ClickHouse is an open-source, column-oriented database management system that allows generating analytical data reports in real time. It is designed for online analytical processing (OLAP) and is particularly effective for real-time analytics on large datasets.
ClickHouse Anti-Patterns Overview
ClickHouse is a powerful columnar database designed for analytical workloads, but using it effectively requires understanding its architecture and avoiding common anti-patterns. Here are the most important anti-patterns to avoid when working with ClickHouse.
Using ClickHouse for OLTP Workloads
ClickHouse is designed for OLAP (analytical) workloads, not OLTP (transactional) workloads. It excels at processing large volumes of data for analytical queries but performs poorly for frequent single-row updates or deletes. Use a traditional RDBMS like PostgreSQL or MySQL for transactional workloads, and use ClickHouse for analytics.
Not Using Appropriate Primary Keys
In ClickHouse, the primary key (defined by the ORDER BY clause) determines data locality and query performance. Choosing a poor primary key, such as a UUID or other high-cardinality field with no relation to common query patterns, can lead to poor performance. Choose primary keys based on your most common query patterns, typically starting with the fields most frequently used in WHERE clauses.
Not Using Partitioning Effectively
Partitioning in ClickHouse allows for efficient data management and query performance. Not using partitioning can lead to poor query performance and difficult data management, while overly granular partitioning (e.g., daily partitions for years of data) can lead to too many small parts and filesystem issues. Choose a partitioning scheme that balances query performance with management overhead, typically using monthly or weekly partitions for time-series data.
Using String Instead of Enum
Using String data types for fields with a limited set of possible values wastes storage space and reduces query performance. Use Enum8 or Enum16 for fields with a small, fixed set of possible values to improve storage efficiency and query performance.
Not Using LowCardinality for Appropriate Columns
The LowCardinality data type in ClickHouse is a special encoding that improves performance for columns with a relatively small number of distinct values (typically less than 10,000). Not using LowCardinality for appropriate columns can result in missed performance optimizations. Apply LowCardinality to String columns with a limited number of distinct values, such as categories, status codes, country codes, etc.
Using Inefficient Data Types
Using inefficient data types wastes storage space and reduces query performance. Choose the smallest possible data type that can accommodate your data: use UInt8/16/32 instead of UInt64 when possible, use DateTime instead of String for timestamps, use Float32 instead of Float64 when the precision is sufficient, and use UInt8 (0 or 1) for boolean values.
Not Using Materialized Views for Common Aggregations
Repeatedly calculating the same aggregations is inefficient and can lead to high CPU usage. Use materialized views to pre-compute and incrementally update common aggregations, significantly improving query performance for frequently accessed aggregated data.
Not Using Projections for Query Optimization
ClickHouse tables are optimized for specific query patterns based on their primary key. Not using projections for different query patterns can lead to suboptimal performance for queries that don’t align with the table’s primary key. Use projections to create additional physical data organizations optimized for different query patterns.
Using GROUP BY Without Combinators
Using GROUP BY without combinators for high-cardinality grouping can lead to excessive memory usage and slow query performance. Use GROUP BY combinators like WITH ROLLUP, WITH CUBE, or WITH TOTALS to optimize aggregation queries, especially for high-cardinality grouping.
Not Using Distributed Tables Properly
Not using distributed tables properly can lead to manual query coordination and result aggregation. Use distributed tables to transparently query data across multiple shards, allowing ClickHouse to handle the distribution and aggregation of queries.
Not Monitoring Query Performance
Not monitoring query performance can lead to undetected performance issues and resource bottlenecks. Use ClickHouse’s system tables, such as system.processes, system.query_log, and system.metrics, to monitor query performance, resource usage, and identify optimization opportunities.
Using FINAL Modifier Excessively
The FINAL modifier forces ClickHouse to perform deduplication at query time, which can be very expensive for large tables. Use FINAL only when necessary, and consider alternative approaches like materialized views with AggregatingMergeTree for frequently accessed data that needs deduplication.
Not Using Asynchronous Inserts
Synchronous inserts can limit throughput in high-volume ingestion scenarios. Use asynchronous inserts for high-throughput scenarios to improve insert performance, and batch multiple rows into a single INSERT statement for even better performance.