DevOps Metrics Explained: How to Track and Optimize Your Development Pipeline

We humans like to blur lines, one such attempt is DevOps. With DevOps in the SDLC ring things got different and what’s that? Tracking key factors like application performance, quality, and velocity becomes a continuous process. Without these measurements, making reliable, data-driven decisions can be tough.

But on what scale do we measure performance? There comes metrics.

DevOps metrics provide the insights needed to adapt, optimize, and ultimately deliver better software, faster.

This blog talks about why DevOps metrics are important. It also talks about various points to think about when using them to make decisions.

Why Keep an Eye on DevOps Metrics?

You can accomplish the following by keeping an eye on DevOps metrics:

• Examine long-term patterns with data on observability.
• Configure helpful alerts with a strong signal and little noise.
• Create practical dashboards to aid with reporting and planning.
• Create attainable SLAs, SLOs, and SLIs.
• Create an internal repository for best practices in software engineering.

It’s good to be “data-driven,” but there’s a risk of becoming overwhelmed by analytics. To demonstrate value, you simply need a few measures.

Four Essential DevOps Metrics to Monitor

The four DevOps metrics are also known by the name, ‘The Four Keys’, and they serve as a benchmark for evaluating a DevOps team’s performance, ranging from low to elite. A “low” score indicates poor performance, while “elite” signifies exceptional progress toward achieving DevOps goals.

Four primary measures, divided into velocity metrics (DF and CLT) and stability metrics (MTTR and CFR), are identified by Google’s DevOps Research and Assessment (DORA) team as indicators of DevOps performance:

DORA Metrics indicators of DevOps performance:

1. Deployment frequency (DF)
2. Change Lead Time (CLT)
3. Mean Time to Recovery (MTTR)
4. Change Failure Rate (CFR)

More frequent deployments, shorter change lead times, fewer failures, and quicker service restorations are all characteristics of high-performing teams. The converse is true for underperforming teams, which results in inefficiency and poor customer experience.

Let’s take a closer look at each metric.

1. Deployment Frequency

The frequency at which code updates are pushed to staging or production is measured by deployment frequency. DevOps team productivity, reaction time, developer skills, team cohesion, and tool efficacy can all be measured directly or indirectly. Effective teams are able to implement adjustments as needed and frequently do so throughout the day. Conversely, low-performing teams are sometimes restricted to weekly or monthly deployments.

Deployment frequency is calculated differently by each company. For example, you may want to calculate deployment frequency for successful pipeline runs. However, the measurement may also change depending on how “deployment” is defined. Deploying each minor Pull Request or code update will result in a high frequency. However, things start to look a little different if your deployment is planned to run after a certain amount of time.

Although switching from weekly to daily deployments, for example, demonstrates how continuous deployment can enhance your development process, relying solely on this DevOps indicator may lead to incorrect assumptions. For example, if you increased your daily deployments from three to 3.3 from the previous quarter, you are technically 10% more “successful,” but that isn’t a good indicator of success. To assess overall development, you would need to consider additional metrics.

Also Read: 6 Reasons Why You Need DevOps as a Service

2. Change Lead Time

The time taken for a code change to move from committed to deployed is called the “change lead time.” In essence, it poses the question, “How quickly can we modify a line of code and have it operational in production?”

While a medium- or low-performing team measures its mean lead time in days, weeks, or even months, a high-performing team usually calculates its mean lead time in hours. There are performance constraints in your software development or deployment process if the lead time for changes is very long. Reducing the change lead time increases your ability to respond to evolving needs. Working in small batches, automating tests, and using trunk-based development can all help shorten change lead times.

The average difference between the time it takes to create pull requests and when they are merged to the master branch is used to determine the change lead time. The average lead time for changes would be 90 divided by 10 if you had 10 changes in a month and the total days from commit to deployment for all changes was 90. This would mean that each change would take an average of 9 days.

It may take months to identify the product’s needs and prospects, yet it may just take a few days to commit the code. Therefore, even while the lead time for changes is a valuable statistic, using it alone is naive.

3. MTTR (Mean Time to Recovery)

The speed at which service outages or complete failures are detected and fixed is measured by your mean time to recovery.

While low-performing teams may take up to a week to recover from a system breakdown, high-performing teams often recover in less than an hour. The average time it takes for a problem to be closed as fixed is how you determine your MTTR. This measure can be impacted via monitoring, automated testing, and effective issue response.

Since statistical design can skew meaning in one way or another, the value of MTTR has been questioned on numerous occasions in recent years. Additionally, averages have the potential to be deceptive and conceal the actual behavior of systems. Therefore, attempt to consider the measure in conjunction with other metrics, even though you shouldn’t ignore it completely.

