We need to go beyond the known properties of the system; we need to discover new properties. Product organizations set expectations for availability and own definitions of SLAs—what must not fail and the fallbacks for things that can. If you see the same symptoms in real-life, those can be reverse-engineered to find the failure with certain probability. This article describes some of the common tools that the Chaos Engineering community considers when starting to implement the practice in an organization. However, we suspect most users are not working on these kinds of safety-critical systems. Adapt. This lets them make informed decisions around prioritizing tasks to upgrade their systems. That being said, there’s always a risk of doing harm to the system and causing customer pain. The second strategy involves designing the experiment to limit the potential harm to be as small as possible while still obtaining meaningful results from the experiment. Prioritize investment between these two metrics as you see fit, knowing that a certain amount of balance is required for the program to be at all effective. A company provides its software to meet the demands of its users. Keeping sight of implementation, sophistication, and adoption concerns can help you figure out where you are on the road to Chaos, and where you need to apply resources to build a successful practice in your organization. With thousands of instances running, it was virtually guaranteed that one or more of these virtual machines would fail and blink out of existence on a regular basis. The most important feature in the example above is that all of the individual behaviors of the microservices are completely rational. For more information on how this work was applied at Netflix, see the paper “Automating Failure Testing Research at Internet Scale” published in the Proceedings of the Seventh ACM Symposium on Cloud Computing (SoCC ’16). Perhaps you recently had an outage that was triggered by timeouts when accessing one of your Redis caches, and you want to ensure that your system is vulnerable to timeouts in any of the other caches in your system. These features center over the common areas like the variety of chaos experiments and the portability of the code, but also critical safety and security features. And to ensure consistent performance and constant availability, healthcare, educational, and finance organizations are implementing chaos experiments. This mindset results inefficiencies down the road, when things do break. Many issues exposed by Chaos Engineering experiments will involve interactions among multiple services. It uses the CI/CD system Spinnaker. System metrics can be useful to help troubleshoot performance problems and, in some cases, functional bugs. To check that a canary cluster is functioning properly, we use an internal tool called Automated Canary Analysis (ACA) that uses steady state metrics to check if the canary is healthy. The complexity of the socio-technical systems we engineer, operate, and exist within is staggering. During the holiday season in 2012, a particularly onerous outage in our single AWS region at the time encouraged us to pursue a multiregional strategy. Chaos principles are the best approach to test a system’s ability against failures when it comes to DevOps-driven software development. With a multi-regional failover strategy, we move all of our customers out of an unhealthy region to another, limiting the size and duration of any single outage and avoiding outages similar to the one in 2012. However, nothing provides more certainty that your system can withstand a given failure scenario than subjecting all of your users to it in production. Some of our chaos tools take advantage of the ACA service to test hypotheses about changes in steady state. The beauty of Chaos Monkey is that it brings the pain of instances disappearing to the forefront, and aligns the goals of engineers across the organization to build resilient systems. Questions chaos engineering answers tend to go like this: Can we provision the resources to provide our service if…, At the top of its field, Netflix is pushed to innovate. This approach provides insight into the onset and duration of availability risks in production. Here’s an overview of the process: The first thing you need to do is decide what hypothesis you’re going to test, which we covered in the section Vary Real-World Events. What if the test we’ve automated doesn’t reveal the problem we’re looking for? In this session, Ana discusses the benefits of using Chaos Engineering to inject failures in order to make your container infrastructure more reliable. During the handoff of responsibility between A42 and A11, microservice E timed out its request to A. Developed by Prof. Peter Alvaro of University of California, Santa Cruz, LDFI can identify combinations of injected faults that can induce failures in distributed systems. ), and unplanned or uncommon combinations of messages. Here are some points that justify chaos engineering: The first concept of testing is that it has several sets of inputs and predicted outputs to obtain desired system behaviors. In 2015, Peter Alvaro worked in collaboration with Netflix engineers to determine if LDFI could be implemented on our systems. One of the key barriers to adopting chaos engineering