clickhouse-io

ClickHouse-specific patterns for high-performance analytics and data engineering. 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: Remember : ClickHouse excels at analytical workloads. Design tables for your query patterns, batch inserts, and leverage materialized views for real-time aggregations. - 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 - 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...