How do you optimize performance in a LAMP stack?

Long Answer

Optimizing a LAMP stack (Linux, Apache, MySQL/MariaDB, PHP) is a multi-layer task. I approach it systematically: measure bottlenecks, optimize database queries, implement caching, enable opcode caching, and tune HTTP delivery.

1) Profiling and measurement

I start with metrics. Using slow query logs, MySQL EXPLAIN, and PHP profiling (Xdebug, Blackfire), I map which endpoints burn the most time. I capture baseline latency percentiles and throughput. Without measurements, optimizations risk being cosmetic.

2) Query optimization

• Indexes: I create composite indexes aligned with WHERE filters and ORDER BY clauses. Equality columns go first, followed by range, then sort.
  
• Projections: I select only needed columns instead of SELECT *.
  
• Joins: I prefer joins that use indexed foreign keys and eliminate unnecessary joins by denormalizing read models where appropriate.
  
• Pagination: I avoid deep offset queries (LIMIT 100000,20) and switch to keyset pagination with indexed columns.
  
• Prepared statements: I parameterize queries for plan reuse and safety.
  
• Query batching: I replace per-row queries with IN queries, set-based updates, or bulk inserts.
  
• EXPLAIN plans: Before shipping, I validate queries are using the intended index and not full table scans.
  

3) Caching layers

Caching absorbs load and smooths response times:

• Data caching: I cache expensive queries or aggregates in Redis/Memcached, with versioned keys and time-based invalidation. For example, popular product listings can be cached for 30 seconds with tags.
  
• Session caching: I store PHP sessions in Redis for faster reads/writes compared to file storage.
  
• Opcode caching: With OPcache enabled, compiled PHP bytecode is stored in memory. I configure memory size, enable JIT for CPU-heavy workloads, and precompile common scripts.
  
• Application caching: Laravel, Symfony, or custom frameworks can wrap cache layers around repositories. I guard against cache stampedes by using a lock so only one worker rebuilds while others serve stale.
  

4) PHP optimization

• OPcache and JIT: Enable and size OPcache correctly; configure JIT for math-heavy workloads.
  
• Autoload optimization: Run composer install --optimize-autoloader --no-dev in production.
  
• Session and file I/O: Move session and cache storage to Redis, and keep file I/O off hot paths.
  
• Asynchronous tasks: Offload heavy jobs (report generation, email sending) to queues (Beanstalk, RabbitMQ).
  
• Concurrency models: PHP-FPM is tuned with pm.max_children and pm.max_requests aligned to CPU and memory.
  

5) Apache and HTTP-level improvements

• Persistent connections: Enable KeepAlive with tuned timeouts.
  
• Compression: Use Gzip or Brotli for text assets.
  
• Caching headers: Add Cache-Control and ETag headers for static assets.
  
• Minification and bundling: Minify CSS/JS, combine requests, and use HTTP/2 multiplexing.
  
• CDN: Offload static assets and media to a CDN close to users.
  
• TLS tuning: Enable modern ciphers and OCSP stapling for HTTPS performance.
  
• Reverse proxy: Place Nginx or Varnish in front of Apache for caching and SSL offload.
  

6) Database and server tuning

• Connection pooling: Use mysqlnd persistent connections or a pooler to avoid handshake overhead.
  
• Buffer sizes: Tune InnoDB buffer pool to fit hot data in memory.
  
• Query cache: Disabled in MySQL 8; instead rely on Redis caching.
  
• Horizontal scaling: For very high read loads, I add read replicas and route queries accordingly.
  

7) Monitoring and regression control

I monitor metrics: query execution time, cache hit ratio, OPcache hit/miss, Apache RPS, p95 latencies, and error budgets. Load testing simulates concurrent sessions and varied network conditions. If performance regresses, I rollback and analyze.

By layering query optimization, multi-level caching, opcode caching, and HTTP improvements, a LAMP application can scale gracefully and deliver consistent low-latency responses.

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Table

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Common Mistakes

• Leaving SELECT * queries in production, forcing wide scans.
  
• Creating too many overlapping indexes, which slow down writes.
  
• Relying on MySQL query cache in new versions instead of Redis.
  
• Failing to configure OPcache properly (small memory, disabled validation).
  
• Allowing cache stampedes with hot keys, flooding the database on expiry.
  
• Neglecting Cache-Control headers, forcing clients to re-download assets.
  
• Running Apache with default settings, missing HTTP/2, compression, or proper KeepAlive.
  
