GitHub Events Stream

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

Pull Request Merged Events Stream

In this recipe, we will

  1. Set up a Pinot cluster, in the steps

    a. Start zookeeper

    b. Start controller

    c. Start broker

    d. Start server

  2. Set up a Kafka cluster

  3. Create a Kafka topic - pullRequestMergedEvents

  4. Create a realtime table - pullRequestMergedEvents and a schema

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

  6. Query the realtime data

Steps

Using Docker images or Launcher Scripts

Docker
Launcher scripts
Docker

Pull 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 setup the Pinot cluster with the components:

  1. Zookeeper

  2. Controller

  3. Broker

  4. Server

  5. 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 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"
}
],
"timeFieldSpec": {
"incomingGranularitySpec": {
"timeType": "MILLISECONDS",
"timeFormat": "EPOCH",
"dataType": "LONG",
"name": "mergedTimeMillis"
}
}
}

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.size": "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

For a single command to setup all the above steps, use the following command. Make sure to stop any previous 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>
Launcher scripts

Get Pinot

Follow instructions in Build from source to get the latest Pinot code

Long Version

Set up the Pinot cluster

Follow the instructions in Advanced Pinot Setup to setup the Pinot cluster with the components:

  1. Zookeeper

  2. Controller

  3. Broker

  4. Server

  5. Kafka

Create a Kafka topic

Download Apache Kafka release.

Create a Kafka topic called pullRequestMergedEvents for the demo.

$ bin/kafka-topics.sh \
--create \
--bootstrap-server localhost:19092 \
--replication-factor 1 \
--partitions 1 \
--topic pullRequestMergedEvents

Add Pinot table and schema

Schema can be found at /examples/stream/githubevents/ in the release, and is also pasted below:

{
"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"
}
],
"timeFieldSpec": {
"incomingGranularitySpec": {
"timeType": "MILLISECONDS",
"timeFormat": "EPOCH",
"dataType": "LONG",
"name": "mergedTimeMillis"
}
}
}

Table config can be found at /examples/stream/githubevents/ in the release, 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.

{
"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": "localhost:2191/kafka",
"stream.kafka.broker.list": "localhost:19092",
"realtime.segment.flush.threshold.time": "12h",
"realtime.segment.flush.threshold.size": "100000",
"stream.kafka.consumer.prop.auto.offset.reset": "smallest"
}
},
"metadata": {
"customConfigs": {}
}
}

Add the table and schema using the command

$ bin/pinot-admin.sh AddTable \
-tableConfigFile $PATH_TO_CONFIGS/examples/stream/githubEvents/pullRequestMergedEvents_realtime_table_config.json \
-schemaFile $PATH_TO_CONFIGS/examples/stream/githubEvents/pullRequestMergedEvents_schema.json \
-exec

Publish events

Start streaming GitHub events into the Kafka topic

Prerequisites

Generate a personal access token on GitHub.

$ bin/pinot-admin.sh StreamGitHubEvents \
-topic pullRequestMergedEvents \
-personalAccessToken <your_github_personal_access_token> \
-kafkaBrokerList localhost:19092 \
-schemaFile $PATH_TO_CONFIGS/examples/stream/githubEvents/pullRequestMergedEvents_schema.json

Short Version

For a single command to setup all the above steps

$ bin/pinot-admin.sh 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 and then setup the table and streaming using

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

Query

Head over to the Query Console to checkout the data!

Visualizing on SuperSet

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

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.