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 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)

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)

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 Optimization Patterns

Efficient Filtering

-- 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

-- 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
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;

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;

Funnel Analysis

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;

Performance Monitoring

-- 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;

Best Practices

1. Partitioning Strategy: Partition by time (usually month or day)
2. Ordering Key: Put most frequently filtered columns first
3. Data Types: Use smallest appropriate type (UInt32 vs UInt64)
4. Avoid: SELECT *, FINAL, too many JOINs, small frequent inserts

Remember: ClickHouse excels at analytical workloads. Design tables for your query patterns and batch inserts.