docs(ko-KR): add Korean translation for skills

- 15 skill categories (17 files): coding-standards, tdd-workflow, frontend-patterns, backend-patterns, security-review (2 files), postgres-patterns, verification-loop, continuous-learning, continuous-learning-v2, eval-harness, iterative-retrieval, strategic-compact, golang-patterns, golang-testing, clickhouse-io, project-guidelines-example
2026-04-17 15:43:30 +08:00 · 2026-03-10 13:29:00 +09:00
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+---
+name: clickhouse-io
+description: 고성능 분석 워크로드를 위한 ClickHouse 데이터베이스 패턴, 쿼리 최적화, 분석 및 데이터 엔지니어링 모범 사례.
+origin: ECC
+---
+
+# ClickHouse 분석 패턴
+
+고성능 분석 및 데이터 엔지니어링을 위한 ClickHouse 전용 패턴.
+
+## 활성화 시점
+
+- ClickHouse 테이블 스키마 설계 시 (MergeTree 엔진 선택)
+- 분석 쿼리 작성 시 (집계, 윈도우 함수, 조인)
+- 쿼리 성능 최적화 시 (파티션 프루닝, 프로젝션, 구체화된 뷰)
+- 대량 데이터 수집 시 (배치 삽입, Kafka 통합)
+- PostgreSQL/MySQL에서 ClickHouse로 분석 마이그레이션 시
+- 실시간 대시보드 또는 시계열 분석 구현 시
+
+## 개요
+
+ClickHouse는 온라인 분석 처리(OLAP)를 위한 컬럼 지향 데이터베이스 관리 시스템(DBMS)입니다. 대규모 데이터셋에 대한 빠른 분석 쿼리에 최적화되어 있습니다.
+
+**주요 특징:**
+- 컬럼 지향 저장소
+- 데이터 압축
+- 병렬 쿼리 실행
+- 분산 쿼리
+- 실시간 분석
+
+## 테이블 설계 패턴
+
+### MergeTree 엔진 (가장 일반적)
+
+```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 (중복 제거)
+
+```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 (사전 집계)
+
+```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;
+```
+
+## 쿼리 최적화 패턴
+
+### 효율적인 필터링
+
+```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';
+```
+
+### 집계
+
+```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
+    quantile(0.50)(trade_size) AS median,
+    quantile(0.95)(trade_size) AS p95,
+    quantile(0.99)(trade_size) AS p99
+FROM trades
+WHERE created_at >= now() - INTERVAL 1 HOUR;
+```
+
+### 윈도우 함수
+
+```sql
+-- Calculate running totals
+SELECT
+    date,
+    market_id,
+    volume,
+    sum(volume) OVER (
+        PARTITION BY market_id
+        ORDER BY date
+        ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
+    ) AS cumulative_volume
+FROM markets_analytics
+WHERE date >= today() - INTERVAL 30 DAY
+ORDER BY market_id, date;
+```
+
+## 데이터 삽입 패턴
+
+### 배치 삽입 (권장)
+
+```typescript
+import { ClickHouse } from 'clickhouse'
+
+const clickhouse = new ClickHouse({
+  url: process.env.CLICKHOUSE_URL,
+  port: 8123,
+  basicAuth: {
+    username: process.env.CLICKHOUSE_USER,
+    password: process.env.CLICKHOUSE_PASSWORD
+  }
+})
+
+// ✅ Batch insert (efficient)
+async function bulkInsertTrades(trades: Trade[]) {
+  const values = trades.map(trade => `(
+    '${trade.id}',
+    '${trade.market_id}',
+    '${trade.user_id}',
+    ${trade.amount},
+    '${trade.timestamp.toISOString()}'
+  )`).join(',')
+
+  await clickhouse.query(`
+    INSERT INTO trades (id, market_id, user_id, amount, timestamp)
+    VALUES ${values}
+  `).toPromise()
+}
+
+// ❌ Individual inserts (slow)
+async function insertTrade(trade: Trade) {
+  // Don't do this in a loop!
+  await clickhouse.query(`
+    INSERT INTO trades VALUES ('${trade.id}', ...)
