch/airlineStats/hadoopIngestionJobSpec.yaml

Tuning

You can set Environment Variable: JAVA_OPTS to modify:

Log4j2 file location with -Dlog4j2.configurationFile
Plugin directory location with -Dplugins.dir=/opt/pinot/plugins
JVM props, like -Xmx8g -Xms4G

Note that you need to config above three all together in JAVA_OPTS. If you only config JAVA_OPTS="-Xmx4g" then plugins.dir is empty usually will cause job failure.

E.g.

docker run --rm -ti -e JAVA_OPTS="-Xms8G -Dlog4j2.configurationFile=/opt/pinot/conf/pinot-admin-log4j2.xml  -Dplugins.dir=/opt/pinot/plugins" --name pinot-data-ingestion-job apachepinot/pinot:latest LaunchDataIngestionJob -jobSpecFile /path/to/ingestion_job_spec.yaml

You can also add your customized JAVA_OPTS if necessary.

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