Four critical DevOps metrics: wait time for changes. One of the fundamental DevOps metrics to track is the wait time for changes. The error rate in changes is the percentage of code changes that require urgent corrections or other solutions after production. The research identified that only four key metrics distinguish the performance of various technology organizations.
These North Star metrics serve as indicators of the overall state of software engineering. How often do you deploy code? There are different ways to answer this seemingly simple question. You can implement code in test or production environments without delivering it to end users. At MinDK, we believe that these types of deployments should not be considered for the frequency of deployment.
Pre-production changes implemented in a test environment are instead referred to as “delivery” (which is also important for achieving high performance). Tracking the frequency of implementation on a daily or weekly basis allows you to identify the changes that provide the greatest benefits and the areas that require additional work. An abrupt drop in deployment frequency may indicate that a workflow is affected by other projects or staffing issues. What is a good transmission failure rate? For the most advanced DevOps teams, the percentage of deployments that need corrections ranges from 0 to 15%.
You can reduce the error rate in changes with the help of solid monitoring and progressive delivery practices, such as working in small increments, trunk-based development, and a solid test automation strategy. Most processes require manual intervention. To create an application, engineers would launch Ansible on a local machine. Releases were unstable and unpredictable, making on-demand delivery a pipe dream.
The waiting time for the changes was counted in weeks. The gearbox failure rate reached 25%. The project needed serious rework. The truth is simple: you can't improve something that you can't measure.
Deployment frequency, wait time for changes, MTTR, and change failure rate are the most important metrics DevOps measures. Together, they provide the basis for identifying any waste in your DevOps processes and improving the entire product value stream. Duration is defined as the time it takes for a workflow to run. It's the most important metric on the list because creating a fast feedback loop (including performance and average recovery time) depends on the duration.
In other words, you can't implement a solution, even a much needed one, faster than the time it takes for your workflow to run. The duration also represents the speed with which developers can get a meaningful signal (“Did my workflow run successfully or failed? ). A short duration requires an optimized workflow. Not all workflows produce the same final state.
For example, some workflows only run specific tests depending on the part of the application's code base that changed. Duration, therefore, is not an explicit measure of the time it takes for implementation to production. It's just a measure of the time it takes to complete a workflow. It's important to note here that speed alone is not the goal.
A workflow without tests can run quickly and turn green again, a sign that doesn't help anyone. Teams must be able to respond to a fault as quickly as possible and with as much information as they can obtain from the fault. Without a set of quality tests, workflows with short durations don't add valuable information to the feedback cycle. The goal, then, is rich information combined with a short duration.
Four Keys classifies events into changes, deployments, and incidents using the `WHERE` instructions, and normalizes and transforms the data with the `SELECT` command. The control panel is designed to provide you with high-level categorizations based on DORA research for the four key metrics, and also to show you an updated record of your recent performance. The Four Keys pipeline is the ETL pipeline that collects your DevOps data and transforms it into DORA metrics. The four key metrics are the frequency of deployment (the frequency with which new versions go to production), the delivery time of the changes (the time left until a confirmation goes to production), the average restoration time (the time it takes to resolve a service deficiency in production) and the failure rate in the changes (the ratio between implementations and production that causes errors and successful deployments).
The four key metrics are described in the book Accelerate (see Q&A on Accelerate in InfoQ) and have been further improved in the DevOps status reports. So I had to find solutions and ended up analyzing the deployment job log and the Git history for keywords such as revert and hotfix to calculate the error rate in the changes. Four Keys uses the WHERE filter to extract only the relevant rows from the events_raw table and the SELECT statement to assign the corresponding fields from the JSON to the confirmation identifier. A rigorous evaluation of these four key metrics (in research led by Dr.
Nicole Forsgren) has demonstrated that the best-performing people are twice as likely to meet their business objectives (productivity, profitability, market share, number of customers) and non-commercial (quantity of products or services, operational efficiency, customer satisfaction, quality of products or services, and achievement of organizational or mission objectives). Then, add your data and compile it into a dashboard with these key metrics, which you can use to track your progress over time. Now that all the data is aggregated and processed in BigQuery, you can view it in the Four Keys control panel. The Four Keys configuration script uses a DataStudio connector, which allows you to connect your data to the Four Keys control panel template.
In the Four Keys process, known data sources are properly analyzed to include changes, incidents, and implementations. Combined, these are four key metrics for DevOps that give you an objective way to assess your performance and track progress over time. .
