Jump Trading's Petabyte Analytics: ClickHouse Meets Iceberg

Petabyte Scale Analytics Unlocked

Jump Trading's adoption of ClickHouse with Iceberg for petabyte-scale financial trading log analytics presents a compelling case study for organizations grappling with massive data volumes and diverse analytical needs. The key insight is the strategic decoupling of real-time, low-latency ingestion and querying from batch processing and broader data accessibility. By leveraging ClickHouse for its unparalleled real-time performance and Iceberg as an open table format on object storage, Jump achieves both immediate operational insights and the flexibility for extensive batch analytics, reporting, and research without compromising either workload. This architecture effectively addresses the common challenge of scaling data platforms where a single system struggles to optimize for both near real-time responsiveness and cost-effective, broad data access.

The innovation lies not just in the choice of technologies, but in their synergistic integration. Jump's 'double-write' pipeline, inspired by Netflix, ensures data consistency across both systems while maintaining Kafka as the single entry point. Crucially, ClickHouse's ability to directly query Iceberg tables provides a unified view, allowing users to seamlessly transition between systems with consistent query logic. This approach minimizes data movement and complexity, enabling users to leverage ClickHouse's familiar features and performance characteristics across both real-time and batch contexts. The emphasis on open-source contributions and tailoring ClickHouse to specific needs highlights a mature approach to data infrastructure, maximizing value and fostering community growth. The article effectively demonstrates how established real-time databases can be extended to handle modern data lakehouse paradigms, offering a blueprint for other data-intensive firms.

Key Points

• Jump Trading manages petabyte-scale financial trading logs using a self-managed ClickHouse platform.
• Zero data loss and sub-20-second p99 latency are critical requirements for their real-time analytics.
• A parallel Apache Iceberg pipeline was introduced to separate batch analytics (reporting, research) from the real-time ClickHouse cluster.
• This architecture allows for independent scaling of compute and storage, moving batch data to Parquet files on object storage.
• Iceberg was chosen for its scalability, long-term retention, and accessibility to multiple readers (Spark, Polars, DuckDB).
• ClickHouse can directly query Iceberg tables, providing a unified data view and enabling seamless switching between systems.
• The integration allows core ClickHouse features (Keeper, replication, distributed tables, access control) to extend to Iceberg tables.
• Jump Trading actively contributes to the ClickHouse open-source project, highlighting the benefits of open-source flexibility.

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📖 Source: How Jump Trading uses ClickHouse with Iceberg for analytics