release-1.0.0

GitHub Events Stream

Steps for setting up a Pinot cluster and a real-time table which consumes from the GitHub events stream.

In this recipe you will set up an Apache Pinot cluster and a real-time table which consumes data flowing from a GitHub events stream. The stream is based on GitHub pull requests and uses Kafka.

In this recipe you will perform the following steps:

  1. Set up a Pinot cluster, to do which you will:

    a. Start zookeeper.

    b. Start the controller.

    c. Start the broker.

    d. Start the server.

  2. Set up a Kafka cluster.

  3. Create a Kafka topic, which will be called pullRequestMergedEvents.

  4. Create a real-time table called pullRequestMergedEvents and a schema.

  5. Start a task which reads from the GitHub events API and publishes events about merged pull requests to the topic.

  6. Query the real-time data.

Steps

Use either Docker images or launcher scripts

Pull the Docker image

Get the latest Docker image.

export PINOT_VERSION=latest
export PINOT_IMAGE=apachepinot/pinot:${PINOT_VERSION}
docker pull ${PINOT_IMAGE}

Long version

Set up the Pinot cluster

Follow the instructions in Advanced Pinot Setup to set up a Pinot cluster with the components:

  • Zookeeper

  • Controller

  • Broker

  • Server

  • Kafka

Create a Kafka topic

Create a Kafka topic called pullRequestMergedEvents for the demo.

docker exec \
  -t kafka \
  /opt/kafka/bin/kafka-topics.sh \
  --zookeeper pinot-zookeeper:2181/kafka \
  --partitions=1 --replication-factor=1 \
  --create --topic pullRequestMergedEvents

Add a Pinot table and schema

The schema is present at examples/stream/githubEvents/pullRequestMergedEvents_schema.json and is also pasted below

pullRequestMergedEvents_schema.json
{
  "schemaName": "pullRequestMergedEvents",
  "dimensionFieldSpecs": [
    {
      "name": "title",
      "dataType": "STRING",
      "defaultNullValue": ""
    },
    {
      "name": "labels",
      "dataType": "STRING",
      "singleValueField": false,
      "defaultNullValue": ""
    },
    {
      "name": "userId",
      "dataType": "STRING",
      "defaultNullValue": ""
    },
    {
      "name": "userType",
      "dataType": "STRING",
      "defaultNullValue": ""
    },
    {
      "name": "authorAssociation",
      "dataType": "STRING",
      "defaultNullValue": ""
    },
    {
      "name": "mergedBy",
      "dataType": "STRING",
      "defaultNullValue": ""
    },
    {
      "name": "assignees",
      "dataType": "STRING",
      "singleValueField": false,
      "defaultNullValue": ""
    },
    {
      "name": "authors",
      "dataType": "STRING",
      "singleValueField": false,
      "defaultNullValue": ""
    },
    {
      "name": "committers",
      "dataType": "STRING",
      "singleValueField": false,
      "defaultNullValue": ""
    },
    {
      "name": "requestedReviewers",
      "dataType": "STRING",
      "singleValueField": false,
      "defaultNullValue": ""
    },
    {
      "name": "requestedTeams",
      "dataType": "STRING",
      "singleValueField": false,
      "defaultNullValue": ""
    },
    {
      "name": "reviewers",
      "dataType": "STRING",
      "singleValueField": false,
      "defaultNullValue": ""
    },
    {
      "name": "commenters",
      "dataType": "STRING",
      "singleValueField": false,
      "defaultNullValue": ""
    },
    {
      "name": "repo",
      "dataType": "STRING",
      "defaultNullValue": ""
    },
    {
      "name": "organization",
      "dataType": "STRING",
      "defaultNullValue": ""
    }
  ],
  "metricFieldSpecs": [
    {
      "name": "count",
      "dataType": "LONG",
      "defaultNullValue": 1
    },
    {
      "name": "numComments",
      "dataType": "LONG"
    },
    {
      "name": "numReviewComments",
      "dataType": "LONG"
    },
    {
      "name": "numCommits",
      "dataType": "LONG"
    },
    {
      "name": "numLinesAdded",
      "dataType": "LONG"
    },
    {
      "name": "numLinesDeleted",
      "dataType": "LONG"
    },
    {
      "name": "numFilesChanged",
      "dataType": "LONG"
    },
    {
      "name": "numAuthors",
      "dataType": "LONG"
    },
    {
      "name": "numCommitters",
      "dataType": "LONG"
    },
    {
      "name": "numReviewers",
      "dataType": "LONG"
    },
    {
      "name": "numCommenters",
      "dataType": "LONG"
    },
    {
      "name": "createdTimeMillis",
      "dataType": "LONG"
    },
    {
      "name": "elapsedTimeMillis",
      "dataType": "LONG"
    }
  ],
  "dateTimeFieldSpecs": [
    {
      "name": "mergedTimeMillis",
      "dataType": "TIMESTAMP",
      "format": "1:MILLISECONDS:TIMESTAMP",
      "granularity": "1:MILLISECONDS"
    }
  ]
}

The table config is present at examples/stream/githubEvents/docker/pullRequestMergedEvents_realtime_table_config.json and is also pasted below.

