Optimizing Indexes in ClickHouse for High-Velocity, High-Volume Data Ingestion


Optimizing indexes in ClickHouse is crucial for maintaining excellent query performance and efficiently handling high-velocity, high-volume data ingestion in real-time. This guide explores key strategies to fine-tune and optimize indexes in ClickHouse for rapid data ingestion.

Strategies to Optimize Indexes in ClickHouse for Ingestion

1. Choose the Right Index Type

  • ClickHouse provides index types, such as primary, secondary, and merge tree indices. The index type selection should be based on your data and query patterns.
  • The primary index (SortedMergeTree) is often recommended for high-velocity and high-volume data as it prioritizes sequential writes and allows efficient data merging.

2. Utilize Merge Tree Indices

  • Merge tree indices are designed to efficiently insert and store time-series data, making them well-suited for high-velocity data.
  • Configure the merge policy to control the timing and method of data merging in the MergeTree tables. Adjust merge settings according to the rate at which data is being ingested and the patterns of your queries.

3. Properly Define Primary Keys

  • Select a primary key that aligns with the access patterns of your data.
  • Avoid using columns with high cardinality as primary keys, as they can result in inefficient indexing.

4. Use Low Cardinality for Secondary Indices

  • When creating secondary indices, prefer columns with low cardinality. High-cardinality columns can lead to larger index sizes and slower insertions.
  • Consider using bitmap indices for columns with low cardinality.

5. Batch Insertions

  • Instead of inserting one row at a time, use batch insertions to reduce overhead and improve the speed of data ingestion.
  • ClickHouse offers efficient mechanisms like the INSERT statement with multiple rows or the INSERT query.

6. Monitor System Resources

  • Continuously monitor system resources, such as CPU, memory, and disk I/O, as high-velocity data ingestion can strain these resources.
  • Utilize system monitoring tools to identify bottlenecks and adjust resource allocation accordingly.

7. Tune Buffer Settings

  • ClickHouse uses buffers to optimize write operations. Adjust buffer settings such as insert_block_size, merge_tree_min_rows_in_total, and merge_tree_min_rows_in_block to match your data ingestion rate.

8. Parallelize Data Ingestion

  • If you have a distributed ClickHouse cluster, distribute data ingestion tasks across multiple nodes. This can significantly increase ingestion throughput.

9. Compression and Encoding

  • Choose appropriate compression and encoding settings for your data to minimize storage requirements and improve query performance.

10. Periodic Maintenance

  • Regularly perform maintenance tasks, such as table optimization, refreshing secondary indices, and deleting obsolete data, to keep ClickHouse operating efficiently.

11. Scale Horizontally

  • As data volume and velocity increase, consider horizontally scaling your ClickHouse cluster by adding more nodes to distribute the workload.

12. Test and Benchmark

  • Continuously test and benchmark your ClickHouse configuration and index choices against realistic workloads. Adjust settings as necessary to achieve optimal performance.


Optimizing indexes in ClickHouse for high-velocity, high-volume data ingestion is an ongoing process that requires careful consideration of your data characteristics and query patterns. By implementing these strategies and regularly monitoring and fine-tuning your ClickHouse setup, you can ensure that your system performs efficiently even under demanding data ingestion scenarios.

To know more about ClickHouse Indexes, do consider reading the following articles:

About Shiv Iyer 227 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.