Net flix embracingfailure re-invent2014-141113085858-conversion-gate02
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This document discusses Netflix's approach to embracing failure through fault injection testing. Netflix has over 50 million members across 50+ countries and streams over 1 billion hours of content per month. To ensure high availability, Netflix designs its complex distributed systems for failure by implementing exception handling, fault tolerance, fallbacks, auto-scaling, and redundancy. However, testing failures at such a large scale is challenging. Netflix developed several "monkey" tools that randomly inject failures like instance reboots or availability zone outages to validate system resilience. More advanced tools like Fault Injection Testing (FIT) allow simulating specific failure scenarios by injecting errors or latency at various points. This helps Netflix continuously validate assumptions and discover issues to further improve availability.
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Net flix embracingfailure re-invent2014-141113085858-conversion-gate02
- 1. © 2014 Amazon.com, Inc. and its affiliates. All rights reserved. May not be copied, modified, or distributed in whole or in part without the express consent of Amazon.com, Inc. Embracing Failure Fault Injection and Service Resilience at Netflix Josh Evans – Director of Operations Engineering Naresh Gopalani – Software Engineer and Architect
- 2. • ~50 million members, ~50 countries • > 1 billion hours per month • > 1000 device types • 3 AWS Regions, hundreds of services • Hundreds of thousands of requests/second • CDN serves petabytes of data at terabits/second Netflix Ecosystem Service Partners Static Content Akamai Netflix CDN AWS/Netfli x Control Plane Internet
- 3. Availability means that members can ● sign up ● activate a device ● browse ● watch
- 4. What keeps us up at night
- 5. Failures can happen any time • Disks fail • Power outages • Natural disasters • Software bugs • Human error
- 6. We design for failure • Exception handling • Fault tolerance and isolation • Fall-backs and degraded experiences • Auto-scaling clusters • Redundancy
- 7. Testing for failure is hard • Web-scale traffic • Massive, changing data sets • Complex interactions and request patterns • Asynchronous, concurrent requests • Complete and partial failure modes Constant innovation and change
- 8. What if we regularly inject failures into our systems under controlled circumstances?
- 10. Blast Radius • Unit of isolation • Scope of an outage • Scope a chaos exercise Zone Region Instance Global
- 11. An Instance Fails Edge Cluster Cluster A Cluster B Cluster D Cluster C
- 12. Chaos Monkey • Monkey loose in your DC • Run during business hours • What we learned – Auto-replacement works – State is problematic
- 13. A State of Xen - Chaos Monkey & Cassandra Out of our 2700+ Cassandra nodes • 218 rebooted • 22 did not reboot successfully • Automation replaced failed nodes • 0 downtime due to reboot
- 14. An Availability Zone Fails EU-West US-EastUS-West AZ1 AZ2
- 15. Chaos Gorilla Simulate an availability zone outage • 3-zone configuration • Eliminate one zone • Ensure that others can handle the load and nothing breaks Chaos Gorilla
- 16. Challenges • Rapidly shifting traffic – LBs must expire connections quickly – Lingering connections to caches must be addressed • Service configuration – Not all clusters auto-scaled or pinned – Services not configured for cross-zone calls – Mismatched timeouts – fallbacks prevented fail-over
- 18. AZ1 AZ2 AZ3 Regional Load Balancers Zuul – Traffic Shaping/Routing Data Data Data Geo-located Chaos Kong Chaos Kong AZ1 AZ2 AZ3 Regional Load Balancers Zuul – Traffic Shaping/Routing Data Data Data Customer Device
- 19. Challenges ● Rapidly shifting traffic ○ Auto-scaling configuration ○ Static configuration/pinning ○ Instance start time ○ Cache fill time
- 20. Challenges ● Service Configuration ○ Timeout configurations ○ Fallbacks fail or don’t provide the desired experience ● No minimal (critical) stack ○ Any service may be critical!
- 22. Services Slow Down and Fail Simulate latent/failed service calls • Inject arbitrary latency and errors at the service level • Observe for effects Latency Monkey
- 23. Latency Monkey Device ZuulELB Edge Service B Service C Internet Service A
- 24. Challenges • Startup resiliency is an issue • Services owners don’t know all dependencies • Fallbacks can fail too • Second order effects not easily tested • Dependencies are in constant flux • Latency Monkey tests function and scale – Not a staged approach – Lots of opt-outs
- 25. More Precise and Continuous
- 27. Distributed Systems Fail ● Complex interactions at scale ● Variability across services ● Byzantine failures ● Combinatorial complexity
- 28. Any service can cause cascading failures ELB
- 29. Fault Injection Testing (FIT) Device Service B Service C Internet Edge Device or Account Override Zuul Service A Request-level simulations ELB
- 30. Failure Injection Points IPC Cassandra Client Memcached Client Service Container Fault Tolerance
- 31. FIT Details ● Common Simulation Syntax ● Single Simulation Interface ● Transported via Http Request header
- 32. Integrating Failure Service Filter Ribbon Service Filter Ribbon ServerRcv ServerRcv ClientSend request Service A response Service B [sendRequestHeader] >>fit.failure: 1|fit.Serializer| 2|[[{"name”:”failSocial, ”whitelist":false, "injectionPoints”: [“SocialService”]},{} ]], {"Id": "252c403b-7e34-4c0b-a28a-3606fcc38768"}]]
- 33. Failure Scenarios ● Set of injection points to fail ● Defined based on ○ Past outages ○ Specific dependency interactions ○ Whitelist of a set of critical services ○ Dynamic tracing of dependencies
- 34. FIT Insights : Salp ● Distributed tracing inspired by Dapper paper ● Provides insight into dependencies ● Helps define & visualize scenarios
- 35. Functional Validation ● Isolated synthetic transactions ○ Set of devices Validation at Scale ● Dial up customer traffic - % based ● Simulation of full service failure Dialing Up Failure Chaos!
- 37. Don’t Fear The Monkeys
- 38. Take-aways • Don’t wait for random failures – Cause failure to validate resiliency – Remove uncertainty by forcing failures regularly – Better to fail at 2pm than 2am • Test design assumptions by stressing them Embrace Failure
- 39. The Simian Army is part of the Netflix open source cloud platform http://netflix.github.com
- 40. Netflix talks at re:Invent Talk Time Title BDT-403 Wednesday, 2:15pm Next Generation Big Data Platform at Netflix PFC-306 Wednesday, 3:30pm Performance Tuning EC2 DEV-309 Wednesday, 3:30pm From Asgard to Zuul, How Netflix’s proven Open Source Tools can accelerate and scale your services ARC-317 Wednesday, 4:30pm Maintaining a Resilient Front-Door at Massive Scale PFC-304 Wednesday, 4:30pm Effective Inter-process Communications in the Cloud: The Pros and Cons of Micro Services Architectures ENT-209 Wednesday, 4:30pm Cloud Migration, Dev-Ops and Distributed Systems APP-310 Friday 9:00am Scheduling using Apache Mesos in the Cloud
- 41. Please give us your feedback on this presentation © 2014 Amazon.com, Inc. and its affiliates. All rights reserved. May not be copied, modified, or distributed in whole or in part without the express consent of Amazon.com, Inc. Join the conversation on Twitter with #reinvent Josh Evans jevans@netflix.com @josh_evans_nflx Naresh Gopalani ngopalani@netflix.com