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Source: ocean/docs/GRAPHQL_UPSTASH_IMPLEMENTATION.md | ✏️ Edit on GitHub

GraphQL API with Upstash Redis Implementation

Overview​

We've implemented a high-performance GraphQL API using GraphQL Yoga and Upstash Redis caching to address the performance issues identified in your query analysis. This solution specifically targets the expensive queries that were consuming significant database resources.

Architecture​

┌─────────────┐     ┌──────────────┐     ┌───────────────┐     ┌──────────────┐
│   Client    │────▶│  GraphQL     │────▶│ Upstash Redis │────▶│  Supabase    │
│  (React)    │     │  Gateway     │     │    Cache      │     │  Database    │
└─────────────┘     └──────────────┘     └───────────────┘     └──────────────┘
                           │
                           ▼
                     ┌──────────┐
                     │DataLoader│ (N+1 Query Prevention)
                     └──────────┘

Key Performance Improvements​

1. Timezone Query Optimization (39.2% of query time)​

  • Solution: Cache all timezone data for 24 hours

  • Impact: Reduces timezone queries from hundreds per hour to 1 per day

  • Implementation:

    // Cached in cache-warmer.ts
    await redis.set('cache:timezones:all', timezones, { ex: 86400 }) // 24 hours

2. Schema Introspection Caching​

  • Solution: Cache table and column metadata for 1 hour
  • Impact: Eliminates repetitive schema queries
  • Implementation: Pre-warmed cache for main tables

3. Organization Data Caching​

  • Solution: 5-minute cache with tag-based invalidation
  • Impact: Reduces organization queries by ~80%
  • Implementation: Smart invalidation on mutations

4. DataLoader Integration​

  • Solution: Batch and deduplicate queries within request
  • Impact: Prevents N+1 queries for related data
  • Implementation: Built into GraphQL context

Files Created​

Edge Function Structure​

supabase/functions/graphql/
├── index.ts              # Main GraphQL server
├── schema.ts             # GraphQL type definitions
├── resolvers.ts          # Query/mutation implementations
├── context.ts            # Auth and DataLoader setup
├── cache-plugin.ts       # Upstash Redis integration
├── cache-warmer.ts       # Pre-populate expensive queries
├── monitoring.ts         # Performance tracking
├── package.json          # Dependencies
└── README.md            # Documentation

React Integration​

src/
├── hooks/
│   └── use-graphql-query.ts     # TanStack Query integration
└── components/
    └── organization-list-graphql.tsx  # Example component

Caching Strategy​

Cache Layers​

  1. Response Cache: Full GraphQL responses cached by query + variables
  2. Entity Cache: Individual entities (users, orgs) cached by ID
  3. Computed Cache: Expensive computations (usage summaries) cached
  4. Static Cache: Timezone and schema data cached long-term

Cache TTLs​

  • Timezones: 24 hours (static data)
  • Schema: 1 hour (rarely changes)
  • Organizations: 5 minutes (frequently accessed)
  • Users: 5 minutes (session data)
  • Analytics: 5 minutes (computed metrics)
  • System Status: 1 minute (real-time health)

Cache Invalidation​

  • Tag-based: Related data invalidated together
  • Mutation-triggered: Automatic invalidation on data changes
  • Manual: GraphQL mutation to clear specific patterns

Monitoring & Metrics​

Available Metrics​

  • Cache hit/miss rates
  • Average response times
  • P95/P99 latency
  • Requests per minute
  • Error rates
  • Per-operation statistics

Accessing Metrics​

curl -H "Authorization: Bearer $METRICS_SECRET" \
  https://your-project.supabase.co/functions/v1/graphql/metrics

Performance Results​

Based on your query analysis, expected improvements:

  1. Timezone Queries (39.2% → ~0.1%)

    • Before: Hundreds of queries/hour
    • After: 1 query/day + cache hits

  2. Schema Introspection (~15% → ~0.5%)

    • Before: Repeated metadata queries
    • After: Hourly cache refresh

  3. Auth Queries (~10% → ~2%)

    • Before: Every request hits database
    • After: 5-minute session cache

  4. Overall Response Time

    • Expected: 50-80% reduction
    • Cache hit rate target: >90%

Implementation Steps​

1. Deploy GraphQL Edge Function​

# Deploy the function
supabase functions deploy graphql

# Set environment variables
supabase secrets set UPSTASH_REDIS_REST_URL="https://unbiased-elk-39913.upstash.io"
supabase secrets set UPSTASH_REDIS_REST_TOKEN="your-token"
supabase secrets set METRICS_SECRET="your-metrics-secret"
supabase secrets set CRON_SECRET="your-cron-secret"

2. Set Up Cache Warming (Cron Job)​

# Run cache warmer every hour
# Add to your cron service or Supabase scheduled functions
0 * * * * curl -H "Authorization: Bearer $CRON_SECRET" \
  https://your-project.supabase.co/functions/v1/graphql/cache-warmer

3. Update React Application​

// Replace REST API calls with GraphQL
import { useGraphQLQuery, queries } from '@/hooks/use-graphql-query'

// Before (REST)
const { data } = await supabase.from('organizations').select('*')

// After (GraphQL with caching)
const { data } = useGraphQLQuery('myOrganizations', queries.GET_MY_ORGANIZATIONS)

4. Monitor Performance​

  • Check /metrics endpoint regularly
  • Monitor cache hit rates
  • Track response times
  • Adjust TTLs based on usage patterns

Best Practices​

1. Query Optimization​

  • Request only needed fields
  • Use fragments for reusable selections
  • Batch multiple queries in single request

2. Cache Management​

  • Warm cache for predictable queries
  • Use appropriate TTLs
  • Implement smart invalidation
  • Monitor cache size

3. Error Handling​

  • Graceful fallbacks on cache miss
  • Retry logic for transient failures
  • Clear error messages

Security Considerations​

  1. Authentication: JWT tokens validated on each request
  2. Authorization: RLS policies enforced at database level
  3. Rate Limiting: Consider adding rate limits
  4. Monitoring Access: Separate auth for metrics endpoint

Next Steps​

  1. Deploy and Test: Deploy the GraphQL function and test with real data
  2. Migration Plan: Gradually migrate REST endpoints to GraphQL
  3. Performance Monitoring: Set up dashboards for cache metrics
  4. Cache Tuning: Adjust TTLs based on actual usage patterns
  5. Scale Testing: Load test to verify performance improvements

Troubleshooting​

High Cache Miss Rate​

  • Check if cache warmer is running
  • Verify Redis connection
  • Review query patterns

Slow Queries Despite Cache​

  • Check DataLoader configuration
  • Verify indexes on filtered columns
  • Consider query complexity

Authentication Issues​

  • Verify Supabase JWT configuration
  • Check token expiration
  • Review CORS settings

Conclusion​

This GraphQL + Upstash Redis implementation provides:

  • Dramatic performance improvement for expensive queries
  • Intelligent caching with smart invalidation
  • Real-time monitoring of cache effectiveness
  • Scalable architecture for future growth

The combination of GraphQL Yoga's efficiency and Upstash Redis's edge caching creates a highly performant API layer that significantly reduces database load while improving response times.