Airbnb has developed Impulse, an internal load testing framework designed to improve the reliability and performance of its microservices. The tool enables distributed, large-scale testing and allows engineering teams to run self-service, context-aware load tests integrated with CI pipelines. By simulating production-like traffic and interactions, Impulse helps engineers identify bottlenecks and errors before changes reach production.
According to the Airbnb engineering team, Impulse is already in use in several customer support backend services and is under review for broader adoption. It is intended to help service owners establish performance baselines, address issues proactively, and support the resilience of Airbnb’s global infrastructure.
Several teams within Airbnb reported:
Impulse helped us identify and address potential issues in our services. During testing, it detected thread pool exhaustion, occasional timeout errors in client API calls, and high memory usage in service containers, allowing us to optimize performance and resource usage. We highly recommend using Impulse as an integral part of development and testing processes.
Impulse integrates widely used tools, including Docker for containerization, Java and Kotlin for test execution, and CI/CD pipelines for automation. Airbnb notes that this combination enables realistic tests that mirror production conditions and provide confidence before code changes are deployed.
Impulse interfaces integrated with other testing frameworks (Source: Airbnb Tech Blog)
Impulse is built from four independent components: a load generator, a dependency mocker, a traffic collector, and a testing API generator. Teams can adopt individual components as needed, depending on the complexity and requirements of their services.
Impulse framework and its four core components (Source: Airbnb Tech Blog)
The load generator allows teams to write testing logic in Java or Kotlin and run containerized tests at scale against the service under test. Using programming languages instead of a domain-specific language provides flexibility for complex scenarios, supports code reuse, and leverages IDE features such as debugging and testing. Each test run triggers a fresh set of containers, improving isolation and cost efficiency. Load workers are distributed across data centers, and the generator integrates with CI/CD pipelines to automate tests with configurable warm-up, steady-state, and peak phases.
The dependency mocker simulates downstream services by introducing controlled latency and predefined responses. This capability is useful when services depend on external vendors or need to be isolated during regression or integration testing. By mocking dependencies, teams can evaluate system behavior under various load conditions without impacting other services.
The traffic collector captures upstream and downstream interactions from live environments, including latency profiles, and replays this data in testing environments. This ensures that load tests reflect real-world patterns and can surface bottlenecks that synthetic traffic might not reveal.
The testing API generator converts asynchronous, event-driven workflows into synchronous API calls, enabling comprehensive load testing of processes that might otherwise be difficult to evaluate under load. This approach helps identify performance issues that traditional synchronous tests may overlook. The framework integrates tools such as Docker for containerization, Java and Kotlin for test execution, and CI/CD pipelines for automation.
