Architectural Patterns of Resilient Distributed Systems
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This document contains the slides from a presentation given by Ines Sombra at Strangeloop 2015 about architectural patterns for building resilient distributed systems. The presentation discusses defining resilience, models of resilience from literature like probabilistic availability and decomposition/orthogonality. It also discusses insights from industry systems like Netflix, Google Chubby, Twitter Truce, and resilient architectural patterns like redundancies, gossip protocols, and prioritizing operability and the ability to handle unknown unknowns.
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Architectural Patterns of Resilient Distributed Systems
- 1. ! OMG Strangeloop 2015!! Architectural Patterns of Resilient Distributed Systems
- 3. Globally distributed & highly available
- 4. Today’s Journey Why care? Resilience literature Resilience in industry Conclusions @randommood ♥
- 5. OBLIGATORY DISCLAIMER SLIDE All from a practitioner’s perspective! @randommood Things you may see in this talk Pugs Fast talking Life pondering Un-tweetable moments Rantifestos What surprised me this year Wedding factoids and trivia
- 7. How can I make a system more resilient? @randommood ♥
- 8. @randommood Resilience is the ability of a system to adapt or keep working when challenges occur
- 12. Harvest & Yield Model
- 13. @randommood Fraction of successfully answered queries Close to uptime but more useful because it directly maps to user experience (uptime misses this) Focus on yield rather than uptime Yield
- 14. @randommood From Coda Hale’s “You can’t sacrifice partition tolerance” Server A Server B Server C Baby AnimalsCute Harvest Fraction of the complete result
- 15. @randommood From Coda Hale’s “You can’t sacrifice partition tolerance” Server A Server B Server C Baby AnimalsCute X 66% harvest Harvest Fraction of the complete result
- 16. @randommood #1: Probabilistic Availability Graceful harvest degradation under faults Randomness to make the worst-case & average-case the same Replication of high-priority data for greater harvest control Degrading results based on client capability
- 17. @randommood #2 Decomposition & Orthogonality Decomposing into subsystems independently intolerant to harvest degradation but the application can continue if they fail You can only provide strong consistency for the subsystems that need it Orthogonal mechanisms (state vs functionality) ♥
- 18. @randommood “If your system favors yield or harvest is an outcome of its design” Fox & Brewer
- 19. Cook & Rasmussen model
- 20. Economic failure boundary Unacceptable workload boundary Accident boundary Cook & Rasmussen Operating point
- 22. Economic failure boundary Unacceptable workload boundary Accident boundary Pressure towards efficiency Cook & Rasmussen
- 23. Economic failure boundary Unacceptable workload boundary Accident boundary Pressure towards efficiency Reduction of effort Cook & Rasmussen
- 24. Economic failure boundary Unacceptable workload boundary Accident boundary Pressure towards efficiency Reduction of effort Cook & Rasmussen Incident!
- 25. Economic failure boundary Unacceptable workload boundary Accident boundary Pressure towards efficiency Reduction of effort Safety Campaign Cook & Rasmussen
- 26. Economic failure boundary Unacceptable workload boundary Accident boundary Pressure towards efficiency Reduction of effort error margin Marginal boundary Safety Campaign Cook & Rasmussen
- 27. error margin Original marginal boundary R.I.Cook - 2004 Acceptable operating point Accident boundary Flirting with the margin
- 28. error margin Original marginal boundary R.I.Cook - 2004 Acceptable operating point Accident boundary Flirting with the margin
- 29. error margin Original marginal boundary R.I.Cook - 2004 Acceptable operating point Accident boundary Flirting with the margin
- 30. error margin Original marginal boundary R.I.Cook - 2004 Acceptable operating point Accident boundary Flirting with the margin
- 31. error margin Original marginal boundary R.I.Cook - 2004 Acceptable operating point Accident boundary Flirting with the margin
- 32. error margin Original marginal boundary R.I.Cook - 2004 Acceptable operating point Accident boundary Flirting with the margin
- 33. R.I.Cook - 2004 Accident boundary Flirting with the margin New marginal boundary!
- 34. R.I.Cook - 2004 Accident boundary Flirting with the margin New marginal boundary!
