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clickhouse-io

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

Invoke via:use clickhouse-io
Origin:ECC

ClickHouse Analytics Patterns

ClickHouse-specific patterns for high-performance analytics and data engineering.

When to Activate

  • Designing ClickHouse table schemas (MergeTree engine selection)
  • Writing analytical queries (aggregations, window functions, joins)
  • Optimizing query performance (partition pruning, projections, materialized views)
  • Ingesting large volumes of data (batch inserts, Kafka integration)
  • Migrating from PostgreSQL/MySQL to ClickHouse for analytics
  • Implementing real-time dashboards or time-series analytics

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)

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)

-- 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)

-- 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

-- PASS: 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;
-- FAIL: 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

-- PASS: 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;
-- PASS: 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

-- 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)

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
  }
})
// PASS: 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 ${values}
  ).toPromise()
}
// FAIL: Individual inserts (slow)
async function insertTrade(trade: Trade) {
  // Don't do this in a loop!
  await clickhouse.query(
    INSERT INTO trades VALUES ('${trade.id}', ...)
  ).toPromise()
}

Streaming Insert

// 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

-- 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

-- 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

-- 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

-- 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

-- 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

-- 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

// 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)

// 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.

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