Apache Pinot

Apache Pinot is a real-time, distributed OLAP database designed for sub-second analytics on massive datasets, targeting use cases like user-facing dashboards, real-time business intelligence, and operational observability.

Reviewed by 7wData

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Apache Pinot is a real-time, distributed OLAP database designed for sub-second analytics on massive datasets, targeting use cases like user-facing dashboards, real-time business intelligence, and operational observability. It is built for organizations that need to ingest high-velocity streaming data (e.g., from Kafka) and run complex analytical queries with low latency, making it suitable for e-commerce product analytics, rideshare marketplace monitoring, and anomaly detection. Pinot is recognized as one of 2024’s Top 3 Big Data and AI Open Source Projects to Watch, with over 5,000 members in its Slack channel and more than 5,500 GitHub stars from a community of dozens of developers contributing over 11,000 commits.

Pinot uses columnar storage for efficient analytical queries and supports real-time ingestion from streaming sources like Kafka alongside batch data ingestion via a hybrid model. It offers multiple indexing techniques—inverted, range, and bitmap—to optimize query performance, and uniquely supports upserts for updating existing records. The database provides multi-tenancy support, Role-Based Access Control (RBAC), and custom OIDC-compatible identity service providers for security. StarTree Cloud, a managed service built on Pinot, adds proprietary features like tiered storage (leveraging object stores like S3 for cost savings), sparse indexing, and the StarTree ThirdEye application for real-time anomaly detection and root cause analysis.

In the real-time OLAP market, Pinot competes directly with Apache Druid, ClickHouse, and StarRocks. Compared to Druid, Pinot is often preferred for user-facing analytics requiring upserts and richer indexing; Druid excels at fast aggregations for ad-hoc analysis. ClickHouse offers superior SQL-based pre-processing and data type support, while StarRocks is noted for better BI integration and outbound connectors. Pinot’s managed service, StarTree Cloud, differentiates with flexible deployment models—Bring Your Own Cloud (BYOC) and SaaS—and 24×7 support backed by SLAs, as used by companies like Grab for real-time observability across 300 cities with p95 query latency of one second.

Honest trade-offs include weaker data type support compared to ClickHouse, limited outbound connectors for BI tool integration, and less mature SQL-based pre-processing capabilities. The open-source version lacks enterprise features like tiered storage and anomaly detection, which are only available in StarTree Cloud. Additionally, while Pinot handles high-dimensional data well, its complexity in cluster management may require dedicated expertise or a managed service for optimal performance.

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How it works

  1. Columnar Storage

    Stores data column-by-column, enabling analytical queries to read only needed columns for faster performance on large datasets.

  2. Real-time Ingestion

    Ingests high-velocity streaming data from sources like Kafka with low latency, supporting sub-second query responses.

  3. SQL Support

    Allows users to query data using standard SQL, lowering the learning curve for analysts familiar with relational databases.

  4. Indexing Techniques

    Employs inverted, range, and bitmap indexes to accelerate query performance on specific columns and filter conditions.

  5. Hybrid Ingestion Model

    Supports both real-time streaming and batch data ingestion, enabling unified analytics across historical and live data.

  6. Support for Upserts

    Enables updating existing records in real-time, critical for use cases like correcting late-arriving data or maintaining state.

  7. Multi-tenancy Support

    Allows multiple tenants to share a single cluster with isolated resources and query routing, reducing operational overhead.

Strengths and trade-offs

Strengths

  • Supports upserts for updating existing records, a unique capability among real-time OLAP databases that enables use cases like correcting late-arriving data.
  • Offers flexible cloud deployment models including BYOC and SaaS via StarTree Cloud, with 24×7 support backed by SLAs for mission-critical workloads.
  • Provides true multi-tenancy support with isolated resources and query routing, allowing multiple teams to share a cluster without interference.
  • Used by Grab to power real-time observability across 300 cities with p95 query latency of one second, handling 10 million requests per month.

Trade-offs

  • Data type support is less comprehensive than ClickHouse, limiting compatibility with certain data formats and requiring workarounds.
  • Outbound connectors for BI tool integration are underdeveloped, making it harder to connect directly to visualization platforms like Tableau or Power BI.
  • SQL-based pre-processing capabilities are weaker than competitors like ClickHouse, requiring external ETL tools for complex transformations.
  • Open-source version lacks enterprise features like tiered storage and anomaly detection, which are only available in the managed StarTree Cloud offering.

Pricing context

Not explicitly mentioned in the sources; StarTree Cloud offers a managed service with pricing likely based on cluster size and features, but no specific tiers or figures are provided.

Getting started with Apache Pinot

  1. Download and install Pinot

    Download the latest Apache Pinot binary from the official website or clone the GitHub repository. Extract the archive and set the JAVA_HOME environment variable to Java 11 or later. Run the quick-start script to launch a local cluster for testing.

