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![© Electric Cloud | www.electric-cloud.com
COMMIT PIPELINE [B]
DEV BUILD TEST
DevOps Use Cases managed by ElectricFlow
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RELEASE PIPELINE
INTEGRATION PRE-PROD PROD
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OPTIONAL ACCEPTANCE
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Pipeline Orchestration
Build/Test Automation
Deployment Automation
Release Management
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Unit / Other Tests
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Uploaded byDeborah Schalm
“Shift-Left.” Performance And Architecture Validation with Continuous Integration
The document emphasizes the need for 'shift-left' strategies and automated 'self-healing' processes to enhance DevOps efficiency and quality. It discusses the integration of Dynatrace and Electric Cloud to improve software delivery through early detection of performance issues and automated rollback for service restoration. The unified platform aims to streamline deployment and monitoring while reducing release cycle times and increasing operational effectiveness across various industries.
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“Shift-Left.” Performance And Architecture Validation with Continuous Integration
- 1. © Electric Cloud| www.electric-cloud.com Shift-left Performance Automated ‘self-healing’
- 2. © Electric Cloud| www.electric-cloud.com Agenda • Why we need Shift-Left and Auto-Heal to accelerate DevOps! • Dynatrace and Electric Cloud introductions • Dynatrace + Electric Cloud = Shift left and Automatic healing
- 3. © Electric Cloud| www.electric-cloud.com Shift-Left?
- 4. © Electric Cloud| www.electric-cloud.com https://puppet.com/resources/whitepaper/2017-state-devops-report/ http://www.gartner.com/binaries/content/assets/events/keywords/infrastructure-operations-management/iome5/gartner-predicts-for-it-infrastructure-and-operations.pdf “By 2020, half of the CIOs who have not transformed their teams' capabilities will be displaced from their organizations' digital leadership teams” – Gartner Adoption: DevOps Teams in Organizations 16% 2014 27% 2017 DevOps is in Top 3 CIO Initiatives Gartner & State of DevOps Report 2017
- 5. © Electric Cloud| www.electric-cloud.com • Speed to Market • Operational Efficiency • Lowering Costs • Better Quality • Employee Retention * • Technology Disruption * Employees are twice as likely to recommend their employer to others https://puppet.com/resources/whitepaper/2017-state-devops-report/ http://www.gartner.com/binaries/content/assets/events/keywords/infrastructure-operations-management/iome5/gartner-predicts-for-it-infrastructure-and-operations.pdf Driving Factors for DevOps Adoption?
- 6. © Electric Cloud| www.electric-cloud.com https://puppet.com/resources/whitepaper/2017-state-devops-report/ http://www.gartner.com/binaries/content/assets/events/keywords/infrastructure-operations-management/iome5/gartner-predicts-for-it-infrastructure-and-operations.pdf High Performers vs Low Performers Speed Gap Closing but Quality Gap Increasing
- 7. © Electric Cloud| www.electric-cloud.com Technical Complexity DevOps promotes choice: “the best stack for your problem” Bad Quality DevOps today mainly driven by Biz “faster to market” but not “quality to market” Alert Drowning Tool diversity results in way too many “non actionable alerts” Disruption of Traditional Ops Overwhelmed with change/work “IT-less IT Teams” DevOps Adoption Challenges
- 8. © Electric Cloud| www.electric-cloud.com 3 Steps to Mature from “Faster to Market” to “Faster Quality to Market”
- 9. © Electric Cloud| www.electric-cloud.com Dev Perf/Test Ops Biz Faster Innovation with Quality Gates Faster Acting on BizDevOps Feedback CI CD Lower Costs Happy Users Benefit: Speed up Delivery! Measure Business Success Step 1: Building an Automated Delivery Pipeline
- 10. © Electric Cloud| www.electric-cloud.com Dev Perf/Test Ops Biz Shift-Left Performance: Break Pipeline Earlier Auto-Healing: Recover Faster in Ops Unit Perf Cont. Perf New Deploy New Capability CI CD Remove/Promote Triage/Optimize Update Tests Innovate/Design Lower Costs Happy Users Benefit: Increase Quality! React Faster and Better! Step 2: Add Monitoring as Quality Gates
- 11. © Electric Cloud| www.electric-cloud.com Feature Team A Feature Team B Feature Team X Shift-Left to Improve “Efficiency” Cloud Ops Auto-Healing ensures “Up Time” PM/Biz Improve“Business” Step 3: Ready to Scale Up
- 12. © Electric Cloud| www.electric-cloud.com What you need is DevOps Automation AI-powered quality gates+ +
- 13. © Electric Cloud| www.electric-cloud.com Who is Electric Cloud ?
