How is Thread Handling implemented in ClickHouse?

Introduction

ClickHouse uses a thread pool to handle and manage threads. The thread pool is responsible for handling incoming client connections, executing SQL queries, and performing other tasks.

Each client connection is handled by a separate thread. When a client connects to the ClickHouse server, a thread is allocated from the thread pool to handle the connection. The thread reads the client’s SQL query, parses it, and passes it to the appropriate internal component for execution. Once the query is executed, the thread returns the result to the client and releases the thread back to the thread pool.

Thread Pool Size

The thread pool size can be configured in the ClickHouse configuration file. The default size is the number of CPU cores on the server, but it can be adjusted based on the workload and the number of concurrent clients.

Types of Threads in ClickHouse

Threads in ClickHouse can be divided into two types:

  1. I/O threads: These threads handle incoming client connections and I/O operations, such as reading and writing data to disk.
  2. Background threads: These threads handle internal tasks such as data compression, garbage collection, and replication.

ClickHouse uses a thread pool for the background threads as well, which can be configured separately from the I/O threads.

Conclusion

The thread pool uses a work-stealing algorithm that allows threads to take tasks from other threads if their own queue is empty. This helps to ensure that all threads are utilized effectively and that no single thread becomes a bottleneck.

Additionally, ClickHouse allows to configure number of threads for read and write operations, so you can tune the performance based on the workload and the number of concurrent clients.

To read more about Threads in ClickHouse, please do consider giving the following articles a read

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.