Systems | Development | Analytics | API | Testing

There are many ways to bootstrap tests and mocks within Speedscale. Matt LeRay goes over various ways, eg. by using sidecars, agents, postman collections, or even request response pairs.

Building and debugging Kubernetes microservices can be tough, especially when you don't have realistic data or environments. See how Speedscale can quickly mock DBs and APIs based on observed production behavior, so you can debug and develop features quickly. People familiar with GoReplay will notice a more modern and automated approach to turning user behavior into reproducible developer environments.

Check out Matt LeRay's talk on How to Test in Kubernetes at Star WEST 2024. Distributed architectures like Kubernetes present unique performance challenges. Autoscaling, Load Balancing and other mechanisms help with resiliency but can also serve to cover up fundamental problems. In this video, learn best practices and high level concepts around Kubernetes and achieving high throughput.

Mocks can be useful, but hard to build. You can use them as backends for development, or even tests (like load and performance testing). Speedscale takes the legwork out of building mocks, by modeling them after real observed traffic. This video covers a real-world example of how to use mocks to backend a JMeter load test.

Speedscale's Traffic Viewer is the perfect complement to your production monitoring or observability system because it provides detailed information (like request and response payloads, headers, cookies, and more) that actually helps developers debug any issues and requires zero developer intervention--all of the data is provided from traffic.

In a conversation with Sephora's Senior Performance Engineer, Diana Manulik discusses why their current load testing tool, JMeter, wasn't meeting their needs for reporting, and why they chose Speedscale.

In this conversation with Sephora's Senior Performance Engineer, Diana Manulik discusses how she uses Speedscale and WireMock to generate mocks much faster.

When working with #AI in cloud environments, traditional data provisioning and software testing methods don't work because of the behavior of AI and LLM APIs. In this Cloud Native Computing Foundation (CNCF) webinar recording, we discuss the top 4 challenges of scaling cloud-native AI workloads, and the solutions developers are turning to instead.

In this brief demo, we show how engineers can build and test quickly by autogenerating traffic simulations, load and mocks from actual traffic using Speedscale.

How does Speedscale compare to Observability tools? CTO Matt LeRay quickly explains the differences in this one-minute video.