Observabilidade e monitoramento de DevOps: guia definitivo

DevOps Observability and Monitoring: Definitive Guide

May 31, 2024 Roberto Magalhães

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DevOps has become an important part of the software development lifecycle. Technique and philosophy, it is the mix of development (Dev) and operations (Ops), with the aim of cultivating a collaborative environment and bringing greater efficiency to the entire process. Teams achieve this by fostering a closer connection between development and operations teams, which are often more separated in development methodologies.

In modern software development, DevOps has become an important concept, enabling teams to improve productivity, communicate more effectively, collaborate closely, and ultimately increase the scalability and reliability of their software, a central goal of site reliability engineering.

Two important concepts in this process are observability and monitoring. DevOps observability allows teams to gain a better understanding of how their systems are performing throughout the development cycle, a vital aspect of system performance. This allows them to resolve defects and bugs immediately, before they affect the larger system, improving the customer experience.

Observability depends on collecting information and using this observability data to gain insights into the internal state of the system, while also moving forward to improve it. Although related to observability, monitoring is a separate concept that generally involves supervising and tracking system behavior. It includes analyzing data from different aspects of the product to identify problems, using various infrastructure metrics. About that,

In short, both DevOps practices are important, but they are different. Monitoring is the “what”, while observability is the “why”. Here we will look at observability in detail. But to understand observability, it's important to also look at monitoring, both of which are fundamental to a cloud-native approach.

Understanding DevOps Observability

First, let's unpack what DevOps observability means and why it is used.

What is observability?

In software engineering, observability is a comprehensive approach to understanding a system based on collecting large amounts of data and analyzing it in depth. When your DevOps team achieves observability, you will be better equipped to review your system and keep it running smoothly. This is critical to maintaining strong performance.

Not only will you know what your system is doing, but also why it is behaving the way it is. So, you can solve any problems.

Observability allows:

Report on the overall health of a system.
Report a system status.
Monitor key metrics.
Debug production systems.
Track previously unknown information about a system.
See the side effects of updates and other changes to a system.
Track, understand and diagnose problems between systems and services.
Stay ahead of outages and degradation.
Better manage capacity planning.

The main components of observability are logs, metrics, and traces. We will look at this in more detail below.

Observability vs. Monitoring

Observability and monitoring are important for a DevOps environment. However, although they are sometimes used interchangeably, they are different concepts.

Monitoring means you collect and analyze data from your technology systems, usually through dashboards. Thanks to this information, you can detect problems, including problem patterns that are occurring in the production environment.

Observability takes monitoring a step further. Based on the data collected thanks to monitoring, you can determine why the behavior is occurring. From there, you can figure out how to resolve the issue with the appropriate technical solution.

In other words, monitoring means you've gathered the data, while observability automates the process of evaluating these metrics to resolve issues using DevOps deployment tools and techniques, an essential aspect of continuous integration, or CI for DevOps.

Key Components of DevOps Observability

Observability involves three components: logs, metrics, and traces. Here, we'll take a closer look at each component and how they are fundamental to infrastructure as code strategies.

Historic

Logs are records of what happened on a specific system. They contain important information that gives the DevOps team greater insight into how the software is working, providing them with the materials they need to make decisions.

In software development, there are several different types of logs that DevOps uses. Examples include application logs, server logs, and error logs.

Application logs are specific to applications. They provide information about how the application is behaving and offer insights such as error messages, performance metrics, and more.

Server logs include critical information about the server's operating system. By providing this data, logs help teams monitor performance, resolve errors, and identify potential cybersecurity issues.

Error logs specifically refer to errors that are occurring within the system. By using these logs, you can better assess problems and their causes, which helps you resolve them.

To make the best use of logs, follow the following steps:

Establish procedures for using logs
Use a consistent format for logs
Continuously monitor logs
Use automation tools to evaluate logs
Store records in a central location
Maintain best security practices

Metrics

Metrics are specific quantitative measurements that provide information about what a system is doing at a given time and in general. By using metrics effectively, you will be better able to make data-driven decisions about your software.

As with logs, there are several different types of metrics to consider in DevOps observability. Examples include:

Performance metrics: These measurements evaluate different aspects of system performance such as downtime, response time, latency, and so on. With the help of these metrics, DevOps teams will be able to improve overall system performance as well as resolve issues.

