Clickhouse for Developers

---

name: clickhouse description: > Comprehensive ClickHouse skill covering everything you need to work with a ClickHouse analytics database: schema design, query optimization, insert strategies, CLI usage, table creation and migrations, backend integration (Node.js, Python, Go), Redis caching strategy, cluster vs single-node differences, and how to test/debug data in the database. MUST USE whenever the user mentions ClickHouse, asks about analytics tables, high-volume insert pipelines, MergeTree schemas, ORDER BY / PRIMARY KEY design, materialized views, ClickHouse query performance, connecting to ClickHouse from code, or running ClickHouse CLI commands to inspect data.

---

ClickHouse Skill

A complete reference for designing, operating, querying, and integrating ClickHouse from backend services. ClickHouse is a columnar, append-optimized analytics database — it is NOT a transactional database. Design everything around that fact.

> Official docs: https://clickhouse.com/docs/best-practices

---

Quick Reference — Read the Right Section

| Task | Go To | |------|-------| | Design a new table | Schema Design | | Write a migration | Migrations | | Insert data from code | Insert Strategy | | Run queries / inspect the DB | CLI Reference | | Connect from Node.js / Python / Go | Backend Integration | | Optimize a slow query | Query Optimization | | Decide on Redis vs direct query | Redis Caching Strategy | | Understand cluster behavior | Cluster Considerations |

---

Core Mental Model

ClickHouse is an append-only, batch-oriented analytics database. The biggest performance wins come from:

1. Writing large batches (10K–100K rows), not individual rows 2. Choosing ORDER BY carefully — it is immutable and drives all query performance 3. Using native types — never store everything as String 4. Reading many rows across few columns — not few rows across many columns

Avoid ClickHouse for:

OLTP workloads (frequent single-row reads/writes)

Complex multi-table JOINs on huge tables

Frequent UPDATE/DELETE patterns

---

Schema Design

Step 1 — Plan ORDER BY Before Creating Any Table

ORDER BY is immutable. Changing it requires creating a new table and migrating all data. Get it right before writing a single row.

Questions to answer first:

What columns appear in WHERE clauses most often?

What is the cardinality (number of distinct values) of each filter column?

Is there a mandatory filter that every query has (e.g. tenant_id, app_id)?

Are date ranges a common filter?

-- BAD: UUID as first ORDER BY column — no index benefit
CREATE TABLE events (
    id UUID,
    timestamp DateTime,
    event_type String,
    user_id UInt64
) ENGINE = MergeTree()
ORDER BY (id);
-- GOOD: Low cardinality first, then date, then higher cardinality
CREATE TABLE events (
    id UUID,
    timestamp DateTime,
    event_type LowCardinality(String),
    user_id UInt64
) ENGINE = MergeTree()
ORDER BY (event_type, toDate(timestamp), user_id);

Cardinality ordering rule: Put columns with fewer distinct values first.

| Position | Cardinality | Examples | |----------|-------------|----------| | 1st | Low (2–1,000) | event_type, status, country | | 2nd | Date (coarse) | toDate(timestamp) | | 3rd+ | Medium-High | user_id, session_id | | Last | High (if needed) | event_id, UUID |

Index usage by query pattern (for ORDER BY (event_type, event_date, user_id)):

| Filter | Index Used? | |--------|-------------| | WHERE event_type = 'X' | ✅ Yes | | WHERE event_type = 'X' AND event_date = '...' | ✅ Yes | | WHERE event_date = '...' | ❌ No — skips first column | | WHERE user_id = 123 | ❌ No — skips first two |

For columns that can't be in ORDER BY, add a data skipping index (see Query Optimization).

---

Step 2 — Choose the Right Engine

| Engine | Use When | |--------|----------| | MergeTree | Standard append-only analytics | | ReplacingMergeTree(ver) | Need logical "upserts" (new version replaces old) | | AggregatingMergeTree | Pre-aggregated data for materialized views | | CollapsingMergeTree(sign) | Logical deletes via insert pattern | | SummingMergeTree | Automatically sum numeric columns on merge | | ReplicatedMergeTree | Any engine on a cluster with replication |

For clusters, prefix engine name with Replicated: ReplicatedMergeTree(...).

