Linux Performance Optimization
Systematic analysis and tuning of Linux production systems. We identify real bottlenecks — not assumed ones — and apply targeted improvements with measured, documented results. Faster response times, better resource utilization, more stable operation under load.
The right approach: measure first, tune second
Performance optimization without measurement is guesswork. The most common mistake is applying generic tuning recommendations without understanding where the actual constraint is. We start with profiling and data collection, then apply targeted changes with defined success criteria.
Profile & measure
Collect baseline metrics: server load, memory utilization, disk I/O wait, query execution times, slow log analysis, response time percentiles and error rates. Establish a clear picture of current state before touching any configuration.
Identify bottlenecks
Analyze the profiling data to identify where time and resources are actually being consumed. Distinguish between application-layer issues, database query inefficiencies, I/O constraints and configuration problems.
Apply targeted changes
Apply configuration and tuning changes systematically, one area at a time, with documented rationale. Test each change in a controlled way where possible before applying to production.
Measure & report
Re-collect metrics after changes. Compare against baseline. Document what was changed, why, and what the measured effect was. Produce a clear before/after report.
Who this is for
Web applications that are slow under load
Applications that perform acceptably in low-traffic conditions but degrade under real production load — slower page times, increased error rates, resource exhaustion.
Databases with performance problems
MySQL or PostgreSQL instances with slow queries, high I/O wait, poor buffer pool utilization or configurations that have never been tuned beyond default installation values.
E-commerce platforms preparing for traffic spikes
Online retailers before peak seasons, sales events or marketing campaigns who need to verify their infrastructure can handle projected traffic volumes without degrading.
Companies on oversized hardware
Organizations paying for more server capacity than they should need, where the root cause is inefficient application configuration or missing caching layers rather than genuine resource requirements.
SaaS platforms growing past their current infrastructure
Software companies whose infrastructure is straining under growth — not because they need to throw more hardware at the problem, but because the configuration and architecture needs to evolve.
Teams after a failed performance fix
Organizations that have already attempted performance improvements — applied "best practice" tuning from blog posts, upgraded hardware, added caching — but haven't seen the expected results.
What we optimize
Web server configuration
Nginx and Apache worker processes, connection limits, keepalive settings, gzip/brotli compression, static file caching, proxy buffer configuration, SSL session reuse and upstream connection pooling.
PHP-FPM tuning
Process manager mode (static/dynamic/ondemand), pool sizing, max children, request timeouts, OPcache configuration, memory limits and process lifecycle management for PHP-based applications.
Database performance
MySQL/MariaDB buffer pool sizing, query cache configuration, slow query analysis, index review, connection pooling, InnoDB configuration and PostgreSQL vacuum settings, shared buffers, effective cache size and query planning statistics.
Cache strategy
Redis configuration, eviction policy, maxmemory settings, pipeline and connection handling. Application-level caching strategy review — whether object caching, page caching or query result caching is most appropriate for the workload.
Disk I/O & storage
I/O scheduler selection, filesystem mount options, SSD vs rotational disk tuning, swappiness configuration, tmpfs for ephemeral workloads and storage layout analysis for database workloads.
CPU & memory utilization
Process priority, CPU affinity for latency-sensitive workloads, memory overcommit settings, NUMA topology awareness on multi-socket systems and identifying memory-hungry processes or leaks.
Kernel & network parameters
TCP connection settings, backlog queue sizing, TIME_WAIT handling, network buffer tuning and sysctl parameters appropriate to the specific workload type — web server, database, high-connection-count application.
Load testing & capacity planning
Load test design, tool selection and execution to measure actual infrastructure capacity under realistic traffic models. Capacity planning based on measured results rather than estimates.
Typical outcomes
Measurably faster response times
Documented improvement in application response time, page load times and database query execution — not theoretical gains but measured before/after numbers.
Higher concurrency capacity
Infrastructure that handles significantly more concurrent users or requests before performance degrades — often achievable through configuration alone on existing hardware.
Reduced infrastructure cost
For organizations running oversized servers, efficient tuning can reduce the hardware or cloud tier required — turning a performance engagement into direct cost savings.
Stability under load
Systems that degrade gracefully under high load rather than crashing, queueing into failure or generating cascading errors — consistent behavior at the performance limits of the infrastructure.
Documented configuration baseline
All tuning changes documented with rationale, so your team understands what was done, can maintain it and has a reference point for future changes.
Clear roadmap for further improvement
Remaining bottlenecks identified and prioritized, with a realistic assessment of what can be achieved through further infrastructure tuning versus application changes.