• Optimizing blindly without measuring with slow query logs, profiling, or load testing.
  
  

Sample Answers

Junior:
“I profile queries with EXPLAIN, add indexes that match filters, and use with() queries to avoid N+1 in ORM layers. I enable OPcache in PHP and configure Redis for sessions and hot data. On the HTTP side I add Gzip compression and cache headers.”

Mid:
“I design composite indexes, confirm with EXPLAIN, and replace offset pagination with keyset. I cache aggregates and query results in Redis with stampede protection. PHP is tuned with OPcache and Composer optimizations. I enable HTTP/2, CDN for static assets, and KeepAlive tuning in Apache.”

Senior:
“I maintain a performance budget: queries are shaped, caches layered, and OPcache sized with JIT where it helps. Apache is fronted by Nginx or Varnish, serving compressed static assets via CDN. I monitor OPcache hit ratio, Redis hit rate, and p95 latency, running load tests before/after changes. Every optimization ships with metrics and rollback options.”

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Evaluation Criteria

Strong answers describe a multi-layer strategy:

• SQL queries optimized with indexes, projections, keyset pagination, and EXPLAIN.
  
• Caching at data, session, and opcode layers with Redis/Memcached and OPcache tuned.
  
• HTTP-level improvements like compression, caching headers, HTTP/2, and CDN.
  
• Apache tuned with KeepAlive and reverse proxies where needed.
  
• Monitoring via slow query logs, cache metrics, and load tests ensures gains are proven.
  Weak answers focus only on one layer (e.g., just Redis), ignore opcode caching, or skip proof. Red flags: using MySQL query cache in v8, no OPcache, or editing blindly without profiling.

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Preparation Tips

• Capture slow queries with MySQL logs; run EXPLAIN to confirm index usage.
  
• Create a composite index matching a hot query’s WHERE and ORDER BY; measure latency.
  
• Enable Redis for sessions and cache a hot query with versioned keys; add stampede locks.
  
• Enable OPcache with adequate memory and preloading; verify hit/miss ratio.
  
• Minify and compress assets, enable Gzip/Brotli, and test load times with Lighthouse.
  
• Configure Apache KeepAlive and enable HTTP/2.
  
• Run a load test simulating concurrent users; measure p95 latency, CPU, cache hit ratio, and bandwidth.
  
• Document results and rollback options.
  
  

Real-world Context

A retailer optimized product queries by replacing offset pagination with keyset and adding a composite (category_id, created_at desc) index; query time dropped from 1.2s to 50ms. A news site cached homepage queries and sessions in Redis with stampede control, cutting database load by 70%. An e-commerce shop enabled OPcache with preloading and Composer autoload optimization, reducing PHP CPU usage by 40%. A SaaS startup offloaded static assets to a CDN, enabled Brotli, and fronted Apache with Nginx; first-byte latency dropped significantly in Asia. In all cases, layering query optimization, caching, opcode tuning, and HTTP-level improvements delivered measurable and stable gains.

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Key Takeaways

• Shape SQL queries with indexes, projections, and keyset pagination.
  
• Use Redis/Memcached caching with tags and stampede prevention.
  
• Enable OPcache and tune Composer autoloader for PHP.
  
• Add HTTP-level improvements: compression, cache headers, HTTP/2, CDN.
  
• Prove gains with profiling, load testing, and metrics on cache hit ratios and latency.
  
  

Practice Exercise

Scenario:
Your LAMP e-commerce app slows during sales events. Product listing queries take seconds, the homepage stalls on cache expiry, and static assets load slowly on mobile.

Tasks:

1. Record a baseline with MySQL slow query log, OPcache hit/miss, and p95 latency.
   
2. Create a composite index on (category_id, created_at desc) and switch to keyset pagination for product listings. Validate with EXPLAIN.
   
3. Cache the homepage query in Redis with a versioned key and short TTL; implement a lock to prevent stampedes.
   
4. Enable Redis sessions and measure reduction in file I/O.
   
5. Configure OPcache with sufficient memory and preload common scripts. Verify with status metrics.
   
6. Enable Brotli compression, Cache-Control headers, and HTTP/2 on Apache. Serve static assets via CDN.
   
7. Rerun a load test simulating sale traffic. Record cache hit ratio, database CPU, and p95 latency.
   
8. Document improvements and provide a rollback (disable cache, revert pagination).
   
   

Deliverable:
A performance report showing measurable improvements in LAMP stack response time through query optimization, caching, opcode caching, and HTTP-level tuning, validated under realistic load.