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    Headline:    

    15 Metrics for DevOps Success

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    5 minutes, 27 seconds

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    Main keyword:    

    Offshoring

    Sub keyword:    

    DevOps

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    • How fast you can do this will vary wildly based on your type of product, team, and risk if you don’t track any Dev Ops Metrics around your velocity, you should at least measure how you are doing on quality.
    • Finding bugs in QA is important to keep your defect escape rate time This might seem like a weird one, but tracking how long it takes to do an actual deployment is another good metric.
    • Tools like Retrace can provide valuable visualizations like this one below that helps make it easy to spot time to detection (MTTD) When problems do happen, it is important that you identify them quickly.

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    By tracking these DevOps Metrics, you can evaluate just how fast you can move before you start breaking frequencyChange volumeDeployment timeLead timeCustomer ticketsAutomated test pass %Defect escape rateAvailabilityService level agreementsFailed deploymentsError ratesApplication usage and trafficApplication performanceMean time to detection (MTTD) Mean time to recovery (MTTR) Goals of DevOps: Velocity, Quality, PerformanceThe main goals of DevOps are velocity, quality and application want to ship code as fast and often as possible. How fast you can do this will vary wildly based on your type of product, team, and risk if you don’t track any DevOps Metrics around your velocity, you should at least measure how you are doing on quality. Perhaps you try to ship when you can, and you don’t really care how fast exactly. However, you always care about quality. The last thing you want is to be chasing production fires all the third piece of the equation is performance. You could argue that it is also at odds with your goals of high velocity and quality. Performance is also related to quality, but perhaps a little sizeTracking how many stories, feature requests, and bug fixes are being deployed is another good DevOps metric. Depending on how large your individual work items are, their counts could vary wildly. You could also track how many story points or days ’ worth of development work are being frequencyTracking how often you do deployments is a good DevOps metric. Ultimately, the goal is to do more smaller deployments as often as possible. Reducing the size of deployments makes it easier to test and release. I would suggest counting both production and non-production deployments separately. How often you deploy to QA or pre-production environments is also important. You need to deploy early and often in QA to ensure time for testing. Finding bugs in QA is important to keep your defect escape rate timeThis might seem like a weird one, but tracking how long it takes to do an actual deployment is another good metric. One of our applications at Stackify is deployed with Azure worker roles and it takes about an hour to deploy. It is a nightmare. Tracking such things could help identify potential problems. It is much easier to deploy more often when the task of actually doing it is quick. Lead timeIf the goal is shipping code quickly, this is a really key DevOps metric. I would define lead time as the amount of time that occurs between starting on a work item until it is deployed. This helps you know that if you started on a new work item today, how long would it take on average until it gets to production. This is also a good metric to help with ticketsThe best and worst indicator of application problems is customer support tickets and feedback. The last thing you want is for your users to find bugs or have problems with your software. Because of this, they also make a good indicator of application quality and performance tests pass %To increase velocity, it is highly recommended that your team makes extensive usage of unit and functional testing. Since DevOps relies heavily on automation, tracking how well your automated tests work is a good DevOps Metrics. It is good to know how often code changes are causing your tests to escape rateDo you know how many software defects are being found in production versus QA? If you want to ship code fast, you need to have confidence that you can find software defects before they get to production. Your defect escape rate is a great DevOps metric to track how often those defects make it to last thing you ever want is for your application to be down. Depending on your type of application and how you deploy it, you may have a little downtime as part of scheduled maintenance . I would suggest tracking that and all unplanned level agreementsMost companies have some service level agreement (SLA) that they operate with. It is also important that you track your compliance with your SLAs. Even if there are no formal SLA, there probably are application requirements or expectations to be deploymentsWe all hope this never happens, but how often do your deployments cause an outage or major issues for your users? Reversing a failed deployment is something we never want to do, but it is something you should always plan for. If you have issues with failed deployments, be sure to track this metric over time. This could also be seen as tracking mean time to failure ( MTTF).Error ratesTracking error rates within your application is super important. Not only are they an indicator of quality problems, but also ongoing performance and uptime related issues. Good exception handling best practices are critical for good – Identify new exceptions being thrown in your code after a deploymentProduction issues – seize issues with database connections, query timeouts, and other related are a fact of life for most applications. At Stackify, we process millions of messages an hour across a couple hundred servers and over a thousand SQL databases. A few errors here and there are just part of the noise of a busy system. It is important that you keep a pulse on your error rates and look for usage & trafficAfter a deployment, you want to see if the amount of transactions or users accessing your system looks normal. If you suddenly have no traffic or a giant spike in traffic, something could be last thing you ever want to see is no traffic at all . You could also see a spike in traffic if you are using microservices and one of your applications is causing a lot more traffic all of a performanceBefore you even do a deployment, you should use a tool like Retrace to look for performance problems, hidden errors, and other issues. During and after the deployment, you should also look for any changes in overall application might be common after a deployment to see major changes in the usage of specific SQL queries, web service calls, and other application dependencies. Tools like Retrace can provide valuable visualizations like this one below that helps make it easy to spot time to detection (MTTD) When problems do happen, it is important that you identify them quickly. The last thing you want is to have a major partial or broad system outage and not know about it. Having robust application monitoring and good coverage in place will help you detect issues quickly. Once you detect them, you also have to fix them quickly!
