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Tutorials

Here you will find a collection of how-to guides for operators or developers tha

Build Docker Images

Overview

The scripts to build Pinot related docker images is located at .

You can access those scripts by running below command to checkout Pinot repo:

You can find current supported 3 images in this directory:

Running Pinot in Production

Requirements

You will need the following in order to run pinot in production:

Hardware for controller/broker/servers as per your load
Working installation of Zookeeper that Pinot can use. We recommend setting aside a path within zookpeer and including that path in pinot.controller.zkStr. Pinot will create its own cluster under this path (cluster name decided by pinot.controller.helixClusterName)
Shared storage mounted on controllers (if you plan to have multiple controllers for the same cluster). Alternatively, an implementation of PinotFS that the Pinot hosts have access to.
HTTP load balancers for spraying queries across brokers (or other mechanism to balance queries)
HTTP load balancers for spraying controller requests (e.g. segment push, or other controller APIs) or other mechanisms for distribution of these requests.

Deploying Pinot

In general, when deploying Pinot services, it is best to adhere to a specific ordering in which the various components should be deployed. This deployment order is recommended in case of the scenario that there might be protocol or other significant differences, the deployments go out in a predictable order in which failure due to these changes can be avoided.

The ordering is as follows:

pinot-controller
pinot-broker
pinot-server

Managing Pinot

Pinot provides a web-based management console and a command-line utility (pinot-admin.sh) in order to help provision and manage pinot clusters.

Pinot Management Console

The web based management console allows operations on tables, tenants, segments and schemas. You can access the console via http://controller-host:port/help. The console also allows you to enter queries for interactive debugging. Here are some screen-shots from the console.

Listing all the schemas in the Pinot cluster:

Rebalancing segments of a table:

Command line utility (pinot-admin.sh)

The command line utility (pinot-admin.sh) can be generated by running mvn install package -DskipTests -Pbin-dist in the directory in which you checked out Pinot.

Here is an example of invoking the command to create a pinot segment:

Here is an example of executing a query on a Pinot table:

Monitoring Pinot

Pinot exposes several metrics to monitor the service and ensure that pinot users are not experiencing issues. In this section we discuss some of the key metrics that are useful to monitor. A full list of metrics is available in the section.

Pinot Server

Missing Segments -
- Number of missing segments that the broker queried for (expected to be on the server) but the server didn’t have. This can be due to retention or stale routing table.
Query latency -

Pinot Broker

Incoming QPS (per broker) -
- The rate which an individual broker is receiving queries. Units are in QPS.
Dropped Requests - , ,

Pinot Controller

Many of the controller metrics include a table name and thus are dynamically generated in the code. The metrics below point to the classes which generate the corresponding metrics.

To get the real metric name, the easiest route is to spin up a controller instance, create a table with the desired name and look through the generated metrics.

Todo

Give a more detailed explanation of how metrics are generated, how to identify real metrics names and where to find them in the code.

Percent Segments Available -
- Percentage of complete online replicas in external view as compared to replicas in ideal state.
Segments in Error State -

Kubernetes Deployment

Pinot community has provided Helm based Kubernetes deployment template.

You can deploy it as simple as run a helm install command.

However there are a few things to be noted before starting the benchmark/production.

Container Resources

We recommend to run Pinot with pre-defined resources for the container, and make requests and limits to be the same.

This will ensure the container won't be killed if there is a sudden bump of workload.

It will also be simpler to benchmark the system, e.g. get broker qps limit.

Below is an example for values to set in values.yaml file. Default resources is not set.

JVM Setting

Pinot Controller/Broker

JVM setting should be complaint with the container resources for Pinot Controller and Pinot Broker.

You can make JVM setting like below to make -Xmx the same size as your container.

Pinot Server

For Pinot Server, heap is majorly used for query processing, metadata management. It uses off-heap memory for data loading/persistence, memory mapped files page caching. So we recommend just keep minimal requirement for JVM, and leave the rest of the container for off-heap data operations.

E.g. Assuming data is 100 GB on disk, the container size is 4 CPU, 10GB Memory.

For JVM, limit -Xmx to not exceed 50% container memory limit, so that the rest of the container could be leveraged by the off-heap operations.

Deep storage

Pinot uses remote storage as deep storage to backup segments.

Default deployment creates a mount disk(e.g Amazon EBS) as deep storage in controller.

You can configure your own S3/Azure DataLate/Google Cloud Storage following this .

Amazon EKS (Kafka)

If you need to connect non-EKS AWS jobs (Lambdas/EC2) to a Kafka running inside an AWS EKS

General steps: update Kafka's advertised.listeners and make sure Kafka is accessible (e.g. allow inputs on Security Groups).

You will probably face the following problems.

If you want to connect to Kafka outside of EKS, you will need to change advertised.listeners. When a client connects to a single Kafka bootstrap server (like other brokers), a bootstrap server sends a list of addresses for all brokers to the client. If you want to connect to a EKS Kafka, these default values will not be correct. This provides an excellent explanation of the field.

If you use Helm to deploy Kafka to AWS EKS, please review the . It describes multiple setups for communicating into EKS.

Running helm upgrade on the Kafka chart does not always update the pods. The exact reason is unknown. It's probably an issue with the chart's implementation. You should run kubectl describe pod and other commands to see the current status of the pods. During initial development, you can run helm uninstall and then helm installto force the values to update.

Amazon MSK (Kafka)

How to Connect Pinot with Amazon Managed Streaming for Apache Kafka (Amazon MSK)

This wiki documents how to connect Pinot deployed in Amazon EKS to Amazon Managed Kafka.

Prerequisite

Please follow this AWS Quickstart Wiki to run Pinot on Amazon EKS.

Create an Amazon MSK Cluster

Please go to to create a Kafka Cluster.

Note:

For demo simplicity, this MSK cluster reuses same VPC created by EKS cluster in the previous step. Otherwise a is required to ensure two VPCs could talk to each other.

Below is a sample screenshot to create an Amazon MSK cluster.

After click on Create button, you can take a coffee break and come back.

Once the cluster is created, you can view it and click View client information to see the Zookeeper and Kafka Broker list.

Sample Client Information

Connect to MSK

Config SecurityGroup

Until now, the MSK cluster is still not accessible, you can follow this to create an EC2 instance to connect to it for topic creation, run console producer and consumer.

In order to connect MSK to EKS, we need to allow the traffic could go through each other.

This is configured through Amazon VPC Page.

Record the Amazon MSK SecurityGroup from the Cluster page, in the above demo, it's sg-01e7ab1320a77f1a9.
Open , click on SecurityGroups on left bar. Find the EKS Security group: eksctl-${PINOT_EKS_CLUSTER}-cluster/ClusterSharedNodeSecurityGroup.

In SecurityGroup, click on MSK SecurityGroup (sg-01e7ab1320a77f1a9), then Click on Edit Rules , then add above ClusterSharedNodeSecurityGroup (sg-0402b59d7e440f8d1) to it.

Click EKS Security Group ClusterSharedNodeSecurityGroup (sg-0402b59d7e440f8d1), add In bound Rule for MSK Security Group (sg-01e7ab1320a77f1a9).

Now, EKS cluster should be able to talk to Amazon MSK.

Create Kafka topic

To run below commands, please ensure you set two environment variable with ZOOKEEPER_CONNECT_STRING and BROKER_LIST_STRING (Use plaintext) from Amazon MSK client information, and replace the Variables accordingly.

E.g.

You can log into one EKS node or container and run below command to create a topic.

E.g. Enter into Pinot controller container:

Then install wget then download Kafka binary.

Create a Kafka topic:

Topic creation succeeds with below message:

Write sample data into Kafka

Once topic is created, we can start a simple application to produce to it.

You can download below yaml file, then replace:

${ZOOKEEPER_CONNECT_STRING} -> MSK Zookeeper String
${BROKER_LIST_STRING} -> MSK Plaintext Broker String in the deployment
${GITHUB_PERSONAL_ACCESS_TOKEN}

And apply the YAML file by.

Once the pod is up, you can verify by running a console consumer to read from it.

Try to run from the Pinot Controller container entered in above step.

Create a Pinot table

This step is relatively easy.

Since we already put table creation request into the ConfigMap, we can just enter into pinot-github-events-data-into-msk-kafka pod to execute the command.

Check if the pod is running:

Sample output:

Enter into the pod

Create Table

Sample output:

Then you can open Pinot Query Console to browse the data

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].

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)

Executing the job

Below command will create example table into Pinot cluster.

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

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

Executing the job using Spark

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

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):

Please ensure parameter PINOT_ROOT_DIR and PINOT_VERSION are set properly.

Please ensure you set

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

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):

Please ensure parameter PINOT_ROOT_DIR and PINOT_VERSION are set properly.