---

Step 3 — Pick Native Types (Never Store Everything as String)

| Data | Wrong | Right | Savings | |------|-------|-------|---------| | UUID | String | UUID | 56% | | Timestamp | String | DateTime / DateTime64(3) | 58–79% | | Integer ID | String | UInt32 / UInt64 | varies | | Boolean | String | Bool | 75–80% | | IPv4 | String | IPv4 | 43–73% | | Decimal amount | String | Decimal(10,2) | significant |

Use the smallest numeric type that fits:

| Type | Range | Use For | |------|-------|---------| | UInt8 | 0–255 | age, rating, status code | | UInt16 | 0–65,535 | year, port | | UInt32 | 0–4.2B | most IDs, unix timestamps | | UInt64 | 0–18E | very large counters |

Use LowCardinality for repeated strings with < 10,000 unique values:

country LowCardinality(String),   -- ~200 unique values
browser LowCardinality(String),   -- ~50 unique values
event_type LowCardinality(String) -- ~100 unique values

Use Enum for fixed, known value sets:

-- Provides insert-time validation + 1-byte storage
status Enum8('pending' = 1, 'processing' = 2, 'shipped' = 3, 'delivered' = 4)

Avoid Nullable unless the null is semantically meaningful:

-- BAD: Nullable everywhere
name Nullable(String),
login_count Nullable(UInt32)
-- GOOD: Use defaults; Nullable only when null has distinct meaning
name String DEFAULT '',
login_count UInt32 DEFAULT 0,
deleted_at Nullable(DateTime),   -- NULL = "not deleted" is semantically distinct
parent_id Nullable(UInt64)       -- NULL = "no parent" is semantically distinct

---

Step 4 — Partitioning Strategy

Partition for lifecycle management, NOT for query performance. Query performance comes from ORDER BY. Partitions enable fast data expiry.

-- GOOD: Monthly partitions for TTL and lifecycle
CREATE TABLE events (
    timestamp DateTime,
    event_type LowCardinality(String),
    user_id UInt64
) ENGINE = MergeTree()
PARTITION BY toStartOfMonth(timestamp)
ORDER BY (event_type, toDate(timestamp), user_id)
TTL timestamp + INTERVAL 90 DAY;
-- Instant deletion of a month
ALTER TABLE events DROP PARTITION '2024-01';

Keep partition count between 100–1,000. Daily partitions grow unbounded; monthly is usually safe.

Tiered storage:

TTL
    timestamp + INTERVAL 7 DAY TO VOLUME 'hot',
    timestamp + INTERVAL 30 DAY TO VOLUME 'warm',
    timestamp + INTERVAL 365 DAY DELETE;

If you're unsure, start without partitioning. You can add it later by creating a new table, migrating data, and renaming.

---

Complete Table Example

CREATE TABLE page_events (
    -- Identifiers
    event_id     UUID DEFAULT generateUUIDv4(),
    tenant_id    UInt32,
    user_id      UInt64,
    -- Low-cardinality dimensions (great for ORDER BY)
    event_type   LowCardinality(String),
    country      LowCardinality(String) DEFAULT '',
    browser      LowCardinality(String) DEFAULT '',
    platform     Enum8('web'=1, 'ios'=2, 'android'=3, 'api'=4),
    -- Timestamps
    occurred_at  DateTime64(3),
    inserted_at  DateTime DEFAULT now(),
    -- Metrics
    duration_ms  UInt32 DEFAULT 0,
    revenue      Decimal(12,4) DEFAULT 0,
    -- Flexible properties
    properties   JSON,
    -- Skipping index for user lookups
    INDEX idx_user_id user_id TYPE bloom_filter GRANULARITY 4
) ENGINE = MergeTree()
PARTITION BY toStartOfMonth(occurred_at)
ORDER BY (tenant_id, event_type, toDate(occurred_at), user_id)
TTL occurred_at + INTERVAL 365 DAY
SETTINGS index_granularity = 8192;

---

Migrations

ClickHouse does NOT support transactional DDL. There is no rollback. Plan carefully.

ORMs / Migration Tools Compatibility

| Tool | ClickHouse Support | |------|--------------------| | Prisma | ❌ No native support — use raw SQL migrations | | Drizzle | ❌ No native support — use raw SQL migrations | | TypeORM | ⚠️ Unofficial community driver only | | Flyway | ✅ Supported via JDBC driver | | Liquibase | ✅ Supported | | golang-migrate | ✅ Works well — recommended for Go | | Custom SQL files | ✅ Always works |

For Node.js projects: maintain a migrations/ folder with numbered .sql files and a small runner script.

For Go projects: use golang-migrate with the ClickHouse driver.

Do NOT use Prisma/Drizzle to generate ClickHouse DDL. They have no concept of MergeTree engines, ORDER BY, or PARTITION BY.

