LOAD PERFORMANCE
Find the bottleneck.
Check acceleration
We optimize queries, server code, and caching based on measurement results. We verify changes against a repeatable load scenario: response time, errors, and resource consumption before and after.
TASK
SCENARIO
DATA
VERIFICATION
LAUNCH
HANDOVER
to launch and handover
MEASUREMENTS
BOTTLENECKS
CHANGE VERIFICATION
projects
of studio
models
Premier Partner status
marketing, analytics
Acceleration begins from measurement
A slow response may be related to a database query, external API, blocking, memory, or a task queue. First, we collect data on real scenarios and identify operations that impact users and business.
We establish the baseline: data volume, configuration, load profile, and response time distribution. We look not only at the average but also at slow queries, error rates, and system stability during traffic spikes.
We implement changes by priority: index, query, data access structure, cache, or code. Scaling is considered after diagnostics. We confirm the result with a repeat test under comparable conditions and describe limitations.
- Initial measurements and agreed-upon scenarios
- Query plans and server code profiles
- Cache with update and clearing rules
- Repeatable test before and after changes
- Report with results and limitations
Scenario
The load reflects user actions
Delay
We evaluate response time distribution
Errors
Acceleration should not hide glitches
Resources
We check CPU, memory, disk, and connections
Cache must remain correct
We define the lifespan, invalidation, and behavior when the cache is inaccessible. User data is separated by access context; an outdated value should not silently change an important decision.
Optimization from verifiable effect
We do not promise the same acceleration to all projects: the result depends on the limitation found.
Priority by impact
We start with operations that create a queue or delay in a key scenario. We check if the problem for the user has decreased, not just the time of a single request.
Comparable tests
We save the load profile, data, and cache state. We compare response time, errors, and resources under the same conditions; we show warmed and cold cache separately.
Architecture as needed
Replication and stream splitting add consistency constraints and operational costs. We select them after evaluation rather than using them as a mandatory feature of Highload.
From slow scenario to a confirmed result
Every hypothesis is verified by measurement and re-testing.
Initial measurements
We collect logs, metrics, and key operations. We agree on access and diagnostic boundaries for the live project.
Cause localization
We check query plans, code profiles, and expectations of external services. We choose bottlenecks with a noticeable impact.
Change plan
We agree on priorities, expected criteria, and test scenarios. We assess migration risks and the possibility of a rollback.
Optimization
We change queries, indexes, data references, or cache in the selected volume. We verify the accuracy of the result.
Load check
We launch an agreed test on a prepared staging environment. We increase the load gradually and set stop conditions.
Report and release
We compare results before and after, showing limitations. We release changes according to the plan and check performance metrics.
Requests, code and load scenarios
The toolset depends on the application and the identified bottleneck.
Database
Execution plans, slow queries, and DBMS statistics. We evaluate index and schema changes together with their impact on write operations.
Profiles and cache
Profiler of the appropriate stack and Redis if necessary. We do not include heavy diagnostics on the production server without approval.
Load tests
k6 or another suitable tool. We set scenarios, metrics, error thresholds, and stop conditions; the number of requests alone does not characterize quality.
— COST
Pricing
to Highload optimization
From slow SQL queries to scaling architecture. The scope of work and test profile are fixed after diagnostics.
| Scope of work | SQL and index optimization Approved slow queries and database settings 85 000 ₽ up to 7 days | POPULAR Caching and code Profiling and caching of key scenarios 150 000 ₽ up to 14 days | Highload architecture Scaling and traffic distribution per task from 260 000 ₽ from 20 days |
|---|---|---|---|
|
SQL queries
|
Up to 30 agreed requests | Up to 70 requests and caching | Requests and structure by task |
|
Redis
|
Not included | Basic caching per assignment | Distributed cache by architecture |
|
Code profiling
|
Not included | Key operations | Bottlenecks and refactoring as assigned |
|
Database replication
|
Not included | Not included | Read/write as per task |
|
Load test
|
Check of selected queries | Test plan up to 1 000 RPS when the environment is ready | Test plan up to 10 000 RPS after environment assessment |
|
Environment settings
|
DBMS based on diagnostic results | DBMS and memory according to assignment | Application, server and DBMS per task |
Cost of work in the agreed volume. Stand resources, licenses, and servers are paid separately. The RPS in the table refers to the planned test load, not the guaranteed project throughput. The timeline depends on access, data, and complexity of changes.
— QUESTIONS AND ANSWERS
Everything that matters
before start
Scope of work, source materials, approval order, and launch conditions.
-
How to understand that optimization is needed?
Due to degraded response time, increase in errors, queues, or resource consumption in critical scenarios. We start with measurements and causes; database size itself does not determine the need for refactoring. -
Is it enough to buy a more powerful server?
Sometimes resources really limit the system. But requests, locks, and extra work can keep the problem on a larger server. We compare options after measurements and consider operating costs. -
When is sharding needed?
When requirements for volume and load cannot be reasonably covered by simpler solutions. Before splitting data, we evaluate the distribution key, operations between parts, migration, and maintenance. -
Is the test safe for a live website?
The main test is performed on a prepared staging environment with agreed data. Production checks require a separate window, constraints, and stop conditions. Live payments and mass notifications are disabled in the test scenario. -
How is the acceleration confirmed?
We repeat the agreed scenario with comparable data and settings. We show response time distribution, errors, and resources before and after. We do not make universal promises like 100x acceleration.