Running Pinot in Production

Requirements

You will need the following in order to run pinot in production:

Hardware for controller/broker/servers as per your load
Working installation of Zookeeper that Pinot can use. We recommend setting aside a path within zookpeer and including that path in pinot.controller.zkStr. Pinot will create its own cluster under this path (cluster name decided by pinot.controller.helixClusterName)
Shared storage mounted on controllers (if you plan to have multiple controllers for the same cluster). Alternatively, an implementation of PinotFS that the Pinot hosts have access to.
HTTP load balancers for spraying queries across brokers (or other mechanism to balance queries)
HTTP load balancers for spraying controller requests (e.g. segment push, or other controller APIs) or other mechanisms for distribution of these requests.

Deploying Pinot

The ordering is as follows:

pinot-controller
pinot-broker
pinot-server

Managing Pinot

Pinot provides a web-based management console and a command-line utility (pinot-admin.sh) in order to help provision and manage pinot clusters.

Pinot Management Console

Listing all the schemas in the Pinot cluster:

Rebalancing segments of a table:

Command line utility (pinot-admin.sh)

The command line utility (pinot-admin.sh) can be generated by running mvn install package -DskipTests -Pbin-dist in the directory in which you checked out Pinot.

Here is an example of invoking the command to create a pinot segment:

Here is an example of executing a query on a Pinot table:

Monitoring Pinot

Pinot Server

Missing Segments -
- Number of missing segments that the broker queried for (expected to be on the server) but the server didn’t have. This can be due to retention or stale routing table.
Query latency -

Pinot Broker

Incoming QPS (per broker) -
- The rate which an individual broker is receiving queries. Units are in QPS.
Dropped Requests - , ,

Pinot Controller

Many of the controller metrics include a table name and thus are dynamically generated in the code. The metrics below point to the classes which generate the corresponding metrics.

To get the real metric name, the easiest route is to spin up a controller instance, create a table with the desired name and look through the generated metrics.

Todo

Give a more detailed explanation of how metrics are generated, how to identify real metrics names and where to find them in the code.

Percent Segments Available -
- Percentage of complete online replicas in external view as compared to replicas in ideal state.
Segments in Error State -

Amazon MSK (Kafka)

How to Connect Pinot with Amazon Managed Streaming for Apache Kafka (Amazon MSK)

This wiki documents how to connect Pinot deployed in Amazon EKS to Amazon Managed Kafka.

Prerequisite

Please follow this AWS Quickstart Wiki to run Pinot on Amazon EKS.

Create an Amazon MSK Cluster

Please go to to create a Kafka Cluster.

Note:

For demo simplicity, this MSK cluster reuses same VPC created by EKS cluster in the previous step. Otherwise a is required to ensure two VPCs could talk to each other.

Below is a sample screenshot to create an Amazon MSK cluster.

After click on Create button, you can take a coffee break and come back.

Once the cluster is created, you can view it and click View client information to see the Zookeeper and Kafka Broker list.

Sample Client Information

Connect to MSK

Config SecurityGroup

Until now, the MSK cluster is still not accessible, you can follow this to create an EC2 instance to connect to it for topic creation, run console producer and consumer.

In order to connect MSK to EKS, we need to allow the traffic could go through each other.

This is configured through Amazon VPC Page.

Record the Amazon MSK SecurityGroup from the Cluster page, in the above demo, it's sg-01e7ab1320a77f1a9.
Open , click on SecurityGroups on left bar. Find the EKS Security group: eksctl-${PINOT_EKS_CLUSTER}-cluster/ClusterSharedNodeSecurityGroup.

In SecurityGroup, click on MSK SecurityGroup (sg-01e7ab1320a77f1a9), then Click on Edit Rules , then add above ClusterSharedNodeSecurityGroup (sg-0402b59d7e440f8d1) to it.

Click EKS Security Group ClusterSharedNodeSecurityGroup (sg-0402b59d7e440f8d1), add In bound Rule for MSK Security Group (sg-01e7ab1320a77f1a9).

Now, EKS cluster should be able to talk to Amazon MSK.

Create Kafka topic

E.g.

You can log into one EKS node or container and run below command to create a topic.

E.g. Enter into Pinot controller container:

Then install wget then download Kafka binary.

Create a Kafka topic:

Topic creation succeeds with below message:

Write sample data into Kafka

Once topic is created, we can start a simple application to produce to it.

You can download below yaml file, then replace:

${ZOOKEEPER_CONNECT_STRING} -> MSK Zookeeper String
${BROKER_LIST_STRING} -> MSK Plaintext Broker String in the deployment
${GITHUB_PERSONAL_ACCESS_TOKEN}

And apply the YAML file by.

Once the pod is up, you can verify by running a console consumer to read from it.

Try to run from the Pinot Controller container entered in above step.

Create a Pinot table

This step is relatively easy.

Since we already put table creation request into the ConfigMap, we can just enter into pinot-github-events-data-into-msk-kafka pod to execute the command.