---

Migration Patterns

#### Adding a Column

-- Safe: adding a column with a DEFAULT
ALTER TABLE events ADD COLUMN IF NOT EXISTS session_id UUID DEFAULT generateUUIDv4();
-- For a cluster: ON CLUSTER must come first
ALTER TABLE events ON CLUSTER '{cluster}' ADD COLUMN IF NOT EXISTS session_id UUID DEFAULT generateUUIDv4();

#### Changing an ORDER BY (requires table recreation)

-- 1. Create new table with correct ORDER BY
CREATE TABLE events_v2 AS events;   -- Copies structure
ALTER TABLE events_v2 MODIFY ORDER BY (tenant_id, event_type, toDate(occurred_at), user_id);
-- Or create from scratch:
CREATE TABLE events_v2 (...) ENGINE = MergeTree() ORDER BY (...);
-- 2. Migrate data
INSERT INTO events_v2 SELECT * FROM events;
-- 3. Swap
RENAME TABLE events TO events_old, events_v2 TO events;
-- 4. Verify, then drop
DROP TABLE events_old;

#### Adding a Skipping Index

ALTER TABLE events ADD INDEX IF NOT EXISTS idx_user_id user_id TYPE bloom_filter GRANULARITY 4;
ALTER TABLE events MATERIALIZE INDEX idx_user_id;  -- Backfill existing data

#### Node.js Migration Runner Example

// migrations/runner.js
import { createClient } from '@clickhouse/client';
import fs from 'fs';
import path from 'path';
const client = createClient({
  url: process.env.CLICKHOUSE_URL,
  username: process.env.CLICKHOUSE_USER,
  password: process.env.CLICKHOUSE_PASSWORD,
  database: process.env.CLICKHOUSE_DB,
});
// Track applied migrations
await client.command({
  query: CREATE TABLE IF NOT EXISTS _migrations (
    name String,
    applied_at DateTime DEFAULT now()
  ) ENGINE = MergeTree() ORDER BY (applied_at, name)
});
const applied = new Set(
  (await client.query({ query: 'SELECT name FROM _migrations', format: 'JSONEachRow' }))
    .json().map(r => r.name)
);
const files = fs.readdirSync('./migrations').filter(f => f.endsWith('.sql')).sort();
for (const file of files) {
  if (applied.has(file)) continue;
  const sql = fs.readFileSync(path.join('./migrations', file), 'utf-8');
  // Execute each statement separately (ClickHouse doesn't support multi-statement by default)
  for (const stmt of sql.split(';').map(s => s.trim()).filter(Boolean)) {
    await client.command({ query: stmt });
  }
  await client.command({ query: INSERT INTO _migrations (name) VALUES ('${file}') });
  console.log(Applied: ${file});
}

#### Go Migration Runner (golang-migrate)

import (
    "github.com/golang-migrate/migrate/v4"
    _ "github.com/golang-migrate/migrate/v4/database/clickhouse"
    _ "github.com/golang-migrate/migrate/v4/source/file"
)
m, err := migrate.New(
    "file://migrations",
    "clickhouse://localhost:9000?database=mydb&username=default&password=",
)
if err != nil { log.Fatal(err) }
if err := m.Up(); err != nil && err != migrate.ErrNoChange {
    log.Fatal(err)
}

---

Insert Strategy

Rule 1 — Batch 10,000–100,000 Rows Per INSERT

Each INSERT creates a data part. Many small inserts = many small parts = merge pressure = cluster instability.

# BAD: one row at a time
for event in events:
    client.execute("INSERT INTO events VALUES", [event])  # Creates 10,000 parts!
GOOD: batch appropriately
BATCH_SIZE = 10_000
for i in range(0, len(events), BATCH_SIZE):
    client.execute("INSERT INTO events VALUES", events[i:i+BATCH_SIZE])

Monitor part health:

SELECT table, count() as parts, sum(rows) as total_rows
FROM system.parts
WHERE active AND database = currentDatabase()
GROUP BY table
ORDER BY parts DESC;
-- Warning: > 3,000 parts per table is trouble

Rule 2 — Use Async Inserts for Many Small Producers

When batching client-side isn't practical (many microservices, IoT, etc.):

SET async_insert = 1;
SET async_insert_max_data_size = 10000000;   -- 10MB buffer
SET async_insert_busy_timeout_ms = 1000;      -- Flush every 1s
SET wait_for_async_insert = 1;                -- Wait for durability confirmation

Rule 3 — Avoid Mutations (UPDATE/DELETE)

ClickHouse is not built for mutations. They rewrite entire data parts.

| Need | Use Instead | |------|-------------| | UPDATE rows | ReplacingMergeTree + insert new version | | DELETE rows frequently | Lightweight DELETE (23.3+): DELETE FROM events WHERE ... | | Delete old data in bulk | DROP PARTITION | | Track deletions | CollapsingMergeTree(sign) with sign = -1 row |

ReplacingMergeTree pattern:

