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# ClickHouse 分析模式

用于高性能分析和数据工程的 ClickHouse 特定模式。

## 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
-- ✅ GOOD: Use indexed columns first
SELECT *
FROM markets_analytics
WHERE date >= '2025-01-01'
  AND market_id = 'market-123'
  AND volume > 1000
ORDER BY date DESC
LIMIT 100;

-- ❌ BAD: Filter on non-indexed columns first
SELECT *
FROM markets_analytics
WHERE volume > 1000
  AND market_name LIKE '%election%'
  AND date >= '2025-01-01';
```

### 聚合

```sql
-- ✅ GOOD: Use ClickHouse-specific aggregation functions
SELECT
    toStartOfDay(created_at) AS day,
    market_id,
    sum(volume) AS total_volume,
    count() AS total_trades,
    uniq(trader_id) AS unique_traders,
    avg(trade_size) AS avg_size
FROM trades
WHERE created_at >= today() - INTERVAL 7 DAY
GROUP BY day, market_id
ORDER BY day DESC, total_volume DESC;

-- ✅ Use quantile for percentiles (more efficient than percentile)
SELECT

From Luohaothu/everything-codex, MIT.

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