+  `).toPromise()
+}
+```
+
+### 스트리밍 삽입
+
+```typescript
+// For continuous data ingestion
+import { createWriteStream } from 'fs'
+import { pipeline } from 'stream/promises'
+
+async function streamInserts() {
+  const stream = clickhouse.insert('trades').stream()
+
+  for await (const batch of dataSource) {
+    stream.write(batch)
+  }
+
+  await stream.end()
+}
+```
+
+## 구체화된 뷰
+
+### 실시간 집계
+
+```sql
+-- Create materialized view for hourly stats
+CREATE MATERIALIZED VIEW market_stats_hourly_mv
+TO market_stats_hourly
+AS SELECT
+    toStartOfHour(timestamp) AS hour,
+    market_id,
+    sumState(amount) AS total_volume,
+    countState() AS total_trades,
+    uniqState(user_id) AS unique_users
+FROM trades
+GROUP BY hour, market_id;
+
+-- Query the materialized view
+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 >= now() - INTERVAL 24 HOUR
+GROUP BY hour, market_id;
+```
+
+## 성능 모니터링
+
+### 쿼리 성능
+
+```sql
+-- Check slow queries
+SELECT
+    query_id,
+    user,
+    query,
+    query_duration_ms,
+    read_rows,
+    read_bytes,
+    memory_usage
+FROM system.query_log
+WHERE type = 'QueryFinish'
+  AND query_duration_ms > 1000
+  AND event_time >= now() - INTERVAL 1 HOUR
+ORDER BY query_duration_ms DESC
+LIMIT 10;
+```
+
+### 테이블 통계
+
+```sql
+-- Check table sizes
+SELECT
+    database,
+    table,
+    formatReadableSize(sum(bytes)) AS size,
+    sum(rows) AS rows,
+    max(modification_time) AS latest_modification
+FROM system.parts
+WHERE active
+GROUP BY database, table
+ORDER BY sum(bytes) DESC;
+```
+
+## 일반적인 분석 쿼리
+
+### 시계열 분석
+
+```sql
+-- Daily active users
+SELECT
+    toDate(timestamp) AS date,
+    uniq(user_id) AS daily_active_users
+FROM events
+WHERE timestamp >= today() - INTERVAL 30 DAY
+GROUP BY date
+ORDER BY date;
+
+-- Retention analysis
+SELECT
+    signup_date,
+    countIf(days_since_signup = 0) AS day_0,
+    countIf(days_since_signup = 1) AS day_1,
+    countIf(days_since_signup = 7) AS day_7,
+    countIf(days_since_signup = 30) AS day_30
+FROM (
+    SELECT
+        user_id,
+        min(toDate(timestamp)) AS signup_date,
+        toDate(timestamp) AS activity_date,
+        dateDiff('day', signup_date, activity_date) AS days_since_signup
+    FROM events
+    GROUP BY user_id, activity_date
+)
+GROUP BY signup_date
+ORDER BY signup_date DESC;
+```
+
+### 퍼널 분석
+
+```sql
+-- Conversion funnel
+SELECT
+    countIf(step = 'viewed_market') AS viewed,
+    countIf(step = 'clicked_trade') AS clicked,
+    countIf(step = 'completed_trade') AS completed,
+    round(clicked / viewed * 100, 2) AS view_to_click_rate,
+    round(completed / clicked * 100, 2) AS click_to_completion_rate
+FROM (
+    SELECT
+        user_id,
+        session_id,
+        event_type AS step
+    FROM events
+    WHERE event_date = today()
+)
+GROUP BY session_id;
+```
+
+### 코호트 분석
+
+```sql
+-- User cohorts by signup month
+SELECT
+    toStartOfMonth(signup_date) AS cohort,
+    toStartOfMonth(activity_date) AS month,
+    dateDiff('month', cohort, month) AS months_since_signup,
+    count(DISTINCT user_id) AS active_users
+FROM (
+    SELECT
+        user_id,
+        min(toDate(timestamp)) OVER (PARTITION BY user_id) AS signup_date,
+        toDate(timestamp) AS activity_date
+    FROM events
+)
+GROUP BY cohort, month, months_since_signup
+ORDER BY cohort, months_since_signup;
+```
+
+## 데이터 파이프라인 패턴
+
+### ETL 패턴
+
+```typescript
+// Extract, Transform, Load
+async function etlPipeline() {
+  // 1. Extract from source
+  const rawData = await extractFromPostgres()
+
+  // 2. Transform
+  const transformed = rawData.map(row => ({
+    date: new Date(row.created_at).toISOString().split('T')[0],
+    market_id: row.market_slug,
+    volume: parseFloat(row.total_volume),
+    trades: parseInt(row.trade_count)
+  }))
+
+  // 3. Load to ClickHouse
+  await bulkInsertToClickHouse(transformed)
+}
+
+// Run periodically
+setInterval(etlPipeline, 60 * 60 * 1000)  // Every hour
+```
+
+### 변경 데이터 캡처 (CDC)
+
+```typescript
+// Listen to PostgreSQL changes and sync to ClickHouse
+import { Client } from 'pg'
+
+const pgClient = new Client({ connectionString: process.env.DATABASE_URL })
+
+pgClient.query('LISTEN market_updates')
+
+pgClient.on('notification', async (msg) => {
+  const update = JSON.parse(msg.payload)
+
+  await clickhouse.insert('market_updates', [
+    {
+      market_id: update.id,
+      event_type: update.operation,  // INSERT, UPDATE, DELETE
+      timestamp: new Date(),
+      data: JSON.stringify(update.new_data)
+    }
+  ])
+})
+```
+
+## 모범 사례
+
+### 1. 파티셔닝 전략
+- 시간별 파티셔닝 (보통 월 또는 일)
+- 파티션이 너무 많은 것 방지 (성능 영향)
+- 파티션 키에 DATE 타입 사용
+
+### 2. 정렬 키
+- 가장 자주 필터링되는 컬럼을 먼저 배치
+- 카디널리티 고려 (높은 카디널리티 먼저)
+- 정렬이 압축에 영향을 미침
+
+### 3. 데이터 타입
+- 가장 작은 적절한 타입 사용 (UInt32 vs UInt64)
+- 반복되는 문자열에 LowCardinality 사용
+- 범주형 데이터에 Enum 사용
+
+### 4. 피해야 할 것
+- SELECT * (컬럼을 명시)
+- FINAL (쿼리 전에 데이터를 병합)
+- 너무 많은 JOIN (분석을 위해 비정규화)
+- 작은 빈번한 삽입 (배치 처리)
+
+### 5. 모니터링
+- 쿼리 성능 추적
+- 디스크 사용량 모니터링
+- 병합 작업 확인
+- 슬로우 쿼리 로그 검토
+
+**기억하세요**: ClickHouse는 분석 워크로드에 탁월합니다. 쿼리 패턴에 맞게 테이블을 설계하고, 배치 삽입을 사용하며, 실시간 집계를 위해 구체화된 뷰를 활용하세요.