GRAPHQL Schema Analyzer

GraphQL Schema Analyzer

Analyze GraphQL schemas for performance bottlenecks, security vulnerabilities, query complexity issues, and best practices. Detects N+1 resolver patterns, circular references, excessive query depth, missing authorization, and schema design anti-patterns.

Usage

"Analyze my GraphQL schema for issues"
"Check for N+1 queries in my resolvers"
"Audit GraphQL authorization and security"
"Optimize my GraphQL schema performance"

How It Works

1. Schema Discovery

# Find schema files
find . -name "*.graphql" -o -name "*.gql" -o -name "schema.ts" -o -name "typeDefs*" | head -20
# Find resolver files
find . -name "*resolver*" -o -name "*resolvers*" | head -20
# Check for schema-first vs code-first
grep -rn "buildSchema\|makeExecutableSchema\|@ObjectType\|@Field" src/ 2>/dev/null | head -10

2. Schema Analysis

Type design:

• Nullable vs non-nullable fields (default to non-nullable, opt into nullable)
• Proper use of interfaces and unions
• Enum types vs string fields
• Input types vs reusing output types
• Relay-style pagination (connections) vs offset pagination
• Proper ID types

Naming conventions:

• PascalCase for types, camelCase for fields
• Consistent verb prefixes for mutations (create/update/delete)
• Query names reflect what they return
• Input type naming (CreateUserInput, not UserInput)

3. Performance Analysis

N+1 query detection:

• List resolvers that fetch related data individually
• Missing DataLoader usage for batched lookups
• Nested resolvers triggering individual database queries

Query complexity:

• Fields that return large lists without pagination
• Deeply nested types (>4 levels)
• Expensive computed fields without caching
• Missing query depth/complexity limits

Resolver efficiency:

• Overfetching from database (SELECT * when only 2 fields needed)
• Missing field-level resolvers (resolving entire object when partial suffices)
• Redundant resolver calls for the same data

4. Security Audit

Authorization:

• Fields/mutations without auth checks
• Authorization at resolver level vs directive level
• Introspection enabled in production
• Sensitive fields exposed (email, phone, internal IDs)

Denial of service:

• No query depth limit
• No query complexity limit
• No rate limiting
• Circular references enabling infinite depth queries
• Aliases allowing query multiplication
• Batch queries without limits

Data leakage:

• Error messages exposing internal details
• Stack traces in production responses
• Field suggestions in production (did you mean X?)

5. Best Practices

• Deprecation annotations on legacy fields
• Description/documentation on types and fields
• Proper error handling (user errors vs system errors)
• Subscription efficiency (pub/sub vs polling)
• Schema stitching/federation best practices
• Versioning strategy (no versioning in GraphQL — evolve the schema)

Output

## GraphQL Schema Analysis

**Types:** 34 | **Queries:** 12 | **Mutations:** 18 | **Subscriptions:** 3

### 🔴 Critical (3)
1. **N+1 on User.posts resolver** — resolvers/user.ts:23
   Each user in a list query triggers individual posts query
   → Implement DataLoader: batch user IDs → single query

2. **No query depth limit** — server.ts
   Allows queries nested to unlimited depth
   → Add depth limiting plugin (max 8-10 levels)

3. **Introspection enabled in production** — server.ts:15
   Full schema discoverable by anyone
   → Disable in production: `introspection: process.env.NODE_ENV !== 'production'`

### 🟡 Warnings (5)
4. 3 mutations without authorization middleware
5. User.email exposed without field-level auth
6. `posts` query returns unbounded list (no pagination)
7. Circular reference: User → Posts → Author → Posts
8. 5 deprecated fields without migration timeline

### 📊 Schema Metrics
- Average type size: 6.2 fields
- Deepest nesting: 7 levels (User → Posts → Comments → Author → ...)
- Largest type: Order (18 fields — consider splitting)
- Undocumented types: 12/34 (35%)

### ✅ Good Practices
- Relay-style pagination on main collections
- Input validation via custom scalars (Email, DateTime)
- Proper union types for polymorphic returns
- Error union pattern for typed error handling