clickhouse-io

# ClickHouse 分析模式

针对高性能分析和数据工程的 ClickHouse 特定模式。

## 概览

ClickHouse 是一款用于联机分析处理（OLAP）的列式数据库管理系统（DBMS）。它针对大规模数据集上的快速分析查询进行了优化。

**核心特性：**
- 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
-- 针对可能存在重复的数据（例如来自多个源）
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（预聚合）

```sql
-- 用于维护聚合指标
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);

-- 查询聚合数据
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
-- ✅ 推荐：优先使用索引列
SELECT *
FROM markets_analytics
WHERE date >= '2025-01-01'
  AND market_id = 'market-123'
  AND volume > 1000
ORDER BY date DESC
LIMIT 100;

-- ❌ 不推荐：优先过滤非索引列
SELECT *
FROM markets_analytics
WHERE volume > 1000
  AND market_name LIKE '%election%'
  AND date >= '2025-01-01';
```

### 聚合

```sql
-- ✅ 推荐：使用 ClickHouse 特有的聚合函数
SELECT
    toStartOfDay(created_at) AS day,
    market_id,
    sum(volume) AS total_volume,