- 14. © Electric Cloud| www.electric-cloud.com Electric Cloud Every Business is a Software Business DevOps and Continuous Delivery = new Agile Automation & Acceleration are essential 1 2 3 Our View 15 Years of experience accelerating software delivery 60 Fortune 500 Customers operating in many verticals 85 % Average Reduction in release cycle time Who We Are 1 Singular goal to help the world deliver better software faster
- 15. © Electric Cloud| www.electric-cloud.com ElectricFlow One unified platform for teams to automate deployments and coordinate releases.
- 16. © Electric Cloud| www.electric-cloud.com ElectricFlow Unified DevOps Release Automation Unified. Single end-to-end platform ensures shared control and visibility for all teams Flexible. Models any processes across environments and toolchains without being prescriptive Scalable. Securely orchestrates legacy and CD pipelines, across any toolchains, at any scale Accessible. Rich UX and APIs (CLI, DSL, REST, SDKs) speak your language: “Engineer,” “Admin,” “Exec” Industry Leader. Rated #1 in 2016 Gartner ARA MQ
- 17. © Electric Cloud| www.electric-cloud.com DEV | OPS INFRAAPPSElectric Cloud in DevOps Landscape OPERATEBUILD TEST PROVISION MONITORDEPLOYDEV Dev Tools CI CT Service Mgmt & Monitoring SW-Defined Infrastructure Configuration Mgmt Release Automation 17 Electric Cloud
- 18. © Electric Cloud| www.electric-cloud.com Our Customers Telecom/Networking Other Systems ISV, Internet & Entertainment Heavy Industry Financial Services Automotive Medical Devices Semiconductor Aerospace / Defense Mobile Devices
- 19. © Electric Cloud| www.electric-cloud.com Gap and Huawei: Two tales of DevOps at scale Developer Build Production Build Regression Test Full Test Feature Delivery Time 10 minutes 300 minute 240 minutes 24 hours 30 daysHuawei – Before 1 minute 10 minutes 60 minutes 6 hours 7 daysHuawei – After 20 minutes 150 minutes 300 minutes 24 hours 15 daysGap – Before 20 minutes 120 minutes 150 minutes 6 hours 1 dayGap – After
- 20. © Electric Cloud| www.electric-cloud.com ElectricFlow: Unified DevOps Release Automation Transforms software release from a chore to a competitive advantage. INTEGRATE FlowUX, 100’s Plugins, REST API, SDK, DSL, CLI, “future-proof!” H ElectricFlow Unified DevOps Release Automation MODEL Applications, Environments, Pipelines, Releases, Resources AUTOMATE Tools, Resources, Pipelines, Workflows, Procedures, Jobs COORDINATE Security, Approvals, Reporting, Artifacts ANY APP. ANY VERSION. ANYWHERE. ANYTIME. TRADITIONAL AND CD PIPELINES AND RELEASES. ORCHESTRATE ANY PROCESS AND TOOLCHAIN. DEPLOY PIPELINE AND RELEASE ANY USE CASE FLEXIBLE AND SCALABLE AUTOMATION FOR DEV/QA. BUILD/TEST CLOUD PROVISIONING | RUNBOOK AUTOMATION | AND MORE
- 21. © Electric Cloud| www.electric-cloud.com COMMIT PIPELINE [B] DEV BUILD TEST DevOps Use Cases managed by ElectricFlow COMMIT PIPELINE [A] DEV BUILD TEST RELEASE PIPELINE INTEGRATION PRE-PROD PROD Provision Configure Smoke, Other Tests . . . OPTIONAL ACCEPTANCE Deploy (1…N) Provision Configure Smoke, Other Tests . . . OPTIONAL APPROVAL Deploy (1…N) OPTIONAL ACCEPTANCE COMMIT PIPELINE [“N”] DEV BUILD TEST Pipeline Orchestration Build/Test Automation Deployment Automation Release Management Environment Provisioning Build Unit / Other Tests . . . Publish Artifact OPTIONAL APPROVAL Provision Configure Functional, Other Tests . . . OPTIONAL APPROVAL De-provision Deploy OPTIONAL ACCEPTANCE Provision Configure Smoke, Performance, Other Tests . . . De-provision Deploy (1…N) OPTIONAL ACCEPTANCE OPTIONAL APPROVAL
- 22. © Electric Cloud| www.electric-cloud.com Who is Dynatrace ?
