Testing

Testing

k6 vs Locust: Comparing Modern Load Testing Tools

Both k6 and Locust are excellent modern load testing tools that replaced JMeter for many teams.

Testing

Hypothesis: Property-Based Testing for Python

Hypothesis is Python's most powerful property-based testing library. It generates test inputs automatically, shrinks failures to minimal examples, and remembers past failures across test runs. If you write Python and care about test quality, Hypothesis is worth learning. Installation pip install hypothesis Copy Hypothesis integrates with pytest,

Testing

k6 vs JMeter: Which Load Testing Tool to Choose?

JMeter has been the go-to load testing tool for over two decades. k6 emerged as a modern alternative that trades JMeter's GUI and feature breadth for developer-friendliness and simplicity. Which one should you use? This comparison cuts through the noise. Quick Comparison Feature k6 JMeter Scripting

Testing

k6 Load Testing: Complete Getting Started Guide

Load testing tells you how your application behaves under pressure — before real users find out the hard way.

Testing

fast-check: Property-Based Testing for JavaScript and TypeScript

fast-check is the leading property-based testing library for JavaScript and TypeScript.

Testing

k6 Scenarios: Ramp-up, Spike, and Stress Testing

k6's scenarios system gives you fine-grained control over how load is applied to your system.

Testing

Integration Testing Anti-Patterns to Avoid

Integration tests fail for predictable reasons: shared mutable state, timing assumptions, over-mocking, wrong scope, and ordering dependencies. Most integration test suites that "work most of the time" are failing for these reasons. This guide identifies the most common anti-patterns and shows how to fix them. Key

Testing

Service Mesh Testing Patterns: Istio and Linkerd

Service meshes (Istio, Linkerd) add traffic management, observability, and security at the infrastructure layer. Testing mesh behavior requires testing at a different level than application code — you're testing retry policies, circuit breakers, mTLS, and traffic routing rules, not business logic. This guide covers the test patterns specific to

Testing

TestContainers for Microservices: Spinning Up Real Dependencies

TestContainers runs real Docker containers (PostgreSQL, Redis, Kafka, Elasticsearch, etc.) inside your integration tests. Each test gets a clean, isolated instance with no shared state. This eliminates the "works on my machine but fails in CI" problem that plagues integration tests using shared test databases or in-memory

Testing

WireMock Tutorial: Mocking HTTP Services in Integration Tests

WireMock is an HTTP mock server that lets you stub external API responses and verify that your code makes the expected calls. It's the right tool when your integration tests need a real HTTP server to call (not a mock object) without depending on an actual external service.

Testing

Testing Async Messaging with Kafka: Consumer and Producer Tests

Testing Kafka producers and consumers requires handling asynchrony — you publish a message and the consumer processes it on its own schedule. Embedded Kafka (for unit-like tests) and TestContainers Kafka (for integration tests) solve the "real Kafka without real infrastructure" problem. Consumer group offset management, idempotency, and retry

Testing

Microservices Testing Strategy: Unit vs Integration vs Contract Tests

Testing microservices requires a layered strategy: unit tests for individual service logic, contract tests to validate service interfaces, integration tests for key interaction points, and E2E tests for critical user journeys. The classic test pyramid applies but needs adjustments for distributed systems — contract tests occupy a unique layer that doesn&