supabase-performance-tuning

# Supabase Performance Tuning

## Prerequisites
- Supabase SDK installed
- Understanding of async patterns
- Redis or in-memory cache available (optional)
- Performance monitoring in place

## Instructions

### Step 1: Establish Baseline
Measure current latency for critical Supabase operations.

### Step 2: Implement Caching
Add response caching for frequently accessed data.

### Step 3: Enable Batching
Use DataLoader or similar for automatic request batching.

### Step 4: Optimize Connections
Configure connection pooling with keep-alive.

## Output
- Reduced API latency
- Caching layer implemented
- Request batching enabled
- Connection pooling configured

## Error Handling

See `{baseDir}/references/errors.md` for comprehensive error handling.

## Examples

See `{baseDir}/references/examples.md` for detailed examples.

## Resources
- [Supabase Performance Guide](https://supabase.com/docs/performance)
- [DataLoader Documentation](https://github.com/graphql/dataloader)
- [LRU Cache Documentation](https://github.com/isaacs/node-lru-cache)