DevOps Pipelines and Metrics Driven Feedback Loops

Editor's Notes

• #2: Most screenshots are from Dynatrace AppMon – http://bit.ly/dtpersonal – but presented concepts should work with many other tools
• #3: The first DevOpsDays held in Ghent, Belgium: https://legacy.devopsdays.org/events/2009-ghent/
• #4: http://www.telehouse.com/2016/03/devops-how-a-culture-of-empathy-creates-massive-productivity/
• #6: In case you are a “DevOps Virgin” I definitely recommend checking out The Phoenix Project (the DevOps Bible) and Continuous Delivery (which is what we actually all want to achieve): Deliverying software faster with great quality and without all potential mistakes that a manual and rigid process brings with it This inspired many companies which have been talking about their successes!
• #7: Minimize feature cycle time and
• #8: See the full webinar: https://info.dynatrace.com/apm_wc_gene_kim_na_registration.html
• #10: My blog on this: http://apmblog.dynatrace.com/2016/11/16/transformation-to-continuous-innovation-and-optimization/
• #11: The new way is how we take Pictures Right Now: We see what we are about to build – we optimize it on the spot based on best practices – we deploy it into production and immediately get feedback from our customers / friends: then we can decide whehter we „built“ the right thing or not. Whether we take another picture of the scene or not – or whether we delete some of those that nobody likes
• #12: Resource: http://www.spikelab.org/blog/the-word-devops-and-a-marketing-problem.html
• #13: Several companies changed their way they develop and deploy software over the years. Here are some examples (numbers from 2011 – 2014) Cars: from 2 deployments to 700 Flicks: 10+ per Day Etsy: lets every new employee on their first day of employment make a code change and push it through the pipeline in production: THAT’S the right approach towards required culture change Amazon: every 11.6s Remember: these are very small changes – which is also a key goal of continuous delivery. The smaller the change the easier it is to deploy, the less risk it has, the easier it is to test and the easier is it to take it out in case it has a problem.
• #14: But not only the hipsters / unicorns have been doing it – it is catching on – even in enterprises that seem too big. But because they are too big to fail they had to go through a major transformation! Taken from http://www.hostingadvice.com/blog/cloud-66-devops-as-a-service/
• #15: Such as Target ...
• #16: http://www.americanbanker.com/news/bank-technology/banking-apps-that-matter-will-head-to-the-cloud-in-2016-1078525-1.html
• #17: Check out the recorded webinar and podcast Webinar: http://ow.ly/cEYo305kFEy Podcast: http://bit.ly/pureperf
• #18: Die
• #19: Dynatrace 6.2 – verstärkte burn-down phase im letzten 1/3: Ruxit -  up/down trend in sprints, ideal wäre eine gerade blaue linie, wobei sich rot und grün leicht zeitversetzt überdecken
• #20: A basic key metric for developers should be „Did I break the build“. This is why we at Dynatrace installed these Pipeline State UFOs that are hooked up with Jenkins to tell engineers how good or bad the current Trunk or Latest Sprint build is Key thing here is that this should not only be applied to the build itself but to metrics across the delivery pipeline: from DevToOps. It should include metrics like the next examples
• #21: The most basic metric for everyone operating software. Did my last deployment break anything? Is the software still available from those locations where my users are accessing the software? Use Synthetic Monitoring: http://www.dynatrace.com/en/synthetic-monitoring/
• #22: Monitoring user experience and impact on conversion rate Screenshot from Dynatrace AppMon & UEM
• #23: Even if the deployment seemed good because all features work and response time is the same as before. If your resource consumption goes up like this the deployment is NOT GOOD. As you are now paying a lot of money for that extra compute power Screenshot from Dynatrace AppMon
• #24: Understand user behavior depending on who they are and what they are doing. Screenshot from https://github.com/Dynatrace/Dynatrace-UEM-PureLytics-Heatmap Does the behavior change if they have a less optimal user experience? Screenshot from https://github.com/Dynatrace/Dynatrace-UEM-PureLytics-Heatmap Seems like users that have a frustrating experience are more likely to click on Support Screenshot from https://github.com/Dynatrace/Dynatrace-UEM-PureLytics-Heatmap
