— 01
Pages load slowly and users notice
Speed
Optimization
and Stabilization.
I'll speed up page loading, reduce server load, optimize database queries, and eliminate the root causes of outages. I work from measurements, not intuition — results are visible in numbers before and after.
From measurements to results
Profiling
SQL
Cache
Frontend
Timeline
3–14
Approach
Measure first
Areas
What I speed up
Choose what's slow —
I'll fix it
I'll fix it
Laravel Performance Optimization
I speed up Laravel apps: eliminate N+1 queries, configure caching and queues, optimize slow code paths.
ПодробнееReact / Next.js Performance Optimization
Frontend optimization for React and Next.js: bundle size, code splitting, rendering, and load performance.
ПодробнееCore Web Vitals Optimization
I bring LCP, INP, and CLS into the green zone — improving real metrics that Google uses for ranking.
ПодробнееDatabase Optimization
I speed up your database: indexes, slow queries, structure, and bottleneck elimination under real load.
ПодробнееNginx & Server Optimization
Server and Nginx configuration: caching, compression, HTTP/2, PHP-FPM tuning for stable performance under load.
ПодробнееPerformance Audit
I measure and find the root causes of slow performance: profiling, metrics, and a prioritized report on what to fix and when.
Подробнее01 — Who it's for
Who needs speed and performance optimization?
— 02
Server gets overloaded as traffic grows or during peak hours
— 03
Application crashes or freezes under certain scenarios
— 04
Database is slow — queries take seconds instead of milliseconds
— 05
Load growth is planned and you need to verify the system can handle it
— 06
Google PageSpeed shows low scores, affecting SEO and conversion
02 — What's included
Seven areas of optimization and stabilization
profiling · sql · cache · frontend
I find the real bottleneck — I don't optimize at random
80% of performance gains typically come from 20% of changes — if you identify the right 20%. Without profiling and measurement, 'optimization' is just guessing. I start with measurement, find the bottleneck, and fix exactly that.
EXPLAIN ANALYZE
Laravel Debugbar
Lighthouse / CWV
Redis / Memcached
Queue / Horizon
Sentry / Datadog
01
Profiling and bottleneck identification
Optimization without measurement is guesswork
'Add an index', 'enable cache', 'raise memory' — the typical response to complaints about slow performance. Sometimes it helps. More often it doesn't — because the real bottleneck is somewhere completely different. A page may be 'slow' due to one slow query three levels deep that nobody thought to check.
How to find the real cause
A profiler shows the actual time distribution: how much goes to database queries, how much to computation, how much to external calls, how much to rendering. Without this, any 'improvements' might speed up parts that take 2% of the time while leaving the part that takes 80% untouched.
How I do this
I connect a profiler to the real environment or reproduce the load on staging. I use Laravel Debugbar, Telescope, Blackfire, or xdebug depending on the stack. I build a request waterfall, find the top-N most expensive operations. Only then do I move to fixes — measuring the result after each change.
02
Database and SQL query optimization
N+1 — the most common and costly mistake
An ORM hides the real number of database queries. A list of 100 users with their roles and latest orders can turn into 301 queries instead of 3. On test data, this is invisible. On a real database — the page takes 8 seconds, the server is overloaded, and the DBA reads the logs in disbelief.
Missing indexes and indexes that hurt
Missing an index on a column filtered in 90% of queries means a full table scan on every request. But excessive indexes slow down INSERT and UPDATE. Correct indexing requires analyzing real queries, not intuition.
How I handle this
I enable slow query logging and analyze with EXPLAIN ANALYZE. I find N+1 with a profiler and fix via eager loading or query rewrite. I design missing indexes for specific queries and verify results through the execution plan. For aggregate queries — materialized views or denormalization where justified.
03
Caching
Cache that doesn't cache
'We have Redis' doesn't mean the cache is working effectively. Typical problems: too little cached (every request hits the database), too much cached (memory runs out, eviction breaks functionality), wrong invalidation (users see stale data or cache resets too aggressively).
Cache stampede and other non-trivial problems
When the cache for a popular key expires, hundreds of concurrent requests hit the database for the same data — this is cache stampede. Without protective patterns (lock-based caching, probabilistic early expiration), cache invalidation creates a load spike.
How I handle this
I analyze cache hit rate and TTL distribution. I determine what, where, and for how long to cache: HTTP cache for static assets, application cache for heavy computations, query cache for aggregates. I implement safe invalidation patterns. I configure cache monitoring — hit rate, miss rate, eviction rate.
04
Frontend optimization and Core Web Vitals
PageSpeed 42 — that's not just a score
Google uses Core Web Vitals as a ranking signal. LCP above 4 seconds, CLS above 0.25, INP above 500ms — that's not just poor UX, it's lost search positions. And the causes can be non-obvious: render-blocking resources, unoptimized images, layout shifts from async-loaded content.
