Enhancing Data Ingestion: Integrating RocksDB with ClickHouse for High-Velocity Workloads

Introduction

Integrating RocksDB with ClickHouse for high-velocity, high-volume data ingestion leverages the strengths of both systems to address specific challenges. RocksDB, a high-performance, embedded key-value store optimized for fast storage on flash and high-speed disk, brings its efficient write capabilities, while ClickHouse provides scalable, fast analytics on large datasets.

RocksDB impact on ClickHouse ingestion rate

(1) Efficient Write Operations

  • RocksDB’s LSM-tree (Log-Structured Merge-tree) Storage: RocksDB uses an LSM-tree for data storage, which is optimized for write-intensive operations. This design allows RocksDB to absorb high-velocity data writes by initially writing entries to an in-memory structure (MemTable) and then flushing them to disk in a sequential manner, reducing disk seek times.
  • Batched Writes and Compression: RocksDB batches writes and compresses data before storing it on disk, significantly reducing I/O and storage requirements. This is particularly effective for high-volume data ingestion, as it minimizes disk usage and speeds up write operations.

(2) Tiered Storage Integration

  • Hot and Cold Data Management: Integrating RocksDB with ClickHouse allows for an effective tiered storage strategy, where RocksDB acts as a high-speed ingestion layer (hot storage) that efficiently handles write-heavy workloads. ClickHouse can then asynchronously transfer data from RocksDB to its columnar storage format, optimized for fast reads and analytics (cold storage).
  • Minimized I/O Overhead: This tiered approach minimizes I/O overhead during data ingestion, as RocksDB efficiently handles the initial write load, and ClickHouse manages the long-term storage and analytics.

(3) Improved Durability and Recovery

  • WAL (Write-Ahead Logging) in RocksDB: RocksDB ensures data durability through its use of Write-Ahead Logging (WAL), which records changes before they are committed to the database. This feature is crucial for high-velocity data ingestion systems, ensuring no data loss in case of system failures.
  • Fast Recovery: The LSM-tree structure and WAL enable fast recovery and restarts of RocksDB, ensuring minimal downtime and maintaining high availability for data ingestion pipelines.

(4) Scalability and Parallelism

  • Horizontal Scalability: While ClickHouse inherently supports horizontal scalability for analytical workloads, integrating RocksDB can enhance scalability for write-heavy workloads. RocksDB’s architecture supports concurrent writes and compactions, distributing the load effectively across available hardware.
  • Parallel Data Processing: ClickHouse can process data in parallel across different shards and replicas. By using RocksDB as a front-end for data ingestion, this parallelism extends to the ingestion process, enabling high-throughput writes alongside scalable analytics.

(5) Use Cases and Application

  • Real-Time Analytics Pipelines: The combination is ideal for scenarios requiring real-time data ingestion and analytics, such as monitoring systems, IoT data streams, and high-frequency trading platforms.
  • Log and Event Data Management: For applications generating vast amounts of log or event data, using RocksDB for initial ingestion before batch processing or transferring data to ClickHouse for analysis can significantly improve efficiency.

Conclusion

Integrating RocksDB with ClickHouse for high-velocity, high-volume data ingestion exploits RocksDB’s efficient write operations and ClickHouse’s analytical capabilities. This combination enhances the overall performance of data ingestion pipelines, providing a scalable solution for real-time analytics on large datasets. By leveraging the strengths of both systems, organizations can achieve improved write performance, efficient storage utilization, and robust data durability, all while maintaining the ability to perform fast, scalable analytics with ClickHouse.

About Shiv Iyer 215 Articles
Open Source Database Systems Engineer with a deep understanding of Optimizer Internals, Performance Engineering, Scalability and Data SRE. Shiv currently is the Founder, Investor, Board Member and CEO of multiple Database Systems Infrastructure Operations companies in the Transaction Processing Computing and ColumnStores ecosystem. He is also a frequent speaker in open source software conferences globally.