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Kinesis

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In this guide we're going to learn how to configure Kinesis as a data source in the StarTree Dataset Manager. You should have created an environment and will also need access to an AWS Kinesis account.

The StarTree Data Manager can ingest messages from an AWS Kinesis Stream within your AWS account.

Create dataset​

Enter a name for your dataset and select Provide my own data.

Select data source Select data source

Click the NEXT button.

Data Source​

You'll see the following screen where you should select Streaming and then Kinesis:

Select data sourceSelect data source

Provide a new connection name. Enter the credentials for Basic authentication type, namely, Access Key, Secret Key and Region for your account.

Click TEST CONNECTION to check that StarTree Cloud can access the Kinesis account.

Test Connection Test Connection

You will see a success message if the account has been configured correctly. Click NEXT to go to the next screen.

Data Modeling​

Next we need to select the Kinesis stream that we'd like to connect to Pinot and its format, as shown in the screenshot below:

Select Topic Select Topic

We'll select the github-events topic and JSON for its data format.

tip

The messages that we publish to the github-events stream will have the following structure:


{
  "requestedReviewers": [
    "saneDG"
  ],
  "requestedTeams": [],
  "repo": "ratatiedot-extranet",
  "numAuthors": 1,
  "assignees": [],
  "numCommenters": 0,
  "title": "Chore: RTENU-8 Added missing configs, TODO formatting, updated package-lock.jsn",
  "elapsedTimeMillis": 2089000,
  "numLinesDeleted": 165,
  "committers": [
    "mehtis"
  ],
  "numFilesChanged": 4,
  "authorAssociation": "NONE",
  "numReviewers": 0,
  "numCommitters": 1,
  "numCommits": 3,
  "count": null,
  "numReviewComments": 0,
  "mergedTimeMillis": 1663654063000,
  "userId": "mehtis",
  "reviewers": [],
  "labels": [],
  "numComments": 0,
  "createdTimeMillis": 1663651974000,
  "numLinesAdded": 158,
  "organization": "finnishtransportagency",
  "userType": "User",
  "mergedBy": "NinaDang97",
  "authors": [
    "mehtis"
  ],
  "commenters": []
}

The Dataset Manager will then make an educated guess at the field and data types for each of the fields in the messages on this stream.

Data Transformation Columns and field/data types

Time Column​

We'll change the createdTimeMillis field type to be DATETIME. This will the primary time column for this Pinot table, which will be used by Pinot to maintain the time boundary between offline and realtime data in a hybrid table and also, for retention management. (Read more here).

Time column Time column

(Optional) Enable Upserts​

We can add primary key to enable upsert to this realtime table. We are not going to select a primary key for upsert in this demo. Read more about upserts here.

Primary key Primary key

Once you're happy with the data transformations, scroll down, and click on the NEXT button.

Additional Configuration​

On this screen you'll be able to configure indexes, tenants, ingestion scheduling, and data retention on this data source.

Configure indexes, tenants, scheduling, and data retention Configure indexes, tenants, ingestion scheduling, and data retention

For more information on the different types of indexes and when to use them, see the Apache Pinot Indexing Documentation.

Once you're happy with the configuration, scroll down, and click on the NEXT button.

Review​

You'll now see the review and submit screen, where you can review everything that we've configured in the previous steps.

Review Data Source Review Data Source

Click on the toggle next to Preview Data to see how the data will look once it's imported. If anything doesn't look right, click on the BACK button to go back to the previous screen.

Once you're happy ready to create the data source, click on the SUBMIT button. You'll then see the following screen:

Data Source Created Data Source Created

Query Data Source​

To have a look at the data that we've imported, click on the Query Console link, which will open the Pinot Data Explorer. Click on the kinesis_github_data table and then click RUN QUERY to run a basic query against the data source.