Edmonds Commerce - Laravel Performance Optimisation
Overview
Query tuning, caching strategies, queue management, and Redis optimisation. Optimise your Laravel applications to handle scale and deliver sub-100ms response times.
What We Do
Performance optimisation for Laravel applications. We identify bottlenecks, implement caching strategies, optimise database queries, and tune infrastructure to deliver fast, responsive applications that scale.
Performance Optimisation Areas
Database Query Optimisation
Identify and fix slow queries that bottleneck application performance.
Optimisation Techniques:
- N+1 query prevention through eager loading
- Query analysis and execution plan review
- Database indexing strategies
- Denormalisation for read performance
- Partitioning for large tables
- Query result caching
Eloquent Optimisation
Tune Laravel's ORM for maximum performance.
Eloquent Strategies:
- Relationship eager loading (with, load)
- Select specific columns instead of all
- Chunking large queries
- Custom query building
- Lazy eager loading
- Caching relationships
Caching Strategies
Implement caching at multiple levels to reduce database load.
Cache Levels:
- Redis for query result caching
- View fragment caching
- HTTP caching headers
- Browser caching
- CDN caching for static assets
- Cache invalidation strategies
Redis Optimisation
Leverage Redis for caching, sessions, and queue management.
Redis Use Cases:
- Session storage
- Cache backend
- Rate limiting
- Real-time features
- Pub/sub messaging
- Job queuing
Queue Optimisation
Async processing for heavy operations prevents timeout failures.
Queue Features:
- Background job processing
- Batch job processing
- Failed job handling
- Queue monitoring
- Job prioritisation
- Failed job retries
Performance Metrics
Response Time Targets:
- API endpoints: sub-100ms target
- Web pages: sub-200ms target
- Database queries: sub-10ms target
Key Metrics:
- Time to First Byte (TTFB)
- First Contentful Paint (FCP)
- Largest Contentful Paint (LCP)
- Cumulative Layout Shift (CLS)
- Database query time
- Memory usage
Our Performance Optimisation Process
1. Baseline Measurement
Profile the application to establish baseline performance metrics. Identify slow endpoints and queries.
2. Bottleneck Identification
Use Laravel Debugbar, Telescope, and database query analysers to locate performance problems.
Tools:
- Laravel Debugbar for development profiling
- Laravel Telescope for production monitoring
- New Relic or DataDog for APM
- MySQL EXPLAIN plans
- Blackfire for profiling
3. Targeted Optimisation
Implement specific optimisations:
- Fix N+1 queries through eager loading
- Add database indexes for frequently queried columns
- Implement Redis caching for expensive operations
- Move heavy operations to background queues
- Optimise assets (minification, compression)
4. Testing & Validation
Verify optimisations deliver measurable improvements with production-representative load.
5. Monitoring & Maintenance
Continuous monitoring ensures optimisations remain effective as data and usage patterns change.
Common Performance Issues
N+1 Query Problem: Loading related models one-at-a-time instead of eager loading. Solved through relationship eager loading.
Missing Indexes: Database queries scanning full tables instead of using indexes. Solved through strategic indexing.
Unoptimised Caching: Expensive operations recalculated repeatedly instead of cached. Solved through caching strategies.
Synchronous Heavy Operations: Operations that should be asynchronous timeout or slow down responses. Solved through queue processing.
Large Result Sets: Loading thousands of records into memory. Solved through chunking or pagination.
Technology Stack
- Laravel with performance optimisations
- Redis for caching and sessions
- PostgreSQL or MySQL with indexes
- Laravel Horizon for queue monitoring
- Blackfire for profiling
- New Relic for APM
- DataDog for monitoring
Performance Best Practices
Query Optimization:
- Use eager loading for relationships
- Select specific columns instead of *
- Add database indexes for frequent queries
- Use query chunking for large datasets
- Cache expensive queries
Caching:
- Cache database queries
- Cache expensive computations
- Use view fragment caching
- Implement cache busting strategies
- Monitor cache hit rates
Async Processing:
- Move heavy operations to queues
- Process reports and exports asynchronously
- Send emails through queue jobs
- Import large datasets asynchronously
- Generate PDFs in background
Infrastructure:
- Use Redis for sessions and cache
- Configure PHP opcache
- Implement CDN for static assets
- Monitor server resources
- Load test before production release
Scaling Strategies
Vertical Scaling: More powerful hardware (CPU, RAM, storage).
Horizontal Scaling: More servers behind load balancers.
Database Scaling: Read replicas for read-heavy workloads. Write sharding for write-heavy systems.
Caching Layer: Redis or Memcached to reduce database load.
Async Processing: Queues to prevent timeout failures under load.
Target Audiences
High-Traffic Applications: Sites and APIs serving thousands of requests per second.
E-Commerce Platforms: Optimise checkout flows and product searches for conversions.
Data-Heavy Applications: Analytics platforms processing large datasets.
Real-Time Systems: Optimise for low-latency updates and notifications.
Related Services
Infrastructure & DevOps: Kubernetes for scaling, Docker for containerisation, CI/CD pipelines.
API Development: Performance-optimised REST and GraphQL APIs.
Database Administration: Database optimisation and tuning.
Contact
Based in the UK, serving global clients. Discuss your performance concerns, scaling strategy, or optimisation needs.