clickhouse-architect

# 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`](./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**:

```sql
-- 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