Knowledge Base

Learn how to use the `system.query_log` table to find the most memory-intensive queries in ClickHouse, with examples for clustered and standalone setups.

Learn about automatic schema migration tools for ClickHouse and how to manage changing database schemas over time.

Monitor the number of active or queued mutations in ClickHouse, especially when performing `ALTER` or `UPDATE` operations. Use the `system.mutations` table for tracking mutations.

ClickHouse Keeper improves upon ZooKeeper with features like reduced disk space usage, faster recovery, and less memory consumption, offering better performance for ClickHouse clusters.

Learn how to calculate the ratio of empty or zero values in every column of a ClickHouse table to optimize sparse column serialization.

ClickHouse is popular for logs and metrics analysis because of the real-time analytics capabilities provided. Ready to find out more?

Learn how to connect to ClickHouse Cloud Service using Python with a step-by-step example using the clickhouse-connect driver.

Learn how to validate that two ClickHouse queries produce identical result-sets using hash functions and comparison techniques.

Learn how to resolve the SSL Exception `CERTIFICATE_VERIFY_FAILED` error.

This article explains how to resolve the DB::Exception error related to intersecting parts in ClickHouse, often caused by a race condition or manual intervention in the ZooKeeper data.