Complex distributed systems fail. They fail more frequently, and in different ways, as they scale and evolve over time. In this session, you learn how Netflix embraces failure to provide high service availability. Netflix discusses their motivations for inducing failure in production, the mechanics of how Netflix does this, and the lessons they learned along the way. Come hear about the Failure Injection Testing (FIT) framework and suite of tools that Netflix created and currently uses to induce controlled system failures in an effort to help discover vulnerabilities, resolve them, and improve the resiliency of their cloud environment.

1. © 2014 Amazon.com, Inc. and its affiliates. All rights reserved. May not be copied, modified, or distributed in whole or in partwithout the express consent of Amazon.com, Inc.
November 12th, 2014 | Las Vegas, NV
PFC305Embracing Failure
Fault Injection and Service Resilience at Netflix
Josh Evans and NareshGopalani, Netflix

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/Netflix
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-East
US-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

17. A Region Fails
EU-West
US-East
US-West

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!

21. A Service Fails
Zone
Region
Global
Service

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
Zuul
ELB
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

26. Service Failure Testing:FIT

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!

36. Continuous Validation
Critical
Services
Non-critical Services
Synthetic Transactions

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 Amazon EC2
DEV-309
Wednesday, 3:30pm
From Asgardto 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 InterProcessCommunications in the Cloud: The Pros and Cons of MicroservicesArchitectures
ENT-209
Wednesday, 4:30pm
Cloud Migration, Dev-Ops and Distributed Systems
APP-310
Friday 9:00am
Scheduling using Apache Mesosin the Cloud

41. Please give us your feedback on this session.
Complete session evaluations and earn re:Invent swag.
http://bit.ly/awsevals
Josh Evans
jevans@netflix.com
@josh_evans_nflx
Naresh Gopalani
ngopalani@netflix.com