PyBay 2018: Production-Ready Python Applications
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In 2016, Susan Fowler released the 'Production Ready Microservices' book. This book sets an industry benchmark on explaining how microservices should be conceived, all the way through to documentation. So how does this translate for Python applications? This session will explore how to expertly deploy your Python micro-service to production.
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PyBay 2018: Production-Ready Python Applications
- 1. Production-Ready Python applications Michael Kehoe Staff Site Reliability Engineer
- 2. Today’s agenda 1 Introduction 2 Tenets of Readiness 3 Building Production-Ready Python Applications 4 Recap
- 3. Introduction
- 4. Michael Kehoe $ /USR/BIN/WHOAMI • Staff Site Reliability Engineer @ LinkedIn • Production-SRE Team • Funny accent = Australian + 4 years American • Worked on: • Networks • Micro-services • Traffic Engineering • Databases
- 5. Production-SRE Team @ LinkedIn $ /USR/BIN/WHOAMI • Disaster Recovery - Planning & Automation • Incident Response – Process & Automation • Visibility Engineering – Making use of operational data • Reliability Principles – Defining best practice & automating it
- 6. Production-Ready Python Applications • This talk is a high-level overview of what it takes to build a production-ready service • Each of the topics could be its own separate talk • Focus on standard open-source options • Number of options not mentioned
- 7. What makes an application Production-Ready?
- 8. O’Reilly 2017 Susan J. Fowler Production-Ready Microservices
- 9. “A production-ready application or service is one that can be trusted to serve production traffic…” S U S A N J . F O W L E R
- 10. “… We trust it to behave reasonably, we trust it to perform reliably, we trust it to get the job done and to do its job well with very little downtime.” S U S A N J . F O W L E R
- 12. Tenets of Readine ss 1 Stability 2 Reliability 3 Scalability 4 Performance 5 Fault Tolerance 6 Disaster Recovery 7 Monitoring 8 Documentation
- 14. Stability
- 15. Tenets of Readiness STABILITY • Stable development cycle • Code Linting • Code Review • Central Repository • Build system • See Fabio Fleitas’s talk from Saturday
- 16. Tenets of Readiness STABILITY • Stable deployment cycle • Canary/ Staging environment • Reliable Deployment via: • Docker/ Kubernetes • Heroku • CD tools
- 17. Reliability
- 18. Tenets of Readiness RELIABILITY • Dependency Management • Onboarding + Deprecation procedures • See Documentation section • Routing + Discovery • Etcd • PyDiscover • Consul-service-discovery
- 19. Scalability
- 20. Tenets of Readiness SCALABILITY • Understanding growth-scales • Qualitative vs quantitative growth scale • Resource awareness • Dependency scaling • What services/ databases need to scale
- 21. Performance
- 22. Tenets of Readiness PERFORMANCE • Constant performance evaluation • Understand how to benchmark application • Traffic management • Understand traffic pattern performance • Capacity Planning • Have the right metrics
- 23. Fault Tolerance
- 24. Tenets of Readiness FAULT TOLERANCE • Avoiding Single Points of Failure (SPOF) • Use multiple instances behind a load balancer • Catch exceptions (meaningfully)
- 25. Tenets of Readiness FAULT TOLERANCE • Resiliency Engineering • Add testing to verify that non-standard behavior is handled correctly • Run chaos experiments: • DNS/ Network failures • Consume disk space/ IO • Consume CPU
- 27. Tenets of Readiness DISASTER RECOVERY • Understand common failures • Have an application-specific disaster- recovery plan in place • Have a general incident management plan
- 28. Tenets of Readiness DISASTER RECOVERY: DAEMONS
- 29. Tenets of Readiness DISASTER RECOVERY: FLASK APP
- 30. Monitoring
- 31. Tenets of Readiness MONITORING • Logging • Tracing • Metrics Dashboards/ Alerts
- 32. Tenets of Readiness MONITORING: LOGGING https://docs.python.org/2/library/logging.handlers.html#sysloghandler
- 33. Tenets of Readiness MONITORING: TRACING • Multiple (free/ open-source) Options • Opentracing • Jaeger • Zipkin (various community libraries)
- 34. Tenets of Readiness MONITORING: METRICS • Multiple (free/ open-source) Options • Statsd • Jaeger • prometheus
- 36. Documentation
- 37. In-Code Documentation What you should be covering • Function/ API & Class docstrings • Code usage documentation • Project documentation • Onboarding • Contribution • Testing
- 38. Tenets of Readiness DOCUMENTATION How to do it? • Sphinx • JavaDoc • Doxygen • Etc….
