clickhouse-io
OfficialClickHouse数据库模式、查询优化、分析以及高性能分析工作负载的数据工程最佳实践。
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---
name: clickhouse-io
description: ClickHouse数据库模式、查询优化、分析以及高性能分析工作负载的数据工程最佳实践。
origin: ECC
---
# ClickHouse 分析模式
用于高性能分析和数据工程的 ClickHouse 特定模式。
## 何时激活
* 设计 ClickHouse 表架构(MergeTree 引擎选择)
* 编写分析查询(聚合、窗口函数、连接)
* 优化查询性能(分区裁剪、投影、物化视图)
* 摄取大量数据(批量插入、Kafka 集成)
* 为分析目的从 PostgreSQL/MySQL 迁移到 ClickHouse
* 实现实时仪表板或时间序列分析
## 概述
ClickHouse 是一个用于在线分析处理 (OLAP) 的列式数据库管理系统 (DBMS)。它针对大型数据集上的快速分析查询进行了优化。
**关键特性:**
* 列式存储
* 数据压缩
* 并行查询执行
* 分布式查询
* 实时分析
## 表设计模式
### MergeTree 引擎 (最常用)
```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
-- 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 (预聚合)
```sql
-- For maintaining aggregated metrics
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);
-- Query aggregated data
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, market_id
ORDER BY hour DESC;
```
## 查询优化模式
### 高效过滤
```sql
-- PASS: GOOD: Use indexed columns first
SELECT *
FROM markets_analytics
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