Cloud native API Testing comes with a lot of challenges. In this video see how to overcome these challenges with the novel approach of traffic replay.
Challenges of Cloud Native API Testing
- How do clients call the API?
- Required dependencies can be missing.
- Test data has to be reset in between runs.
What is the Ideal API Testing Method?
- Identify what needs to be tested.
- Generate automation quickly.
- Run in a stable environment with the right data.
Identify what needs to be tested
- See the exact set of calls.
- Identify the technologies used by the API.
- Drill down to deep detail like request and response headers and payloads.
Generate API Test automation quickly
- Turn API traffic into test cases.
- Update security tokens, parameters, timestamps, etc.
- Check responses to every call.
Run in a stable environment
- Detect microservice dependencies.
- Turn API traffic into service mocks.
- Orchestrate the data and environment.
Use Case: Performance Testing for SRE
- Track the SRE Golden Signals:
- Latency: How long the API takes to respond.
- Throughput: How many transactions per second can the API handle.
- Success Rate: How many of the calls returned the correct value.
- Saturation: How did the underlying infrastructure perform under load.
Use Case: Functional Testing for SDET
- Assertions for every single call:
- Compare the actual against expected values.
- Visually demonstrate the differences.
- Integrate with CI to shift-left testing at code check-in or merge.
Value of Speedscale
- Software Engineers
- Spend less time writing scripts and more time building new features.
- Take out the guesswork by building realistic scenarios from live application traffic.
- SRE / DevOps
- The CI system runs faster without full E2E environments with lots of moving parts.
- Automatically produce SLO numbers to ensure the API meets expectations.
- Perform more validations to find issues before they reach production.
- Deployments are safer when more automated testing is performed before each release.
- Cloud spend for non-prod can be up to 40% of the bill, reduce it with smaller environments.
Many businesses struggle to discover problems with their cloud services before they impact customers. For developers, writing tests is manual and time-intensive. Speedscale helps Kubernetes engineering teams gain confidence in how new code will perform in real world scenarios. Collect and replay API traffic, simulate load or chaos, and measure latency, throughput, saturation and errors before the code is released. Speedscale Traffic Replay is an alternative to legacy testing approaches which take days or weeks to run and do not scale well for modern architectures. If you would like more information, schedule a demo today!