CREATE TABLE users (
    user_id UInt64,
    name String,
    status LowCardinality(String),
    updated_at DateTime DEFAULT now()
) ENGINE = ReplacingMergeTree(updated_at)
ORDER BY user_id;
-- "Update" by inserting a new version
INSERT INTO users (user_id, name, status) VALUES (123, 'Alice', 'inactive');
-- Query deduplicated (FINAL is slower but consistent)
SELECT * FROM users FINAL WHERE user_id = 123;
-- Or use argMax for better performance at scale
SELECT user_id, argMax(status, updated_at) as status
FROM users GROUP BY user_id;

Rule 4 — Never Run OPTIMIZE TABLE FINAL in Production

Background merges handle part consolidation automatically. Forcing it:

Blocks other operations

Causes severe disk I/O spikes

Provides no lasting benefit

---

CLI Reference

Connect

# Basic
clickhouse-client -h  -u  --password  -d 
Using env vars (recommended — hides password from process list)
export CLICKHOUSE_PASSWORD=yourpassword
clickhouse-client -h 127.0.0.1 -u app_user -d app_db
With SSL
clickhouse-client -h  -u  -d  --secure --port 9440

Parse a JDBC URL (jdbc:clickhouse://host:8123/db):

JDBC="jdbc:clickhouse://myhost.com:8123/mydb"
HOST=$(echo $JDBC | sed 's|.*://\([^:]*\):.*|\1|')
PORT=$(echo $JDBC | sed 's|.*:\([0-9]*\)/.*|\1|')
DB=$(echo $JDBC   | sed 's|.*/||')
clickhouse-client -h "$HOST" --port "$PORT" -d "$DB"