Explore our Solutions: DevOps as a Service

4. Change Failure Rate

The percentage of code modifications or deployments that require hotfixes after production is known as the change failure rate. To put it in plain, “How frequently is a change you made truly ‘successful’?”

As a defect progresses through the pipeline, from failing a pre-commit unit test to creating a production outage, its cost or damage increases. The statistics become considerably more difficult to measure when a pipeline stage fails, meaning that all previous stages were unable to identify the problem.

Failure can be broadly defined as an error or problem that causes issues for customers following a deployment to production. Testing-identified problems that are resolved before deployment are not included in this important measure. Change failure rates can reach 60% for low-performing teams, whereas they are less than 15% for high-performing teams.  Change failure rates can be decreased by the same strategies that reduce lead times, such as trunk-based development, automated testing, and working in small batches.

How to Calculate DevOps Metrics?

The following advice will help you get the most out of DevOps metrics:

• Establish baselines: Before you can truly monitor your progress or celebrate victories, you must know where you are beginning from. It’s similar to looking at the scoreboard before the start of the game.
• Avoid making measurements become goals: “When a measure becomes a target, it ceases to be a good measure,” according to Goodhart’s Law. You risk losing sight of what is important for your DevOps success when you become fixated on a single figure.
• Start small: You can stay ahead of the competition by adding new features fast. Make changes or deployments in smaller, easier-to-manage batches. Smaller changes are easier to understand, get through the deployment pipeline faster, and are easier to undo. Failure recovery is also quicker.
• Be wary of simplicity: It can be alluring to focus on a single, easily understood metric, but the reality is that no one figure can capture the complete picture. DevOps is more than just a statistic; it’s a way of thinking and a journey around software development.
• Make fair comparisons: Measurements might not quite match if you’re comparing metrics from two separate projects or even two different periods. Situations vary, and context is crucial.
• Take a closer look: Choose metrics that clearly demonstrate value for your team and your projects, even if they need a little more work to monitor. It’s simple to measure what’s right in front of you.
• Examine client comments: Users may still have problems even if your deployment is going smoothly. You cannot determine user happiness with DORA data. To find out what needs to be fixed, see what people are saying. All of this falls under value stream management.
• Look for trends in problems: Do the same types of difficulties keep coming up? This may indicate more serious problems that require attention.
• Keep an eye out for near misses: Not only the issues that occurred, but the ones that nearly occurred as well. You can learn a lot from them about how to steer clear of software delivery problems in the future.
• Monitor cost reductions: Demonstrate how DevOps reduces costs or prevents further expenses. Its budget is justified by this.
• Reduce downtime: You want your system to remain operational at all times because downtime can damage your company’s reputation and result in lost revenue. Make every effort to stay away from it.

How Are DevOps Metrics Interpreted?

You must be astute with the statistics you collect while using DevOps metrics. If you’re not vigilant, averages can easily mislead you. Just as one outlier can distort perceptions, relying only on averages to make conclusions might obscure the true distribution of data.

Think about eliminating the outliers from your data. Your perception of ordinary performance may be distorted by these extreme figures. It shouldn’t be the norm for your usual deployment time, for example, if one deployment takes a lot longer than the rest because of unanticipated problems.

Data bucketing can also be beneficial. To do this, related data types are grouped together so that patterns within those groupings can be examined. For instance, when figuring out lead times, keep bug patches and new feature deployments apart. This enables you to determine whether and why one type routinely takes longer than the other.

Last, not the least, constantly challenge the conclusions drawn from your data. What narrative do the facts tell you? What could it be omitting? Does a dramatic improvement in your change failure rate, for example, indicate that your deployments are indeed more reliable, or are you deploying less frequently, which lowers the chance of failure?

Investing more time and energy in the early phases of product development will reduce the likelihood of issues throughout deployment. Consider it like repairing a boat’s leaks when it’s still docked instead of out at sea. This proactive strategy guarantees that your product is reliable and used more quickly while also saving time.  However, be aware that your DevOps team cannot directly affect every area of your product’s performance. Despite their value, DORA measurements might occasionally reveal elements that are out of direct control, such overall corporate procedures or decision-making schedules. For instance, your lead times would inevitably increase if your business had a policy requiring thorough security checks before implementation.

Check out: Zuci joins hands with the global cloud-based test platform provider, Sauce Labs

Setting reasonable goals are aided by understanding the limits of what you can directly influence. For example, you can try to streamline other aspects of your deployment process to make up for the time lost, even though you might not be able to expedite those company-mandated security checks.

Better Measure DevOps Metrics DORA offers a helpful framework to enhance the development and management of tech services, particularly if you’re thinking about switching to Kubernetes.

However, it needs to be customized for your situation, used in conjunction with other metrics, seen as a single moment in time, and not manipulated.

Determining the pipeline’s performance and identifying areas for improvement is the overarching objective of these crucial DevOps KPIs.