  2. Configure a data source

    Define a table schema in a JSON file specifying column names, data types, and indexing options. Create a table configuration file that includes ingestion settings, such as the Kafka topic for real-time data or a batch file path. Use the Pinot controller API to upload the schema and table config.

  3. Ingest sample data

    Start a Kafka producer to send sample events to the configured topic, or place a CSV file in the batch directory. Pinot automatically ingests streaming data in near real-time or processes batch files on a schedule. Verify ingestion by checking the table's segment count in the Pinot UI.

  4. Run a SQL query

    Open the Pinot query console at http://localhost:9000. Write a SQL query, such as SELECT COUNT(*) FROM yourTable WHERE eventTime > NOW() - INTERVAL 1 HOUR. Execute the query and observe sub-second response times. Experiment with filters, aggregations, and group-by clauses.

  5. Set up multi-tenancy

    Define tenants in the Pinot configuration by assigning tables to tenant namespaces. Configure resource isolation using tenant-specific broker and server tags. Use the controller API to create tables with the tenant property, ensuring queries from different teams are routed to isolated resources.

Frequently Asked Questions

What is Apache Pinot used for?

Apache Pinot is a real-time, distributed OLAP database for sub-second analytics on massive datasets. It handles high-velocity streaming data from sources like Kafka, making it ideal for user-facing dashboards, real-time business intelligence, and operational observability in e-commerce and rideshare monitoring.

How does Apache Pinot handle real-time data ingestion?

Pinot ingests high-velocity streaming data from sources like Kafka with low latency, supporting sub-second query responses. It also supports a hybrid model that combines real-time streaming with batch data ingestion, enabling unified analytics across both historical and live data for comprehensive insights.

What indexing techniques does Apache Pinot use?

Pinot employs multiple indexing techniques to optimize query performance, including inverted indexes, range indexes, and bitmap indexes. These accelerate queries on specific columns and filter conditions, allowing efficient analytical queries on large datasets with columnar storage for faster data retrieval.

Does Apache Pinot support upserts for updating records?

Yes, Pinot uniquely supports upserts, enabling updates to existing records in real-time. This is critical for use cases like correcting late-arriving data or maintaining state in applications, a capability not commonly found in other real-time OLAP databases like Druid or ClickHouse.

How does Apache Pinot compare to Druid and ClickHouse?

Pinot is preferred for user-facing analytics requiring upserts and richer indexing, while Druid excels at fast aggregations for ad-hoc analysis. ClickHouse offers superior SQL-based pre-processing and data type support, but Pinot provides true multi-tenancy and flexible cloud deployment via StarTree Cloud.

What are the main weaknesses of Apache Pinot?

Pinot has weaker data type support than ClickHouse, limited outbound connectors for BI tools, and less mature SQL-based pre-processing. The open-source version lacks enterprise features like tiered storage and anomaly detection, which are only available in the managed StarTree Cloud service.

Alternatives

How Apache Pinot compares

Direct head-to-head against 3 competitors. Picked by 7wData.

This tool

Apache Pinot

Pricing
Not explicitly mentioned in the sources; StarTree Cloud offers a managed service with pricing likely based on cluster size and features, but no specific tiers or figures are provided.
Target
Apache Pinot is a real-time, distributed OLAP database designed for sub-second analytics on massive datasets, targeting use cases like user-facing dashboards, real-time business intelligence, and
Strength
Supports upserts for updating existing records, a unique capability among real-time OLAP databases that enables use cases like correcting late-arriving data.
Watch for
Data type support is less comprehensive than ClickHouse, limiting compatibility with certain data formats and requiring workarounds.

Apache Druid

Pricing
Open source (Imply Enterprise: custom pricing)
Target
Real-time analytics with automated indexing
Deployment
Self-hosted/cloud
Strength
Automated schema management for dynamic sources
Watch for
Complex setup for manual tuning

ClickHouse

Pricing
Open source (ClickHouse Cloud: $1.20/GB/month)
Target
High-speed OLAP with full SQL
Deployment
Self-hosted/cloud
Strength
Vectorized query engine for complex analytics
Watch for
No native real-time upserts

StarRocks

Pricing
Open source (CelerData Enterprise: custom pricing)
Target
Sub-second P99 latency at scale
Deployment
Self-hosted/cloud
Strength
Cost-based optimizer for complex queries
Watch for
Younger ecosystem than Pinot/Druid

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Sources

Reporting on this tool draws on these publicly available sources.

  1. www.linkedin.com
  2. startree.ai
  3. startree.ai
  4. startree.ai
  5. www.ksolves.com
  6. startree.ai
  7. medium.com