Backend Performance

About industry

In the ever-changing digital domain today, backend systems are an integral element of any business experience-centric digitized products — both functional and dynamic. Back-end performance Testing goes a long way in assessing the performance, reliability, and resource consumption of APIs, databases, and server-side components across multiple load scenarios. Back-end Performance Testing @ QO-BOX Our work focus in back-end performance testing is to find bottlenecks, to the throughput of any system and make sure behind-end resources well serve real-user flows. Using automated performance test framework for collecting insights to improve system reliability on data that is CPU, memory and network consumption and CI/CD loaded testing.

Key Strategies

API Performance Testing

Test under load on API Response time, performance handling errors etc.

Database Performance Testing

The tests for measuring query performance, connection pooling, and indexing techniques.

Infrastructure Performance Monitoring

Measuring Consumption of CPU, Memory, Network, and Process to find the root cause of degradation.

Individual scalability & stress tests

To ensure backend systems stay stable under heavy load conditions We leverage a combative testing framework, automation pipelines, and performance analysis to enable businesses to build robust, high-performing backend architectures that are operations at scale. Do you want adjustments that apply to your specific website or testing goals?

Key Approaches

Develop Test Strategy

Establish APIs, load patterns, and performance baseline

Spot Bottlenecks

Examine APIs, database queries, and limitations at the infrastructure level.

Automate Testing

Write code using JMeter, and Gatling and run via CI/CD Pipelines Simulate Concurrent users, peak load, and failure points of API load & stress testing Optimize the Database – fine-tuning of queries, indexing connection pooling APM Monitoring CPU, Memory, and network-related Infrastructure Monitoring

Optimization throughout

automated tests run and tuned; reports analysed.

Testing Essentials Key Results

Based on the analysis of the DTC ABB Performance Test Automation Proposal, these are the top key benefits from the evaluation:

Better System Reliability

Find performance bottlenecks in APIs, databases, and infrastructure that can help us deliver a smooth system.

Faster response time

more efficient API and database resulting in lower latency leading to improved end-user experience.

Scalable Testing

This ensures the system is working as expected under increased loads, providing you confidence for high-traffic scenarios.

Cost Advantage

Performance problems are found early, which reduces downtime and lowers costs in the infrastructure.

Continuous Monitoring & Opportunities for Growth

Leverages automated performance testing in CI/CD pipelines for preventive optimization

Data-Driven Summary

Eagle-Eyes for informing system architecture and resource allocation decisions

Integrated Seamlessly

Facilitates the relationship of front-end, back-end, and third-party services.

Tools for the Back-end Performance Testing Implementation

Performance Testing Tools

Load누/un
Gatling
K6
LoadRunner

API Testing & Automation Tools

Postman
Rest Assured
SoapUI

Tools for Monitoring & Analysis of Infrastructure

Prometheus
Grafana
New Relic
Dynatrace
AppDynamics
Elasticsearch
Logstash
Kibana
Jenkins
GitHub Actions
GitLab CI
Docker
Kubernetes
Postman
Swagger

Database Performance Tuning Tools

MySQL Performance Schema
Pg Admin
AWR Reports
Oracle Database

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