Inspect the Database

# List databases
clickhouse-client -h  -u  -q "SHOW DATABASES;" --format=TSV
List tables
clickhouse-client -h  -u  -d  -q "SHOW TABLES;" --format=TSV
Describe a table
clickhouse-client -h  -u  -d  -q "DESCRIBE TABLE my_table;" --format=TSV
Show CREATE statement (includes engine, ORDER BY, partitioning)
clickhouse-client -h  -u  -d  -q "SHOW CREATE TABLE my_table;" --format=TSV
Table sizes
clickhouse-client -h  -u  -d  -q "
SELECT table, total_rows as rows,
       formatReadableSize(total_bytes) as size,
       formatReadableSize(data_bytes) as data
FROM system.tables WHERE database = currentDatabase()
ORDER BY total_bytes DESC;" --format=PrettyCompact
Check primary key / partition key columns
clickhouse-client -h  -u  -d  -q "
SELECT name, type, is_in_primary_key, is_in_partition_key
FROM system.columns
WHERE database = '' AND table = ''
ORDER BY position;" --format=PrettyCompact
Part health check
clickhouse-client -h  -u  -d  -q "
SELECT table, count() as parts, sum(rows) as rows,
       formatReadableSize(sum(bytes_on_disk)) as size
FROM system.parts WHERE active AND database = currentDatabase()
GROUP BY table ORDER BY parts DESC;" --format=PrettyCompact
Query Data
# Basic query — JSON output
clickhouse-client -h  -u  -d  \
  -q "SELECT * FROM events LIMIT 10;" --format=JSONEachRow | jq -s '.'
Aggregation
clickhouse-client -h  -u  -d  \
  -q "SELECT event_type, count() as n FROM events GROUP BY event_type ORDER BY n DESC;" \
  --format=PrettyCompact
Export to CSV
clickhouse-client -h  -u  -d  \
  -q "SELECT * FROM events FORMAT CSV" > /tmp/events.csv
Analyze Query Performance
# Execution plan
clickhouse-client -h  -u  -d  \
  -q "EXPLAIN SELECT * FROM events WHERE user_id = 123;" --format=TSV
With actual timing (ClickHouse 21.1+)
clickhouse-client -h  -u  -d  \
  -q "EXPLAIN ANALYZE SELECT * FROM events WHERE user_id = 123;" --format=TSV
See which indexes were used
clickhouse-client -h  -u  -d  \
  -q "EXPLAIN indexes = 1 SELECT * FROM events WHERE user_id = 123;" --format=TSV
Look for:
Rows in EXPLAIN output — fewer is better
Skip entries showing granules skipped by indexes
Full scan — indicates missing index coverage
Insert / Modify Data
# Insert from file (CSV)
clickhouse-client -h  -u  -d  \
  -q "INSERT INTO events FORMAT CSV" < data.csv
Insert from file (JSONEachRow)
clickhouse-client -h  -u  -d  \
  -q "INSERT INTO events FORMAT JSONEachRow" < data.ndjson
Run a SQL script
clickhouse-client -h  -u  -d  --multiquery < migration.sql
Lightweight delete (23.3+)
clickhouse-client -h  -u  -d  \
  -q "DELETE FROM events WHERE occurred_at < '2023-01-01';"
Drop partition (instant, for lifecycle)
clickhouse-client -h  -u  -d  \
  -q "ALTER TABLE events DROP PARTITION '2023-01';"
Backend Integration
Node.js
Install:
npm install @clickhouse/client
Module setup (src/clickhouse.js or src/clickhouse.ts):
// src/clickhouse.js
import { createClient } from '@clickhouse/client';
let _client = null;
export function getClickHouseClient() {
  if (_client) return _client;
  _client = createClient({
    url: process.env.CLICKHOUSE_URL ?? 'http://localhost:8123',
    username: process.env.CLICKHOUSE_USER ?? 'default',
    password: process.env.CLICKHOUSE_PASSWORD ?? '',
    database: process.env.CLICKHOUSE_DB ?? 'default',
    clickhouse_settings: {
      async_insert: 1,                       // Buffer small inserts server-side
      wait_for_async_insert: 1,              // Confirm durability
      async_insert_busy_timeout_ms: 1000,
    },
    compression: { request: true },           // Compress inserts
    request_timeout: 30_000,
  });
  return _client;
}
Environment variables (.env):
CLICKHOUSE_URL=http://localhost:8123
CLICKHOUSE_USER=default
CLICKHOUSE_PASSWORD=secret
CLICKHOUSE_DB=analytics
Insert (batch — always batch):
import { getClickHouseClient } from './clickhouse.js';
// Accumulate rows, then flush in a batch
const BATCH_SIZE = 10_000;
const buffer = [];
export async function trackEvent(event) {
  buffer.push(event);
  if (buffer.length >= BATCH_SIZE) {
    await flush();
  }
}
export async function flush() {
  if (buffer.length === 0) return;
  const rows = buffer.splice(0, buffer.length);
  const client = getClickHouseClient();
  await client.insert({
    table: 'events',
    values: rows,
    format: 'JSONEachRow',
  });
}
// Also flush on process exit / interval
setInterval(flush, 5_000);
process.on('beforeExit', flush);
Query (analytics — aggregate, don't fetch rows one by one):
export async function getEventStats({ startDate, endDate, eventType }) {
  const client = getClickHouseClient();
  const result = await client.query({
    query: 
      SELECT
        toStartOfHour(occurred_at) AS hour,
        count() AS events,
        uniq(user_id) AS unique_users
      FROM events
      WHERE
        event_type = {eventType: String}
        AND occurred_at >= {startDate: DateTime}
        AND occurred_at < {endDate: DateTime}
      GROUP BY hour
      ORDER BY hour
