Apache Pinot
Apache Pinot is an open-source, real-time distributed OLAP datastore built to answer analytical queries with very low latency over continuously streaming and batch data, originally developed at LinkedIn.
Definition
Apache Pinot is an open-source, real-time distributed OLAP datastore built to answer analytical queries with very low latency over continuously streaming and batch data, originally developed at LinkedIn.
Overview
Pinot was purpose-built for a specific class of problem: powering user-facing analytics dashboards that need sub-second query responses over datasets that are constantly updated, such as LinkedIn's "who viewed my profile" feature. It ingests data from both streaming sources like Apache Kafka and batch sources such as Hadoop, indexing incoming records so that aggregation queries can be answered quickly even as new data arrives continuously. Internally, Pinot partitions data into segments distributed across servers, and it applies multiple indexing techniques — including inverted, sorted, and star-tree indexes — to accelerate the specific kinds of filtering and aggregation queries common in dashboards. This heavy investment in indexing is what allows Pinot to serve highly concurrent, low-latency OLAP queries directly to end users rather than only to internal analysts. Pinot occupies a niche alongside real-time analytics engines like Apache Druid and ClickHouse, all of which target the same broad problem of fast analytics over fresh data, but each with different tradeoffs in ingestion architecture, indexing strategy, and operational model. Organizations building customer-facing analytics products, rather than internal BI tools, are typically the ones that reach for Pinot specifically.
Key Features
- Real-time ingestion from streaming sources like Apache Kafka
- Sub-second query latency over continuously updating data
- Multiple indexing strategies including star-tree indexes
- Horizontally scalable, segment-based storage architecture
- Designed to serve analytics directly to end users, not just analysts
- SQL-based query interface for OLAP-style aggregations
- Hybrid support for both streaming and batch data ingestion