High Load

When the database slows down and the server is overloaded

As website traffic or database size grows to millions of records, standard development methods stop coping. Pages start taking several seconds to load, CPU server load hits 100%, the database locks tables, and users see 504 Gateway Timeout errors.

Simply buying a more expensive server solves the problem only temporarily — suboptimal queries scale extremely poorly. We approach high-load (Highload) system optimization at the architecture and code level. We analyze slow query logs (Slow Query Log), build efficient index bases, rewrite heavy SQL queries, and deploy in-memory caching (Redis, Memcached).

For projects with terabytes of data, we implement distributed architectures: database replication (Master-Slave) to separate read and write streams, as well as horizontal scaling (sharding).

  • Database performance audit (MySQL, PostgreSQL, MongoDB, ClickHouse)
  • Database structure optimization, normalization/denormalization, and indexes
  • Caching of "heavy" data in RAM using Redis
  • Database Replication Setup (Separation of Read/Write Streams at Code Level)
  • Backend server code profiling (PHP, Node.js, Python, Go)

100x

Speeding up slow SQL query execution time after index optimization

10K+

RPS (requests per second) — target performance of the optimized database

<50 ms

Backend API response time under peak load in cached mode

10 million pathway

Data rows — the minimum volume of tables we work with during optimization

Don't scale up hardware — optimize algorithms

An sub-optimal SQL query with nested loops takes exponentially longer to execute as the database grows. We write clean, fast backend code and configure proper indexes, saving you money on server rentals.

Our method

Three steps to fast backend performance

We conduct a detailed study of system metrics and optimize bottlenecks.

Backend code profiling

Using specialized profilers (Blackfire, Xdebug, V8 Profiler), we break down server script execution by milliseconds. We find "heavy" functions, RAM memory leaks, and unnecessary loops.

Optimizing the server code itself reduces the load on the processor (CPU) several times over.

RAM caching

Queries to traditional hard drives and DBMS take relatively long to execute. We set up Redis/Memcached — in-memory databases. 'Hot' and rarely changing data is delivered in fractions of a millisecond.

The server instantly serves a cached page or block without accessing the main database at all.

Replication and stream separation

With simultaneous reads and writes, the database can lock tables (Database locks). We configure replication: the main server (Master) accepts write operations, while a pool of replica servers (Slaves) serves read operations.

This eliminates deadlocks and distributes the read load across machines.

Stages

How Highload optimization is carried out

Sequential process of log collection, query debugging, cache setup, and load testing.

01

Log collection and Slow Query analysis

We enable monitoring of slow SQL queries. We identify queries with execution times exceeding 100 ms. We build a database load map.

02

Index and structure optimization

We analyze query execution plans (EXPLAIN). We add composite indexes, eliminate Full Table Scan, and optimize JOIN connections.

03

Server code profiling

We launch backend code profiling. We look for memory leaks, inefficient data processing algorithms, and optimize internal loops.

04

Redis Cache Integration

We design caching schemes: cache-aside for static website blocks, product lists, and menus. We configure time-to-live (TTL) and cache invalidation.

05

DBMS configuration tuning

We fine-tune DBMS configuration files (my.cnf, postgresql.conf): memory buffer pool distribution, connection cache, and disk write parameters.

06

Load stress testing

Using k6/wrk utilities, we simulate a peak influx of thousands of users. We measure response time stability, CPU usage, and prove results with figures.

Our stack

Highload development tool stack

We use advanced database profiling and caching software.

Blackfire.io & Xdebug

Leading code profilers. Provide interactive Call Graphs, allowing you to visually see which line of PHP or Node.js consumes CPU resources.

Redis & Sentinel / Cluster

Ultra-fast RAM cache storage. Used to store results of heavy queries, user sessions, and tokens, responding to requests within microseconds.

k6.io & wrk

Modern load testing software. It generates asynchronous HTTP requests across thousands of threads, allowing you to evaluate backend durability before launching to production.

Pricing

Database and Backend Optimization Pricing

The price depends on the database size, server-side logic complexity, and required RPS performance.

Features SQL and index optimization Database cleanup and slow query acceleration 60 000 ₽ Timeframe: up to 7 days Order Popular Caching and code Redis Integration, Backend Code Profiling 95 000 ₽ Timeframe: up to 14 days Order Highload architecture Turnkey database replication and horizontal scaling from 180 000 ₽ Timeframe: from 20 days Discuss
Number of optimized SQL queries up to 30 heavy requests up to 70 requests + caching Complete redesign of DB structure and queries
Redis Caching Integration Basic block caching Distributed Redis Sentinel Cache
Backend code profiling (Blackfire / Node) Basic profiling Deep refactoring of bottlenecks
Replication Setup (Master-Slave) Read/Write stream splitting
Stress testing (k6 / wrk) Testing up to 1,000 RPS Tests up to 10,000+ RPS with report
Server and DBMS configuration tuning my.cnf/postgresql.conf Setup RAM buffer optimization Comprehensive tuning of OS kernel, Nginx, and DB
FAQ

FAQ about Highload optimization

Is your server constantly overloaded, and users complaining about a slow website? Write to us — we will capture load snapshots and show where milliseconds are lost.

  • How to know if our project really needs database optimization?

    Main signs of database issues: 1) website pages (especially personal account or product catalog with filters) take longer than 1–2 seconds to open; 2) with an increase in online users, the site starts lagging heavily or throws a 504 Gateway Timeout error; 3) in the hosting monitoring panel, the server CPU usage chart hits 100%, and the DBMS process (mysqld or postgres) consumes maximum resources; 4) the database size exceeds 5–10 GB. In all these cases, query profiling allows significantly reducing the load.
  • Will simply buying a more powerful server help instead of optimizing the backend?

    Most often, no. If you have an SQL query performing an unindexed search on a 1-million-row table, the server CPU is forced to read all data from the disk on every click. Buying a 32-core server instead of an 8-core one will simply allow running more such inefficient queries simultaneously, but won't speed up page load for a specific user. Moreover, as the database grows to 10 million rows, the site will freeze anyway. Proper indexes speed up searches thousands of times, making expensive hardware purchases unnecessary.
  • What is database sharding and when is it used?

    Sharding (horizontal partitioning) is a method of dividing a huge database table into parts and distributing them across different physical servers. For example, an online store's order table can be divided: store Moscow users' orders on server No. 1, and Saint Petersburg orders on server No. 2. Sharding is used in Enterprise-scale projects when data volume exceeds the hard drive or RAM capacity of a single server, and classic replication can no longer handle the load.
  • How do you perform load testing, and is it safe for a live website?

    We never conduct stress tests on a live production site, as this can lead to downtime and loss of clients. We deploy a full isolated copy of the project (Staging server), identical in capacity to the production server. Then, using the k6.io utility, we generate virtual users who perform typical actions (searching, adding to cart, viewing articles). We smoothly ramp up the load from 10 to 1000+ requests per second (RPS), tracking at what numbers the server starts to degrade performance.
High loads

Preparing a large-scale ad campaign or a sale?

Submit a request — our Highload architects will prepare your website for an influx of hundreds of thousands of visitors, optimize queries, set up caching, and guarantee stable performance under load.

Optimize backend for high load