• #27: Unfortunately not every story is a good story. But the bad stories are often not told – even though we can learn even more. PrepSportswear failed 80% of their deployments after speading up deployments
• #28: Podcasts https://www.spreaker.com/user/pureperformance/pureperformance-guest-host-series-01-alo https://www.spreaker.com/user/pureperformance/006-how-to-sell-performance-to-marketing_1 https://www.spreaker.com/user/pureperformance/007-attack-of-the-bots-spiders-from-mars Webinars & eBooks https://info.dynatrace.com/apm_all_17q2_cs_prep_sportswear_case_study_en_fulfilment.html?_sf_s=prep https://info.dynatrace.com/apm_wc_prepsportswear_na_registration_devops.html?_sf_s=prep https://info.dynatrace.com/apm_wc_prep_sportswear_na_registration.html?_sf_s=prep
• #30: They had a monolithic app that couldnt scale endlessly. Their popularity caused them to think about re-architecture and allowing developers to make faster changes to their code. The were moving towards a Service Approach
• #31: Separating frontend logic from backend (search service). The idea was to also host these services potentially in the public cloud (frontend) and in a dynamic virtual enviornment (backend) to be able to scale better globally
• #32: On Go Live Date with the new architecture everything looked good at 7AM where not many folks were yet online!
• #33: By noon – when the real traffic started to come in the picture was completely different. User Experience across the globe was bad. Response Time jumped from 2.5 to 25s and bounce rate trippled from 20% to 60%
• #35: The backend service itself was well tested. The problem was that they never looked at what happens under load „end-to-end“. Turned out that the frontend had direct access to the database to execute the initial query when somebody executed a search. The returned list of search result IDs was then iterated over in a loop. For every element a „Micro“ Service call was made to the backend which resulted in 33! Service Invokations for this particular use case where the search result returned 33 items. Lots of wasted traffic and resources as these Key Architectural Metrics show us
• #37: They fixed the problem by understanding the end-to-end use cases and then defined backend service APIs that provided the data they really needed by the frontend. This reduced roundtrips, elimiated the architectural regression and improved performance and scalability
• #38: Lessons Learned!
• #40: Got this story also covered here: https://www.infoq.com/articles/Diagnose-Microservice-Performance-Anti-Patterns If we monitor these key metrics in dev and in ops we can make much better decisions on which builds to deploy We immediately detect bad changes and fix them. We will stop builds from making it into Production in case these metrics tell us that something is wrong. We can also take features out that nobody uses if we have usage insights for our services. Like in this case we monitor % of Visitors using a certain feature. If a feature is never used – even when we spent time to improve performance – it is about time to take this feature out. This removes code that nobody needs and therefore reduces technical debt: less code to maintain – less tests to maintain – less bugs in the system!
• #42: You should know these books
• #43: The Phoenix Project explains in details the 3 Ways on how to mature your Organization: http://itrevolution.com/the-three-ways-principles-underpinning-devops/
• #45: They come from tools. I work for Dynatrace and we provide all these metrics – but there are also other tools out there that do that job
• #46: Do it manually
• #47: Or do some of it automated through these tools such as Dynatrace
• #60: And this place nicely into what our friends from CapitalOne do to optimize their pipeline throughput: Shift Quality Left; Find problems earlier to avoid too many „bad builds“ wasting time in later pipeline stages that take longer to execute
• #64: Monitor your production deployments and monitor the impact on your end users, performance and conversion rates. Take this data to respond fast to issues
• #65: Using Real User Feedback also allows us to start experimenting, optimizing what is good and remove what nobody really needs This is the Second Way (Feedback Loops) and alread the Third Way (Continuous Experimentation)
• #66: If we do all that we become successful as a business as we outpace our competition with new innovative ideas