A bundle that weighs as much as the internet
500 KB of JavaScript on a homepage that uses 10% of the code. Images in PNG where WebP is needed. Fonts loaded synchronously and blocking rendering. All of this adds up to seconds of wait time for the user — especially on mobile with a slow connection.
How I handle this
I audit with Lighthouse and Chrome DevTools Performance. I eliminate render-blocking resources, configure code splitting and lazy loading. I optimize images: conversion to WebP/AVIF, correct sizing, lazy loading. I configure font-display and preconnect for fonts. I verify results against real CWV data from Google Search Console.
05
Background jobs and queues
Heavy operations in the request — a source of slowness
Sending email, generating PDFs, calling an external API, resizing images — none of this should execute synchronously in the user's request. Each such operation adds seconds to the response time and potentially crashes the request when the external service times out.
A queue that doesn't work in production
Jobs are added to the queue, but the worker crashed and nobody noticed. Or there's one worker and thousands of jobs — the queue grows, emails arrive an hour late. Or a job fails and goes to failed jobs with no alert sent.
How I handle this
I move heavy operations into async jobs. I configure Laravel Horizon or equivalent: worker monitoring, queue size, processing time. I set up alerts on failed jobs and queue length. For critical tasks — retry with exponential backoff and a dead letter queue for manual inspection.
06
Stabilization and outage elimination
'Sometimes crashes' is worse than 'always crashes'
Unstable behavior is the hardest to diagnose: the error doesn't reproduce locally, there's nothing in the logs, users complain occasionally. Behind this usually sits a race condition, a memory leak during extended operation, a concurrent request problem, or a dependency on an external service without timeouts.
A memory leak that surfaces on day three
A worker that every three days starts consuming 100% memory and crashes. PHP-FPM that gradually slows down until restart. These problems don't reproduce in manual testing — only under sustained load over time.
How I handle this
I configure detailed logging and tracing via Sentry or equivalent — so every crash leaves a trace with context. I analyze the crash pattern: time, conditions, frequency. For race conditions — I add locks with the right isolation level. Memory leaks are diagnosed by monitoring worker memory consumption over time.
07
Monitoring and observability
Finding out about outages from users
Without monitoring, the time to detect a problem is the time of the first support ticket. That could be hours. In that time you've lost transactions, data, and trust. Monitoring isn't 'nice to have' — it's basic hygiene for any production project.
Metrics that nobody looks at
A dashboard with twenty charts opened once a quarter — that's not monitoring. Monitoring is alerts on deviations: response time spiked, error rate increased, queue is growing, disk is almost full. The right metrics with the right thresholds, sending a signal when it matters.
How I set this up
I connect Sentry or equivalent for error tracking with context: stack trace, user, request. I configure uptime monitoring with alerts via Telegram or email. I add response time, error rate, and queue length metrics. I set alert thresholds for the project's actual characteristics — not defaults, but tuned to your load.
03 — How we work
Four stages — from measurements to confirmed results
Measure the current state
I connect a profiler, collect response time metrics, analyze slow query logs, and audit the frontend with Lighthouse. I establish a baseline — the starting point for measuring results.
Find the bottlenecks
I build a bottleneck map with an estimate of each one's contribution to total time. I identify the top-5 changes with the highest potential impact. We agree on priorities before touching the code.
Fix and verify
I make changes iteratively — each step with result measurement. I don't move to the next change until the effect of the previous one is confirmed. This way each change's contribution is visible.
Confirm the results
I compare final metrics against the baseline. I prepare a brief report: what was done, what improvement each metric showed, and recommendations for further optimization.
04 — Result
What you get after performance optimization
Not 'feels somewhat faster', but
measurable gains in numbers
Pages load faster, the server handles load, outages are detected before users notice. Every improvement is documented with metrics — you see a concrete result, not abstract 'we optimized it'.
Measurable performance gains
Response time, LCP, TTFB, number of queries — before and after in numbers.
Stable operation under load
Root causes of outages eliminated, not symptoms. System doesn't crash at peak traffic.
Monitoring and alerts
You learn about problems first — before your users notice them.
When to reach out
Don't wait for slowness to become downtime
A slow page loses conversion. An unstable service loses trust. Both scenarios cost more through inaction than through fixing. Even without a specific complaint — a performance audit will show where the system is running at its limit. Cost is fixed after initial analysis — no hidden fees.
I usually respond within an hour
first call is free
working together
online
<1 day
response time
1:1
direct, no middlemen
0 ₽
for the first call
Quick replies
within the day
Single developer
full context, always
05 — FAQ
Common questions about speed optimization
How much can a project be sped up?
Depends on the current state. If there are obvious problems — N+1 queries, missing indexes, heavy synchronous operations — gains can be multiplicative: 3–10x on response time. If the project is already optimized, expect 20–50%. I'll give an honest answer after initial analysis.
Is production server access required?
Will optimization break anything in the project?
What if the problem comes back after optimization?
06 — Other services
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