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Digital solution teams often face delays or other disruptions while fixing unexpected challenges during the development of systems & applications. But are you familiar with Chaos Engineering, a popular practice that aims to proactively identify potential issues in a system before they become actual problems for real-time users?

What is Chaos Engineering?

Chaos engineering is a planned approach where you experiment with the IT system & applications by deliberately introducing errors with a focus on identifying weaknesses & improving the system’s resilience.

Implementing Chaos Engineering can improve the stability of a system and better understand potential risks it may hold. The technique involves running controlled experiments and has the capacity to convert systems to their previous state without disturbing users. This way, Enterprises can securely understand how their systems behave under different scenarios and configure them to become more resilient and avoid potential system outages.

Chaos Engineering Principles

Chaos Engineering pre-requisites a well-thought-out approach that covers a course of actions starting from planning out the hypothesis – what could go wrong, then spans into introducing the stress/failure, assessing the impact & deriving actionable insights to improvise the system. Here is the simplified version of standardized principles of chaos engineering, which engineering teams have widely practised.

Establish

A hypothesis through a ‘steady state’, which indicates normal behaviour and is the measurable output of a system.

Consider

The ‘steady state’ to exist in both the control group and the experimental group.

Introduce

‘Variables’ into the mix that are potential threats in a real-world scenario, like server crashes, system malfunctions, severed network connections, etc.

Disprove

The hypothesis that your ‘steady state’ or the system is resilient by automating the experiment and trying to find differences between the two groups.

A slew of enterprises across the verticals has been implementing chaos engineering to improve the confidence of their IT ecosystem. Below are use cases of how some of the top brands in the world used chaos engineering to better their system operations for reliable digital offerings.

Netflix

With users across time zones, Netflix is the choice of entertainment for many at various hours of the day. The impact of outages on paying subscribers was proven damaging to the perception of their streaming service. Chaos Monkey was created by Netflix engineers in 2011. and the name was given to a tool that randomly “unplugs” instances during business hours – enabling product developers to learn about real-time vulnerabilities and solve problems faster.

Uber

The ridesharing behemoth- Uber is backed by one of the largest micro-services architectures that are often stressed by both business means –user surges, network outages, data regulations & technical means – frequent deployments, CI/CD. To make their application systems more ‘Anti-Fragile’, they incrementally opted for chaos engineering instead of unit testing, removing instances to asses how they could improve their continuous integration & change management.

Capital One

Being the first-ever bank to run entirely on the public cloud, CapitalOne resorted to chaos engineering to sustain its banking performance & security. They built their ecosystem with the Infrastructure as Code (IaC) approach. And with the help of chaos engineering experiments, they found the right strategies to strike the right balance between lower latency & high capacity.

Twilio

Twilio embraced chaos engineering with a drive to make their systems high-available & instilling self-healing capabilities. Distributed queuing and rate limiting systems are core areas of this cloud communication platform major, and they built their chaos engineering tool, Rate queue – an internally-developed distributed queueing system built over Redis that provides load balancing & isolation to provide automatic failure detection & avoids message loss.

Chaos Engineering – Best Practices

It’s essential to continuously focus on improving your chaos engineering experiments based on the results and feedback you receive. Use what you learn to make your systems more resilient and to improve your processes and procedures. Some of the best practices to follow while implementing chaos engineering would be:

  • Grasp the system’s behaviour in a steady state and learn about measurable outputs so that you can identify the anomalies produced as the result of the experiments.
  • Understand the variables related to real-world scenarios which would impact system outputs. And always include those variables in your chaos experiment. This helps widen the experiment coverage for the maximum extent of possible failures.
  • Induce the experiments in production experiment in a controlled manner. That’s the ideal way to assess the system’s performance in its real working condition & concentrate on fruitful enhancements.
  • Minimize the blast radius – the spread of impact caused by conducting the chaos experiments. While planning, chaos engineering teams should count on the blast radius factor and conduct the experiments without creating a negative effect on users & customers.

There are several tools and platforms available to support Chaos Engineering experiments. Some are open-source and freely available, while others are commercial products that require a license or subscription. Here are some popular ones used widely:

To produce successful digital offerings, building systems that are capable and reliable in any possible scenario is as essential as developing great functionalities. This fact necessitates a proactive approach to examine & strengthen your IT ecosystem’s confidence. And chaos engineering is such a radical approach where you tear apart your system & applications in a controlled manner so that you can eliminate weaknesses & vulnerabilities that otherwise might put you behind in the market.

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