Comment on page
Use S3 and Pinot in Docker

Setup Pinot Cluster
In order to setup Pinot in Docker to use S3 as deep store, we need to put extra configs for Controller and Server.
Create a docker network
docker network create -d bridge pinot-demo
Start Zookeeper
docker run \
    --name zookeeper \
    --restart always \
    --network=pinot-demo \
    -d zookeeper:3.5.6
Prepare Pinot configuration files
Below sections will prepare 3 config files under /tmp/pinot-s3-docker to mount to the container.
/tmp/pinot-s3-docker/
                     controller.conf
                     server.conf
                     ingestionJobSpec.yaml
Start Controller
Below is a sample controller.conf file.
Please config:
controller.data.dirto your s3 bucket. All the uploaded segments will be stored there. 
And add s3 as a pinot storage with configs:
pinot.controller.storage.factory.class.s3=org.apache.pinot.plugin.filesystem.S3PinotFS
pinot.controller.storage.factory.s3.region=us-west-2
Regarding AWS Credential, we also follow the convention of DefaultAWSCredentialsProviderChain.
You can specify AccessKey and Secret using:
Environment Variables - AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY (RECOMMENDED since they are recognized by all the AWS SDKs and CLI except for .NET), or AWS_ACCESS_KEY and AWS_SECRET_KEY (only recognized by Java SDK)
Java System Properties - aws.accessKeyId and aws.secretKey
Credential profiles file at the default location (~/.aws/credentials) shared by all AWS SDKs and the AWS CLI
Configure AWS credential in pinot config files, e.g. set pinot.controller.storage.factory.s3.accessKey and pinot.controller.storage.factory.s3.secretKey in the config file. (Not recommended)
pinot.controller.storage.factory.s3.accessKey=****************LFVX
pinot.controller.storage.factory.s3.secretKey=****************gfhz
Add s3 to pinot.controller.segment.fetcher.protocols 
and set pinot.controller.segment.fetcher.s3.class toorg.apache.pinot.common.utils.fetcher.PinotFSSegmentFetcher
pinot.role=controller
pinot.controller.storage.factory.class.s3=org.apache.pinot.plugin.filesystem.S3PinotFS
pinot.controller.storage.factory.s3.region=us-west-2
controller.data.dir=s3://<my-bucket>/pinot-data/pinot-s3-example-docker/controller-data/
controller.local.temp.dir=/tmp/pinot-tmp-data/
controller.helix.cluster.name=pinot-s3-example-docker
controller.zk.str=zookeeper:2181
controller.port=9000
controller.enable.split.commit=true
pinot.controller.segment.fetcher.protocols=file,http,s3
pinot.controller.segment.fetcher.s3.class=org.apache.pinot.common.utils.fetcher.PinotFSSegmentFetcher
Then start pinot controller with:
docker run --rm -ti \
    --name pinot-controller \
    --network=pinot-demo \
    -p 9000:9000 \
    --env AWS_ACCESS_KEY_ID=<aws-access-key-id> \
    --env AWS_SECRET_ACCESS_KEY=<aws-secret-access-key> \
    --mount type=bind,source=/tmp/pinot-s3-docker,target=/tmp \
    apachepinot/pinot:0.6.0-SNAPSHOT-ca8545b29-20201105-jdk11 StartController \
    -configFileName /tmp/controller.conf
Start Broker
Broker is a simple one you can just start it with default:
docker run --rm -ti \
    --name pinot-broker \
    --network=pinot-demo \
    --env AWS_ACCESS_KEY_ID=<aws-access-key-id> \
    --env AWS_SECRET_ACCESS_KEY=<aws-secret-access-key> \
    apachepinot/pinot:0.6.0-SNAPSHOT-ca8545b29-20201105-jdk11 StartBroker \
    -zkAddress zookeeper:2181 -clusterName pinot-s3-example-docker
Start Server
Below is a sample server.conf file
Similar to controller config, please also set s3 configs in pinot server. 
