Temporal
Temporal workflows coordinate long-running, fault-tolerant business processes. Testing them requires a different approach than testing regular functions — workflows encode history, run across multiple servers, and must be deterministic across replays. Why Temporal Testing Is Different A Temporal workflow is a durable function that can pause, resume after failures, and replay
Temporal
Temporal and AWS Step Functions both orchestrate distributed workflows. But they differ significantly in how easy they are to test. This comparison covers unit testing, local development, mock support, replay testing, and CI integration. The Core Difference Temporal workflows are code. You write Go, Java, Python, or TypeScript. Tests use
WebAssembly
WebAssembly modules in browsers are instantiated via JavaScript and expose their functionality through JS exports. End-to-end and integration tests for WASM web apps use the same tools as regular web tests — Playwright for browser automation and Jest for unit-level JS/WASM integration — but with a few WASM-specific techniques: waiting for
Rust
Tokio is Rust's most popular async runtime. Testing async Rust code requires a Tokio runtime to drive futures to completion. Tokio provides #[tokio::test] for exactly that. The Basics: #[tokio::test] Add tokio with the test feature to Cargo.toml: [dev-dependencies] tokio = { version = "1", features = ["
Java
Concurrent Java code is notoriously difficult to test. The JVM's threading model, JIT compilation, and memory model create bugs that appear only under load, on specific hardware, or with particular GC settings. Good concurrent testing is a combination of the right synchronization primitives, stress testing, and race detection
C++
ThreadSanitizer (TSan) is a runtime data race detector for C and C++ built into GCC and Clang. It instruments your code to track memory accesses and reports races the moment they occur. Unlike manual code review, TSan catches actual races with exact stack traces pointing to the lines that raced.
Airflow
Apache Airflow DAGs are Python code. That means you can test them like any other Python code — validate structure, unit test operators, and run DAGs against test data. This guide covers the full testing spectrum for Airflow. DAG Integrity Tests The first thing to test is that your DAGs load
Python
Testing async Python code requires an event loop to run coroutines. pytest-asyncio and anyio provide that infrastructure and make async tests look nearly identical to synchronous ones. The Problem with Async Tests You can't call await outside a coroutine, so this doesn't work: # Won't
Conductor
Netflix Conductor is an open-source workflow orchestration engine. Workflows are defined as JSON, tasks are implemented as microservices. Testing Conductor workflows requires validating the workflow definition, testing task workers independently, and verifying end-to-end execution. Conductor Architecture for Testing Understanding what to test: * Workflow definition (JSON) — the orchestration logic: task order,
Temporal
Temporal activities are where your workflow touches the outside world — HTTP calls, database writes, email sends. Testing workflows in isolation means replacing activities with mocks that return predictable values without making real external calls. Why Mock Activities Activities in Temporal are the "impure" part of the system — they
AWS
AWS Step Functions orchestrate Lambda functions, ECS tasks, and other AWS services into durable workflows. Testing them requires either expensive real AWS calls or local simulation. AWS provides Step Functions Local for exactly this purpose, and moto mocks the underlying AWS services. Testing Approaches Step Functions testing has three levels:
JavaScript
Async JavaScript is everywhere — fetch calls, timers, event handlers, database queries. Testing async code correctly requires understanding how the JavaScript event loop works and how Jest and Vitest provide tools to control it. Async Test Functions Both Jest and Vitest support async/await in test functions natively: // Jest or Vitest