Outages are inevitable, but their impact on customers doesn‘t have to be. By proactively putting systems under stress to surface weaknesses, organizations around the world are reinforcing reliability and safeguarding user experience. This emerging discipline known as "chaos engineering" has quickly become a cornerstone practice for tech leaders aiming to reduce incident rates.
In this comprehensive guide, you‘ll learn what chaos engineering entails, its real-world benefits, an overview of popular tools, and best practices to implement it successfully within your organization.
What is Chaos Engineering?
Chaos engineering is the practice of intentionally injecting failures into systems to uncover latent weaknesses before they surface as outages that impact customers. By proactively putting systems under stress in a controlled manner, you can surface the cascading failures, bottlenecks, single points of failure and other weaknesses that can lead to incidents.
The learnings from chaos experiments allow you to address architectural and design flaws through better patterns and redundancy for higher resilience. By surface weaknesses in pre-production environments, you gain confidence that systems can withstand inevitable real-world failures when they do happen.
Core Principles
Chaos engineering builds on principles from chaos theory and non-linear system analysis:
- Systems behave differently under turbulent conditions – the only way to gain confidence is to observe them under such conditions. Outages uncover new, unexpected failure modes.
- Experiment in pre-production environments – GameDays and blameless post-mortems look backwards. Chaos engineering provides failures as a first-class citizen to engineer more resilience upfront.
- Hypothesize around steady state – Define what it means for your system to be in a good, stable state so you can detect deviations more easily.
- Vary real-world events – Match the kind of failures that occur in prod like servers crashing, networks glitching, etc.
Why Chaos Engineering Matters
Look no further than tech leaders like Amazon, Netflix, Apple, and Google who have publicly praised chaos engineering for lowering incident rates and reinforced trust with customers through better availability, reliability and resilience to outages.
Some benefits include:
- Up to 80% reduction in incident frequency
- 62% faster MTTR from better runbooks, redundancy, and team experience responding
- Increased customer satisfaction and brand loyalty
By learning ahead of time how cascading failures ripple through production systems, organizations large and small can significantly harden reliability through chaos experiments…
Overview of Chaos Tools
The good news is teams don‘t have to build custom solutions from scratch to reap the benefits of chaos engineering. There are over two dozen excellent open source and commercial tools designed specifically for chaos testing across application stacks, infrastructure, and cloud environments. Let‘s explore some leading options:
Kubernetes-Native Tools
For orchestrated chaos on Kubernetes infrastructure and application workloads, Chaos Mesh and Litmus lead the way.
Chaos Mesh offers a superior dashboard and visibility into fault injection impact through time series metrics. It focuses specifically on Kubernetes-layer crashes of pods and containers.
Litmus on the other hand provides a wider breadth of over 20 fault injection methods encompassing pods, network, Docker daemons, Kubernetes APIs, disks, and more. It also offers a marketplace of pre-packaged experiments developed by the community.
Public Cloud Services
All major cloud provides like AWS, Google Cloud (GCP) and Microsoft Azure offer first-party chaos services focused mainly on instance and infrastructure fault injection:
- AWS FIS
- GCP Cloud Failover Toolkit
- Azure Fault Analysis Service
For workload testing, Gremlin provides the most mature SaaS solution with automated agent install, unified cross-cloud/hybrid visibility, and a library of failure modes like Blackhole, Clock Skew, CPU Spike, Packet Loss, Latency, etc.
On-Prem Environments
For traditional on-prem environments, Chaos Toolkit is likely the most flexible starting point given its agentless architecture. It relies on a simple, declarative OpenAPI approach to integrate with any infrastructure stack.
Steadybit is another intriguing self-hosted option combining intelligent monitoring and chaos experiments to correlate system health telemetry with weakness exposures.
Other Notable Tools
Beyond the major platforms above, other open source chaos tools like Chaos Monkey, Pumba, Muxy, PowerfulSeal and more exist to stress specific layers like network, infrastructure, cloud instances, and custom workloads.
The key is matching your critical components with a chaos tool purpose-built to test resilience at that layer. Over time, leading teams use multiple chaos solutions for comprehensive coverage across stacks.
Best Practices for Running Experiments
While tools make it operationally easier to orchestrate chaos, there are important best practices around establishing an incrementally safer approach:
Start Small, Expand Slowly
Define an initial blast radius in pre-production to limit risk. For example, target only 1 node or pod replica set. As you gain confidence, slowly expand the magnitude and combination of failures.
Establish Steady State
Determine and codify metrics like application response time, error rates, pod restart rate etc that indicate overall system health. Watch these closely during experiments to detect deviations or customer-impacting issues.
Automate Early
Manual testing can only go so far. Evolve towards automating chaos experiments directly in CI/CD pipelines to run tests continually against the latest builds.
Remediate, Rinse, Repeat
For each weakness uncovered, fix the root cause through better redundancy, resource provisioning, timeouts etc. Rerun experiments to demonstrate improved resilience.
In Closing
This guide covered what chaos engineering entails, why tech leaders are embracing chaos, an overview of popular tools, and best practices for safe adoption within your organization.
The tools and techniques exist to take the learnings from the inevitability of failures and shift them left – to reinforce resilience proactively instead of reactively. Increase trust in your team and systems by putting chaos engineering principles into practice across critical services. Your customers will thank you.
