clickhouse-iolisted

# ClickHouse Analytics Patterns ClickHouse-specific patterns for high-performance analytics and data engineering. ## When to Activate - 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 ## Overview 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 ## Table Design Patterns ### MergeTree Engine (Most Common) ```sql CREATE TABLE markets_analytics ( date Date, market_id String, market_name String, volume UInt64, trades UInt32, unique_traders UInt32, avg_trade_size Float64, created_at DateTime ) ENGINE = MergeTree() PARTITION BY toYYYYMM(date) ORDER BY (date, market_id) SETTINGS index_granularity = 8192; ``` ### ReplacingMergeTree (Deduplication) ```sql -- For data that may have duplicates (e.g., from multiple sources) CREATE TABLE user_events ( event_id String, user_id String, event_type String, timestamp DateTime, pr