Fatal Background Processes in ClickHouse


ClickHouse is an open-source columnar database management system that is designed to handle high volumes of data and support high concurrency. However, like any complex system, it is possible for background processes to fail, which can cause various types of issues.

Common Fatal Background processes

Here are some of the most common fatal background processes in ClickHouse:

  1. MergeTree mutations: MergeTree tables in ClickHouse use a background process to apply mutations to data. If this process fails or stops for any reason, it can cause the table to become inconsistent or unusable.
  2. Replication: ClickHouse supports replication to provide high availability and redundancy. If the replication background process fails, it can cause data inconsistencies between replicas and potentially data loss.
  3. Background operations on Distributed tables: Distributed tables in ClickHouse use background processes to perform certain operations, such as shard rebalancing or data copying. If these processes fail, it can cause inconsistencies in the data across different shards.
  4. Materialized views: ClickHouse supports materialized views, which are pre-computed views that can improve query performance. If the background process responsible for maintaining materialized views fails, it can cause queries to fail or return incorrect results.
  5. Background process for Dictionary reloads: ClickHouse uses dictionaries to map data between different tables. If the background process responsible for reloading these dictionaries fails, it can cause queries to fail or return incorrect results.


In order to prevent these fatal background processes from causing issues, it is important to monitor the health of the system and ensure that these processes are running properly. In addition, it is important to have backup and recovery procedures in place to minimize the impact of any failures that do occur.

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

  1. Deep Dive into ClickHouse’s Streaming and Blocking Operators
  2. ClickHouse Data Types: LowCardinality
  3. Introduction to Working Datasets in ClickHouse
  4. Overview of Regular Expressions in ClickHouse
About Shiv Iyer 227 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.