By now, most industries employ data analysis to provide their teams with insights that assist in product development—and software development is no exception. The move to DevOps and CI/CD practices has led to a significant increase in release velocity, with changes now being deployed to production multiple times per day. To maximize the impact of these changes and ensure software quality across releases, development teams rely on DevOps intelligence to help them gain a deeper understanding of how their applications are performing in their production environments.
Keep reading for a discussion of the types of metrics and analysis that can help coders identify opportunities to improve their application’s performance and stability.
Increased Velocity Means Increased Risk
Agile development techniques have enabled organizations to shorten release cycles and release applications more frequently. Along with these shorter cycles, however, comes an increased risk of introducing bugs into an application’s code base.
Of course, having a thorough, automated testing strategy that validates each feature to the greatest possible extent is critical for mitigating this increased risk. Organizations that leverage DevOps dashboards can see improvements in both delivery speed and overall software quality. These dashboards, which display insights generated through real-time application performance analysis, can help organizations a great deal when it comes to diagnosing and resolving issues that require developer intervention.
Real-Time Analysis Enables Quicker Developer Response
Proper incident response requires that information be interpreted and disseminated in an efficient manner so that development teams can be notified of problems. This is known as reducing the mean time to acknowledgement, and it allows pertinent development personnel to begin the process of root cause analysis as early as possible.
Reducing the time that it takes to begin root cause analysis is just one way that real-time analysis helps organizations improve performance with great efficiency. In addition, the insights gleaned from application analysis can minimize the time that it takes to actually get to the root cause of the problem itself. This allows organizations to implement more permanent solutions while also improving their mean time to resolution, both of which help limit the impact of performance problems on end users.
DevOps Dashboards and the Metrics That Matter
A DevOps dashboard is essentially a monitoring interface for an application and the processes associated with its development and deployment. When utilized properly, these types of tools can help break down silos between development and operations folks and enable them to work together to provide an improved user experience. Certain metrics are more useful than others for developers who are trying to head off potential application quality issues, including:
An application with slow response times will be considered intolerable by almost any end user. Users expect virtually instant responses on a consistent basis, and in many cases, this is necessary to meet an organization’s service-level objectives. Keeping a close eye on this metric over time enables organizations to do a few different things.
First, teams can better identify what their benchmark performance expectations should be for their applications. This will enable them to set proper service-level objectives that allow them to meet their service-level agreements even if they hit a slow patch.
Second, slow response times will likely drive customers away, but they won’t always be reported. It may be the case that requests are technically resulting in successful responses but taking a little extra time to do so. Tracking this metric is critical for alerting development teams to problems that might otherwise linger in production for a month or more.
Finally, the analysis of a slow response time is often all that developers need to reproduce and determine the root cause of the problem. An inefficiently designed query, for instance, may be the reason for slow responses, and as long as the developers know the details of the sluggish request, they will likely have all they need to redesign the query to improve performance.
Unlike sluggish response times, repeated error responses will almost always trigger bug reports from end users. But wouldn’t it be nice to know about the issue ahead of time? Error rate is an extremely useful metric for the development staff to monitor, especially in real-time. The frequency of error responses can indicate when a bug was introduced. For instance, if this metric spikes just after a release, the development staff can immediately examine the most recent changes to quickly identify and resolve the issue, and they can rollback to a previous release in order to limit the impact of the unstable release on end users.
Incident Reports Over Time
Sadly, not all bugs will be caught prior to a release, and there will be many times when end users discover problems before the development staff. Tracking incident reports over time, therefore, allows both development and operations personnel to gain a better handle on how their development processes are maturing. Are developers properly analyzing application issues to provide permanent fixes that eliminate related bug reports in the future? Does the automated testing process provide enough coverage to eliminate problems that should be caught before a release?
These are the types of questions that a quality DevOps team needs to be asking themselves in order to consistently provide high-quality service for their end users.
Enabling Continuous Improvement with Real-Time Analysis
One of the primary goals of any forward-thinking development organization should be to continuously improve the overall performance and stability of their applications. DevOps dashboards help organizations accomplish this by tracking key performance metrics (such as those mentioned above) in real-time. With this information, development organizations can identify opportunities for improvement and gain insight into past mistakes (so as to not repeat them in the future), resulting in more stable and high performance applications. This helps organizations provide top-notch products that will be competitive for years to come.
It’s not just development teams that gain insights from DevOps intelligence. Operational teams will use every piece of data they can access if it can help them model and understand the workloads they are supporting.
Better Targeted Improvements
The additional metrics and logs that automation of application pipelines bring into the operations side of the DevOps world, if nothing else, prove their value by showing what activities development teams engage in the most often. These metrics and logs also help development teams track the increasing magnitude of individual applications—both the size and the sheer velocity with which new applications are being built and released into the environment.
Ideally, all infrastructure is cloud-based. Adding new capacity can happen on demand and with little thought. But as adoption of the cloud skyrockets, even the largest hyperscalers need to limit how much capacity their customer can access, as hardware takes time to order, assemble, and ship.
Many organizations share this pain. The longer the lead time, the better the ability for the capacity-planning lifecycle to happen seamlessly.
Insight into what features and functions development teams are accessing also helps with product selection. They can more effectively eliminate bottlenecks—both performance-based and where human intervention is still required.
Operational teams have years of experience using automation to eliminate single points of failure. They’re also an extremely effective second set of eyes to help their partners in the development organization. The additional insight they gain through improved metrics and logs helps them them to be as effective as possible.
Improved Incident Management
Every operational team has someone who is stuck being on-call on any given night. The bigger the team and the more moving parts they support, the less likely it is that the person on-call knows all the recent changes. Add into that mix the ability for development teams to run their own pipelines in a DevOps-friendly organization where production changes can happen all the time. The situation can become impossible.
Yet, with those self-service capabilities came the ability to automatically roll back a failed application to a known working version. Often, enhanced monitoring is made available to the on-call staff and network operation centers they support. This enables them to quickly identify what has changed in an environment so they can start tracking where errors are coming from and identify the source of performance problems.
AIOps Powered by DevOps
The real power for operational teams in a DevOps environment is when they can integrate all the metrics and logs, and consolidate them into proper AIOps solutions. This includes metrics and logs generated across the application development pipelines, and those generated by the underpinning infrastructure.
Application performance management (APM) solutions give developers what they need. But the machine learning and AI capabilities that an AIOps solution brings to the table allow for operational teams to become proactive and start to resolve events before they become incidents that affect customers.