How to Configure ClickHouse Server for High Performance

Introduction

Configuring the ClickHouse Servercan significantly impact the performance of your queries. While this may not affect the performance of ClickHouse systems with smaller datasets, it significantly impacts the performance of datasets at scale with data size in the petabytes. It is hence an absolutely crucial consideration and an activity in which you should actively explore engaging the services of ClickHouse experts (such as ChistaDATA’s server engineering team) to ensure configurations appropriate for and specific to large workloads.

Top 20 ClickHouse Server Configuration Parameters

Here are 20 key configuration parameters to consider in ClickHouse:

1. MaxThreadsForQuery:
   • This configuration option controls the maximum number of threads that can be used for query execution.
   • Set a value based on the available CPU resources and the expected workload. Increasing the value can improve parallel query execution, especially for complex and resource-intensive queries.
2. MaxMemoryUsage:
   • Specifies the maximum amount of memory that a single query can utilize.
   • Adjust this value based on the available memory on your system and the memory requirements of your queries. Allocating sufficient memory can prevent excessive disk I/O and improve query performance.
3. MaxQuerySize:
   • Defines the maximum size of a query in bytes.
   • Increase this value if you often execute large queries to avoid query truncation and improve performance. However, be cautious about increasing it excessively, as it may impact the system’s memory usage.
4. MaxMergeTreeBlockSize:
   • Determines the maximum size of a block in MergeTree tables.
   • Adjusting this value can affect query performance for MergeTree tables. Smaller block sizes may improve query execution for queries that scan only a portion of the data, while larger block sizes can optimize disk I/O for queries scanning large portions of data.
5. join_algorithm:
   • Controls the join algorithm used by ClickHouse.
   • Experiment with different join algorithms (e.g., auto, hash_join, merge_join, or prefer_mergeable) to find the one that performs best for your specific workload. Consider factors like the size of the joined tables and the available memory.
6. force_index_by_date:
   • Forces the use of an index for filtering by date in MergeTree tables.
   • If your queries commonly filter data by date in MergeTree tables, enabling this option can improve performance by utilizing the index.
7. compile_expressions:
   • Determines whether expressions within queries are compiled.
   • Enabling this option can improve query performance by compiling expressions during query execution. However, it may increase initial query compilation time.
8. optimize_move_to_prewhere:
   • Controls whether ClickHouse automatically moves filters to the preWHERE clause for better query performance.
   • Enabling this option can improve query execution by pushing filters closer to the data source, reducing the amount of data processed.
9. enable_optimize_predicate_expression:
   • Enables the optimization of predicate expressions by ClickHouse.
   • When enabled, ClickHouse optimizes predicate expressions to reduce the number of calculations performed during query execution, leading to improved performance.
10. max_parallel_replicas:
    • Specifies the maximum number of parallel query replicas to use for distributed query execution.
    • Adjust this value based on the available resources and the desired level of parallelism for distributed queries. Increasing the value can improve query performance for distributed environments.
11. use_uncompressed_cache:
    • Determines whether uncompressed data is stored in the cache.
    • Enabling this option can improve performance for queries that frequently access cached data. However, it increases the memory footprint of the cache.
12. max_columns_to_read:
    • Sets the maximum number of columns to read from a table during query execution.
    • Adjust this value based on the number of columns required by your queries. Limiting the number of columns read can improve query performance by reducing data transfer and memory usage.
13. low_cardinality_max_dictionary_size:
    • Defines the maximum size of the dictionary for low cardinality columns.
    • Adjust this value based on the number of distinct values in your low cardinality columns. A larger dictionary size can improve performance by reducing dictionary lookups.
14. max_distributed_connections:
    • Specifies the maximum number of connections to use for distributed query execution.
    • Adjust this value based on the available network resources and the desired level of parallelism for distributed queries. Increasing the value can improve query performance in distributed environments.
15. preferred_block_size_bytes:
    • Sets the preferred block size in bytes for reading data from storage.
    • Adjust this value based on the characteristics of your storage system and the expected data transfer rate. Choosing an appropriate block size can help optimize I/O operations and improve query performance.
16. merge_tree_uniform_read_distribution:
    • Controls the uniform distribution of read operations across MergeTree replicas.
    • Enabling this option helps distribute read operations evenly across replicas, preventing hotspots and improving overall query performance in distributed environments.
17. max_query_size:
    • Specifies the maximum size of a query in bytes that ClickHouse can process.
    • Adjust this value based on the complexity and size of your queries. Increasing the limit can prevent query truncation and allow larger queries to execute successfully.
18. compile_expressions:
    • Determines whether ClickHouse compiles expressions during query execution.
    • Enabling this option can improve query performance by reducing the overhead of expression evaluation. However, it may increase the initial query compilation time.
19. max_threads:
    • Controls the maximum number of threads ClickHouse can use for query execution.
    • Adjust this value based on the available CPU resources and the expected concurrency level of your workload. Increasing the number of threads can improve parallel query execution and overall performance.
20. max_block_size:
    • Sets the maximum size of a block in ClickHouse.
    • Adjust this value based on the available memory resources and the desired trade-off between memory usage and query performance. Larger block sizes can reduce memory overhead but may increase latency.

Remember to carefully evaluate and test the impact of each configuration option based on your specific workload and hardware resources. It’s recommended to monitor query performance and adjust the configuration settings iteratively to achieve optimal performance in ClickHouse.

Conclusion

We saw how to configure your ClickHouse server and some key configuration options for optimizing query performance. Regular monitoring and iterative adjustments ensure sustained efficiency and smooth operation.

To know more about ClickHouse Configuration Parameters, do read the following articles:

• 9 Guidelines for Tuning ClickHouse Configuration Parameters for High Performance
• Tuning ClickHouse for Performance Immediately after Installation
• Configuring ClickHouse Server for High Performance
• ClickHouse Server Configuration for High-volume Data Ingestion
• ClickHouse Configuration: Tuning max_insert_threads and max_bytes_before_external_group_by