KPIs extend far beyond merely assessing a company's overall success; they permeate every facet of business operations and strategies, allowing for precise insights into performance. In the realm of software development, KPIs also stretch past the visible success markers.
How often do we think about metrics for evaluating backend development though? It seems sometimes to appear pivotal yet often overlooked aspect. We know a huge amount of real cases of successful companies which seem to prosper due to tracking KPIs like active user growth and daily active users, but indicators such as bug rates or crash rates are much less visible. Certainly, the backend is backend since it is at the back, so it is less noticeable. However, the bug rate is an indicator of quite high importance for Airbnb, since minimizing bugs allowed the company to create in-house tools and extensive automated testing frameworks to catch issues before they reach the production environment. Meanwhile, Uber is known for closely monitoring application crash rates, and if crashes increase after a new release, it indicates a problem that needs immediate attention.
With this article, I aim to introduce some of the most important KPIs for backend developers and the ways the developer can maintain efficient performance. While software development harmoniously integrates frontend and backend into a cohesive process, focusing on backend indicators can elucidate overarching goals and underscore their significance.
Looking closely: What KPIs Are There When Talking About Software Development
KPIs in software development provide tangible and objective metrics to take account of the progress and overall quality of a project or team's performance. When we address software development in general, we tend to think about such things as lead time, cycle time, commit-to-deploy time, customer satisfaction, ROI or project completion rate. Of course, there are many more of them.
Some of the metrics overlap with both frontend and backend development, as they are crucial for all the layers of the project. These might be metrics concerning collaboration and teamwork, learning and adaptability as well as narrower ones like code review metrics.
The metrics are not one-size-fits-all; each organization or project might have unique KPIs based on their goals and needs. It is crucial, however, to remember that for each frontend and backend, there are specific indicators too. As promised earlier, I aim to focus on the backend metrics, and not merely those which exist out there, but those I perceive the most crucial for a healthy assessment of the backend team and its workflow.
KPIs for Backend Developers
Here are some most vital KPIs for backend developers with some advice of how to maintain effective indicators.
1. Response Time
In real-world scenarios, fast response times is critical for user experience, especially in applications where real-time feedback is a necessity.
Optimize your code, make efficient database queries, and consider caching mechanisms to reduce response times. Evaluate middleware and ensure you're not overloading the server with non-foreground tasks. Use monitoring tools like New Relic, Datadog, or Grafana integrated with your backend service.
New Relic’s graph showing Web transactions time. Source: New Relic
Tools like New Relic can assist you not only in monitoring response times but also diagnosing the root causes of slowdowns, ensuring optimal performance for your end-users.
2. Throughput, or Requests Per Second
Throughput is also a common metric used to evaluate the system's performance and scalability.
Scale your services either vertically or horizontally. Analyze your server configurations and make sure they are optimized for the traffic you're aiming to handle. Monitoring tools like Apache Benchmark or JMeter can help simulate user traffic and measure throughput.
3. Uptime
Implement redundancy, failover strategies, and regular backups. Always monitor your server's health and set up notifications for downtimes. Use uptime monitoring tools like Pingdom, UptimeRobot, or StatusCake.
Uptime checks using Pingdom. Source: Pingdom
Such tools allow for configuring the checks and receiving real-time alerts in case of downtimes. Other than that, instruments like Pingdom provide information on what might have caused the issue, whether it's a server error, DNS issues, or something else, in case if your site goes down.
4. Error Rate
Regularly review your logs. If an operation fails, the system or the user might attempt the same operation multiple times, leading to increased load and, in turn, potentially more errors.
Ensure proper error handling in the code and consider using automatic error reporting tools like Sentry or Rollbar. Using tools like Logstash, ELK stack, or Splunk might also help in monitoring application logs. And don’t forget to set up alerts for unexpected errors.
Splunk APM, as an example, offers customizable features that allow users to differentiate between errors and root cause errors, enhancing error diagnosis. As seen in the image above, utilizing tools like the request and error graph in Tag Spotlight can help visually distinguish root cause errors from total errors through color variations.
5. Resource Utilization (CPU, Memory, Disk, Network)
Regularly profile your application to check for memory leaks or unnecessary resource consumption. Optimize or refactor areas of code that are resource-intensive. For these means, use server monitoring tools like Nagios, Prometheus, or Zabbix.
6. Database Performance
Check for slow or inefficient queries on a regular basis. Consider indexing, optimizing schema, or even denormalization in some cases. Use tools specific to your database, e.g., slow query logs for SQL databases or monitoring solutions like Percona Monitoring and Management for MySQL.
7. Code Deployment Frequency
Frequent deployments with smaller change sets can simplify identification of errors, but if not managed correctly, can also introduce instability or unanticipated performance blockages.
Adopt a Continuous Delivery approach, ensuring that you have a robust testing and deployment pipeline in place. Employ CI/CD tools' logs and analytics like Jenkins, CircleCI, and GitLab CI.
8. Rollback Frequency
Make sure there is thorough testing in staging environments before pushing to production. The goal is to catch potential issues early and minimize the need for rollbacks. Track deployments and their outcomes. Measure how often rollbacks occur in relation to deployments. For measurement, you can use Docker Swarm, LaunchDarkly or New Relic introduced earlier. For example, LaunchDarkly, by using feature flags, allows you to "roll back" a feature without necessarily redeploying the code.
9. Time To Resolve Critical Bugs
Since the "critical" typically means that the bug has a significant impact on the application's functionality, user experience, security, or system stability, make sure to prioritize critical bug fixes over new features. Implement a robust testing strategy, including unit, integration, and end-to-end tests, to identify and address issues before they reach production. Issue-tracking systems like Jira, Trello, or GitHub Issues to measure the time from bug detection to resolution can assist you in that.
While there are numerous KPIs to measure the performance of a backend developer, in my opinion, these highlighted ones stand out as the most pivotal, offering a comprehensive insight into efficiency.
Diving deep into the world of KPIs, we've seen how they're not just shiny badges but vital checkpoints guiding our path in the tech landscape. Isn't it fascinating how something as hidden as the backend can hold such power? After all, it's the behind-the-scenes efforts that often set the stage for a main performance, as examples of UUber, Airbnb and manu other successful stories have shown.
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