- 44. Documentation • Relevant PEP’s: • PEP -257: Docstring Conventions • PEP-287: reStructured Docstring Format (Official Python Documentation Standard) • Further info: • https://pythonhosted.org/an_example_ pypi_project/sphinx.html • https://realpython.com/documenting- python-code
- 45. Recap
- 46. Tenets of Readine ss 1 Stability 2 Reliability 3 Scalability 4 Performance 5 Fault Tolerance 6 Disaster Recovery 7 Monitoring 8 Documentation
- 47. Further Resources • Find me at: • michael-kehoe.io • @matrixtek • linkedin.com/in/michaelkkehoe • Slides will be available in multiple locations shortly
Notas del editor
- Michael So we’re apart of a team at LinkedIn called Production-SRE The key tenants of production-sre at LinkedIn is: Assist in restoring stability during site-critical issues Developing applications to reduce MTTD and MTTR Provide direction and guidelines for site-troubleshooting Build tools for efficient site-issue troubleshooting, issue detection and correlation As this presentation goes on, you’ll notice how an Event Correlation system fits into these
- Michael So we’re apart of a team at LinkedIn called Production-SRE The key tenants of production-sre at LinkedIn is: Assist in restoring stability during site-critical issues Developing applications to reduce MTTD and MTTR Provide direction and guidelines for site-troubleshooting Build tools for efficient site-issue troubleshooting, issue detection and correlation As this presentation goes on, you’ll notice how an Event Correlation system fits into these
- In this context, stability is about having a consistent pre-production experience Development Continuous Integration with central code repo and code review Reproducible builds Unit/ Integration testing Deployment Simple repeatable deploys Canary/ Dark Canary/ Staging Canary testing
- In this context, stability is about having a consistent pre-production experience Development Continuous Integration with central code repo and code review Reproducible builds Unit/ Integration testing Deployment Simple repeatable deploys Canary/ Dark Canary/ Staging Canary testing
- Dependency management Unreliability in microservices usually comes from either changes in inbound traffic or changes in behavior from downstream traffic Knowing all of these and understanding impact matters Onboarding/ Deprecation Documented manner to start using API’s Access Control Best Practices Deprecation ACL/ Firewalls Code Cleanup Routing + Discovery Is there a standard way to discover how to get to your service Does your application have reliable health-checks Does your load-balancer respect these Circuit breakers/ Degraders
- Growth Scales: How your service scales with the business goals/ metrics How your application scales as it gets more traffic (how do you make it serve more traffic) What resource “bounds” the application throughput Resource Awareness: What is your resource usage What are your bottlenecks Horizontal vs Vertical scaling (don’t do this) Dependency Scaling: Going back to reliability…how do your downstreams scale with your services growth
- Performance This is essential Ideally should be done every time a change is made to the service (deployment/ AB flag) Measure and report performance Traffic Management QoS Scaling for bursts/ failover Capacity: This really ties everything together Do you have the right numbers to know how many resources you’ll need in the future
- SPOF: In 2018, this really shouldn’t be a problem. Hardware failures Rack failures Do the right thing early to avoid problems down the road Resiliency Engineering May be known as chaos engineering Deliberately break your service to find weakpoints and look to make things fail more gracefully Outages happen, be prepared for them
- SPOF: In 2018, this really shouldn’t be a problem. Hardware failures Rack failures Do the right thing early to avoid problems down the road Resiliency Engineering May be known as chaos engineering Deliberately break your service to find weakpoints and look to make things fail more gracefully Outages happen, be prepared for them
- SPOF: In 2018, this really shouldn’t be a problem. Hardware failures Rack failures Do the right thing early to avoid problems down the road Resiliency Engineering May be known as chaos engineering Deliberately break your service to find weakpoints and look to make things fail more gracefully Outages happen, be prepared for them
- Failure Scenarios: Understand what ways your service can break – resiliency engineering can help here if you’re ensure Have plans to respond to these Disaster Recovery: For larger scale outages, what’s your plan IM: What’s the process to manage & respond to the outage Gamedays are a great way to test this
- Failure Scenarios: Understand what ways your service can break – resiliency engineering can help here if you’re ensure Have plans to respond to these Disaster Recovery: For larger scale outages, what’s your plan IM: What’s the process to manage & respond to the outage Gamedays are a great way to test this
- Dashboards: Dashboards are for high-level system health Not for regression validation Monitor service/ resources/ infrastructure All alerts should be actionable and have pre-planned responses Anything other than this creates alert fatigue Logging is an underrated aspect of software development. During resiliency testing, see what the logs say and if they’re helpful
- Dashboards: Dashboards are for high-level system health Not for regression validation Monitor service/ resources/ infrastructure All alerts should be actionable and have pre-planned responses Anything other than this creates alert fatigue Logging is an underrated aspect of software development. During resiliency testing, see what the logs say and if they’re helpful
- In code documentation has two main forms, Writing documentation (or docstrings) for your function, Class, module Writing code examples So how can we do this nice and easily Sphinx JavaDoc Doxygen And there’s many others
- Michael So we’re apart of a team at LinkedIn called Production-SRE The key tenants of production-sre at LinkedIn is: Assist in restoring stability during site-critical issues Developing applications to reduce MTTD and MTTR Provide direction and guidelines for site-troubleshooting Build tools for efficient site-issue troubleshooting, issue detection and correlation As this presentation goes on, you’ll notice how an Event Correlation system fits into these