Performance testing

Performance testing is a critical aspect of software evaluation that assesses how well a system operates under different workload conditions. This type of testing focuses on key metrics such as response time, throughput, resource usage, and stability to ensure the system can handle expected and peak user demands. By simulating various scenarios, including high traffic loads and simultaneous user interactions, performance testing helps identify potential bottlenecks and weaknesses in the system, allowing developers to optimize performance and enhance user experience.

In this post, we will focus on standard testing methods rather than reviewing all types of testing.

Load testing

Tasks:
- Run tests with gradually increasing users/requests
- Monitor response time and resource usage
- Identify system breaking points
- Test main business flows


Purpose:
- Evaluate system performance under normal and peak loads
- Determine system limitations
- Ensure system meets SLA requirements


Success Metrics:
- Response time < x seconds (according to SLA)
- Throughput achieves y requests/second
- Error rate < z%
- CPU/Memory usage < threshold

• Testing flow

• Examples of some tools/libraries.

JMeter: Test scripting & execution
K6: Modern load testing tool
Gatling: High-scale load testing
Grafana + Prometheus: Monitoring
ELK Stack: Log analysis

Stress testing

Tasks:
- Test with load exceeding capacity
- Test with sudden spike traffic
- Test recovery after overload


Purpose:
- Evaluate stability under high pressure
- Test recovery capability
- Identify system weaknesses


Success Metrics:
- No system crashes
- Recovery time < x minutes
- No data loss
- Graceful degradation

• Testing flow

• Examples of some tools/libraries.

Apache AB: HTTP stress testing
Locust: Python-based stress testing
Siege: HTTP stress testing
Chaos Monkey: Failure injection
Datadog: Advanced monitoring

Endurance/Stability Testing

Tasks:
- Run continuous tests for extended periods (24h+)
- Monitor memory usage and performance
- Check for memory leaks


Purpose:
- Evaluate long-term stability
- Detect memory leaks
- Monitor resource usage over time


Success Metrics:
- No memory leaks
- Stable performance
- No degradation
- Meets uptime requirements

• Testing flow

• Examples of some tools/libraries.

Marathon: Long-running tests
AppDynamics: Performance monitoring
New Relic: Resource tracking
DynaTrace: Memory analysis
VisualVM: Memory profiling

Scalability Testing

Tasks:
- Test with scaling up/out resources
- Evaluate auto-scaling effectiveness
- Test performance with additional nodes


Purpose:
- Verify scalability capabilities
- Evaluate efficiency of adding resources
- Optimize scaling configuration


Success Metrics:
- Linear scaling ratio
- Auto-scaling time < x minutes
- Performance increases proportionally with resources

• Testing flow

• Examples of some tools/libraries.

AWS CloudWatch: Auto-scaling monitoring
Azure Monitor: Scale metrics
Terraform: Infrastructure scaling
Kubernetes: Container orchestration
Prometheus: Metrics collection

Volume Testing

Tasks:
- Test with large data sets
- Check database performance
- Test file uploads/downloads


Purpose:
- Evaluate big data processing
- Check storage capacity
- Test I/O operations


Success Metrics:
- Query time < x seconds
- Backup/restore time meets requirements
- Optimized storage usage

• Testing flow

• Examples of some tools/libraries.

Faker: Test data generation
DBMonster: Database load testing
pgbench: PostgreSQL benchmarking
Cassandra Stress: NoSQL testing
YCSB: Database benchmarking

Integrated Performance Testing Flow