pinot.server.netty.port=8098
pinot.server.adminapi.port=8097
pinot.server.instance.dataDir=/tmp/pinot-tmp/server/index
pinot.server.instance.segmentTarDir=/tmp/pinot-tmp/server/segmentTars
​
​
pinot.server.storage.factory.class.s3=org.apache.pinot.plugin.filesystem.S3PinotFS
pinot.server.storage.factory.s3.region=us-west-2
pinot.server.segment.fetcher.protocols=file,http,s3
pinot.server.segment.fetcher.s3.class=org.apache.pinot.common.utils.fetcher.PinotFSSegmentFetcher
Then start pinot server with:
docker run --rm -ti \
    --name pinot-server \
    --network=pinot-demo \
    --env AWS_ACCESS_KEY_ID=<aws-access-key-id> \
    --env AWS_SECRET_ACCESS_KEY=<aws-secret-access-key> \
    --mount type=bind,source=/tmp/pinot-s3-docker,target=/tmp \
    apachepinot/pinot:0.6.0-SNAPSHOT-ca8545b29-20201105-jdk11 StartServer \
    -zkAddress zookeeper:2181 -clusterName pinot-s3-example-docker \
    -configFileName /tmp/server.conf
Setup Table
In this demo, we just use airlineStats table as an example which is already packaged inside the docker image.
You can also mount your table conf and schema files to the container and run it.
docker run --rm -ti \
    --name pinot-ingestion-job \
    --network=pinot-demo \
    apachepinot/pinot:0.6.0-SNAPSHOT-ca8545b29-20201105-jdk11 AddTable \
    -controllerHost pinot-controller \
    -controllerPort 9000 \
    -schemaFile examples/batch/airlineStats/airlineStats_schema.json \
    -tableConfigFile examples/batch/airlineStats/airlineStats_offline_table_config.json \
    -exec
Set up Ingestion Jobs
Standalone Job
Below is a sample standalone ingestion job spec with certain notable changes:
jobType is SegmentCreationAndMetadataPush (this job will bypass controller download segment )
inputDirURI is set to a s3 location  s3://my.bucket/batch/airlineStats/rawdata/
outputDirURI is set to a s3 location  s3://my.bucket/output/airlineStats/segments
Add a new PinotFs under pinotFSSpecs
- scheme: s3
  className: org.apache.pinot.plugin.filesystem.S3PinotFS
  configs:
    region: 'us-west-2'
Sample 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'
​
  # segmentMetadataPushJobRunnerClassName: class name implements org.apache.pinot.spi.batch.ingestion.runner.IngestionJobRunner interface.
  segmentMetadataPushJobRunnerClassName: 'org.apache.pinot.plugin.ingestion.batch.standalone.SegmentMetadataPushJobRunner'
​
# jobType: Pinot ingestion job type.
# Supported job types are:
#   'SegmentCreation'
#   'SegmentTarPush'
#   'SegmentUriPush'
#   'SegmentCreationAndTarPush'
#   'SegmentCreationAndUriPush'
jobType: SegmentCreationAndMetadataPush
​
# inputDirURI: Root directory of input data, expected to have scheme configured in PinotFS.
inputDirURI: 's3://<my-bucket>/pinot-data/rawdata/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: 's3://<my-bucket>/pinot-data/pinot-s3-docker/segments/airlineStats'
​
# segmentCreationJobParallelism: The parallelism to create egments.
segmentCreationJobParallelism: 5
​
# 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.spi.filesystem.LocalPinotFS
​
​
  - scheme: s3
    className: org.apache.pinot.plugin.filesystem.S3PinotFS
    configs:
      region: 'us-west-2'
​
# 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://pinot-controller: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://pinot-controller:9000/tables/airlineStats'
​
# 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://pinot-controller:9000'
​
# pushJobSpec: defines segment push job related configuration.
pushJobSpec:
​
  # 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
​
Launch the data ingestion job:
docker run --rm -ti \
    --name pinot-ingestion-job \
    --network=pinot-demo \
    --env AWS_ACCESS_KEY_ID=<aws-access-key-id> \
    --env AWS_SECRET_ACCESS_KEY=<aws-secret-access-key> \
    --mount type=bind,source=/tmp/pinot-s3-docker,target=/tmp \
    apachepinot/pinot:0.6.0-SNAPSHOT-ca8545b29-20201105-jdk11 LaunchDataIngestionJob \
    -jobSpecFile /tmp/ingestionJobSpec.yaml
​
Use S3 as Deep Storage for Pinot
Batch Data Ingestion In Practice
Last modified 1yr ago