claudegoodies
Skill

clickhouse-io

From affaan-m

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

Provides ClickHouse SQL patterns for table design, query optimization, batch inserts, and materialized views.

Use it when

  • Designing MergeTree, ReplacingMergeTree, or AggregatingMergeTree tables
  • Writing analytical SQL with aggregation, quantiles, or window functions
  • Setting up bulk or streaming inserts via @clickhouse/client in TypeScript
  • Building materialized views for real-time hourly stats or monitoring slow queries

Skip it if

  • Not using ClickHouse as your database
  • TypeScript examples assume @clickhouse/client, irrelevant for other languages/drivers
  • Reference document of SQL/code snippets, not an executable tool

Facts

Repository
affaan-m/ECC
Status
Actively maintained
Last commit
Source file
docs/ja-JP/skills/clickhouse-io/SKILL.md

Source preview

The instructions Claude Code reads when this skill runs.

# ClickHouse 分析パターン

高性能分析とデータエンジニアリングのためのClickHouse固有のパターン。

## 概要

ClickHouseは、オンライン分析処理（OLAP）用のカラム指向データベース管理システム（DBMS）です。大規模データセットに対する高速分析クエリに最適化されています。

**主な機能:**
- カラム指向ストレージ
- データ圧縮
- 並列クエリ実行
- 分散クエリ
- リアルタイム分析

## テーブル設計パターン

### MergeTreeエンジン（最も一般的）

```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（重複排除）

```sql
-- 重複がある可能性のあるデータ（複数のソースからなど）用
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（事前集計）

```sql
-- 集計メトリクスの維持用
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);

-- 集計データのクエリ
SELECT
    hour,
    market_id,
    sumMerge(total_volume) AS volume,
View full source on GitHub →

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