ClickHouse database patterns, query optimization, analytics, and data engineering best practices for high-performance analytical workloads.
apm install @worldflowai/clickhouse-io
[](https://apm-p1ls2dz87-atlamors-projects.vercel.app/packages/@worldflowai/clickhouse-io)---
name: clickhouse-io
description: ClickHouse database patterns, query optimization, analytics, and data engineering best practices for high-performance analytical workloads.
---
# ClickHouse Analytics Patterns
ClickHouse-specific patterns for high-performance analytics and data engineering.
## Overview
ClickHouse is a column-oriented database management system (DBMS) for online analytical processing (OLAP). It's optimized for fast analytical queries on large datasets.
**Key Features:**
- Column-oriented storage
- Data compression
- Parallel query execution
- Distributed queries
- Real-time analytics
## Table Design Patterns
### MergeTree Engine (Most Common)
```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 (Deduplication)
```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 (Pre-aggregation)
```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;
```
## Query Optimization Patterns
### Efficient Filtering
```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';
```
### Aggregations
```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;
```
### Window Functions
```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;
```
## Data Insertion Patterns
### Bulk Insert (Recommended)
```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()
}
```
### Streaming Insert
```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()
}
```
## Materialized Views
### Real-time Aggregations
```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;
```
## Performance Monitoring
### Query Performance
```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;
```
### Table Statistics
```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;
```
## Common Analytics Queries
### Time Series Analysis
```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;
```
### Funnel Analysis
```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;
```
### Cohort Analysis
```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;
```
## Data Pipeline Patterns
### ETL Pattern
```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
```
### Change Data Capture (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)
}
])
})
```
## Best Practices
### 1. Partitioning Strategy
- Partition by time (usually month or day)
- Avoid too many partitions (performance impact)
- Use DATE type for partition key
### 2. Ordering Key
- Put most frequently filtered columns first
- Consider cardinality (high cardinality first)
- Order impacts compression
### 3. Data Types
- Use smallest appropriate type (UInt32 vs UInt64)
- Use LowCardinality for repeated strings
- Use Enum for categorical data
### 4. Avoid
- SELECT * (specify columns)
- FINAL (merge data before query instead)
- Too many JOINs (denormalize for analytics)
- Small frequent inserts (batch instead)
### 5. Monitoring
- Track query performance
- Monitor disk usage
- Check merge operations
- Review slow query log
**Remember**: ClickHouse excels at analytical workloads. Design tables for your query patterns, batch inserts, and leverage materialized views for real-time aggregations.