Check if the pod is running:

Sample output:

Enter into the pod

Create Table

Sample output:

Then you can open Pinot Query Console to browse the data

# 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

# 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:

  # 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

# 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

# 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

# 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

# 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:

  # 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

# 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

# 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

Tutorials

Build Docker Images

hashtagOverview

Running Pinot in Production

hashtagRequirements

hashtagDeploying Pinot

hashtagManaging Pinot

hashtagPinot Management Console

hashtagCommand line utility (pinot-admin.sh)

hashtagMonitoring Pinot

hashtagPinot Server

hashtagPinot Broker

hashtagPinot Controller

Kubernetes Deployment

hashtagContainer Resources

hashtagJVM Setting

hashtagPinot Controller/Broker

hashtagPinot Server

hashtagDeep storage

Amazon EKS (Kafka)

hashtagIf you need to connect non-EKS AWS jobs (Lambdas/EC2) to a Kafka running inside an AWS EKS

Amazon MSK (Kafka)

hashtagPrerequisite

hashtagCreate an Amazon MSK Cluster

hashtagConnect to MSK

hashtagConfig SecurityGroup

hashtagCreate Kafka topic

hashtagWrite sample data into Kafka

hashtagCreate a Pinot table

Batch Data Ingestion In Practice

hashtagTable Layout

hashtagConfiguring batch ingestion job

hashtagExecuting the job

hashtagExecuting the job using Spark

hashtagExecuting the job using Hadoop

Kubernetes Deployment

hashtagContainer Resources

hashtagJVM Setting

hashtagPinot Controller/Broker

hashtagPinot Server

hashtagDeep storage

Build Docker Images

hashtagOverview

hashtagPinot

hashtagHow to build a docker image

hashtagHow to publish a docker image

hashtagKubernetes Examples

hashtagPinot Presto

hashtagHow to build

hashtagHow to push

hashtagConfiguration

hashtagVolumes

hashtagKubernetes Examples

hashtagPinot Superset

hashtagHow to build

hashtagHow to push

hashtagConfiguration

hashtagVolumes

hashtagKubernetes Examples

Running Pinot in Production

hashtagRequirements

hashtagDeploying Pinot

hashtagManaging Pinot

hashtagPinot Management Console

hashtagCommand line utility (pinot-admin.sh)

hashtagMonitoring Pinot

hashtagPinot Server

hashtagPinot Broker

hashtagPinot Controller

Tutorials

Amazon EKS (Kafka)

hashtagIf you need to connect non-EKS AWS jobs (Lambdas/EC2) to a Kafka running inside an AWS EKS

Amazon MSK (Kafka)

hashtagPrerequisite

hashtagCreate an Amazon MSK Cluster

hashtagConnect to MSK

hashtagConfig SecurityGroup

hashtagCreate Kafka topic

hashtagWrite sample data into Kafka

hashtagCreate a Pinot table

Overview

Requirements

Deploying Pinot

Managing Pinot

Pinot Management Console

Command line utility (pinot-admin.sh)

Monitoring Pinot

Pinot Server

Pinot Broker

Pinot Controller

Container Resources

JVM Setting

Pinot Controller/Broker

Pinot Server

Deep storage

If you need to connect non-EKS AWS jobs (Lambdas/EC2) to a Kafka running inside an AWS EKS

Prerequisite

Create an Amazon MSK Cluster

Connect to MSK

Config SecurityGroup

Create Kafka topic

Write sample data into Kafka

Create a Pinot table

Table Layout

Configuring batch ingestion job

Executing the job

Executing the job using Spark

Executing the job using Hadoop

Container Resources

JVM Setting

Pinot Controller/Broker

Pinot Server

Deep storage

Overview

Pinot

How to build a docker image

How to publish a docker image

Kubernetes Examples

Pinot Presto

How to build

How to push

Configuration

Volumes

Kubernetes Examples

Pinot Superset

How to build

How to push

Configuration

Volumes

Kubernetes Examples

Requirements

Deploying Pinot

Managing Pinot

Pinot Management Console

Command line utility (pinot-admin.sh)

Monitoring Pinot

Pinot Server

Pinot Broker

Pinot Controller

If you need to connect non-EKS AWS jobs (Lambdas/EC2) to a Kafka running inside an AWS EKS

Prerequisite

Create an Amazon MSK Cluster

Connect to MSK

Config SecurityGroup

Create Kafka topic

Write sample data into Kafka

Create a Pinot table

Table Layout

Configuring batch ingestion job

Executing the job

Executing the job using Spark

Executing the job using Hadoop