Chaos Engineering: Why Breaking Things Should Be Practiced

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© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Failures are a given and everything will eventually fail over time. Werner Vogels CTO – Amazon.com “ “

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© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. “Chaos Engineering is the discipline of experimenting on a distributed system in order to build confidence in the system’s capability to withstand turbulent conditions in production.” http://principlesofchaos.org

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© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Building Confidence Through Testing Unit testing of components: • Tested in isolation to ensure function meets expectations. Functional testing of integrations: • Each execution path tested to assure expected results. Is it enough???

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© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Failure Injection • Start small & build confidence • Application level • Host failure • Resource attacks (CPU, memory, …) • Network attacks (dependencies, latency, …) • Region attacks!

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© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Availability Availability Downtime per year 99% (2-nines) 3 days 15 hours 99.99% (4-nines) 52 minutes 99.999% (5-nines) 5 minutes 99.9999% (6-nines) 31 seconds

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© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. System Availability Availability = Normal Operation Time Total Time MTBF** MTBF** + MTTR* = * Mean Time To Repair (MTTR) **Mean Time Between Failure (MTBF)

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© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Availability in Parallel Component Availability Downtime X 99% (2-nines) 3 days 15 hours Two X in parallel 99.99% (4-nines) 52 minutes Three X in parallel 99.9999% (6-nines) 31 seconds

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© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Infrastructure as Code • Template of the infrastructure in code. • Version controlled infrastructure. • Repeatable template. • Testable infrastructure. • Automate it!

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© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Immutable Infrastructure • No updates on live systems • Always start from a new resource being provisioned • Deploy the new software • Test in different environments (dev, staging) • Deploy to prod (inactive) • Change references (DNS or Load Balancer) • Keep old version around (inactive) • Fast rollback if things go wrong

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© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. CAP Theorem Consistency Availability Partition Tolerance Data is consistent. All nodes see the same state. Every request is non-failing. Service still responds as expected if some nodes crash. Distributed System In the presence of a network partition, you must choose between consistency and availability!

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© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Eventual Consistency … if no new updates are made to a given data item, eventually all accesses to that item will return the last updated value. Availability An eventually consistent system can return any value before it converges!! https://en.wikipedia.org/wiki/Eventual_consistency Distributed System Every request is non-failing.

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© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Web Instances Worker Instance Worker Instance Queue API Instance API Instance API Instance API: {DO foo} PUT JOB: {JobID: 0001, Task: DO foo} API: {JobID: 0001} GET JOB: {JobID: 0001, Task: DO foo} Cache Result: { JobID: 0001, Result: bar }

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© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Read / Write Sharding RDS DB Instance Read Replica App Instance App Instance App Instance RDS DB Instance Master (Multi-AZ) RDS DB Instance Read Replica RDS DB Instance Read Replica

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© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Cascading Failures a.k.a the Punisher! • Timeouts • Retries & Exponential Backoff • Idempotent operations • Exception Handling • Rejection • Intermittent and transient errors • Service degradation & fallbacks

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© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Circuit Breaker • Wrap a protected function call in a circuit breaker object, which monitors for failures. • If failures reach a certain threshold, the circuit breaker trips.

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What is Steady State? • ”normal” behavior of your system • Business Metric https://medium.com/netflix-techblog/sps-the-pulse-of-netflix-streaming-ae4db0e05f8a

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© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Business Metrics at work Amazon: 100 ms of extra load time caused a 1% drop in sales (Greg Linden). Google: 500 ms of extra load time caused 20% fewer searches (Marissa Mayer). Yahoo!: 400 ms of extra load time caused a 5–9% increase in the number of people who clicked “back” before the page even loaded (Nicole Sullivan).

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© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. What if…? “What if this load balancer breaks?” “What if Redis becomes slow?” “What if a host on Cassandra goes away?” ”What if latency increases by 300ms?” ”What if the database stops?” Make it everyone’s problem!

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© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Designing Experiment • Pick hypothesis • Scope the experiment • Identify metrics • Notify the organization

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© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Rules of thumbs • Start with very small • As close as possible to production • Minimize the blast radius. • Have an emergency STOP!

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© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Quantifying the result of the experiment • Time to detect? • Time for notification? And escalation? • Time to public notification? • Time for graceful degradation to kick-in? • Time for self healing to happen? • Time to recovery – partial and full? • Time to all-clear and stable?

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© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. More questions to ask. • Can you clarify if there were any preceding events? • Why would they believe acting in this way was the best course of action to deliver the desired outcome? • Is there another failure mode that could present here? • What decisions or events prior to this made this work before? • Why stop there – are there places to dig deeper that could shine a light more on this? • Did others step in to help, to advise, or to intercede?

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© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Big Challenges to Chaos Engineering Mostly Cultural • no time or flexibility to simulate disasters. • teams already spending all of its time fixing things. • can be very political. • might force deep conversations. • deeply invested in a specific technical roadmap (microservices) that chaos engineering tests show is not as resilient to failures as originally predicted.

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© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. More Resources • https://mvdirona.com/jrh/talksAndPapers/JamesRH_Lisa.pdf • https://www.gremlin.com • https://queue.acm.org/detail.cfm?id=2353017 • https://softwareengineeringdaily.com/ • https://github.com/dastergon/awesome-sre • https://www.usenix.org/system/files/conference/osdi14/osdi14-paper-yuan.pdf • https://medium.com/@NetflixTechBlog • http://principlesofchaos.org • https://speakerdeck.com/tammybutow/chaos-engineering-bootcamp • https://github.com/adhorn/awesome-chaos-engineering • https://www.infoq.com/presentations/netflix-chaos-microservices • http://royal.pingdom.com/wp-content/uploads/2015/04/pingdom_uptime_cheat_sheet.pdf • http://willgallego.com/2018/04/02/no-seriously-root-cause-is-a-fallacy

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