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    15 Metrics for DevOps Success
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    By tracking these DevOps metrics, you can evaluate just how fast you can move before you start breaking frequencyChange volumeDeployment timeLead timeCustomer ticketsAutomated test pass %Defect escape rateAvailabilityService level agreementsFailed deploymentsError ratesApplication usage and trafficApplication performanceMean time to detection (MTTD) Mean time to recovery (MTTR) Goals of DevOps: Velocity, Quality, PerformanceThe main goals of DevOps are velocity, quality and application want to ship code as fast and often as possible. How fast you can do this will vary wildly based on your type of product, team, and risk if you don’t track any DevOps metrics around your velocity, you should at least measure how you are doing on quality. Perhaps you try to ship when you can, and you don’t really care how fast exactly. However, you always care about quality. The last thing you want is to be chasing production fires all the third piece of the equation is performance. You could argue that it is also at odds with your goals of high velocity and quality. Performance is also related to quality, but perhaps a little sizeTracking how many stories, feature requests, and bug fixes are being deployed is another good DevOps metric. Depending on how large your individual work items are, their counts could vary wildly. You could also track how many story points or days ’ worth of development work are being frequencyTracking how often you do deployments is a good DevOps metric. Ultimately, the goal is to do more smaller deployments as often as possible. Reducing the size of deployments makes it easier to test and release. I would suggest counting both production and non-production deployments separately. How often you deploy to QA or pre-production environments is also important. You need to deploy early and often in QA to ensure time for testing. Finding bugs in QA is important to keep your defect escape rate timeThis might seem like a weird one, but tracking how long it takes to do an actual deployment is another good metric. One of our applications at Stackify is deployed with Azure worker roles and it takes about an hour to deploy. It is a nightmare. Tracking such things could help identify potential problems. It is much easier to deploy more often when the task of actually doing it is quick. Lead timeIf the goal is shipping code quickly, this is a really key DevOps metric. I would define lead time as the amount of time that occurs between starting on a work item until it is deployed. This helps you know that if you started on a new work item today, how long would it take on average until it gets to production. This is also a good metric to help with ticketsThe best and worst indicator of application problems is customer support tickets and feedback. The last thing you want is for your users to find bugs or have problems with your software. Because of this, they also make a good indicator of application quality and performance tests pass %To increase velocity, it is highly recommended that your team makes extensive usage of unit and functional testing. Since DevOps relies heavily on automation, tracking how well your automated tests work is a good DevOps metrics. It is good to know how often code changes are causing your tests to escape rateDo you know how many software defects are being found in production versus QA? If you want to ship code fast, you need to have confidence that you can find software defects before they get to production. Your defect escape rate is a great DevOps metric to track how often those defects make it to last thing you ever want is for your application to be down. Depending on your type of application and how you deploy it, you may have a little downtime as part of scheduled maintenance. I would suggest tracking that and all unplanned level agreementsMost companies have some service level agreement (SLA) that they operate with. It is also important that you track your compliance with your SLAs. Even if there are no formal SLA, there probably are application requirements or expectations to be deploymentsWe all hope this never happens, but how often do your deployments cause an outage or major issues for your users? Reversing a failed deployment is something we never want to do, but it is something you should always plan for. If you have issues with failed deployments, be sure to track this metric over time. This could also be seen as tracking mean time to failure ( MTTF).Error ratesTracking error rates within your application is super important. Not only are they an indicator of quality problems, but also ongoing performance and uptime related issues. Good exception handling best practices are critical for good – Identify new exceptions being thrown in your code after a deploymentProduction issues – Capture issues with database connections, query timeouts, and other related are a fact of life for most applications. At Stackify, we process millions of messages an hour across a couple hundred servers and over a thousand SQL databases. A few errors here and there are just part of the noise of a busy system. It is important that you keep a pulse on your error rates and look for usage & trafficAfter a deployment, you want to see if the amount of transactions or users accessing your system looks normal. If you suddenly have no traffic or a giant spike in traffic, something could be last thing you ever want to see is no traffic at all. You could also see a spike in traffic if you are using microservices and one of your applications is causing a lot more traffic all of a performanceBefore you even do a deployment, you should use a tool like Retrace to look for performance problems, hidden errors, and other issues. During and after the deployment, you should also look for any changes in overall application might be common after a deployment to see major changes in the usage of specific SQL queries, web service calls, and other application dependencies. Tools like Retrace can provide valuable visualizations like this one below that helps make it easy to spot time to detection (MTTD) When problems do happen, it is important that you identify them quickly. The last thing you want is to have a major partial or broad system outage and not know about it. Having robust application monitoring and good coverage in place will help you detect issues quickly. Once you detect them, you also have to fix them quickly!



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    https://stackify.com/15-metrics-for-devops-success/

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