Chaos engineering - why breaking things should be practised.

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© 2018, 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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© 2018, 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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© 2018, 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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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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© 2018, 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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© 2018, 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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© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Netflix team members Casey Rosenthal, Lorin Hochstein, Aaron Blohowiak, Nora Jones, and Ali Basiri, suggest the following inputs for Chaos experiments: • Simulating the failure of an entire region or datacenter. • Partially deleting Kafka topics over a variety of instances to recreate an issue that occurred in production. • Injecting latency between services for a select percentage of traffic over a predetermined period of time. • Function-based chaos (runtime injection): Randomly causing functions to throw exceptions. • Code insertion: Adding instructions to the target program and allowing fault injection to occur prior to certain instructions. • Time travel: Forcing system clocks out of sync with each other. • Executing a routine in driver code emulating I/O errors. • Maxing out CPU cores on an Elasticsearch cluster.

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© 2018, 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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© 2018, 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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© 2018, 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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© 2018, 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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© 2018, 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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© 2018, 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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© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Availability in Series Component Availability Downtime X 99% (2-nines) 3 days 15 hours Y 99.99% (4-nines) 52 minutes X and Y Combined 98.99% 3 days 16 hours 33 minutes

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© 2018, 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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© 2018, 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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© 2018, 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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© 2018, 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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© 2018, 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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© 2018, 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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© 2018, 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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© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Avoiding Cascading Failures • Timeouts • Retries & Exponential Backoff • Idempotent operations • Exception Handling • Rejection • Intermittent and transient errors • Service degradation & fallbacks

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© 2018, 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. Producer Circuit Breaker Consumer Connection Monitoring Timeouts Breaking Circuit

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© 2018, 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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