Understand the Impact Code Changes Have on Your Pipeline

Learning Objectives

  • Introduction to the DevOps lifecycle
  • Planning and coding
  • Building and testing
  • Releasing and deploying
  • Monitoring and operating

In previous articles, we’ve explored the importance of logging to monitor distributed applications or systems built using a microservices architecture. We’ve also discussed how a comprehensive log management solution is invaluable in continually improving your release cycle. We’ve also looked at DevOps and the importance of DevOps toolsets in accomplishing a digital transformation within your organization.

This article will take a closer look at how a log management system can provide insights into the impact that code changes have at each stage in the development lifecycle, including within your continuous integration / continuous deployment (CI/CD) pipeline. We’ll discuss the different effects that code changes have and how to observe, identify and mitigate those effects. 

The DevOps LifeCycle

The DevOps lifecycle is a cycle of identifying a need, designing and implementing a solution, and then deploying it through an automated pipeline into a production environment. Once deployed, various systems monitor the code. Using this information, the team can identify additional features or modifications and repeat the process. Within the lifecycle, a core philosophy at play is that of feedback loops. At each step, the team uses procedures to validate the code and determine if it can proceed. If validation fails, the team rectifies the problem and resubmits the modified code. Rapid and frequent feedback gives the engineers time to react, prioritize, and address issues before they can significantly impact the process.

Let’s walk through each step within the lifecycle and identify how code changes impact the process and how teams can use tools and automation to ensure problems are identified and mitigated. When this lifecycle is automated and proven by the team, it establishes a sense of trust with the team. The certainty that the process works increases a team’s confidence in their code, accelerating the development process.

Planning and Coding

The planning and coding phase of the lifecycle is the most difficult to automate. But the planning and coding phase is essential to establish a firm foundation for the remainder of the process. This phase includes the process of identifying and refining the requirements and building the solution. Building the solution involves coding and adding tests to a test suite that validates the new or modified functionality. Engineers typically execute these tests locally. It is typical for teams to have a process run the test suite and other analytics tools when introducing changes to the code repository.

Techniques like API-first development and test-driven development help ensure that the logic within the new or modified code is sound and meets the requirements established during the planning phase. In addition to executing the test suite, additional utilities like static code analysis, mutation testing, and vulnerability scanning complement any checks that are part of a compiler or preprocessor.

Finally, in this part of the process, the engineers instrument their code to support observability and monitoring later in the lifecycle. This instrumentation takes the form of adding log statements and implementing libraries and frameworks that support distributed tracing and performance metric reporting.

Build and Test

The build and test actions are the initial steps that the pipeline performs on newly submitted code changes. Ideally, the actions performed should mirror those already performed manually by the engineer. Performing them validates that the compilation, test suites, and packaging of the application or service are complete as expected.

The pipeline relies on the build and test processes’ results to validate this part of the lifecycle. If failures occur, the pipeline utility typically sends an alert to the engineering team, and they research and rectify the problem before submitting the change again.

Release and Deploy

Having validated that the code passed all tests, and packaging the code into a new deployment unit, the pipeline proceeds to the release and deploy phases. This pipeline stage is when the instrumentation and logging are of the utmost importance. The pipeline deploys the new code to validate the new release’s performance without a significant impact on users of the system. Some examples of different approaches that teams use are canary deployment, blue/green deployment, or red/black deployments.

The pipeline relies on data from the newly deployed code to validate that the deployment was successful and that consumer interactions execute as expected. System and application logs are the core atomic unit for gathering the necessary information to make these decisions. A log management system such as Mezmo, formerly known as LogDNA, is essential to perform this task. The log management system aggregates,  analyzes, and then provides data to the pipeline to make appropriate decisions based on the results.

If the pipeline determines that the deployment is successful, it orchestrates the complete replacement of previous versions of the application or service and annotates the pipeline as having completed successfully.

Monitor and Operate

Once the pipeline deploys the application, we enter the final phase of the DevOps lifecycle. As with the previous stage, system and application logs and performance metrics are central to this phase. In the past, operations personnel would routinely review logs and manually check each server’s performance metrics as part of their role.  In DevOps' age, and with modern distributed architectures, this is no longer feasible or necessary.

Log management systems do more than collect and aggregate logs. Systems like Mezmo perform complex analytics on the log to detect anomalies and identify potential problems. DevOps teams can establish baseline metrics for performance, error rates, and latency. Modern log management systems automatically consider these baseline metrics and can automatically invoke an incident management process when it identifies problems.

Automating these processes is more efficient, and in many cases, even the incident response can be automated, resulting in fewer interruptions for the engineering team.

Learning More

Log management is a critical component for your DevOps pipeline, enabling you to mitigate the effects of code changes and more effectively monitor your production services. Given the importance of log management, it’s vital you partner with an organization with robust systems and a proven track record of high-performing production systems. 

Mezmo offers a full-featured, free trial to prospective users, allowing you to experiment with their impressive assortment of integrations that ingests your log data. They provide real-time aggregation, monitoring, and analysis, and you can configure your account to send real-time alerts to PagerDuty, Slack, and other means, as required. Furthermore, their developer-friendly UI and features makes it easier for DevOps teams to leverage logs at every stage of the development lifecycle, and feel empowered with the data insights they can provide.

It’s time to let data charge