- 23. +8000 Customers #1 GartnerMarket Share of top twenty retailers of Fortune 500 of ten largest banks 19 386 9 Consumer goods GovernmentTeleco & Media TravelFinance Retail
- 24. 2major releases/year customers deploy& operate on-prem 26 major releases/year 500 prod deployments/day self-service online sales SaaS & Managed 2011 2016 sprint releases (continuous-delivery) 1h: Code -> Prod6months major/minor release
- 25. ruxit theme 2014.05.15 Cloud OS,Disks Containers, Processes, Logs Application- & Webserver Mobile* Services Network Browser 3rd parties FullStackDiscovery,Modeling&Analytics OneAgent
- 26. Confidential, Dynatrace, LLC Monitoringredefined Every user, every app, everywhere. AI powered, full stack, automated. Full lifecycle - development, test, and production
- 27. unified, modern, allyou need
- 28. © Electric Cloud| www.electric-cloud.com Dynatrace + Electric Cloud
- 29. © Electric Cloud| www.electric-cloud.com Dynatrace + Electric Cloud – Key use cases • Shift-Left monitoring: Automatically and Pro-actively detect performance issues early in the Pipeline and stop potential bad versions getting into production environments. • Visibility of Deployments for OPS users: Automatically update Dynatrace with details of deployment incidents. Apply root cause analysis and remediate the performance issues. • Auto healing based on Policies: Using the related action to automate baseline thresholds and trigger rollbacks and enable ‘self-healing’.
- 30. © Electric Cloud| www.electric-cloud.com Shift-left PerformanceDynatraceElectricCloud EFLOW is invoked to start a Pipeline Deploy to QA / Pre-production Run Load tests Compare new version’s performance signature to baseline Notice degradation overtime on QA environment Receive notice and block this version from moving forward to production Notify Release Manager, QA Manager, Dev Manager, IT Manager Inform EFLOW about service degradation
- 31. © Electric Cloud| www.electric-cloud.com ElectricFlow Pipeline is initiated with a bad version
- 32. © Electric Cloud| www.electric-cloud.com Shift-Left Continuous Performance with Dynatrace “Performance Signature” for Build Nov 16 “Performance Signature” for Build Nov 17
- 33. © Electric Cloud| www.electric-cloud.com ElectricFlow’s Gates stop Pipeline from moving forward
- 34. © Electric Cloud| www.electric-cloud.com Automated rollback for “self healing”DynatraceElectricCloud EFLOW is invoked to start a Pipeline New version is deployed into production A deployment event is created automatically in Dynatrace Dynatrace monitors new version in Production Service degradation is noticed on the new release Rollback flow is initiated automatically n EFLOW Rollback is completed and Dynatrace is updated Service is restored to how it was before the deployment EFLOW is informed automatically to trigger Rollback to restore service
- 35. © Electric Cloud| www.electric-cloud.com Add Deployment Information to Dynatrace Monitoring Data From Pipeline
- 36. © Electric Cloud| www.electric-cloud.com AI-Driven Problem Detection is aware of Deployment Business Impact Technical Root Cause Impact Trace in Environment Problem event history includes EC Deployment Details
- 37. © Electric Cloud| www.electric-cloud.com Problem Detection Triggers Auto-Healing Action From Rollback
- 38. © Electric Cloud| www.electric-cloud.com Summary: Faster Quality To Market Dev Perf/Test Ops Biz Shift-Left Performance: Break Pipeline Earlier Auto-Healing: Recover Faster in Ops CI CD Lower Costs Happy Users +
- 39. © Electric Cloud| www.electric-cloud.com Anand Ahire – aahire@electric-cloud.com Andreas Grabner - andreas.grabner@dynatrace.com
Editor's Notes
- #3 Anand
- #4 Andi – Why do we need to invest in Shift-Left and Self-Healing?
