clickhouse-iolisted

# ClickHouse Analytics Patterns ClickHouse-specific patterns for high-performance analytics and data engineering. ## 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 products_analytics ( date Date, product_id String, product_name String, sales UInt64, orders UInt32, unique_buyers UInt32, avg_order_size Float64, created_at DateTime ) ENGINE = MergeTree() PARTITION BY toYYYYMM(date) ORDER BY (date, product_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, properties String ) ENGINE = ReplacingMergeTree() PARTITION BY toYYYYMM(timestamp) ORDER BY (user_id, event_id, timestamp) PRIMARY KEY (user_id, event_id); ``` ### AggregatingMergeTree (Pre-aggregation) ```sql -- For maintaining aggregated metrics CREATE TABLE product_stats_hourly ( hour DateTime, product_id String, total_sales AggregateFunction(sum, UInt64), total_orders AggregateFunction(count, UInt32), u