ClickHouse Architect

<!-- ADR: 2025-12-09-clickhouse-architect-skill -->

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

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 (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

Column Type	Default Codec	Read-Heavy Alternative	Example
DateTime/DateTime64	CODEC(DoubleDelta, ZSTD)	CODEC(DoubleDelta, LZ4)	timestamp DateTime64(3) CODEC(DoubleDelta, ZSTD)
Float prices/gauges	CODEC(Gorilla, ZSTD)	CODEC(Gorilla, LZ4)	price Float64 CODEC(Gorilla, ZSTD)
Integer counters	CODEC(T64, ZSTD)	—	count UInt64 CODEC(T64, ZSTD)
Slowly changing integers	CODEC(Delta, ZSTD)	CODEC(Delta, LZ4)	version UInt32 CODEC(Delta, ZSTD)
String (low cardinality)	LowCardinality(String)	—	status LowCardinality(String)
General data	CODEC(ZSTD(3))	CODEC(LZ4)	Default compression level 3

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+)

Pattern	Severity	Modern Status	Fix
Too many parts (>300/partition)	Critical	Still critical	Reduce partition granularity
Small batch inserts (<1000)	Critical	Still critical	Batch to 10k-100k rows
High-cardinality first ORDER BY	Critical	Still critical	Reorder: lowest cardinality first
No memory limits	High	Still critical	Set max_memory_usage
Denormalization overuse	High	Still critical	Use dictionaries + materialized views
Large JOINs	Medium	180x improved	Still avoid for ultra-low-latency
Mutations (UPDATE/DELETE)	Medium	1700x improved	Use lightweight updates (v24.4+)

Table Engine Selection

Deployment	Engine	Use Case
ClickHouse Cloud	SharedMergeTree	Default for cloud deployments
Self-hosted cluster	ReplicatedMergeTree	Multi-node with replication
Self-hosted single	MergeTree	Single-node development/testing

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

<!-- ADR: 2025-12-10-clickhouse-skill-delegation -->

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

User Need	Invoke Skill	Trigger Phrases
Create database users, manage permissions	devops-tools:clickhouse-cloud-management	"create user", "GRANT", "permissions", "credentials"
Configure DBeaver, generate connection JSON	devops-tools:clickhouse-pydantic-config	"DBeaver", "client config", "connection setup"
Validate schema contracts against live database	quality-tools:schema-e2e-validation	"validate schema", "Earthly E2E", "schema contract"

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

Reference	Content
references/schema-design-workflow.md	Complete workflow with examples
references/compression-codec-selection.md	Decision tree + benchmarks
references/anti-patterns-and-fixes.md	13 deadly sins + v24.4+ status
references/audit-and-diagnostics.md	Query interpretation guide
references/idiomatic-architecture.md	Parameterized views, dictionaries, dedup
references/schema-documentation.md	COMMENT patterns + naming for AI understanding

External Documentation

• ClickHouse Best Practices
• Altinity Knowledge Base
• ClickHouse Blog

Python Driver Policy

<!-- ADR: 2025-12-10-clickhouse-python-driver-policy -->

Use clickhouse-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

Factor	clickhouse-connect	clickhouse-driver
Maintainer	ClickHouse Inc.	Solo developer
Weekly commits	Yes (active)	Sparse (months)
Open issues	41 (addressed)	76 (accumulating)
Downloads/week	2.7M	1.5M
Bus factor risk	Low (company)	High (1 person)

Do NOT use clickhouse-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.

Related Skills

Skill	Purpose
devops-tools:clickhouse-cloud-management	User/permission management
devops-tools:clickhouse-pydantic-config	DBeaver connection generation
quality-tools:schema-e2e-validation	YAML schema contracts
quality-tools:multi-agent-e2e-validation	Database migration validation