seems to be a lack of understanding about the concept, and an unwillingness to create more “technical debt” in trying to integrate it. LDFI works by reasoning about the system behavior of successful requests in order to identify candidate faults to inject. (And... let's face it, as a good coping strategy, too!) How do you recognize its steady state? ‘This is how software will be built in ten years’ – an interview with the CEO of Gremlin. Chaos Mesh is a tool to perform chaos engineering experiment Otherwise, we are just building confidence in a system other than the one we care about, which diminishes the value of the exercise. Only later did our take on it become known as Chaos Engineering. In order to catch the threats to resiliency that Chaos Engineering is interested in, you need to expose experiments to the same state problems that exist in the production environment. We help them with instrumentation, metrics, actionable alerts, and best practices. In particular, safety and security features help adoption with IT, and are difficult to build yourself. You can run experiments directly or through automation. Vertically scaling in the datacenter had led to many single points of failure, some of which caused massive interruptions in DVD delivery. By the second year, things were running pretty smoothly. Running a chaos experiment is a great way to find out. More consumers notice the problem, causing a consumer-induced retry storm. Perhaps the most interesting examples of this are systems where comprehensibility is specifically ignored as a design principle. The system as a whole should make sense but subsections of the system don’t have to make sense. If a human peeks under the hood into any of these algorithms, the series of weights and floating-point values of any nontrivial solution is too complex for an individual to make sense of. Developer This empirical process of verification leads to more resilient systems, and builds confidence in the operational behavior of those systems. The failure scenario will be applied only to the experiment node. Try to operationalize your hypothesis using your metrics as much as possible. But while the name conjures associations with, well, chaos, induced software breakages are methodical. Therefore, the hypotheses in our experiments are usually in the form of “the events we are injecting into the system will not cause the system’s behavior to change from steady state.”. Similarly, failure testing breaks a system in some preconceived way, but doesn’t explore the wide open field of weird, unpredictable things that could happen. that brings together Chaos Engineering practitioners from different organizations, there were participants from Google, Amazon, Microsoft, Dropbox, Yahoo!, Uber, cars.com, Gremlin Inc., Univer‐ Commercial support is available from ChaosIQ. In this time of incremental releases and agile development, continuous examination of software is imperative to offer a seamless, faultless, and consistent experience to the users. While we draw on our experiences at Netflix to provide specific examples, the principles outlined in this book are not specific to any one organization, and our guide for designing experiments does not assume the presence of any particular architecture or set of tooling. Imagine a distributed system that serves information about products to consumers. The API does not have all of the information necessary to respond to the request, so it reaches out to microservices C and F. Each of those microservices also need additional information to satisfy the request, so C reaches out to A, and F reaches out to B and G. A also reaches out to B, which reaches out to E, who is also queried by G. The one request to D fans out among the microservices architecture, and it isn’t until all of the request dependencies have been satisfied or timed out that the API layer responds to the mobile application. What we really want is a metric that captures satisfaction of currently active users, since satisfied users are more likely to maintain their subscriptions. The Systems Thinking community uses the term “steady state” to refer to a property such as internal body temperature where the sys‐ tem tends to maintain that property within a certain range or pat‐ tern. That’s true. Service owners can define custom application metrics in addition to the automatic system metrics. Chaos Engineering is an approach for learning about how your system behaves by applying a discipline of empirical exploration. Once again, only induce events that you expect to be able to handle! From financial, medical, and insurance institutions to rocket, farming equipment, and tool manufacturing, to digital giants and startups alike, Chaos Engineering is finding a foothold as a discipline that improves complex systems. The software to build, orchestrate, and automate experiments usually doesn’t come for free with any existing system, and even the best framework for chaos needs adoption in order to be useful. Then come back to Chaos Engineering and it will either uncover other weaknesses that you didn’t know about, or it will give you more confidence that your system is in fact resilient. With this new formalization, we pushed Chaos Engineering forward at Netflix. A hypothetical example based on real-world events will help illustrate the deficiency.