The microservices world is constantly developing with more and more complexities restricting traceability and eventually, compromising resiliency. Reliability is a factor that drives an infrastructure’s overall and eventual performance. To achieve a reliable system and overall reliability goals, it is vital to understand how your system might react to outages caused on purpose to prevent an actual outage in the future.
“Experience is the teacher of all things”
- Julius Caesar
As the quote very well implicates, experiencing failures helps you evaluate how to build truly reliable systems. Why wait for failures in production when you can test them while building or in staging? To understand these failures, we need to understand how each component of the system reacts to turbulent conditions and hence, the term Chaos Engineering comes into the fray. In this blog, we will understand the meaning of Chaos Engineering and how it drives the true essence of achieving resiliency & reliability by learning from failures & eliminating them.
Chaos Engineering is nothing but the art of injecting faults deliberately & in a controlled way in a system to understand the repercussions and reactions of the system amidst failure before they actually happen. It helps one to gain confidence over his/her system and avoid future failures.
Chaos Engineering helps you simulate worst-case scenarios and find potential failures even before they happen to avoid their occurrence once and for all.
It comes up as an essential and dynamic practice to make distributed systems rigid and stabilized by identifying all the possible faults and inconsistencies, all in all resulting in resilient infrastructure. To counter failures and outages in the system as well as infrastructure, Chaos Engineering comes into play as a means to gauge system behavior, blast radius, and recovery times. It basically involves experimenting with a distributed system to to gain assurance in the system’s ability to withstand chaotic conditions. With today’s frequently changing and highly complex systems, chaos engineering needs to be accepted as an important approach to achieve resilient infrastructure. Through chaos engineering, unanticipated failure scenarios can be discovered and corrected before causing user issues.
All enterprises, SREs, Q&A engineers and developers looking forward to reducing system failures and outages along to reduce revenue loss must use chaos engineering tools for testing purposes. Netflix started applying Chaos Engineering through Chaos Monkey to strengthen its physical infrastructure and now it has become a de-facto in the world of automated and resilient systems. From the e-commerce industry to the finance industry, various companies are switching to Chaos testing and Chaos Engineering tools.
For enterprises & individuals to embrace Chaos engineering as a testing methodology it may require a cultural shift: from using generic testing to protect your applications at all costs , to allowing chaos and a little danger into your life to prevent a chaotic future.
So, What exactly are the principles one must follow to start his/her Chaos Engineering journey? A few essential principles describe an ideal infrastructure for performing Chaos Engineering. Experiments are conducted to identify the weaknesses of a system. Once discovered, it provides a scope for improvement before the weakness manifests in the system in a greater way. You can find a detailed description of the principles here: https://principlesofchaos.org/
The following principles are adhered to for conducting chaos experiments:
I. Formulate a hypothesis: For a start, assuming how the system might react if experimenting goes wrong helps in the formation of a hypothesis for the output of the system that can be measured.
II. Identifying the variables: A probable variable for a Chaos experiment can be identified by comparing a Chaos environment with real-life events as chaos variables are a reflection of real-life events. Hence, it is very important to identify and prioritize the variables according to the probability and estimated impact of the event.
III. Automation of running experiments: It is extremely important to tie the act of Chaos itself with automated orchestration (steady-state condition checks) and analysis (data availability & integrity, application, and storage deployments’ health, etc.,). An experiment can’t be run manually as it demands comprehensive labor work and eventually not sustainable. Hence, Chaos Engineering builds automation into the system by running them continuously.
IV. Reduced Blast Radius: To avoid a negative impact or drastic results, it is crucial to control the Blast Radius and conduct experiments at a reduced blast radius to ensure minimum negative influence or a short-term negative influence.
V. Scaling the Blast Radius: The experiment is a success if a fault is identified with the reduced blast radius or else one can always scale the blast radius until a proper figure is obtained. It helps to improvise the system’s real-life behavior.
Failures help us learn about our shortcomings. Each system is prone to vulnerabilities and it is vital to understand these vulnerabilities before they get out of hand. An outage or a downtime can lead to a hefty loss in time as well as money. Chaos Engineering isn’t a luxury but a necessity for all sorts of systems. The road to adoption gets easier when you think in that direction.
The key to starting the journey to adoption is by running a few generic tests now and adhering to the principles of Chaos Engineering. Remember, you are not just breaking things on purpose, you are breaking things to secure them on purpose!