    ,
    query_params: { eventType, startDate, endDate },
    format: 'JSONEachRow',
  });
  return result.json();
}
TypeScript types:
interface EventRow {
  event_id: string;
  tenant_id: number;
  event_type: string;
  user_id: number;
  occurred_at: string;  // ClickHouse returns DateTime as string
  properties: Record;
}
const result = await client.query({
  query: 'SELECT * FROM events LIMIT 100',
  format: 'JSONEachRow',
});
const rows = await result.json();
Python
Install:
pip install clickhouse-connect   # Official Anthropic-maintained driver
or
pip install clickhouse-driver    # Older but widely used
Module setup (clickhouse.py):
# clickhouse.py
import os
import clickhouse_connect
from functools import lru_cache
@lru_cache(maxsize=1)
def get_client():
    return clickhouse_connect.get_client(
        host=os.environ.get('CLICKHOUSE_HOST', 'localhost'),
        port=int(os.environ.get('CLICKHOUSE_PORT', 8123)),
        username=os.environ.get('CLICKHOUSE_USER', 'default'),
        password=os.environ.get('CLICKHOUSE_PASSWORD', ''),
        database=os.environ.get('CLICKHOUSE_DB', 'default'),
        settings={
            'async_insert': 1,
            'wait_for_async_insert': 1,
            'async_insert_busy_timeout_ms': 1000,
        },
        compress=True,
    )
Batch insert:
from clickhouse import get_client
from datetime import datetime
BATCH_SIZE = 10_000
def insert_events(events: list[dict]):
    """Always insert in batches of 10K+ rows."""
    client = get_client()
    # clickhouse_connect expects column-oriented data
    column_names = ['tenant_id', 'event_type', 'user_id', 'occurred_at', 'properties']
    data = [
        [e['tenant_id'] for e in events],
        [e['event_type'] for e in events],
        [e['user_id'] for e in events],
        [e['occurred_at'] for e in events],
        [e.get('properties', {}) for e in events],
    ]
    client.insert('events', data, column_names=column_names)
def batch_insert(events: list[dict]):
    for i in range(0, len(events), BATCH_SIZE):
        insert_events(events[i:i+BATCH_SIZE])
Query:
def get_event_stats(event_type: str, start_date: str, end_date: str):
    client = get_client()
    result = client.query("""
        SELECT
            toStartOfHour(occurred_at) AS hour,
            count() AS events,
            uniq(user_id) AS unique_users
        FROM events
        WHERE event_type = {event_type:String}
          AND occurred_at >= {start_date:DateTime}
          AND occurred_at < {end_date:DateTime}
        GROUP BY hour
        ORDER BY hour
    """, parameters={'event_type': event_type, 'start_date': start_date, 'end_date': end_date})
    return result.named_results()  # Returns list of dicts
With pandas (for data pipelines):
def get_dataframe(query: str, params: dict = None):
    client = get_client()
    return client.query_df(query, parameters=params or {})
df = get_dataframe("SELECT event_type, count() as n FROM events GROUP BY event_type")
Go
Install:
go get github.com/ClickHouse/clickhouse-go/v2
Module setup (internal/clickhouse/client.go):
package clickhouse
import (
    "context"
    "crypto/tls"
    "fmt"
    "os"
    "sync"
    "time"
    ch "github.com/ClickHouse/clickhouse-go/v2"
    "github.com/ClickHouse/clickhouse-go/v2/lib/driver"
)
var (
    once   sync.Once
    client driver.Conn
)
func GetClient() (driver.Conn, error) {
    var err error
    once.Do(func() {
        options := &ch.Options{
            Addr: []string{fmt.Sprintf("%s:%s",
                getEnv("CLICKHOUSE_HOST", "localhost"),
                getEnv("CLICKHOUSE_PORT", "9000"),
            )},
            Auth: ch.Auth{
                Database: getEnv("CLICKHOUSE_DB", "default"),
                Username: getEnv("CLICKHOUSE_USER", "default"),
                Password: getEnv("CLICKHOUSE_PASSWORD", ""),
            },
            Settings: ch.Settings{
                "async_insert":                1,
                "wait_for_async_insert":       1,
                "async_insert_busy_timeout_ms": 1000,
            },
            DialTimeout:     time.Second * 5,
            MaxOpenConns:    10,
            MaxIdleConns:    5,
            ConnMaxLifetime: time.Hour,
            Compression: &ch.Compression{
                Method: ch.CompressionLZ4,
            },
        }
        // Enable TLS for production
        if os.Getenv("CLICKHOUSE_TLS") == "true" {
            options.TLS = &tls.Config{InsecureSkipVerify: false}
        }
        client, err = ch.Open(options)
    })
    return client, err
}
func getEnv(key, fallback string) string {
    if v := os.Getenv(key); v != "" {
        return v
    }
    return fallback
}
Batch insert:
package clickhouse
import (
    "context"
    "time"
)
type Event struct {
    TenantID   uint32    ch:"tenant_id"
    EventType  string    ch:"event_type"
    UserID     uint64    ch:"user_id"
    OccurredAt time.Time ch:"occurred_at"
}
func InsertEvents(ctx context.Context, events []Event) error {
    conn, err := GetClient()
    if err != nil {
        return err
    }
    batch, err := conn.PrepareBatch(ctx, "INSERT INTO events")
    if err != nil {
        return err
    }
    for _, e := range events {
        if err := batch.AppendStruct(&e); err != nil {
            return err
        }
    }
    return batch.Send()
}
Query:
type HourlyStats struct {
    Hour        time.Time ch:"hour"
    Events      uint64    ch:"events"
    UniqueUsers uint64    ch:"unique_users"
}
func GetEventStats(ctx context.Context, eventType, start, end string) ([]HourlyStats, error) {
    conn, err := GetClient()
    if err != nil {
        return nil, err
    }
    rows, err := conn.Query(ctx, 
        SELECT toStartOfHour(occurred_at) AS hour,
               count() AS events,
               uniq(user_id) AS unique_users
        FROM events
        WHERE event_type = @eventType
          AND occurred_at >= @start
          AND occurred_at < @end
        GROUP BY hour ORDER BY hour,
        ch.Named("eventType", eventType),
        ch.Named("start", start),
        ch.Named("end", end),
    )
    if err != nil {
        return nil, err
    }
    defer rows.Close()
    var stats []HourlyStats
    for rows.Next() {
        var s HourlyStats
        if err := rows.ScanStruct(&s); err != nil {
            return nil, err
        }
        stats = append(stats, s)
    }
    return stats, rows.Err()
}
Query Optimization
Use ORDER BY Prefix in Every WHERE Clause
Always filter on the leftmost columns of ORDER BY first.
If you can't, add a data skipping index.
Data Skipping Indexes
For columns NOT in ORDER BY that you filter on:
-- Add bloom filter for high-cardinality equality lookups
ALTER TABLE events ADD INDEX idx_user_id user_id TYPE bloom_filter GRANULARITY 4;
ALTER TABLE events MATERIALIZE INDEX idx_user_id;  -- Backfill
-- Index types:
-- bloom_filter: equality on high-cardinality (user IDs, session IDs)
-- set(N):       low-cardinality equality (status IN ('a','b'))  
-- minmax:       range queries (amount > 1000)
-- ngrambf_v1:  text search (LIKE '%term%')
-- tokenbf_v1:  token search (hasToken(text, 'word'))
-- Verify it's being used
EXPLAIN indexes = 1
SELECT * FROM events WHERE user_id = 12345;
-- Look for "Skip" entries in output
JOINs
ClickHouse JOINs load the right table into memory. Always put the smaller table on the right.
-- BAD: large table on right
SELECT * FROM small_table s JOIN large_table l ON l.id = s.id;
-- GOOD: small table on right
SELECT * FROM large_table l JOIN small_table s ON s.id = l.id;
Filter BEFORE joining:
-- GOOD: reduce data before the join
SELECT * FROM
    (SELECT * FROM orders WHERE status = 'completed') o
JOIN
    (SELECT * FROM customers WHERE country = 'US') c
ON c.id = o.customer_id;
Choose the right algorithm:
SET join_algorithm = 'auto';          -- Default: ClickHouse decides
SET join_algorithm = 'partial_merge'; -- Large-to-large, memory-constrained
SET join_algorithm = 'grace_hash';    -- Large datasets, can spill to disk
Use ANY JOIN when you only need one match:
SELECT o.*, c.name
FROM orders o
ANY LEFT JOIN customers c ON c.id = o.customer_id;
-- Faster and less memory when right table may have duplicates
Alternatives to JOINs (often faster):
-- Dictionary for dimension lookups
SELECT o.*, dictGet('customers_dict', 'name', o.customer_id) as name
FROM orders o;
-- IN subquery for filtering
SELECT * FROM orders
WHERE customer_id IN (SELECT id FROM customers WHERE country = 'US');
Materialized Views
Use materialized views to pre-aggregate data instead of scanning raw tables.
Incremental MV (updates in real time):
-- Destination table
CREATE TABLE events_hourly (
    hour DateTime,
    event_type LowCardinality(String),
    events AggregateFunction(count, UInt64),
    unique_users AggregateFunction(uniq, UInt64)
) ENGINE = AggregatingMergeTree()
ORDER BY (event_type, hour);
-- MV triggers on every INSERT into events
CREATE MATERIALIZED VIEW events_hourly_mv TO events_hourly AS
SELECT
    toStartOfHour(occurred_at) AS hour,
    event_type,
    countState() AS events,
    uniqState(user_id) AS unique_users
FROM events
GROUP BY hour, event_type;
-- Query (reads thousands instead of billions)
SELECT hour, event_type, countMerge(events), uniqMerge(unique_users)
FROM events_hourly
WHERE hour >= now() - INTERVAL 7 DAY
GROUP BY hour, event_type;
Refreshable MV (periodic rebuild, good for complex JOINs):
CREATE MATERIALIZED VIEW customer_summary
REFRESH EVERY 1 HOUR
ENGINE = MergeTree() ORDER BY customer_id
AS SELECT
    c.customer_id, c.name,
    count() as orders, sum(o.amount) as total_spent
FROM customers c
LEFT JOIN orders o ON o.customer_id = c.customer_id
GROUP BY c.customer_id, c.name;
-- Force refresh
SYSTEM REFRESH VIEW customer_summary;
Avoid Small/Single-Row Queries
ClickHouse is built for scanning many rows and returning aggregates.
Do not use it like a key-value store.
// BAD: Fetching one user's data from ClickHouse on every request
app.get('/user/:id/events', async (req, res) => {
  const events = await ch.query(SELECT * FROM events WHERE user_id = ${req.params.id});
  res.json(events); // This is a key-value access pattern
});
// GOOD: Aggregate query that leverages ClickHouse's strength
app.get('/analytics/summary', async (req, res) => {
  const stats = await ch.query(
    SELECT event_type, count() as n, uniq(user_id) as users
    FROM events
    WHERE occurred_at >= today() - 7
    GROUP BY event_type
  );
  res.json(stats);
});
Redis Caching Strategy
When to Use Redis in Front of ClickHouse
| Scenario | Use Redis? | Reason |
|----------|-----------|--------|
| Dashboard with same query run by many users | ✅ Yes | Prevents redundant large scans |
| Single user fetching their own recent events | ✅ Yes | ClickHouse isn't a KV store |
| Aggregation query taking > 500ms | ✅ Yes | Cache computed result |
| Real-time per-user event counts | ✅ Yes | Maintain counter in Redis, bulk-sync to CH |
| Ad-hoc analytics queries (new filters every time) | ❌ No | Cache hit rate will be low |
| Time-series queries where time range keeps moving | ❌ Careful | Invalidation is complex |
| Backfill / batch ETL pipeline | ❌ No | No user-facing latency concern |
Recommended Cache Pattern
// Cache ClickHouse aggregation results in Redis
async function getDashboardStats(tenantId, dateRange) {
  const cacheKey = stats:${tenantId}:${dateRange};
  const TTL = 300; // 5 minutes
  // 1. Try cache first
  const cached = await redis.get(cacheKey);
  if (cached) return JSON.parse(cached);
  // 2. Run the (potentially expensive) ClickHouse query
  const result = await clickhouse.query({
    query: 
      SELECT event_type, count() as n, uniq(user_id) as users
      FROM events
      WHERE tenant_id = {tenantId: UInt32}
        AND occurred_at >= {start: DateTime}
      GROUP BY event_type
    ,
    query_params: { tenantId, start: dateRange },
    format: 'JSONEachRow',
  });
  const data = await result.json();
  // 3. Cache for TTL
  await redis.setex(cacheKey, TTL, JSON.stringify(data));
  return data;
}
When to Query ClickHouse Directly (No Redis)
The query is already fast (< 100ms) due to good schema design and materialized views
The query parameters are always unique (ad-hoc analytics, no cache benefit)
You have a materialized view pre-aggregating the data — query the MV directly
It's an internal/batch process with no latency requirement
The right answer is usually: build good materialized views so the ClickHouse query is already fast enough that you don't need Redis.
Cluster Considerations
On a ClickHouse cluster, DDL and certain operations must include ON CLUSTER.
Engine Naming
| Single-node | Cluster |
|-------------|---------|
| MergeTree | ReplicatedMergeTree('/clickhouse/tables/{shard}/{database}/{table}', '{replica}') |
| ReplacingMergeTree(ver) | ReplicatedReplacingMergeTree(...) |
| All MergeTree variants | Replicated prefix |
In practice, use macros defined in config.xml:
ENGINE = ReplicatedMergeTree('/clickhouse/tables/{shard}/{database}/{table}', '{replica}')
DDL on Cluster
-- Always include ON CLUSTER for DDL on distributed setups
CREATE TABLE events ON CLUSTER '{cluster}' ( ... )
ENGINE = ReplicatedMergeTree('/clickhouse/tables/{shard}/{database}/{table}', '{replica}')
ORDER BY (...);
ALTER TABLE events ON CLUSTER '{cluster}' ADD COLUMN new_col UInt32 DEFAULT 0;
Distributed Tables
-- Create local table first (on cluster)
CREATE TABLE events_local ON CLUSTER '{cluster}' ( ... )
ENGINE = ReplicatedMergeTree(...)
ORDER BY (...);
-- Then create a Distributed table as the access layer
CREATE TABLE events ON CLUSTER '{cluster}' ( ... )
ENGINE = Distributed('{cluster}', currentDatabase(), 'events_local', rand());
Applications connect to the Distributed table; ClickHouse routes queries to shards transparently.
INSERT Routing
On a cluster, insert into the Distributed table (not the local table) unless you are doing a shard-local operation intentionally.
System Queries on Clusters
-- Check part health across all shards
SELECT hostName(), table, count() as parts
FROM clusterAllReplicas('{cluster}', system.parts)
WHERE active GROUP BY hostName(), table ORDER BY parts DESC;
-- Check replication lag
SELECT database, table, replica_name, queue_size
FROM system.replication_queue
WHERE queue_size > 0;
Rules Reference
Detailed per-rule files are in rules/ (loaded on demand):
Schema / Primary Key: rules/schema-pk-*.md
Schema / Types: rules/schema-types-*.md
Schema / Partitioning: rules/schema-partition-*.md
Schema / JSON: rules/schema-json-when-to-use.md
Query / JOINs: rules/query-join-*.md
Query / Indexes: rules/query-index-skipping-indices.md
Query / Materialized Views: rules/query-mv-*.md
Insert / Batching: rules/insert-batch-size.md
Insert / Async: rules/insert-async-small-batches.md
Insert / Format: rules/insert-format-native.md
Insert / Mutations: rules/insert-mutation-*.md
Insert / Optimize: rules/insert-optimize-avoid-final.md
💬