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What is Apache Pinot?

Apache Pinot is a real-time distributed OLAP datastore, built to deliver scalable real-time analytics with low latency. It can ingest from batch data sources (such as Hadoop HDFS, Amazon S3, Azure ADLS, Google Cloud Storage) as well as stream data sources (such as Apache Kafka).

Pinot was built by engineers at LinkedIn and Uber and is designed to scale up and out with no upper bound. Performance always remains constant based on the size of your cluster and an expected query per second (QPS) threshold.

For getting started guides, deployment recipes, tutorials, and more, please visit our project documentation at https://docs.pinot.apache.org.

<img src="https://pinot.apache.org/static/images/hero_diagram.svg" align="center" alt="Apache Pinot"/>

Features

Pinot was originally built at LinkedIn to power rich interactive real-time analytic applications such as Who Viewed Profile, Company Analytics, Talent Insights, and many more. UberEats Restaurant Manager is another example of a customer facing Analytics App. At LinkedIn, Pinot powers 50+ user-facing products, ingesting millions of events per second and serving 100k+ queries per second at millisecond latency.

<a href="https://docs.pinot.apache.org/basics/getting-started"><img src="https://gblobscdn.gitbook.com/assets%2F-LtH6nl58DdnZnelPdTc%2F-MKaPf2qveUt5cg0dMbM%2F-MKaPmS1fuBs2CHnx9-Z%2Fpinot-ui-width-1000.gif?alt=media&token=53e4c5a8-a9cd-4610-a338-d54ea036c090" align="center" alt="Apache Pinot query console"/></a>

When should I use Pinot?

Pinot is designed to execute real-time OLAP queries with low latency on massive amounts of data and events. In addition to real-time stream ingestion, Pinot also supports batch use cases with the same low latency guarantees. It is suited in contexts where fast analytics, such as aggregations, are needed on immutable data, possibly, with real-time data ingestion. Pinot works very well for querying time series data with lots of dimensions and metrics.

Example query:

SELECT sum(clicks), sum(impressions) FROM AdAnalyticsTable
  WHERE
       ((daysSinceEpoch >= 17849 AND daysSinceEpoch <= 17856)) AND
       accountId IN (123456789)
  GROUP BY
       daysSinceEpoch TOP 100

Contributing to Pinot

Want to contribute to apache/pinot? 👋🍷

Want to join the ranks of open source committers to Apache Pinot? Then check out the Contribution Guide for how you can get involved in the code.

If you have a bug or an idea for a new feature, browse the open issues to see what we’re already working on before opening a new one.

We also tagged some beginner issues new contributors can tackle.

Building Pinot

# Clone a repo
$ git clone https://github.com/apache/pinot.git
$ cd pinot

# Build Pinot
# -Pbin-dist is required to build the binary distribution
# -Pbuild-shaded-jar is required to build the shaded jar, which is necessary for some features like spark connectors
$ mvn clean install -DskipTests -Pbin-dist -Pbuild-shaded-jar

# Run the Quick Demo
$ cd build/
$ bin/quick-start-batch.sh

For UI development setup refer this doc.

Normal Pinot builds are done using the mvn clean install command.

However this command can take a long time to run.

For faster builds it is recommended to use mvn verify -Ppinot-fastdev, which disables some plugins that are not actually needed for development.

More detailed instructions can be found at Quick Demo section in the documentation.

Deploying Pinot to Kubernetes

Please refer to Running Pinot on Kubernetes in our project documentation. Pinot also provides Kubernetes integrations with the interactive query engine, Trino Presto, and the data visualization tool, Apache Superset.

Join the Community

Documentation

Check out Pinot documentation for a complete description of Pinot's features.

License

Apache Pinot is under Apache License, Version 2.0