- #5 Andi: talks to the Gartner portion Anand : talks to the DORA portion Because DevOps has been a driving factor in recent years to transform IT Organizations
- #6 Lead: Anand Andi talks about the employee retention The driving factors are all clear – but too often it is just “Speed to Market” that is driving the change
- #7 Anand leads Based on the State of DevOps Report we know that companies are indeed speeding up. But they are falling behind on quality
- #8 Andi leads The biggest challenges make it clear: Technology Complexity and being Driven by Business leads to bad quality and an overwhelmed IT Organizations that is drowning in alerts and is overwhelmed with the new complexity
- #9 Andi leds The right way is to MATURE towards “Faster Quality to Market” – and here are 3 Mandatory Steps for you
- #10 Anand Starts and Andi brings it home Continuous Delivery is not only about Delivery but also about Feedback Loops. But not only the big feedback loop from Ops/Biz back to Dev. It is also about the smaller feedback loops within a phase of the pipeline Promotional Flow Dev: Execute Performance Unit Tests, do performance and resource profiling, optimize your code before committing it into the Source Repo CI: Continuous Integration as the vehicle to promote code from Dev to QA. Executing our Core Tests + Typically comes with Automated Deployments to deploy into different test environments Perf/Test: Continuous Performance Testing allows to identify performance and scalability regressions for new code commits. Works very well if you can easily spin up load environments and then compare build to build performance metrics -> we call this a “Performance Signature” of a service or app CD: Continuous Delivery brings in more automation and orchestration so that we can deploy the tested and verified code changes into Operations Ops: Monitoring the impact of a new Deployment. All Lights Green? Still meeting SLAs? Deployment Successful? Biz: Monitoring the impact of new Features/Capabilities: Are they accessible for our end users? Are they being used? What is the impact on User Experience, User Behavior, Conversion Rate, … Feedback Flow Biz: Through techniques such as Feature Flagging, A/B Testing, Blue/Green Deployments, … - Biz can independently decide which features to promote or remove depending on the feedback from monitoring. This is the fastest way to react to problems and boost business Ops: Through monitoring and orchestration Ops can triage situations that arise in production. Ops might be able to mitigate performance or resource issues by adding more virtual resources, diverting traffic, changing configuration, … - without having to request a new build from Dev. This requires a good architecture and automation strategy. It also requires good and granular monitoring to understand the dependencies between services, application, processes and the underlying infrastructure Perf/Test: Looking at Production Monitoring Data allows Perf/Test to constantly update their load scenarios to better reflect real user behavior in the next set of continuous performance tests Dev: Take the feedback from all Biz, Ops and Test and have it influence the next product iteration by innovating in areas that end users like, optimize where optimization is essential We want to speed up, to get more features out to make our users happy, but we also need to take care that we are lowering the costs, because if you just push more stuff out, Dev. code, and it runs on an infrastructure that is dynamically (because Amazon and Microsoft, Google provides it), it potentially is very costly if you don't look at quality from a performance perspective, from a resource consumption perspective. Hey, we saw something in production, our system is now responding slower, or faster. Or all of it's unavailable because we made a mistake." The business can see the feedback flow saying, "Hey, we just deployed this new feature, but guess what? Nobody's using it. And we know this thanks to Dynatrace monitoring." We believe there's a lot of chances in the promotion flow to actually empower all these individual roles and people to make better decisions on whether to push it forward. Developers, they can run some unit performance tests, and we actually have within Dynatrace, features to show them if they have any performance regressions in there, and if Dynatrace says, "Not good” then, perhaps don’t push it out. So, our point is there is many options with smaller feedback loops within individual stages, Dynatrace gives you the data so that you can make the right decisions.
