Performance Patterns

Performance Anti-Patterns Reference

N+1 Query Problem

The N+1 problem occurs when code executes N additional queries to fetch related data for N items from an initial query.

Identification:

• Queries inside loops
• Lazy loading of associations during iteration
• GraphQL resolvers fetching per-item

Fix Strategies:

1. Eager Loading: Load related data in initial query
2. Batching: Collect IDs, fetch all at once
3. DataLoader: For GraphQL, batch and cache per-request
4. Denormalization: Store computed/related data together

Severity: HIGH - Scales linearly with data size, causes exponential slowdown

Over-Fetching

Retrieving more data than needed from API or database.

Identification:

• SELECT * queries
• API endpoints returning full objects
• No field selection support
• Loading nested relations by default

Fix Strategies:

1. Field Selection: Only query needed columns
2. Sparse Fieldsets: Support ?fields=id,name parameter
3. GraphQL: Let clients specify exact fields
4. DTOs: Map to response-specific objects

Severity: MEDIUM - Increases bandwidth, memory, serialization time

Under-Fetching

Requiring multiple requests to get needed data.

Identification:

• Waterfall requests (request depends on previous)
• Multiple endpoints for related data
• No include/expand support

Fix Strategies:

1. Compound Endpoints: /users?include=orders
2. GraphQL: Single query for nested data
3. BFF Pattern: Backend aggregates for frontend
4. Parallel Requests: When dependencies allow

Severity: MEDIUM - Increases latency, connection overhead

Missing Pagination

Returning unbounded result sets.

Identification:

• List endpoints without limit
• findAll() without pagination
• No cursor for large datasets

Fix Strategies:

1. Offset Pagination: ?page=1&limit=20
2. Cursor Pagination: ?cursor=abc&limit=20 (better for large sets)
3. Default Limits: Always apply max limit server-side
4. Streaming: For very large exports

Severity: HIGH - Can crash server/client with large data

Inefficient Algorithms

O(n²) or worse complexity where better solutions exist.

Identification:

• Nested loops on collections
• Repeated array.find/includes in loops
• String concatenation in loops
• Sort inside loops

Fix Strategies:

1. Use Maps/Sets: O(1) lookup instead of O(n)
2. Single Pass: Combine operations
3. Pre-compute: Calculate once, reuse
4. Better Algorithms: Binary search for sorted data

Severity: HIGH - Becomes unusable with large data

Unnecessary Re-renders (Frontend)

Components re-rendering when their output hasn't changed.

Identification:

• Inline objects/arrays in JSX
• Inline function handlers
• Missing React.memo/useMemo/useCallback
• Context changes affecting all consumers

Fix Strategies:

1. Memoization: React.memo for components
2. Stable References: useMemo for objects, useCallback for functions
3. Context Splitting: Separate frequently-changing state
4. Selectors: Only subscribe to needed state slices

Severity: MEDIUM-HIGH - Causes janky UI, especially on lists

Sequential Async Operations

Running async operations one-by-one when parallel is possible.

Identification:

• Sequential await statements
• Waterfall promises
• Loop with await inside

Fix Strategies:

1. Promise.all: Run independent operations in parallel
2. Promise.allSettled: When some can fail
3. Batching: Group operations efficiently
4. Pipelining: Stream processing

Severity: MEDIUM - Multiplies latency

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