Error Rates: As you can probably imagine, error rates relate to the amount of errors that are occurring, as well as the frequency of these problems. They enable DevOps teams to build more reliable products.

Resource utilization: These metrics take into account the type and number of resources the system is using, such as memory. By evaluating resource utilization metrics, you can eliminate limitations and improve the system.

DevOps teams must adhere to certain best practices when working with observability metrics. These practices include:

Define the key metrics you want to measure
Evaluate all metrics in real time
Make sure you receive alerts about these measurements
Collect metrics in a standardized format and manner and store them in a centralized location
Use visualizations as data graphs
Target specific metrics

Trace elements

Traces are another important part of DevOps observability. They essentially “track” the path of aspects of a distribution system and allow teams to better understand how system components are performing and how the system is functioning and flowing as a whole. There are different techniques and methods involved such as distributed tracing.

Distributed tracing involves tracking specific requests throughout the system. Each request is assigned a tracking ID so it can be tracked from start to finish. This allows teams to gain insights into system behavior and increase observability.

There are some best practices you can leverage when working with traces in DevOps, such as:

Developing a strategy to capture traces
Ensure you include important and relevant metadata
Be consistent with naming conventions and methods
Using Tracking Platforms to View and Evaluate Tracks
Using traces in conjunction with logs and metrics
Focus on continuous improvement

Implementing DevOps Observability

How do you implement observability solutions into your DevOps process? Here are some steps to follow.

Choosing the right tools

Part of implementing the process involves choosing the right tools. You should look for platforms that meet your needs and research each observability solution you are considering.

Consider factors like performance, features, scalability, usability, learning curve, and more. Whether you're looking to hire a DevOps engineer or choose from the latest DevOps trends, the right tools make all the difference.

There are several observability tools available, such as Prometheus, Grafana and Jaeger.

Integrating observability into your software development lifecycle

It's important to integrate observability into your DevOps pipeline to gain better insight into your entire SDLC. Among other benefits, you will be able to detect and resolve problems more quickly.

The process includes:

Find the right tools for the process
Create a monitoring system for pre- and post-deployment
Automate the testing process
Troubleshoot as needed
Use data to improve your efforts

Establishing Best Practices for Observability Systems

It is important to create a culture of observability within your organization. Best practices include:

Setting goals and benchmarks
Establish a communication and collaboration system
Keep stakeholders informed

Monitoring for Continuous Improvement

Monitoring is essential for continuous improvement. It involves:

Proactive Monitoring

This requires careful oversight of your systems. This means taking control and preventing incidents from occurring. Stay in control of your systems, for example, by configuring alerts to deploy when a metric exceeds a certain threshold.

Performance optimization

Monitoring helps DevOps teams ensure performance optimization. To track and optimize your system, establish key performance indicators (KPIs). Continue monitoring data to identify and resolve any performance bottlenecks.

Ensuring reliability and resilience

Finally, monitoring plays a role in ensuring system reliability and resilience. Some steps you can take to manage and increase resilience are:

Establishing baselines
Configuring alerts
Establishing a system for incident response
Looking at trends
Leveraging predictive analytics
Creating a culture of continuous improvement

Conclusion

Observability is an important and comprehensive process that all DevOps teams should use to understand their systems, identify any issues that are occurring, and troubleshoot issues to keep their system running smoothly. Ultimately, this important process is critical to resolving issues and increasing the performance, usability, and overall quality of your software.

Observability is a multi-faceted, multi-stage process that requires some effort, but it is essential and should not be overlooked. Furthermore, when it involves automation, it helps to aim for a deployment with zero downtime.

When your DevOps team achieves observability, you will be better equipped to review your system and keep it running smoothly. This is critical to maintaining strong performance.

If you liked this, be sure to check out our other DevOps articles.

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What is DevSecOps and why do you need it?
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Why is DevSecOps becoming essential?
Achieving zero downtime deployment with continuous deployment

Common questions

What is the difference between observability and monitoring in DevOps?

Observability and monitoring are different processes in DevOps. Monitoring involves collecting and analyzing data from your systems, before identifying patterns that may be causing problems. Observability goes further, using this data to understand how and why problems are occurring so you can resolve them.

How can I choose the right observability tools for my DevOps teams?

To choose the right observability platform, you must start by identifying your needs and requirements for the tool. From there, based on your needs, research the available tools, evaluating the different options. Consider additional factors in observability platforms, including performance, features, scalability, usability, learning curve, and more.