fix: restore missing files (package.json etc) and fix sync script logic

2026-03-22 14:40:14 +00:00 · 2026-01-31 18:55:45 +08:00
parent b1d03833b9
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85 changed files with 17074 additions and 1 deletions
--- a/docs/zh-TW/skills/clickhouse-io/SKILL.md
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+---
+name: clickhouse-io
+description: ClickHouse database patterns, query optimization, analytics, and data engineering best practices for high-performance analytical workloads.
+---
+
+# ClickHouse 分析模式
+
+用於高效能分析和資料工程的 ClickHouse 特定模式。
+
+## 概述
+
+ClickHouse 是一個列式資料庫管理系統（DBMS），用於線上分析處理（OLAP）。它針對大型資料集的快速分析查詢進行了優化。
+
+**關鍵特性：**
+- 列式儲存
+- 資料壓縮
+- 平行查詢執行
+- 分散式查詢
+- 即時分析
+
+## 表格設計模式
+
+### MergeTree 引擎（最常見）
+
+```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
+-- 用於可能有重複的資料（例如來自多個來源）
+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（預聚合）
+
+```sql
+-- 用於維護聚合指標
+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);
+
+-- 查詢聚合資料
+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;
+```
+
+## 查詢優化模式
+
+### 高效過濾
+
+```sql
+-- ✅ 良好：先使用索引欄位
+SELECT *
+FROM markets_analytics
+WHERE date >= '2025-01-01'
+  AND market_id = 'market-123'
+  AND volume > 1000
+ORDER BY date DESC
+LIMIT 100;
+
+-- ❌ 不良：先過濾非索引欄位
+SELECT *
+FROM markets_analytics
+WHERE volume > 1000
+  AND market_name LIKE '%election%'
+  AND date >= '2025-01-01';
+```
+
+### 聚合
+
+```sql
+-- ✅ 良好：使用 ClickHouse 特定聚合函式
+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;
+
+-- ✅ 使用 quantile 計算百分位數（比 percentile 更高效）
+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;
+```
+
+### 視窗函式
+
+```sql
+-- 計算累計總和
+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;
+```
+
+## 資料插入模式
+
+### 批量插入（推薦）
+
+```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
+  }
+})
+
+// ✅ 批量插入（高效）
+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 ${values}
+  `).toPromise()
+}
+
+// ❌ 個別插入（慢）
+async function insertTrade(trade: Trade) {
+  // 不要在迴圈中這樣做！
+  await clickhouse.query(`
+    INSERT INTO trades VALUES ('${trade.id}', ...)
+  `).toPromise()
+}
+```
+
+### 串流插入
+
+```typescript
+// 用於持續資料攝取
+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()
+}
+```
+
+## 物化視圖
+
+### 即時聚合
+
+```sql
+-- 建立每小時統計的物化視圖
+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;
+
+-- 查詢物化視圖
+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;
+```
+
+## 效能監控
+
+### 查詢效能
+
+```sql
+-- 檢查慢查詢
+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;
+```
+
+### 表格統計
+
+```sql
+-- 檢查表格大小
+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;
+```
+
+## 常見分析查詢
+
+### 時間序列分析
+
+```sql
+-- 每日活躍使用者
+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;
+
+-- 留存分析
+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;
+```
+
+### 漏斗分析
+
+```sql
+-- 轉換漏斗
+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;
+```
+
+### 世代分析
+
+```sql
+-- 按註冊月份的使用者世代
+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;
+```
+
+## 資料管線模式
+
+### ETL 模式
+
+```typescript
+// 提取、轉換、載入
+async function etlPipeline() {
+  // 1. 從來源提取
+  const rawData = await extractFromPostgres()
+
+  // 2. 轉換
+  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. 載入到 ClickHouse
+  await bulkInsertToClickHouse(transformed)
+}
+
+// 定期執行
+setInterval(etlPipeline, 60 * 60 * 1000)  // 每小時
+```
+
+### 變更資料捕獲（CDC）
+
+```typescript
+// 監聽 PostgreSQL 變更並同步到 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)
+    }
+  ])
+})
+```
+
+## 最佳實務
+
+### 1. 分區策略
+- 按時間分區（通常按月或日）
+- 避免太多分區（效能影響）
+- 分區鍵使用 DATE 類型
+
+### 2. 排序鍵
+- 最常過濾的欄位放在最前面
+- 考慮基數（高基數優先）
+- 排序影響壓縮
+
+### 3. 資料類型
+- 使用最小的適當類型（UInt32 vs UInt64）
+- 重複字串使用 LowCardinality
+- 分類資料使用 Enum
+
+### 4. 避免
+- SELECT *（指定欄位）
+- FINAL（改為在查詢前合併資料）
+- 太多 JOINs（為分析反正規化）
+- 小量頻繁插入（改用批量）
+
+### 5. 監控
+- 追蹤查詢效能
+- 監控磁碟使用
+- 檢查合併操作
+- 審查慢查詢日誌
+
+**記住**：ClickHouse 擅長分析工作負載。為你的查詢模式設計表格，批量插入，並利用物化視圖進行即時聚合。