Note If you're setting this up on a pre-configured cluster, set the properties stream.kafka.zk.broker.url and stream.kafka.broker.list correctly, depending on the configuration of your Kafka cluster.

pullRequestMergedEvents_realtime_table_config.json
{
  "tableName": "pullRequestMergedEvents",
  "tableType": "REALTIME",
  "segmentsConfig": {
    "timeColumnName": "mergedTimeMillis",
    "timeType": "MILLISECONDS",
    "retentionTimeUnit": "DAYS",
    "retentionTimeValue": "60",
    "schemaName": "pullRequestMergedEvents",
    "replication": "1",
    "replicasPerPartition": "1"
  },
  "tenants": {},
  "tableIndexConfig": {
    "loadMode": "MMAP",
    "invertedIndexColumns": [
      "organization",
      "repo"
    ],
    "streamConfigs": {
      "streamType": "kafka",
      "stream.kafka.consumer.type": "simple",
      "stream.kafka.topic.name": "pullRequestMergedEvents",
      "stream.kafka.decoder.class.name": "org.apache.pinot.plugin.stream.kafka.KafkaJSONMessageDecoder",
      "stream.kafka.consumer.factory.class.name": "org.apache.pinot.plugin.stream.kafka20.KafkaConsumerFactory",
      "stream.kafka.zk.broker.url": "pinot-zookeeper:2181/kafka",
      "stream.kafka.broker.list": "kafka:9092",
      "realtime.segment.flush.threshold.time": "12h",
      "realtime.segment.flush.threshold.rows": "100000",
      "stream.kafka.consumer.prop.auto.offset.reset": "smallest"
    }
  },
  "metadata": {
    "customConfigs": {}
  }
}

Add the table and schema using the following command:

$ docker run \
    --network=pinot-demo \
    --name pinot-streaming-table-creation \
    ${PINOT_IMAGE} AddTable \
    -schemaFile examples/stream/githubEvents/pullRequestMergedEvents_schema.json \
    -tableConfigFile examples/stream/githubEvents/docker/pullRequestMergedEvents_realtime_table_config.json \
    -controllerHost pinot-controller \
    -controllerPort 9000 \
    -exec
Executing command: AddTable -tableConfigFile examples/stream/githubEvents/docker/pullRequestMergedEvents_realtime_table_config.json -schemaFile examples/stream/githubEvents/pullRequestMergedEvents_schema.json -controllerHost pinot-controller -controllerPort 9000 -exec
Sending request: http://pinot-controller:9000/schemas to controller: 20c241022a96, version: Unknown
{"status":"Table pullRequestMergedEvents_REALTIME succesfully added"}

Publish events

Start streaming GitHub events into the Kafka topic:

Prerequisites

Generate a personal access token on GitHub.

$ docker run --rm -ti \
    --network=pinot-demo \
    --name pinot-github-events-into-kafka \
    -d ${PINOT_IMAGE} StreamGitHubEvents \
    -schemaFile examples/stream/githubEvents/pullRequestMergedEvents_schema.json \
    -topic pullRequestMergedEvents \
    -personalAccessToken <your_github_personal_access_token> \
    -kafkaBrokerList kafka:9092

Short version

The short method of setting things up is to use the following command. Make sure to stop any previously running Pinot services.

$ docker run --rm -ti \
    --network=pinot-demo \
    --name pinot-github-events-quick-start \
     ${PINOT_IMAGE} GitHubEventsQuickStart \
    -personalAccessToken <your_github_personal_access_token>

Kubernetes cluster

If you already have a Kubernetes cluster with Pinot and Kafka (see Running Pinot in Kubernetes), first create the topic, then set up the table and streaming using

$ cd kubernetes/helm
$ kubectl apply -f pinot-github-realtime-events.yml

Query

Browse to the Query Console to view the data.

Visualize with SuperSet

You can use SuperSet to visualize this data. Some of the interesting insights we captures were

List the most active organizations during the lockdown

Repositories by number of commits in the Apache organization

To integrate with SuperSet you can check out the SuperSet Integrations page.

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