- #11 Continuous Delivery is not only about Delivery but also about Feedback Loops. But not only the big feedback loop from Ops/Biz back to Dev. It is also about the smaller feedback loops within a phase of the pipeline Promotional Flow Dev: Execute Performance Unit Tests, do performance and resource profiling, optimize your code before committing it into the Source Repo CI: Continuous Integration as the vehicle to promote code from Dev to QA. Executing our Core Tests + Typically comes with Automated Deployments to deploy into different test environments Perf/Test: Continuous Performance Testing allows to identify performance and scalability regressions for new code commits. Works very well if you can easily spin up load environments and then compare build to build performance metrics -> we call this a “Performance Signature” of a service or app CD: Continuous Delivery brings in more automation and orchestration so that we can deploy the tested and verified code changes into Operations Ops: Monitoring the impact of a new Deployment. All Lights Green? Still meeting SLAs? Deployment Successful? Biz: Monitoring the impact of new Features/Capabilities: Are they accessible for our end users? Are they being used? What is the impact on User Experience, User Behavior, Conversion Rate, … Feedback Flow Biz: Through techniques such as Feature Flagging, A/B Testing, Blue/Green Deployments, … - Biz can independently decide which features to promote or remove depending on the feedback from monitoring. This is the fastest way to react to problems and boost business Ops: Through monitoring and orchestration Ops can triage situations that arise in production. Ops might be able to mitigate performance or resource issues by adding more virtual resources, diverting traffic, changing configuration, … - without having to request a new build from Dev. This requires a good architecture and automation strategy. It also requires good and granular monitoring to understand the dependencies between services, application, processes and the underlying infrastructure Perf/Test: Looking at Production Monitoring Data allows Perf/Test to constantly update their load scenarios to better reflect real user behavior in the next set of continuous performance tests Dev: Take the feedback from all Biz, Ops and Test and have it influence the next product iteration by innovating in areas that end users like, optimize where optimization is essential We want to speed up, to get more features out to make our users happy, but we also need to take care that we are lowering the costs, because if you just push more stuff out, Dev. code, and it runs on an infrastructure that is dynamically (because Amazon and Microsoft, Google provides it), it potentially is very costly if you don't look at quality from a performance perspective, from a resource consumption perspective. Hey, we saw something in production, our system is now responding slower, or faster. Or all of it's unavailable because we made a mistake." The business can see the feedback flow saying, "Hey, we just deployed this new feature, but guess what? Nobody's using it. And we know this thanks to Dynatrace monitoring." We believe there's a lot of chances in the promotion flow to actually empower all these individual roles and people to make better decisions on whether to push it forward. Developers, they can run some unit performance tests, and we actually have within Dynatrace, features to show them if they have any performance regressions in there, and if Dynatrace says, "Not good” then, perhaps don’t push it out. So, our point is there is many options with smaller feedback loops within individual stages, Dynatrace gives you the data so that you can make the right decisions.
