DevOps observability: The complete guide

In DevOps, observability is both a practice and a systems design principle that enables engineering leaders and their teams to understand their software system's overall efficiency. Implementing and leveraging observability within your DevOps  processes helps your team address challenges such as a lack of visibility into performance, inefficient resource utilization, and slow, difficult troubleshooting.

By understanding and implementing DevOps observability practices, your team can gain the insights it needs to improve system reliability and performance. This guide explores the details of DevOps observability as a practice, the benefits it provides for your team, and how you can best implement it in your development process.

Table of contents

• What is observability in DevOps?
• The importance of DevOps observability
• The differences: Observability vs. monitoring
• 3 pillars of DevOps observability
• How to implement DevOps observability
• Factors to consider when selecting the best DevOps observability tools
• 5 tips to overcome DevOps observability challenges
• FAQ

Within the DevOps and SecDevOps methodology, observability is a practice that focuses on improving a software system's transparency and understandability and, consequently, its reliability and efficiency. 

DevOps observability empowers teams to enhance the developer and user experiences by proactively addressing challenges and making data-driven decisions. By leveraging purpose-built tools, you can gain valuable insights into your system's real-time performance, enabling you to identify and resolve issues swiftly.

Key components of the observability process include quantifiable DevOps metrics, text-based logs, traces, or more detailed records of system events. By analyzing these components, your team can find correlations across different data sources and make more informed decisions.

Implementing DevOps observability within your team's development practices is critical to building more reliable, efficient systems. By focusing on DevOps observability, you can create better user and developer experiences while reducing risk throughout your system.

DevOps observability is essential for development teams to minimize response time and stay efficient and competitive. Observability provides the necessary data to quickly pinpoint root causes and resolve problems smoothly when issues arise.

Observability introduces a collection of DevOps benefits for your team, including improved system reliability, enhanced performance, and the ability to make better data-driven decisions. Some of the key benefits your team can experience when implementing DevOps observability in its process include:

• Proactive risk mitigation: With DevOps observability, you don't wait until a risk occurs to address it; through metrics, you can identify potential risks before they happen. By taking proactive measures, you and your team can create a more efficient and reliable system.
• Data-driven decision-making: Don't make decisions in the dark. With DevOps observability in play, you can use real-time insights to help inform your choices, such as resource allocation and feature prioritization. Observability eliminates guesswork, allowing more intelligent decision-making.
• Enhanced performance optimization: Metrics collected as part of observability practices allow teams to identify bottlenecks, such as CPU constraints or network congestion, with greater efficiency. These insights can also lead to better resource allocation and reduced costs.
• Improved user experience: Implementing DevOps observability can deliver a better user experience by anticipating problems before they affect your user base.
• Refined problem detection and resolution: DevOps observability enables real-time visibility into your system's performance, allowing teams to detect issues faster. The metrics collected allow easier pinpointing of the root cause for faster resolutions.
• Upgraded team collaboration: More data gives your team a shared understanding of your system's operations. This enhanced comprehension leads to more efficient communication that can help prevent misunderstandings.

While you may hear the terms "observability" and "monitoring" used interchangeably, they're different concepts. Each approach relies on other metrics, analysis focuses, and scopes to help DevOps teams create a more efficient software system.

Monitoring focuses primarily on collecting and analyzing data to better understand system performance and identify specific issues. The scope is generally limited to set metrics, and the approach—particularly in deciding what to monitor and when—is more reactive, responding to alerts.

Observability focuses on the tools and practices you need to better understand your system, which is ideal for troubleshooting more complex issues. Its scope can go beyond predefined metrics to help identify system behavior and enable a more proactive approach, enabling teams to anticipate problems before they occur.

Telemetry is a subset of observability; it concentrates on gathering and analyzing data on remote systems or devices. Similar to observability, telemetry collects data from an extensive range of data sources and focuses on a system's performance. When an engineer uses the term "telemetry," the most important thing to understand is that a remote system or device is the primary focus rather than a local system.

The three pillars of DevOps observability are metrics, logs, and traces. Utilizing these metrics and records, your team can create a comprehensive view of your system's behavior. While your team can use additional data sources, these three pillars are a solid foundation for understanding system performance and making better decisions. Understanding what to look for within each is key to success.

