ClickHouse Parser Internals

ClickHouse Advanced Education Series – How ClickHouse Parser works? 


ClickHouse uses a SQL parser to process SQL statements and convert them into a query execution plan. The parser is responsible for validating the syntactic and semantic correctness of SQL statements, and for transforming them into a format that can be executed by the query engine.

Here is the high-level overview of how the ClickHouse parser works:

  1. The parser receives an SQL statement as an input.
  2. The lexical analyzer, also known as the scanner, scans the input and converts it into a sequence of tokens. Tokens are the basic building blocks of SQL statements, such as keywords, identifiers, and operators.
  3. The syntactic analyzer, also known as the parser, takes the sequence of tokens and checks the syntax of the SQL statement. The parser checks the grammar rules of the SQL statement and generates an abstract syntax tree (AST). This step also validates the SQL statement, and if there are any syntax errors, the parser raises an exception.
  4. The semantic analyzer takes the AST generated by the syntactic analyzer and checks the semantic correctness of the SQL statement. This step checks for semantic errors such as invalid table or column references, and verifies that the SQL statement is semantically correct.
  5. The query optimizer takes the AST and generates an execution plan for the SQL statement. This step considers the database schema, statistics, and the execution context to generate the most efficient plan for executing the query.
  6. Once the execution plan is generated, the query engine takes the plan and executes it.

It’s important to mention that this is a high-level overview of how the ClickHouse parser works, and it’s a complex process that involves many other optimizations and internal processes like filtering, indexing, etc.

As part of the query execution plan, the parser also applies various levels of optimization such as constant folding, constant propagation, and query simplification.

Also, ClickHouse uses a specific SQL dialect that is based on SQL-92 standards, with additional features and functions to support distributed query processing, data compression and partitioning, and more.

ClickHouse Performance Engineer Checklist

  1. Memory: Make sure that you have enough memory to hold the working set of your data. ClickHouse uses memory for caching data, so having enough memory is crucial for good performance.
  2. Disk I/O: Monitor disk I/O to ensure that it is not a bottleneck. You can use tools like iostat or iotop to monitor disk I/O and identify whether there are any specific processes or devices that are causing high disk I/O.
  3. Data Compression: ClickHouse uses data compression to reduce the amount of disk I/O required to read and write data. Make sure that the compression ratio is high and that you are using the best compression method for your data.
  4. Data Partitioning: ClickHouse uses data partitioning to distribute data across multiple disks. Make sure that the partitioning method is appropriate for your data and that you are not overloading a single disk.
  5. Disk Space: Make sure that there is enough free space on the disk to avoid disk I/O problems.
  6. Hardware: Make sure that all the hardware is working properly and that there are no issues with the disk or controller.
  7. Configuration: Make sure that the configuration of ClickHouse is appropriate for your use case. For example, increasing the number of merge threads or the number of background threads can help with disk I/O performance.
  8. Monitoring: Set up monitoring and logging to track performance metrics and identify potential performance bottlenecks.
  9. Backup: Set up regular backups to ensure that you can recover from any data loss.
  10. Security: Make sure that the security of your ClickHouse cluster is in place, like setting up user authentication and access control.
  11. By following this checklist, you can ensure that your ClickHouse configuration is optimized for performance, and you will be able to identify and resolve any performance issues that may arise. Keep in mind that this checklist is not exhaustive and may vary depending on the specific use case, but it will give you a good starting point for configuring ClickHouse for optimal performance.
About Shiv Iyer 56 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.

Be the first to comment

Leave a Reply