Ten best practices for getting the most out of ClickHouse, from primary key design and data types to materialized views,   ReplacingMergeTree, and join optimization — illustrated with benchmarks on a 150M row dataset.

ClickHouse is a leading platform for open-source columnar database technology and real-time analytics. With ClickHouse, developers can delve into documentation, case studies, and performance benchmarks to learn how to build highly scalable and performant analytical systems. It provides tips about ClickHouse's storage engine, query execution, and distributed architecture. Developers can gain insights into their data in real-time, enabling them to make informed decisions and extract  business insights.

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Ten actionable best practices for ClickHouse performance and efficiency, benchmarked on a 150M-row Amazon reviews dataset. Key topics include: choosing the right ORDER BY/primary key (347x less data scanned), using efficient data types and LowCardinality (2x query speedup, 12% storage reduction), avoiding over-partitioning (unnecessary partitioning can cause 46x slowdowns), using skipping indexes (minmax, bloom_filter, set), handling semi-structured data with the JSON type and path hints, ingestion patterns (async inserts, columnar formats like Parquet), materialized views vs projections for pre-aggregation, monitoring via system.query_log and system.parts, tuning ReplacingMergeTree with FINAL vs argMax, and optimizing joins with dictionaries (3x faster than regular joins).

Top 10 best practices tips for ClickHouse

1. Choose the Right Primary Key and Order By #

3. Consider Your Partitioning strategy, or avoid one #

4. Optimize Data Scans with Skipping Indexes #

5. Leveraging the JSON Data Type for semi-structured data #

6. Getting data into ClickHouse the right way #

7. Compute on write, faster reads with materialized views and projections #