Boosting ClickHouse Ingestion Performance by Disabling Foreign Key Checks

“Disabling foreign key checks in ClickHouse can be a game-changer when it comes to loading large amounts of data quickly and efficiently. By bypassing the verification step, you can significantly improve data loading speed and reduce resource usage. However, it is crucial to remember that this should only be done temporarily, and foreign key checks should be re-enabled once the data is loaded to ensure data integrity.” – ChistaDATA Labs

Introduction

When loading large amounts of data into ClickHouse, one challenge you may encounter is the time it takes to enforce foreign key constraints. By default, ClickHouse performs foreign key checks during data loading, which can considerably slow down the process. However, there are methods to temporarily disable these checks, enabling faster data loading. In this blog post, we will examine the reasons why you might want to disable foreign key checks in ClickHouse and how to do so effectively.

Benefits of Disabling Foreign Key Checks

The main benefit of disabling foreign key checks in ClickHouse is a significant improvement in data loading speed. When ClickHouse verifies foreign key constraints for each row during data ingestion, it adds extra processing time, especially with large datasets. By disabling foreign key checks, you can bypass this verification step, resulting in faster data loading.

Another advantage of disabling foreign key checks is a reduction in resource usage. Since ClickHouse doesn’t need to perform extensive checks on foreign keys, it frees up system resources, enabling more efficient data processing. This is particularly useful when dealing with high-volume data loading scenarios, where every second counts.

How to Disable Foreign Key Checks in ClickHouse

To disable foreign key checks in ClickHouse, you can utilize the set allow_experimental_data_skipping_indicessetting. This setting allows ClickHouse to skip index checks for tables with foreign key constraints during data loading.

Here’s how you can disable foreign key checks using this setting:

  1. Open the ClickHouse client or connect to ClickHouse using a SQL editor.
  2. Execute the following command to enable the experimental data skipping indices feature: 
SET allow_experimental_data_skipping_indices = 1;
  1. Load your data into ClickHouse using the desired method, such as the INSERT INTO statement or the ClickHouse bulk data loading tools.

It is important to note that disabling foreign key checks should only be done temporarily during the data loading process. Once the data is loaded, you should re-enable foreign key checks to ensure data integrity.

Conclusion

Disabling foreign key checks in ClickHouse can be a game-changer when it comes to loading large amounts of data quickly and efficiently. By bypassing the verification step, you can significantly improve data loading speed and reduce resource usage. However, it is crucial to remember that this should only be done temporarily, and foreign key checks should be re-enabled once the data is loaded to ensure data integrity. With the ability to disable foreign key checks in ClickHouse, you can streamline your data loading process and make the most out of this powerful analytical database.

In summary, if you are dealing with large datasets and need to load data into ClickHouse quickly, disabling foreign key checks can be a valuable strategy. By saving time on verification and reducing resource usage, you can optimize your data loading process. Just remember to re-enable foreign key checks after loading the data to maintain data integrity. With these tips, you can take full advantage of ClickHouse’s capabilities and efficiently handle your data loading needs.

To read more about ClickHouse Ingestion, do consider reading the below articles

ClickHouse Server Configuration for High-volume Data Ingestion

Demystifying JSON Data With ClickHouse

Fine-Tuning Data Ingestion in ClickHouse Distributed Tables

About Shiv Iyer 216 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.