- 35. @randommood Insights from Cook’s model Engineering resilience requires a model of safety based on: mentoring, responding, adapting, and learning System safety is about what can happen, where the operating point actually is, and what we do under pressure Resilience is operator community focused
- 36. @randommood Engineering system resilience Build support for continuous maintenance Reveal control of system to operators Know it’s going to get moved, replaced, and used in ways you did not intend Think about configurations as interfaces
- 41. Borrill's model
- 42. Traditional engineering Reactive ops unk-unk @randommood Probability of failure Rank A system’s complexity Cascading or catastrophic failures & you don’t know where they will come from! Same area as other 2 combined
- 43. Traditional engineering Reactive ops unk-unk @randommood Failure areas need != strategies Probability of failure Rank
- 44. Traditional engineering Reactive ops unk-unk @randommood Failure areas need != strategies Probability of failure Rank Kingsbury
- 45. Traditional engineering Reactive ops unk-unk @randommood Failure areas need != strategies Probability of failure Rank Kingsbury VS
- 46. Traditional engineering Reactive ops unk-unk @randommood Failure areas need != strategies Probability of failure Rank Kingsbury Alvaro VS
- 47. Strategies to build resilience Code standards Programming patterns Testing (full system!) Metrics & monitoring Convergence to good state Hazard inventories Redundancies Feature flags Dark deploys Runbooks & docs Canaries System verification Formal methods Fault injection Classical engineering Reactive Operations Unknown-Unknown The goal is to build failure domain independence
- 48. @randommood “Thinking about building system resilience using a single discipline is insufficient. We need different strategies” Borrill
- 52. @randommood API inherently more vulnerable to any system failures or latencies in the stack Without fault tolerance: 30 dependencies w 99.99% uptime could result in 2+ hours of downtime per month! Leveraged client libraries
- 53. @randommood Netflix’s resilient patterns Aggressive network timeouts & retries. Use of Semaphores. Separate threads on per- dependency thread pools Circuit-breakers to relieve pressure in underlying systems Exceptions cause app to shed load until things are healthy
- 54. @randommood We went on a diet just like you!#
- 55. $ $
- 56. @randommood Key insights from Chubby Library vs service? Service and client library control + storage of small data files with restricted operations Engineers don’t plan for: availability, consensus, primary elections, failures, their own bugs, operability, or the future. They also don’t understand Distributed Systems
- 57. @randommood Key insights from Chubby Centralized services are hard to construct but you can dedicate effort into architecting them well and making them failure-tolerant Restricting user behavior increased resilience Consumers of your service are part of your UNK- UNK scenarios
- 58. @randommood And the family arrives!
- 59. @randommood Key insights from Truce Evolution of our purging system from v1 to v3 Used Bimodal Multicast (Gossip protocol) to provide extremely fast purging speed Design concerns & system evolution Tyler McMullen Bruce Spang
- 60. Existing best practices won’t save you @randommood Key insights from NetSys João Taveira Araújo looking suave Faild allows us to fail & recover hosts via MAC- swapping and ECMP on switches Do immediate or gradual host failure & recovery Watch Joao’s talk
- 61. Existing best practices won’t save you @randommood Key insights from NetSys João Taveira Araújo looking suave Faild allows us to fail & recover hosts via MAC- swapping and ECMP on switches Do immediate or gradual host failure & recovery Watch Joao’s talk
- 62. Existing best practices won’t save you @randommood Key insights from NetSys João Taveira Araújo looking suave Faild allows us to fail & recover hosts via MAC- swapping and ECMP on switches Do immediate or gradual host failure & recovery Watch Joao’s talk
- 63. @randommood So we have a myriad of systems with different stages of evolution Resilient systems like Varnish, Powderhorn, and Faild have taught us many lessons but some applications have availability problems, why? But wait a minute! ♥
- 66. @randommood Redundancies are key Redundancies of resources, execution paths, checks, replication of data, replay of messages, anti-entropy build resilience Gossip / epidemic protocols too Capacity planning matters Optimizations can make your system less resilient!
- 67. @randommood Unawareness of proximity to error boundary means we are always guessing Complex operations make systems less resilient & more incident-prone You design operability too! Operations matter
- 68. @randommood Complexity if increases safety is actually good Adding resilience may come at the cost of other desired goals (e.g. performance, simplicity, cost, etc) Not all complexity is bad
- 69. @randommood Leverage Engineering best practices Resiliency and testing are correlated. TEST! Versioning from the start - provide an upgrade path from day 1 Upgrades & evolvability of systems is still tricky. Mixed-mode operations need to be common Re-examine the way we prototype systems
- 71. tl;dr OPERABILITYWHILE IN DESIGN UNK-UNK Are we favoring harvest or yield? Orthogonality & decomposition FTW Do we have enough redundancies in place? Are we resilient to our dependencies? Am I providing enough control to my operators? Would I want to be on call for this? Rank your services: what can be dropped, killed, deferred? Monitoring and alerting in place? The existence of this stresses diligence on the other two areas Have we done everything we can? Abandon hope and resort to human sacrifices ♥ ♥ Theory matters!
- 72. IMPROVING OPERABILITYWHILE IN DESIGN Test dependency failures Code reviews != tests. Have both Distrust client behavior, even if they are internal Version (APIs, protocols, disk formats) from the start. Support mixed-mode operations. Checksum all the things Error handling, circuit breakers, backpressure, leases, timeouts Automation shortcuts taken while in a rush will come back to haunt you Release stability is o"en tied to system stability. Iron out your deploy process Link alerts to playbooks Consolidate system configuration (data bags, config file, etc) tl;dr♥ ♥ Operators determine resilience
- 73. @randommood We can’t recover from lack of design. Not minding harvest/yield means we sign up for a redesign the moment we finish coding. TODAY’S RANTIFESTO♥ ♥
- 74. Thank you!github.com/Randommood/Strangeloop2015 77 Special thanks to Paul Borrill, Jordan West, Caitie McCaffrey, Camille Fournier, Mike O'Neill, Neha Narula, Joao Taveira, Tyler McMullen, Zac Duncan, Nathan Taylor, Ian Fung, Armon Dadgard, Peter Alvaro, Peter Bailis, Bruce Spang, Matt Whiteley, Alex Rasmussen, Aysulu Greenberg, Elaine Greenberg, and Greg Bako.