clickhouse-io

Topics in the source

• ClickHouse Analytics Patterns
• When to Activate
• Overview
• Table Design Patterns
• MergeTree Engine (Most Common)
• ReplacingMergeTree (Deduplication)
• AggregatingMergeTree (Pre-aggregation)
• Query Optimization Patterns

ClickHouse-specific patterns for high-performance analytics and data engineering. Designing ClickHouse table schemas (MergeTree engine selection) Writing analytical queries (aggregations, window functions, joins) Optimizing query performance (partition pruning, projections, materialized views) Ingesting large volumes of data (batch inserts, Kafka integration) Migrating from PostgreSQL/MySQL to ClickHouse for analytics Implementing real-time dashboards or time-series analytics ClickHouse is a column-oriented database management system (DBMS) for online analytical processing (OLAP). It's optimized for fast analytical queries on large datasets. Key Features: Column-oriented storage Data compression Parallel query execution Distributed queries Real-time analytics Partition by time (usually month or day) Avoid too many partitions (performance impact) Use DATE type for partition key Put most frequently filtered columns first Consider cardinality (high cardinality first) Order impacts compression Use smallest appropriate type (UInt32 vs UInt64) Use LowCardinality for repeated strings Use Enum for categorical data SELECT (specify columns) FINAL (merge data before query instead) Too many JO

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