Overview
This article discusses the performance improvements achieved by replacing traditional joins with in-memory dictionaries in ClickHouse Cloud, resulting in significantly faster query times and reduced costs. It provides a detailed guide on how to implement this tuning technique, including benchmarks and code examples.
What You'll Learn
1
How to replace traditional joins with in-memory dictionary lookups in ClickHouse
2
Why using dictionaries can lead to significant performance gains in query execution
3
When to migrate dimension tables to dictionaries for optimal performance
Prerequisites & Requirements
- Familiarity with SQL and ClickHouse
- Access to ClickHouse Cloud or a similar environment(optional)
Key Questions Answered
How much faster can queries run by using dictionaries instead of joins in ClickHouse?
By replacing joins with in-memory dictionary lookups, ClickHouse achieved speedups of up to 6.6× and cost reductions of over 60%. This was demonstrated through benchmarks on queries involving 1.4 billion rows.
What are the steps to migrate dimension tables to dictionaries in ClickHouse?
To migrate dimension tables to dictionaries, create a dictionary using the appropriate SQL syntax, specifying the source and layout. For example, the Locations table can be migrated using a hashed key dictionary for fast lookups.
What types of lookups can be performed with ClickHouse dictionaries?
ClickHouse dictionaries support various lookup types, including simple key lookups for dimension tables and range-based lookups for time-aware data. This flexibility allows for optimized query performance based on the data structure.
What are the performance benchmarks for ClickHouse using dictionaries?
The benchmarks showed that with 2 nodes, the runtime improved from 251.05 seconds to 133.78 seconds after tuning, resulting in a 1.9× speedup. With 4 nodes, the runtime decreased from 182.38 seconds to 71.54 seconds, achieving a 2.5× speedup.
Key Statistics & Figures
Speedup from using dictionaries
up to 6.6×
Achieved by replacing joins with in-memory dictionary lookups in ClickHouse.
Cost reduction
over 60%
Resulting from the performance improvements when using dictionaries instead of traditional joins.
Runtime for 2 nodes (tuned vs. untuned)
133.78s
tuned
Technologies & Tools
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Key Actionable Insights
1Consider migrating your dimension tables to in-memory dictionaries to enhance query performance significantly.This approach is particularly beneficial for queries that involve foreign key joins on small dimension tables, as it can lead to substantial speed improvements and cost savings.
2Utilize the ClickHouse dictionary feature for time-aware lookups to ensure accurate data retrieval based on specific time ranges.This is essential for applications where product versions change over time, allowing for efficient querying of the correct data version.
3Leverage the flexibility of ClickHouse dictionaries to load data from various sources, including S3 and Iceberg tables.This capability enables seamless integration of data from different formats, enhancing the overall performance of your data processing workflows.
Common Pitfalls
1
Failing to ensure that the source data for dictionaries has no overlapping ranges can lead to incorrect results.
If overlapping ranges exist, the dictionary will only return the first match, which may not be accurate. It's crucial to validate the data before migrating to dictionaries.
Related Concepts
Data Optimization Techniques
Performance Tuning In Databases
SQL Query Performance