Apache Pinot
UnclaimedUnlock real-time insights from petabyte-scale data with ultra low-latency analytics.
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What is Apache Pinot?
Apache Pinot (web analytics): Unlock real-time insights from petabyte-scale data with ultra low-latency analytics. Apache Pinot is an open-source, distributed OLAP (Online Analytical Processing) datastore designed for lightning-fast insights and real-time analytics. Originally developed at LinkedIn, it provides ultra low-latency queries at extremely high throughput, making it suitable for user-facing analytical applications. Key capabilities: Fast Queries (P90 latencies in tens of milliseconds), High Concurrency (hundreds of thousands of concurrent queries per second), Batch and Streaming Ingest (from Kafka, Pulsar, Kinesis, Hadoop, Spark, S3), Upserts (handle record updates efficiently), Versatile Joins (fact/dimension and fact/fact joins). Apache Pinot is free to use with no paid tier. Buyers most often compare Apache Pinot against ClickHouse, Amazon DynamoDB, Preset.
TL;DR - Apache Pinot
- Real-time OLAP datastore for ultra low-latency analytics.
- Scalable and fault-tolerant, handling petabyte data and high concurrency.
- Supports batch and streaming ingestion with rich indexing and SQL interface.
Pricing: Free forever
Best for: Individuals & startups
Pros & Cons
Pros
- Provides ultra low-latency analytics on large datasets.
- Highly scalable and fault-tolerant for demanding workloads.
- Supports both real-time streaming and batch data ingestion.
- Offers a wide range of indexing options for performance optimization.
- Standard SQL interface makes it accessible for data professionals.
Cons
- Requires technical expertise for setup and management.
- As an open-source project, enterprise-grade support might require third-party vendors or community engagement.
Ratings Across the Web
4.8(2 reviews)
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Key Features
Fast Queries (P90 latencies in tens of milliseconds)High Concurrency (hundreds of thousands of concurrent queries per second)Batch and Streaming Ingest (from Kafka, Pulsar, Kinesis, Hadoop, Spark, S3)Upserts (handle record updates efficiently)Versatile Joins (fact/dimension and fact/fact joins)Rich Indexing Options (timestamp, inverted, StarTree, Bloom filter, range, text, JSON, geospatial)Horizontally Scalable and Fault-TolerantSQL Query Interface (via built-in editor and REST API)
Pricing
Free
Apache Pinot is completely free to use with no hidden costs.
About Apache Pinot
By Toolradar Team·
Apache Pinot is an open-source, distributed OLAP (Online Analytical Processing) datastore designed for lightning-fast insights and real-time analytics. Originally developed at LinkedIn, it provides ultra low-latency queries at extremely high throughput, making it suitable for user-facing analytical applications.
Pinot is built for businesses and developers who need to perform complex aggregations and filtering on large datasets with sub-second response times. Its distributed architecture and columnar storage enable effortless scaling and cost-effective data-driven decisions. It supports both batch and streaming data ingestion from various sources like Kafka, Pulsar, Kinesis, Hadoop, and S3, allowing for a unified view of data.
Key benefits include the ability to serve hundreds of thousands of concurrent queries per second, versatile indexing options for optimized performance, and built-in upsert functionality to handle frequently updated records efficiently. Its standard SQL query interface and multitenancy features further enhance its usability and manageability for diverse analytical workloads.
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Apache Pinot FAQ
How does Apache Pinot achieve ultra low-latency analytics on large datasets?
Apache Pinot utilizes a distributed architecture and columnar storage to process and query petabyte-scale data. It is specifically designed for real-time OLAP, allowing for P90 latencies in the tens of milliseconds even with complex filter and aggregate operations.
What types of data ingestion does Apache Pinot support for real-time and batch processing?
Apache Pinot supports real-time ingestion from streaming sources like Apache Kafka, Apache Pulsar, and AWS Kinesis. For batch ingestion, it integrates with platforms such as Hadoop, Spark, and AWS S3, and can combine both batch and streaming data into a single queryable table.
Can Apache Pinot handle updates to existing records while maintaining query performance?
Yes, Apache Pinot includes built-in upsert functionality. This feature allows the ingestion of the same record multiple times, ensuring that only the latest value is visible at query time, without compromising performance.
What indexing options are available in Apache Pinot to optimize query execution?
Apache Pinot offers a variety of pluggable indexing options to enhance query performance. These include timestamp, inverted, StarTree, Bloom filter, range, text, JSON, and geospatial indexes, allowing users to choose the most suitable index for their data and query patterns.
How does Apache Pinot facilitate data management and security in cloud environments?
Apache Pinot provides built-in multitenancy, which enables the management and securing of data within isolated logical namespaces. This feature supports cloud-friendly resource management by segmenting data and access for different users or applications.
Source: pinot.apache.org