- #12 Anand starts the slides and Andi finishes Implementing a continuous delivery pipeline with feedback loops currently happens for individual projects. The challenge comes if enterprises adopt these new concepts on large scale: for many projects, building new microservice architectures, pushing code changes on separate streams through multiple pipelines into production. Challenge: Individual Micro-Service (or call them Feature-/App-) Teams are pushing their code changes through the pipeline. Their focus is on their service. In earlier stages they mock away depending services so that they can move faster. The production environment (or a production like environment) is the first environment where all moving pieces are deployed in the way they are supposed to work together. Responsible for that environment are those Cloud Ops Teams that provide the underlying Cloud or PaaS Infrastructure for the applications & services that get deployed. The Cloud Ops Teams are the ones that need to make sure that constant new updates from X Service Teams are not impacting overall Service Quality as well as not consuming too much costs in case bad code changes get deployed Cloud Ops: They need Monitoring for multi-platform/technology environments: Enterprise Stack, Cloud Stack, Virtualization, Containers, … Provide “Monitoring as a Service” for each individual Service Teams components as Feedback Loop for their next development cycle Understand Dependencies between different service teams to optimize deployment, e.g: co-deployment of tightly coupled services, … Understand Resource requirements for each service and how scaling impacts resource consumption of all depending services -> this is great for capacity planning Locate and remediate bad deployments to save costs and lower risks Service Team Through Monitoring from Operations better understand the real behavior, issues and resource consumption of their service in combination with all other depending services Shift-Left Quality and Performance by integrating “Monitoring as a Pipeline Feature”. Do not promote builds that show any type of performance regression when testing your services in isolation or in a combined testing environment Shift-Right Metrics by demanding more End User and Operational Feedback from Production, e.g: User Behavior, Failure, Resource Consumption, … -> let this influence your next iteration as well as help Operations to make better decisions while running your current build in production, e.g: include feature flags to turn on/off features that behave good or bad Business Understand End User Behavior after deploying new features -> which features are well received – which updates are negatively impacting user behavior? Understand the actual Running Costs of the new Features as provided by the Cloud Ops Teams Start Experimenting and Innovating with A/B Testing, Blue/Green Deployments, … -> Service Teams can provide the switches to turn on/off certain features, Cloud Ops provides the granular monitoring required to decide whether a feature is good to promote or better to take out
- #13 Andi will talk to this slide
- #24 Who we are!
- #25 Our own DevOps Transformation Story
- #26 We understood that monitoring had to change
- #27 And that’s really what’s possible with Dynatrace. With Dynatrace you can monitor every user, every app, everywhere; the platform is powered by Artificial Intelligence which is leveraged to identify issues and suggest remedies. Dynatrace monitors the full stack – meaning from the network through database and application tiers, into code-specific analysis all the way out to end user devices and third party add-ins. The same holds true for both in house hosted applications as well as private clouds, public clouds and hybrid clouds where containerization, hyper-scale and elastic computing dominate. And all of this is automated – not only initial set up and instrumentation but problem identification, dashboards, ongoing adaptation to environmental and application changes as well as upgrading the Dynatrace platform itself. All automated.
- #28 We’re here to tell you today about Dynatrace, the unified and most modern digital performance management. It’s a single, modern platform that provides everything you need for all types of applications. Over the next few slides we’ll talk about why we built this platform.
- #30 Andi to talk this slide
- #31 Anand
- #32 Anand ElectricFlow Pipeline is initiated with a bad build
- #33 Andi Continuous Performance Testing or Continuous Performance Validation is a good Pipeline Phase to have before deploying into a Production Environment. It is an envioronment running under continuous load. New builds of individual services or complete applications get deployed on a regular basis. The question is whether a new version of a service, application or component shows any degradation in performance, scalability or resrouce consumption. If so it should not be promoted to the next phase before closer examination Dynatrace automatically understands applications but more importantly services. Dynatrace also integrates with testing tools so that traffic on certain services can be associated to certain test scenarios you run in your continuous performance environment. Based on this information it is possible to see any regressions between builds or different loads. In the example above it is easy to spot that the build from Nov 17 shows a significant performance regression. Instead of allowing this build into production it is better to look into the differences between Build Nov 16 and Build Nov 17
- #34 Anand ElectricFlow’s Gates stop Pipeline from moving forward
- #35 Anand
- #36 Andi
- #37 Andi
- #38 Andi and Anand to finish
- #39 Anand to lead and Andi to finish