database-design（数据库设计）

Database Design

Good database design determines the long-term maintainability, performance, and correctness of any data-driven application. Schema decisions made early are expensive to reverse later. Every table, column, index, and constraint should exist for a reason backed by access patterns and business rules.

Data Modeling Principles

Start from Access Patterns

Design tables around how the application reads and writes data, not around how entities look in a domain model. Two questions drive every schema decision:

1. What queries will run most frequently? -- these determine table structure, indexes, and denormalization choices
2. What consistency guarantees does the data need? -- these determine normalization level, constraints, and transaction boundaries

Entity Relationships

Naming Conventions

Consistent naming prevents confusion across teams and tools:

• Tables: plural nouns in snake_case (order_items, user_addresses)
• Columns: singular snake_case describing the value (created_at, total_amount)
• Foreign keys: <referenced_table_singular>_id (user_id, order_id)
• Indexes: idx_<table>_<columns> (idx_orders_user_id_created_at)
• Constraints: chk_<table>_<rule>, uq_<table>_<columns>, fk_<table>_<referenced>

Normalization vs Denormalization

Normal Forms

When to Denormalize

Normalization prevents anomalies but adds JOINs. Denormalize selectively when:

• Read-heavy workloads dominate and JOIN cost is measurable in profiling
• Reporting tables need pre-aggregated data that would otherwise require expensive queries
• Caching a computed value avoids recalculating on every read (e.g., order_total stored on the order row)
• Document-oriented access retrieves an entire aggregate in one read

Rules for safe denormalization:

1. Always keep the normalized source of truth -- denormalized data is a derived cache
2. Define how and when the denormalized copy is updated (trigger, application event, batch job)
3. Monitor for drift between the source and the copy
4. Document why the denormalization exists and what access pattern it serves

Choosing a Database Type

Polyglot persistence -- using different databases for different parts of the same system -- is valid when access patterns genuinely differ. It is not valid as a way to avoid learning one database well.

Indexing Fundamentals

Indexes accelerate reads at the cost of slower writes and additional storage. Every index must justify its existence through query patterns.

Index Types

Composite Index Design

The order of columns in a composite index matters. The leftmost prefix rule means a composite index on (a, b, c) supports queries filtering on (a), (a, b), or (a, b, c), but not (b, c) alone.

Column ordering guidelines:

1. Equality conditions first -- columns compared with =
2. Range conditions last -- columns compared with >, <, BETWEEN
3. Most selective column first among equals

Covering and Partial Indexes

• Covering index: includes all columns the query needs, so the database reads only the index. Use INCLUDE (PostgreSQL) or just add columns to the index key.
• Partial index: indexes only rows matching a condition, reducing size and write overhead. Ideal for querying a small subset of a large table (e.g., WHERE status = 'pending').

See Indexing Strategies Reference for detailed index types, EXPLAIN analysis, and anti-patterns.

Schema Evolution

Schema changes are inevitable. The question is whether they break running applications.

Backward-Compatible Changes (Safe)

• Adding a new nullable column
• Adding a new table
• Adding a new index (may lock briefly on some engines)
• Widening a column type (e.g., VARCHAR(50) to VARCHAR(100))

Breaking Changes (Require Migration Strategy)

• Renaming or removing a column
• Changing a column type in incompatible ways
• Adding a NOT NULL constraint to an existing column with null data
• Splitting or merging tables

The Expand-Contract Pattern

For breaking changes in production with zero downtime:

1. Expand -- add the new structure alongside the old one
2. Migrate -- backfill data from old to new, dual-write during transition
3. Switch -- update application code to use the new structure
4. Contract -- remove the old structure once nothing references it

See Migration Patterns Reference for zero-downtime strategies, rollback techniques, and multi-tool examples.

Partitioning and Sharding

Table Partitioning (Single Database)

Sharding (Multiple Databases)

Sharding distributes data across separate database instances. Use it only after single-instance optimizations (indexing, caching, read replicas) are exhausted.

Shard key selection criteria:

• High cardinality -- many distinct values to distribute evenly
• Present in most queries -- avoids scatter-gather across all shards
• Stable -- values that do not change after creation
• Avoid hotspots -- do not shard by a value that concentrates writes (e.g., current date)

Quick Reference: Common Design Mistakes

Reference Files

Integration with Other Skills