Last Updated on May 18, 2025
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.
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.
This is free and open source software.
Key Features
- Fast Queries: Filter and aggregate petabyte data sets with P90 latencies in the tens of milliseconds—fast enough to return live results interactively in the UI.
- High Concurrency: With user-facing applications querying Pinot directly, it can serve hundreds of thousands of concurrent queries per second.
- SQL Query Interface: The highly standard SQL query interface is accessible through a built-in query editor and a REST API.
- Versatile Joins: Perform arbitrary fact/dimension and fact/fact joins on petabyte data sets.
- Column-oriented: a column-oriented database with various compression schemes such as Run Length, Fixed Bit Length.
- Pluggable indexing: pluggable indexing technologies including timestamp, inverted, StarTree, Bloom filter, range, text, JSON, and geospatial options.
- Stream and batch ingest: Ingest from Apache Kafka, Apache Pulsar, and AWS Kinesis in real time. Batch ingest from Hadoop, Spark, AWS S3, and more. Combine batch and streaming sources into a single table for querying.
- Upsert during real-time ingestion: update the data at-scale with consistency
- Built-in Multitenancy: Manage and secure data in isolated logical namespaces for cloud-friendly resource management.
- Built for Scale: Pinot is horizontally scalable and fault-tolerant, adaptable to workloads across the storage and throughput spectrum.
- Cloud-native on Kubernetes: Helm chart provides a horizontally scalable and fault-tolerant clustered deployment that is easy to manage using Kubernetes.
Website: pinot.apache.org
Support: GitHub Code Repository
Developer: The Apache Software Foundation
License: Apache License 2.0
Pinot is written in Java. Learn Java with our recommended free books and free tutorials.
Related Software
| Column-Oriented Databases | |
|---|---|
| MariaDB ColumnStore | Uses a massively parallel distributed data architecture |
| DuckDB | In-process SQL OLAP database management system |
| Druid | High performance, real-time analytics database |
| Databend | Cloud data warehouse |
| ClickHouse | Real-time analytics database management system |
| InfluxDB Core | Scalable datastore for metrics, events, and real-time analytics |
| Doris | Modern data warehouse for real-time analytics |
| VictoriaMetrics | Scalable solution for monitoring and managing time series data |
| StarRocks | High-performance analytical database |
| MonetDB | High performance relational database system for analytics |
| Kudu | Distributed data storage engine |
| QuestDB | High-performance time-series database |
| Pinot | Real-time analytics platform |
| IoTDB | High-performance time-series database |
| GreptimeDB | Cloud-native database |
| CrateDB | Distributed SQL database management |
Read our verdict in the software roundup.
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