ClickHouse Query Optimization for Large Datasets

This comprehensive guide explores advanced techniques for optimizing queries in ClickHouse when working with large-scale datasets. ClickHouse is a powerful columnar database management system designed for online analytical processing (OLAP) that excels at handling billions to trillions of rows efficiently. Whether you’re facing performance bottlenecks or preparing to scale your data infrastructure, this document outlines proven strategies to dramatically improve query speed, reduce resource consumption, and maximize throughput in production environments.

Core Optimization Strategies

1. Leverage Primary and Secondary Indices

• Primary Indexes use binary search (O(log₂ n)) to skip terabytes of data by filtering on indexed columns. For example, filtering town = 'LONDON' uses the primary index to process 27M rows in 0.044 seconds.
• Data-Skipping Indices like minmax or bloom_filter reduce I/O by excluding irrelevant data blocks. Use them on high-cardinality columns for range queries.

2. Schema Design Best Practices

• Avoid Nullable Columns where possible, as they add overhead. Replace with default values (e.g., 0 instead of NULL).
• Use LowCardinality for strings with ≤10k unique values (e.g., ratecode_id in taxi data), reducing storage by 34% in some cases.
• Optimize Data Types: Use the smallest numeric type (e.g., UInt16 instead of UInt64) and precise date formats.

3. Precompute Results

• Projections pre-aggregate data (e.g., county-level property prices), eliminating full scans during queries.
• Materialized Views automate real-time aggregation for dashboards, reducing on-the-fly computation.

4. Query Execution Tuning

• Asynchronous Inserts: Use async_insert for high-throughput ingestion, queuing inserts to reduce latency.
• Bulk Inserts: Batch ≥100k rows to improve compression and minimize merge overhead.
• Parallel Processing: ClickHouse pipelines data through processors (filtering, aggregation) concurrently, leveraging multi-core systems.

Storage and Infrastructure

Diagnostic Tools

• Query Profiler: Analyze execution stages (parsing → optimization → pipeline execution) to identify bottlenecks.
• EXPLAIN: Use EXPLAIN PIPELINE to visualize parallel processing steps.

Example Workflow

For a query filtering and aggregating 329M rows:

• Optimize Schema: Replace Nullable columns and apply LowCardinality.
• Add Projection: Precompute aggregates for common dimensions (e.g., county).
• Profile Query: Identify excessive memory usage or full scans using built-in tools.

Resulting improvements:

• 20% faster queries (1.699s → 1.353s)
• 34% smaller disk footprint (7.38 GiB → 4.89 GiB)

By combining these techniques, ClickHouse can efficiently handle petabytes of data with sub-second latency.

Sources

• [1] Super charging your ClickHouse queries https://clickhouse.com/blog/clickhouse-faster-queries-with-projections-and-primary-indexes
• [2] 2 ClickHouse tutorials: Quick start and large-scale data analysis https://www.instaclustr.com/education/2-clickhouse-tutorials-quick-start-and-large-scale-data-analysis/
• [3] Guide for Query optimization | ClickHouse Docs https://clickhouse.com/docs/optimize/query-optimization
• [4] Guide for Query optimization | ClickHouse Docs https://clickhouse.com/docs/en/optimize/query-optimization
• [5] ClickHouse architecture: 4 key components and optimization tips https://www.instaclustr.com/education/clickhouse-architecture-4-key-components-and-optimization-tips/
• [6] Performance and Optimizations | ClickHouse Docs https://clickhouse.com/docs/operations/overview