Netflix Winston meetup presentation 2015-11-18
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Operational Automation - Helping Netflix developers sleep at night!
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Netflix Winston meetup presentation 2015-11-18
- 1. Operational Automation Helping Netflix developers sleep at night! Jean-Sebastien Jeannotte / Sayli Karmarkar
- 2. Jean-Sebastien Jeannotte – JS Senior Software Engineer Platform Automation Engineering jjeannotte@netflix.com @jsjeannotte http://www.linkedin.com/in/jsjeannotte Sayli Karmarkar Senior Software Engineer Platform Automation Engineering skarmarkar@netflix.com @HikerTechy https://www.linkedin.com/in/saylikarmarkar Speakers
- 9. Healthcheck Script Every 30 min Disappearing instance? Launch new instance Is the C* ring healthy? Are all instances healthy? Can we fix automatically? Replace bad instance First failure? Sleep for X minutes and retry First failure? Is there an offline maintenance?
- 11. How Did The Healthcheck Script Handled It Every 30 min Disappearing instance? Launch new instance Is the C* ring healthy? Are all instances healthy? Can we fix automatically? Replace bad instance First failure? Sleep for X minutes and retry First failure? Is there an offline maintenance?
- 13. Let’s Take a Step Back
- 14. Engineer Wakes up Logs in and ACK Checks runbook Studies the alert Fixes the problem Runs diagnostics PagerDuty Alert 2:02 AM 2:07 AM 2:15 AM2:10 AM 2:30 AM2:20 AM2:00 AM On-call, Without Automation
- 15. Non-Automated On-Call Pain Points MTTR Productivity
- 16. New Direction
- 17. Failure / Alert Automation Automation using Building Blocks Integrations with Netflix Ecosystem Platform as a Service Event-driven Automation Platform
- 18. How Are Others Approaching This Problem?
- 19. Evaluation
- 21. SQS queue Atlas SQS Sensor Poll RabbitMQ Atlas Alert Trigger Stackstorm Action Runners Action A Action B Workflow C Rules EngineRule Definitions MongoDB Replica Set Winston Deployment Atlas Telemetry Platform ...
- 22. Cassandra Monitoring with Winston C* Priam C* Priam C* Priam AtlasAtlas
- 23. Engineer Wakes up Logs in and ACK Checks runbook Studies the alert Fixes the problem Runs diagnostics PagerDuty Alert 2:02 AM 2:07 AM 2:15 AM2:10 AM 2:30 AM2:20 AM2:00 AM On-call, Without Automation
- 24. False Positive Winston 2:00 AM 2:05 AM 2:05 AM 2:15 AM Assisted Diagnostics Fixed the problem On-call With Winston
- 25. Runbook patterns
- 26. False Positive
- 28. Auto Remediation
- 29. ● Product ○ Reduced MTTR (Mean Time To Recover) ○ Safety - Reduce risk of human errors ○ Capture operational knowledge as code ● People ○ Reduced pager fatigue for developers ○ Increase in productivity ○ Morale Impact
- 30. Stackstorm Docs - http://docs.stackstorm.com/ Stackstorm Slack Channel - https://stackstorm-community.slack.com/ Netflix OpenSource: https://netflix.github.io/ Check out our https://jobs.netflix.com page for current openings
- 31. … no more questions
Editor's Notes
- We focus on providing a common automation platform for Netflix Teams.
- Who runs a service on AWS? Amazon EC2 is hosted in multiple locations world-wide. These locations are composed of regions and Availability Zones. Each region is a separate geographic area. Each region has multiple, isolated locations known as Availability Zones. Why Re:Boot? Xen security issue. Reboot a lot of instances in all the Availability Zones. Why is it a big deal? For stateless services, it’s not. But for Stateful services it is. C* for example. Missing the 50M Party in L.A.
- Denial: That can’t be true … Anger: Yep, it’s confirmed Bargaining: Tried convincing AWS to delay Depression: They said no. Risk is too high. Acceptance: What now … Actually it’s easy to accept, because of the Simian Army. -
- Anyone heard about the simian army? The Simian Army is a suite of tools for keeping your cloud operating in top form. Janitor Monkey, Security Monkey, Coffee Monkey Chaos Monkey, the first member, is a resiliency tool that helps ensure that your applications can tolerate random instance failures Netflix EMBRACE chaos. We love it so much that we generate it. In PROD. We run it on most of Netflix services, and even on C*
- CDE has Chaos Monkey enabled on our C* clusters Maximum 1 node per day, during business hours Cassandra Team Health Check system detects the missing instance and replaces it Going back to our stages of grief, this made acceptance easier. We test for this Our automation can take it.
- What our stack looked like at the time? Bunch of Python/Shell scripts Jenkins as job scheduler (HC, node-replacements, repairs, upgrades and etc) On C* nodes: C* + Priam tAtlas is already a very powerful metrics and alerting tool, and our metric systems add non-C* related metrics (App metrics for example) that help in correlation.
