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By Matthew LeRay
Observability is a critical element of modern software development, unlocking awareness across complex and distributed systems with ease. This has allowed developers to monitor, understand, and debug their applications effectively, leveraging existing resources for more efficient lifecycle management and iteration. In the context of Python, observability is an engine for boosting and maintaining the performance, reliability, and stability of the implementation. In this guide, we're going to look at the key aspects of building and deploying Python observability, the importance of this process, and the tools available to implement it.
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By Ken Ahrens
Software development requires a lot of things to be highly optimized due to the sheer number of parts and the interconnected nature of those parts. Making your service seamless, efficient, and scalable requires tooling that is itself seamless, efficient, and scalable. As organizations have moved to adopting microservices and distributed cloud-native solutions, being able to effectively scale resources and the systems which operate upon those resources has been made even more critical.
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By Nate Lee
Service virtualization tools have become indispensable for organizations seeking to streamline their testing and development processes. These tools allow teams to simulate the behavior of critical software components, enabling more rapid development with overall cost reduction and improved collaborative outcomes. As demand mounts for service virtualization solutions, identifying the best tools to support this workflow in the software development lifecycle has never been so important.
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By Kush Mansingh
Ensuring seamless data flow and robust application performance is crucial in modern networking and cloud computing. Traffic mirroring enables enhanced network observability, allows for effective troubleshooting, and facilitates the testing of new features without interrupting live traffic. It captures and replicates network traffic, providing a powerful solution for maintaining and optimizing network operations.
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By Nate Lee
When discussing the process of testing an API, one of the most common sets of terms you might encounter are “mocks” and “stubs.” These terms are quite ubiquitous, but understanding exactly how they differ from one another – and when each is the correct method for software testing – is critical to building an appropriate test and validation framework. In this blog, we’re going to talk about the differences and similarities between mocks and stubs.
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By Shaun Duncan
When creating production-level applications, enterprises want to ensure the high availability of services. This often results in a lengthy development process that requires extensive testing for the applications or a new release. This involves testing the behavior of the application under load, measuring the performance metrics, and accounting for the resource consumption. All this is done to ensure that the application does not behave unexpectedly when being used by clients.
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By Shaun Duncan
In this tutorial, you will learn how to work with the gRPC Golang library for microservice communication by creating a simple note-taking application. You will generate a gRPC client that is highly efficient and has a service implementation that handles a diverse range of request and response types. APIs and service-to-service communication are what make modern microservice architecture possible.
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By Matthew LeRay
APIs have never had more connections and requests for data. With variable data types, changing programming languages, and a demand for high performance, even with an increased focus on authentication mechanisms and data security, it’s never been more critical to figure out the efficiency of your systems. Enter GraphQL and gRPC.
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By Kush Mansingh
APIs might not always be in the spotlight, but they’re the unsung heroes of just about every modern software project. APIs make it possible for different apps, services, and platforms to talk to each other seamlessly. When an API falters, so does the entire system. That’s where REST API testing comes in. It’s not just a box to check off—it’s the key to making sure your services are dependable, secure, and prepared to handle the unexpected.
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By Nate Lee
In almost all industries, a standard set of KPIs helps to guide teams on whether they are doing the right things in the right ways, with the right outcomes. In software development, this is something that can sometimes go untouched, allowing ourselves and fellow developers to continually work on our code and platforms without making sure we are paying attention to what matters. Some development frameworks, such as Agile, have some KPIs baked directly into them.
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By Speedscale
Learn how Speedscale Ephemeral Environments can simulate production conditions and data in a cluster or even locally on your laptop. Speedscale is different from other ephemeral environments since it automatically generates environments modeled after real traffic. This is enabled by sidecars and operators, backended with a sanitized cloud data warehouse.
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By Speedscale
The 2024 DORA report had interesting findings on the impact of AI development. But some statistics were surprising. Listen to Speedscale CTO Matt LeRay break down why some of this news makes sense and others are surprising, with research from other sources.
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By Speedscale
Installing Speedscale is quick and easy with our quickstart and available Helm charts.
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By Speedscale
Develop and test applications faster using traffic replay: traffic driven environments and tests.
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By Speedscale
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.
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By Speedscale
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.
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By Speedscale
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.
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By Speedscale
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.
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By Speedscale
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.
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By Speedscale
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.
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Continuous Resiliency from Speedscale gives you the power of a virtual SRE-bot working inside your automated software release pipeline. Forecast the real-world conditions of every build, and know you’ll hit your SLO’s before you go to production.
Feed Speedscale traffic (or let us listen) and we’ll turn it into traffic snapshots and corresponding mock containers. Insert your own service container in between for a robust sanity check every time you commit. Understand latency, throughput, headroom, and errors -- before you release! The best part? You didn’t have to write any scripts or talk to anyone!
Automated Traffic Replay for Every Stakeholder:
- DevOps / SRE Pros: Understand if your app will break or burn up your error budget before you release.
- Engineering Leads: Let Speedscale use traffic to autogenerate tests and mocks. Introduce Chaos testing and fuzzing.
- Application Executives: Understand regression/performance, increase uptime and velocity with automation.
Before you go to production, run the projection.