Overview
The article discusses the new functionality in ClickHouse that allows for multiple lightweight projections to behave like true secondary indexes, significantly enhancing query performance without data duplication. It highlights the benefits of these projections, including granule-level pruning, which leads to faster query execution times.
What You'll Learn
1
How to create lightweight projections in ClickHouse to optimize query performance
2
Why granule-level pruning enhances data retrieval efficiency in ClickHouse
3
When to use multiple projections to speed up complex queries
Prerequisites & Requirements
- Understanding of ClickHouse primary indexes and projections
Key Questions Answered
How do lightweight projections function as secondary indexes in ClickHouse?
Lightweight projections in ClickHouse store only their sorting key and a part_offset pointer back to the base table, allowing them to act like secondary indexes without duplicating full rows. This reduces storage overhead and enables efficient query filtering.
What is granule-level pruning and how does it improve query performance?
Granule-level pruning allows ClickHouse to filter data at a finer granularity than before, enabling it to skip over irrelevant data more effectively. This results in dramatically faster query execution times by reducing the number of rows processed.
What performance improvements can be expected from using projections in ClickHouse?
Using projections can lead to significant performance improvements, as demonstrated by a query that executed in 0.010 seconds with projections enabled, compared to 0.077 seconds without them, resulting in roughly a 90% speedup.
What settings control the use of projection indexes in ClickHouse?
Two new settings, max_projection_rows_to_use_projection_index and min_table_rows_to_use_projection_index, control when ClickHouse applies projection indexes based on the estimated number of rows to read from the projection and the base table.
Key Statistics & Figures
Query execution time without projections
0.077 seconds
This was the time taken for a full table scan of approximately 30 million rows.
Query execution time with projections
0.010 seconds
This demonstrates a roughly 90% speedup when using projections for filtering.
Technologies & Tools
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Key Actionable Insights
1Implement lightweight projections in your ClickHouse tables to optimize query performance, especially for complex filters.By using multiple projections, you can significantly reduce query execution times and improve overall system efficiency, particularly in large datasets.
2Utilize granule-level pruning to enhance data retrieval processes in ClickHouse.This feature allows for more precise data filtering, which can lead to substantial performance gains in query execution, especially when dealing with extensive data.
3Benchmark your queries with and without projection support to understand the performance benefits.Running comparative tests can help you quantify the improvements and make informed decisions about when to use projections in your queries.
Common Pitfalls
1
Failing to enable projection support in queries can lead to suboptimal performance.
Without enabling projection support, queries may revert to full table scans, negating the performance benefits of using projections.
Related Concepts
Clickhouse Primary Indexes
Data Filtering Techniques
Query Optimization Strategies