- - Atlas is a very powerful time series metrics and alerting tool - Atlas is Open Source
- Simplified view of Healthcheck flow Assisted Diagnostics Auto-Remediation Auto-Remediation supported: Disappearing instances Replace instance with bad I/O
- How did the healthcheck behave during Re:boot
- 2 behaviour: Instance rebooting: False positive (transient issue) Instance rebooting, but failing AWS healthcheck and being terminated: Auto-remediation
- 218 C* nodes rebooted 22 nodes didn’t start and were automatically terminated by AWS internal healthcheck Our heathcheck identified the missing nodes and automatically remediated the issue 0 downtime L.A. Party was awesome
- Take the learnings from CDE, abstract it and see how to apply to other teams Increase in scope - How can we maximize impact? So our main focus was to apply the learnings: False Positive, Assisted Diagnostics, Auto-Remediation Help on-call engineers sleep at night (improve on-call automation) Why is it such a big deal? First, you need to understand the DevOps Model at Netflix Everyone is on-call at Netflix, every team manage their own service This means a lot of on-call engineer doing on-call operations. So what does it look like to be On-Call when there is no or limited automation?
- On-call before winston. Long MTTR (Mean Time to Recover).
- Operational knowledge in document - hard to maintain Risk of human errors Pager fatigue - Morale High MTTR (Mean Time To Recover) Impact on productivity
- JS -- Hand it over to Sayli who will cover how the new system help alleviate these pain points … Sayli -- To reduce the pain points that we were facing, we started thinking of new approach. Quality of a good engineer - learn not only from failures but from success as well like our AWS reboot success story. We survived during reboot using a system which automatically diagnosed and fixed a known failure scenario. The problem was that it wasn’t designed to extend for other failure scenarios or could be used by other teams. Proactive automation. Idea of reactive automatic troubleshooting and remediation will be highly useful especially in operations. With this expanded charter for our team, we focused on what will be the key features for a system that will solve these problems for us and the answer was event-driven automation.
- 2. Instead of autonomous systems, ability to share building blocks within a single service or even to multiple services 3. eg. sophisticates telemetry system Atlas and CI platform (spinnaker), jenkins 4. Last but not least, Service owners can focus on the automation and not platform - Make it self-serve
- Problem space not unique to Netflix We started working on initial design of our own in-house , internal POC Looked at Facebook (FBAR) / LinkedIn (Nurse) / DropBox (Naoru). This helped us see how they approached the problem Also came across this meetup group .. 400 auto remediators
- Now that we knew WHAT are requirements, we worked on figuring out HOW. Evaluated building platform from scratch, adopting an existing solution or mix and match - using some existing components and building some. After POC, stackstorm. Stackstorm platform for integration and automation across services and tools The usecases that stackstorm was targeting -- facilitated troubleshooting and auto remediation fitted right into what we are looking for.open source. Quality of the code. Great to collaborate and code with. Great discussions with respect to their usecases, approach and adoption challenges. Helped us validate benefits. Do our own or adopt existing solution? We started with our own POC, then we decided to go with Stackstorm- event-driven automation platform Facilitated Troubleshooting/Event handling Automated remediation
- Stackstorm gave us and event driven automation platform and building blocks ..what about integration with netflix ecosystem?
- Pulp fiction fans in the audience?
- On-call before winston. Long MTTR (Mean Time to Recover).
- And now with Winston. Winston gets the Alert. Using its rule engine decide what the right action is. Action then analyse the issue and if it’s identified as a False Positive, no need to Page the on-call. Another use case is that Winston will identify that it can fix the issue. When it does, again, no need to Page the on-call. Last use case, the one we want you to focus on is Assisted Diagnostics. While the on-call is being Paged, Winston runs a series of pre-defined diagnostics and prepare a report for the On-call so that when he logs in the system, he has comprehensive information like the Discovery status, list of recent exceptions or error, or any other relevant context to help him make a decision faster.
- Let’s look at some of the real-life scenarios Anybody who doesn’t know what a runbook is? a 'runbook' is a routine compilation of procedures and steps that a sys-admin or a person on-call goes through to diagnose and remediate a failure. Generally runbooks have 3 broadly classified steps -- Real-life scenarios Remove False Positive - expected scenario, can safely be ignored Diagnostics - collect troubleshooting information Remediation - fix the problem Now let’s see some examples of how winston can assist in these steps ...
- First example is for False Positive: Data Pipeline Team, Broker Offline. But instance was terminated by AWS, so it’s expected that the broker is offline. Issue resolved. No need to Page on-call for that.
- Another Assisted Diagnostics example for Cassandra: Disk Space issue Gives context around the size of the actual C* data Checks if there is any Repair or Compaction running which temporarily increases disk usage Try some auto-remediation: Clean-up old snapshots Still above disk usage threshold, Paging On-call In this case, on-call doesn’t have to try to cleanup snapshot since it was already done by Winston, and can now focus on other unknown root causes. Faster TTR.
- Last example: Auto-Remediation. For Data Pipeline team: Broker is offline again, like in the first example, but this time, the EC2 instance is still running. So it’s not a false positive. Check if there is any disk failure If not, tries to restart the Kafka broker. Succeeded. Broker is back online. Resolved. Not paging on-call.
- Add resources for stackstorm, slack channel and happy hour Stackstorm guys are here