Overview
This article outlines 13 common mistakes, referred to as the 'deadly sins,' that new users encounter when starting with ClickHouse. It provides actionable insights on how to avoid these pitfalls to enhance performance, reliability, and cost efficiency in both self-managed and cloud environments.
What You'll Learn
1
How to select an appropriate partition key to avoid excessive parts in ClickHouse
2
Why vertical scaling is preferred over horizontal scaling in ClickHouse
3
How to manage mutations effectively to prevent performance degradation
4
When to use lightweight deletes instead of mutations for data removal
5
How to optimize primary key selection for better query performance
Prerequisites & Requirements
- Basic understanding of OLAP concepts and database management
- Familiarity with ClickHouse and its architecture(optional)
Key Questions Answered
What are the common pitfalls when starting with ClickHouse?
The article identifies 13 common pitfalls, including issues like selecting poor partition keys, excessive mutations, and unnecessary use of complex types. Each pitfall is explained with recommendations on how to avoid them, helping users optimize their ClickHouse experience.
How does ClickHouse handle deduplication during inserts?
ClickHouse uses a deduplication strategy that compares newly inserted blocks against hashes of recent blocks. If a match is found, the insert is ignored, which helps prevent duplicate entries and allows safe retries of inserts in case of network issues.
Why is it important to choose the right primary key in ClickHouse?
Choosing the right primary key is crucial because it affects query performance and data compression. A well-chosen primary key allows ClickHouse to efficiently filter and access data, which is essential for handling large datasets effectively.
What are the implications of using too many materialized views in ClickHouse?
Excessive materialized views can slow down insert performance as they create new parts for each view on every insert. Users should limit the number of materialized views to avoid complications and performance issues.
Technologies & Tools
Some links below are affiliate links. We may earn a commission if you make a purchase.
Key Actionable Insights
1To maintain optimal performance in ClickHouse, ensure that your partition key has a cardinality of less than 1000. This prevents the creation of excessive parts, which can degrade query performance.Selecting a sensible partition key is crucial for efficient data management and query execution. High cardinality partition keys can lead to performance bottlenecks.
2Consider using lightweight deletes instead of mutations for data removal tasks. This approach is generally more efficient and can help avoid the performance overhead associated with mutations.Lightweight deletes mark rows as deleted without rewriting entire data parts, making them a better choice for compliance or deduplication scenarios.
3When designing your ClickHouse schema, prioritize the use of primitive types over complex types to enhance insertion and query performance.Complex types can introduce overhead during data processing, so it's best to use them only when necessary.
4Utilize the `max_bytes_before_external_group_by` setting to allow aggregations to spill to disk when memory limits are reached, preventing out-of-memory errors.This setting is particularly useful for memory-intensive queries, ensuring they can complete without crashing the server.
Common Pitfalls
1
Choosing a partition key with high cardinality can lead to the 'too many parts' error, severely impacting query performance.
This occurs because parts belonging to different partitions are never merged, which can result in an overwhelming number of inactive parts that slow down operations.
2
Overusing materialized views can create performance issues during inserts, as each view processes the inserted block separately.
Users should be cautious about the number of materialized views to avoid excessive computational overhead and potential slowdowns.
3
Relying on mutations for data updates can lead to resource pressure and performance degradation.
Mutations are CPU and IO-intensive, and should be scheduled carefully to avoid impacting overall system performance.
Related Concepts
Olap Concepts
Data Partitioning Strategies
Clickhouse Architecture
Performance Optimization Techniques