Effective Strategies for Deleting Old Records in ClickHouse: Methods and Best Practices

Deleting old records in ClickHouse requires careful consideration due to its append-only and columnar nature, which does not inherently support row-level deletions as efficiently as traditional relational databases. ClickHouse is designed for high-speed data writes and aggregation queries, so deletions are handled differently. Below are the methods and best practices for managing and deleting older records from a ClickHouse server:

1. Using TTL (Time-To-Live)

The most recommended and efficient way to manage the deletion of old records in ClickHouse is to use TTL expressions when creating or altering tables. TTL allows automatic deletion of data that has aged beyond a certain period, defined at the column or table level.

Setting TTL on Table Creation

CREATE TABLE events (
    EventDate Date,
    EventDateTime DateTime,
    UserID UInt64,
    EventDetails String,
    TTL EventDate + INTERVAL 1 MONTH  -- Data expires 1 month after EventDate
) ENGINE = MergeTree()
PARTITION BY toYYYYMM(EventDate)
ORDER BY (UserID, EventDateTime);

Altering Existing Tables to Add TTL

ALTER TABLE events
ADD TTL EventDate + INTERVAL 1 YEAR;  -- Data expires 1 year after EventDate

2. Manual Deletion with Partition Drops

If your data is partitioned based on time (e.g., daily, monthly), you can manually drop entire partitions that contain old data. This method is very efficient as it removes large blocks of data without needing to process each row individually.

ALTER TABLE events DROP PARTITION '2021-01';

This command drops all data in the partition for January 2021.

3. Deleting Individual Rows

For deleting specific rows based on conditions (less recommended due to performance costs), ClickHouse supports the ALTER TABLE ... DELETE statement, which is applicable for tables with the *MergeTree family engines except CollapsingMergeTree.

ALTER TABLE events DELETE WHERE EventDate <= '2021-01-01';

This method rewrites data parts without the deleted rows, which can be I/O intensive and slow, especially with large datasets.

Best Practices for Deleting Data in ClickHouse

  • Use TTL for Automatic Cleanup: TTL not only simplifies data management by automating the cleanup process but also optimizes the deletion process to be less resource-intensive.
  • Partition Data by Time: Organizing data by time helps in efficiently managing and deleting old data by dropping whole partitions.
  • Regular Maintenance: Regularly check and optimize your table using OPTIMIZE to merge parts and reduce storage overhead, especially after deleting a significant amount of data.
  • Monitor Impact: Monitor the performance impact when using manual row deletions. Consider scheduling such operations during off-peak hours to minimize the impact on query performance.
  • Backup Before Large Deletions: Always ensure you have backups before performing large deletion operations, particularly when dropping partitions or manually deleting large volumes of data.

By following these methods and best practices, you can efficiently manage and delete old records from ClickHouse, keeping your database optimized for performance while managing storage costs effectively.

 

Mastering Performance Tuning in ClickHouse: Tips for Inspecting Statistics Objects

 

Enhancing ClickHouse Performance: Strategic Insights on Partitioning, Indexing, and Monitoring

 

How to use FREEZE command for ClickHouse Backup

 

Consulting

 

 

About Shiv Iyer 222 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.