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PyBay 2018: Production-Ready Python Applications

Michael Kehoe
Michael Kehoe
Michael KehoeArchitect of reliable, scalable infrastructure at LinkedIn

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.

PyBay 2018: Production-Ready Python Applications

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Production-Ready Python
applications
Michael Kehoe
Staff Site Reliability Engineer
Today’s
agenda
1 Introduction
2 Tenets of Readiness
3 Building Production-Ready Python Applications
4 Recap
Introduction
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
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
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
Ad

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PyBay 2018: Production-Ready Python Applications

Editor's Notes

  1. 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
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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
  14. 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
  15. 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
  16. 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