Apache Pinot™ (Incubating): Realtime distributed OLAP datastore

Features#

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 Apache Pinot?#

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

User-Facing Analytics#

Building Latency Sensitive User Facing Analytics via Apache Pinot

Using Apache Kafka and Apache Pinot for User-Facing Analytics

Installs Everywhere#

Pinot can be installed using docker with presto

Helm or K8s crds

On-Premise

Public Cloud

Locally

Install:

Using Helm
Using Binary
Build From Source

helm repo add pinot https://raw.githubusercontent.com/apache/incubator-pinot/master/kubernetes/helm
kubectl create ns pinot
helm install pinot pinot/pinot -n pinot --set cluster.name=pinot

Or choose your preferred method: