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ClickHouse Architect

Prescriptive schema design, compression selection, and performance optimization for ClickHouse (v24.4+). Covers both ClickHouse Cloud (SharedMergeTree) and self-hosted (ReplicatedMergeTree) deployments.

When to Use This Skill

Use this skill when:

• Designing ClickHouse table schemas with ORDER BY key selection
• Selecting compression codecs for column types
• Configuring partition keys for data lifecycle management
• Adding performance accelerators (projections, indexes, dictionaries)
• Auditing and optimizing existing ClickHouse schemas

Core Methodology

Schema Design Workflow

Follow this sequence when designing or reviewing ClickHouse schemas:

1. Define ORDER BY key (3-5 columns, lowest cardinality first)
2. Select compression codecs per column type
3. Configure PARTITION BY for data lifecycle management
4. Add performance accelerators (projections, indexes)
5. Validate with audit queries (see scripts/)
6. Document with COMMENT statements — ClickHouse table and column COMMENTs are the single source of truth (SSoT) for what each column means, how it was computed, and what constraints apply. No external doc, skill, or wiki supersedes the COMMENT. See references/schema-documentation.md

ORDER BY Key Selection

The ORDER BY clause is the most critical decision in ClickHouse schema design.

Rules :

• Limit to 3-5 columns maximum (each additional column has diminishing returns)
• Place lowest cardinality columns first (e.g., tenant_id before timestamp)
• Include all columns used in WHERE clauses for range queries
• PRIMARY KEY must be a prefix of ORDER BY (or omit to use full ORDER BY)

Example :

-- Correct: Low cardinality first, 4 columns
CREATE TABLE trades (
    exchange LowCardinality(String),
    symbol LowCardinality(String),
    timestamp DateTime64(3),
    trade_id UInt64,
    price Float64,
    quantity Float64
) ENGINE = MergeTree()
ORDER BY (exchange, symbol, timestamp, trade_id);

-- Wrong: High cardinality first (10x slower queries)
ORDER BY (trade_id, timestamp, symbol, exchange);

Compression Codec Quick Reference

When to use LZ4 over ZSTD : LZ4 provides 1.76x faster decompression. Use LZ4 for read-heavy workloads with monotonic sequences (timestamps, counters). Use ZSTD (default) when compression ratio matters or data patterns are unknown.

Note on codec combinations :

Delta/DoubleDelta + Gorilla combinations are blocked by default (allow_suspicious_codecs) because Gorilla already performs implicit delta compression internally—combining them is redundant , not dangerous. A historical corruption bug (PR #45615, Jan 2023) was fixed, but the blocking remains as a best practice guardrail.

Use each codec family independently for its intended data type:

-- Correct usage
price Float64 CODEC(Gorilla, ZSTD)              -- Floats: use Gorilla
timestamp DateTime64 CODEC(DoubleDelta, ZSTD)   -- Timestamps: use DoubleDelta
timestamp DateTime64 CODEC(DoubleDelta, LZ4)    -- Read-heavy: use LZ4

PARTITION BY Guidelines

PARTITION BY is for data lifecycle management , NOT query optimization.

Rules :

• Partition by time units (month, week) for TTL and data management
• Keep partition count under 1000 total across all tables
• Each partition should contain 1-300 parts maximum
• Never partition by high-cardinality columns

Example :

-- Correct: Monthly partitions for TTL management
PARTITION BY toYYYYMM(timestamp)

-- Wrong: Daily partitions (too many parts)
PARTITION BY toYYYYMMDD(timestamp)

-- Wrong: High-cardinality partition key
PARTITION BY user_id

Anti-Patterns Checklist (v24.4+)

DELETE Strategy Guide (v13.49.0+ Best Practices)

Choose the right DELETE strategy based on scope. Ranked fastest to slowest:

Anti-pattern: LightweightDELETE FROM before INSERT

DELETE FROM sets _row_exists=0 masks instead of physically removing rows. These ghost rows:

• Persist until ClickHouse background merge (unpredictable timing)
• Show up in queries without FINAL as phantom data
• Cause false anomalies in monitoring/integrity checks
• Were the root cause of months of phantom Stathera gaps in production (opendeviationbar #269)

UseDELETE FROM only for: ad-hoc data correction where ghost rows don't matter (analytics cleanup, dev/test). Never use in write pipelines where INSERT follows DELETE.

All DELETE mutations should use : SETTINGS mutations_sync = 1 to block until completion (prevents INSERT-DELETE race conditions).

Partition-aware DELETE tip : If your partition key includes the columns you're filtering on (e.g., PARTITION BY (symbol, threshold, toYYYYMM(timestamp))), use DELETE IN PARTITION to scope the scan to a single partition instead of scanning all parts.

Table Engine Selection

Cloud (SharedMergeTree) :

CREATE TABLE trades (...)
ENGINE = SharedMergeTree('/clickhouse/tables/{shard}/trades', '{replica}')
ORDER BY (exchange, symbol, timestamp);

Self-hosted (ReplicatedMergeTree) :

CREATE TABLE trades (...)
ENGINE = ReplicatedMergeTree('/clickhouse/tables/{shard}/trades', '{replica}')
ORDER BY (exchange, symbol, timestamp);

Skill Delegation Guide

This skill is the hub for ClickHouse-related tasks. When the user's needs extend beyond schema design, invoke the related skills below.

Delegation Decision Matrix

Typical Workflow Sequence

1. Schema Design (THIS SKILL) → Design ORDER BY, compression, partitioning
2. User Setup → clickhouse-cloud-management (if cloud credentials needed)
3. Client Config → clickhouse-pydantic-config (generate DBeaver JSON)
4. Validation → schema-e2e-validation (CI/CD schema contracts)

Example: Full Stack Request

User : "I need to design a trades table for ClickHouse Cloud and set up DBeaver to query it."

Expected behavior :

1. Use THIS skill for schema design
2. Invoke clickhouse-cloud-management for creating database user
3. Invoke clickhouse-pydantic-config for DBeaver configuration

Performance Accelerators

Projections

Create alternative sort orders that ClickHouse automatically selects:

ALTER TABLE trades ADD PROJECTION trades_by_symbol (
    SELECT * ORDER BY symbol, timestamp
);
ALTER TABLE trades MATERIALIZE PROJECTION trades_by_symbol;

Materialized Views

Pre-compute aggregations for dashboard queries:

CREATE MATERIALIZED VIEW trades_hourly_mv
ENGINE = SummingMergeTree()
ORDER BY (exchange, symbol, hour)
AS SELECT
    exchange,
    symbol,
    toStartOfHour(timestamp) AS hour,
    sum(quantity) AS total_volume,
    count() AS trade_count
FROM trades
GROUP BY exchange, symbol, hour;

Dictionaries

Replace JOINs with O(1) dictionary lookups for large-scale star schemas :

When to use dictionaries (v24.4+) :

• Fact tables with 100M+ rows joining dimension tables
• Dimension tables 1k-500k rows with monotonic keys
• LEFT ANY JOIN semantics required

When JOINs are sufficient (v24.4+) :

• Dimension tables <500 rows (JOIN overhead negligible)
• v24.4+ predicate pushdown provides 8-180x improvements
• Complex JOIN types (FULL, RIGHT, multi-condition)

Benchmark context : 6.6x speedup measured on Star Schema Benchmark (1.4B rows).

CREATE DICTIONARY symbol_info (
    symbol String,
    name String,
    sector String
)
PRIMARY KEY symbol
SOURCE(CLICKHOUSE(TABLE 'symbols'))
LAYOUT(FLAT())  -- Best for <500k entries with monotonic keys
LIFETIME(3600);

-- Use in queries (O(1) lookup)
SELECT
    symbol,
    dictGet('symbol_info', 'name', symbol) AS symbol_name
FROM trades;

Scripts

Execute comprehensive schema audit:

clickhouse-client --multiquery < scripts/schema-audit.sql

The audit script checks:

• Part count per partition (threshold: 300)
• Compression ratios by column
• Query performance patterns
• Replication lag (if applicable)
• Memory usage patterns

Additional Resources

Reference Files

External Documentation

• ClickHouse Best Practices
• Altinity Knowledge Base
• ClickHouse Blog

Python Driver Policy

Useclickhouse-connect (official) for all Python integrations.

# ✅ RECOMMENDED: clickhouse-connect (official, HTTP)
import clickhouse_connect

client = clickhouse_connect.get_client(
    host='localhost',
    port=8123,  # HTTP port
    username='default',
    password=''
)
result = client.query("SELECT * FROM trades LIMIT 1000")
df = client.query_df("SELECT * FROM trades")  # Pandas integration

Why NOT clickhouse-driver

Do NOT useclickhouse-driver despite its ~26% speed advantage for large exports. The maintenance risk outweighs performance gains:

• Single maintainer (mymarilyn) with no succession plan
• Issues accumulating without response
• Risk of abandonment breaks production code

Exception : Only consider clickhouse-driver if you have extreme performance requirements (exporting millions of rows) AND accept the maintenance risk.

ClickHouse COMMENT = Single Source of Truth

Every ClickHouse table and column MUST have a COMMENT that fully documents its meaning, computation method, and constraints. The COMMENT is the SSoT — no external document, skill, or wiki supersedes it.

Why

• ClickHouse COMMENTs have no length limit , support newlines, URLs, and unicode
• Zero performance impact on queries (pure metadata, never in data path)
• Visible via DESCRIBE table, SHOW CREATE TABLE, system.columns
• Survives schema migrations (preserved through ALTER operations)

What to Include in COMMENTs

• Column purpose in plain English
• Computation formula (if derived/computed)
• Unit (seconds, milliseconds, bps, ratio)
• Valid range or enum values
• Anti-patterns (what NOT to do with this column)
• GitHub issue link for provenance
• Source script that populates the column

Example

ALTER TABLE t COMMENT COLUMN session_label
'STRICT session label. 8 values: sydney_only, tokyo_only, ...
Only set when ENTIRE bar (open→close) falls within one session.
cross_session = bar spans boundary. Use WHERE is_pure_session=1.
GitHub: https://github.com/org/repo/issues/54
Source: scripts/populate-sessions/populate_v3.py';

Anti-Pattern

NEVER create a ClickHouse column without a COMMENT. A column without documentation is a column that will be misused.

Related Skills

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Weekly Installs

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Repository

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GitHub Stars

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First Seen

Jan 24, 2026

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