clickhouse-io

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ClickHouse database patterns, query optimization, analytics, and data engineering best practices for high-performance analytical workloads.

AI & Automation 196,640 stars 30253 forks Updated 2 days ago MIT

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# ClickHouse 分析模式 用於高效能分析和資料工程的 ClickHouse 特定模式。 ## 概述 ClickHouse 是一個列式資料庫管理系統(DBMS),用於線上分析處理(OLAP)。它針對大型資料集的快速分析查詢進行了優化。 **關鍵特性:** - 列式儲存 - 資料壓縮 - 平行查詢執行 - 分散式查詢 - 即時分析 ## 表格設計模式 ### MergeTree 引擎(最常見) ```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(去重) ```sql -- 用於可能有重複的資料(例如來自多個來源) 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(預聚合) ```sql -- 用於維護聚合指標 CREATE TABLE market_stats_hourly ( hour DateTime, market_id String, total_volume AggregateFunction(sum, UInt64), total_trades AggregateFunction(count, UInt32), unique_users AggregateFunction(uniq, String) ) ENGINE = AggregatingMergeTree() PARTITION BY toYYYYMM(hour) ORDER BY (hour, market_id); -- 查詢聚合資料 SELECT hour, market_id, sumMerge(total_volume) AS volume, countMerge(total_trades) AS trades, uniqMerge(unique_users) AS users FROM market_stats_hourly WHERE hour >= toStartOfHour(now() - INTERVAL 24 HOUR) GROUP BY hour,...

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Author
affaan-m
Repository
affaan-m/everything-claude-code
Created
4 months ago
Last Updated
2 days ago
Language
JavaScript
License
MIT

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