releasev0.5.0 has been released! Check the release notes

Apache Pinot™ (Incubating)

Realtime distributed OLAP datastore, designed to answer OLAP queries with low latency

Pinot is proven at scale in LinkedIn powers 50+ user-facing apps and serving 100k+ queries


Blazing Fast

Pinot is designed to answer OLAP queries with low latency on immutable data

Pluggable indexing

Pluggable indexing technologies - Sorted Index, Bitmap Index, Inverted Index

Near Real time ingestion

Near Realtime ingestion with Apache Kafka supports StringSerializer or Avro formats

Horizontally scalable

Horizontally scalable and fault tolerant

Joins using PrestoDB

Joins are currently not supported, but this problem can be overcome by using PrestoDB for querying

SQL-like Query Interface (PQL)

SQL like language that supports selection, aggregation, filtering, group by, order by, distinct queries on data

Hybrid tables

Consist of of both offline and realtime table. Use realtime table only to cover segments for which offline data may not be available yet

Anomaly Detection

Run ML Algorithms to detect Anomalies on the data stored in Pinot. Use ThirdEye with Pinot for Anomaly Detection and Root Cause Analysis

Smart Alerts in ThirdEye

Detect the right anomalies by customizing anomaly detect flow and notification flow

Ingest and Query Options

Ingest with Kafka, Spark, HDFS or Cloud Storages
Query using PQL(Pinot Query Language ), SQL or Presto(supports Joins)

Who Uses?

Pinot powers several big players, including LinkedIn, Uber, Microsoft, Factual, Weibo, Slack and more

Installs Everywhere

Pinot can be installed using docker with presto
Helm or K8s crds
Public Cloud


helm repo add pinot
kubectl create ns pinot
helm install pinot pinot/pinot -n pinot --set

Or choose your preferred method: