cc-skill-clickhouse-io

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

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Repository: ranbot-ai/awesome-skills
Last source activity: February 21, 2026 at 06:57
Detected SKILL.md language: English
Stars: 6
Forks: 4

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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;

-- 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);

-- 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;

-- ✅ 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';

-- ✅ 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;

-- 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;

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()
}

// 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()
}

-- 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

name	cc-skill-clickhouse-io
description	ClickHouse database patterns, query optimization, analytics, and data engineering best practices for high-performance analytical workloads.
category	Business & Marketing
source	antigravity
tags	["typescript","ai","workflow","design","rag"]
url	https://github.com/sickn33/antigravity-awesome-skills/tree/main/skills/cc-skill-clickhouse-io

name	cc-skill-clickhouse-io
description	ClickHouse database patterns, query optimization, analytics, and data engineering best practices for high-performance analytical workloads.
category	Business & Marketing
source	antigravity
tags	["typescript","ai","workflow","design","rag"]
url	https://github.com/sickn33/antigravity-awesome-skills/tree/main/skills/cc-skill-clickhouse-io

cc-skill-clickhouse-io

Install options

Review the source files

ClickHouse Analytics Patterns

Overview

Table Design Patterns

MergeTree Engine (Most Common)

ClickHouse Analytics Patterns

Overview

Table Design Patterns

MergeTree Engine (Most Common)

ReplacingMergeTree (Deduplication)

AggregatingMergeTree (Pre-aggregation)

Query Optimization Patterns

Efficient Filtering

Aggregations

Window Functions

Data Insertion Patterns

Bulk Insert (Recommended)

Streaming Insert

Materialized Views

Real-time Aggregations

cc-skill-clickhouse-io

Source facts

Install options

Review the source files

More from this repository

More from this repository

ClickHouse Analytics Patterns

Overview

Table Design Patterns

MergeTree Engine (Most Common)

ClickHouse Analytics Patterns

Overview

Table Design Patterns

MergeTree Engine (Most Common)

ReplacingMergeTree (Deduplication)

AggregatingMergeTree (Pre-aggregation)

Query Optimization Patterns

Efficient Filtering

Aggregations

Window Functions

Data Insertion Patterns

Bulk Insert (Recommended)

Streaming Insert

Materialized Views

Real-time Aggregations