Metrics are quantifiable data points your team can measure with numeral values that point to your system's performance. Keeping a close eye on metrics can enable your team to identify ongoing trends, detect anomalies, and spot performance bottlenecks. Examples of these metrics include:

• CPU usage

• Errors detected

• Memory consumption

• Network traffic

• Response times

Logs are more detailed, text-based records of your system. They provide information about system events, giving you a deeper understanding of what's happening within your system, including errors and informational messages. Examples of logs your team might reference include:

• Application logs

• Infrastructure logs

• Security logs

• System logs

• Network logs

Traces are detailed records that emphasize exchanges between different components within your system. By examining traces, your team can better understand how requests move throughout a system, noting bottlenecks or errors during the process. Examples of traces you might observe include:

• API calls

• Database queries

• Network requests

• Workflows

DevOps observability can seem complex, but it’s worth the effort. You can easily integrate it into your team’s workflow in three steps.

Begin your DevOps observability journey by establishing a solid data foundation to guide your decision-making. Identify the most critical metrics for your team, basing them on your organization's goals and objectives. For example, a team emphasizing system uptime may want to focus on the mean time to recovery (MTTR) metric, which notes how long it takes to recover from a system failure or begin with DORA metrics.

Once you've identified the critical metrics, begin the data collection process by assembling metrics, logs, and traces. Store this information in a centralized location so your team can assess and ensure the data remains reliable and up-to-date.

Now you have to analyze the data to turn it into actionable insights. Selecting the correct DevOps tools for the job—a process we'll explore in the next section—is critical to the observability process. Such tools allow the data to be interpreted to its full potential within your given system, significantly affecting the outcome of your observability approach.

Create a dashboard within your selected observability tool to visualize metrics and make critical information glanceable. These tools can draw correlations within data to identify patterns, helping you make decisions to reduce risk and increase efficiency in the future. Also, take advantage of your tool's alerts to receive notifications of failures or anomalies, allowing you to respond faster.

You've collected the necessary data and implemented a DevOps observability tool to identify patterns and alert you to critical changes. Next, you'll use those insights to determine how to improve your system and developer experience. As you examine your results, focus on one area at a time that you wish to improve, setting clear goals.

For example, if the data shows high latency in your system between specific API calls, this may lead to slow user response times. Using this information, you can set goals to reduce latency by improving network performance, optimizing database inquiries, or refactoring inefficient code sections.

Choosing the right DevOps observability tool for your system can lead to better results. Your chosen tool should be able to collect and analyze critical data from your system and integrate with your existing software for greater efficiency. We'll explore the top factors you should consider when selecting the best DevOps observability tools for your team.

The key to DevOps observability tools is their ability to obtain needed metrics properly. First, decide on core metrics your software should be able to track, such as CPU usage, memory consumption, and network traffic. If your required metric isn't included, ensure the tool you're considering can create custom metrics and adapt to your team's needs. The tool should also be able to aggregate logs from your required sources and provide alerts when set parameters are out of range.

The DevOps observability process shouldn't add weight to your continuous testing approach, so selecting a tool that integrates with your DevOps pipeline is crucial. Check with your CI/CD pipeline, such as GitLab CI or Jenkins, to see if it's compatible. In addition to your pipeline solution, ensure other tools within your DevOps stack, such as configuration and deployment tools, integrate well with your selected observability platform.

As your team and system advance, you'll want observability software that can scale with your DevOps approach. Understand the scalability factor of the observability tool, looking at factors like data volume and bandwidth. You may want to test the platform by simulating peak usage and observing how it handles more stressful situations. You'll want to find another option if the observability solution can't keep up with your system.

The human factor is critical within the DevOps observability process, and selecting a tool your team can learn to use properly is key. When identifying potential observability tools, examine the user interface and its overall complexity; the more complex the software, the steeper the learning curve will be during implementation. Check to see if any training or documentation is available to make the integration process easier, and take your team's DevOps skills into account when choosing the best solution.

You already know the challenges of balancing development tools and budget; working to select a DevOps observability tool is no different. When choosing a solution, consider the offering's price model, whether subscription or usage-based. Additionally, evaluate the support levels included, as response times and available support may differ. You'll want to factor both short- and long-term costs like upgrades and additional features into the overall picture.

Like any development process, DevOps observability has its own challenges. Here are five tips and best practices to overcome the most common DevOps observability challenges you'll run up against, from technical complexity to effectively working with your team:

Excessive collections of complex data will slow down your progress, introducing inefficiencies. Identifying and prioritizing your team's critical data points for observation is essential. Implementing data retention policies for specific periods, rather than storing and archiving unnecessary data, can help you avoid needless storage requirements.

Example: If your team monitors network response times, you might aggregate the data by hour and set the data retention policy to 30 days.

By opting for a unified DevOps observability platform, your team can centralize data, making it easier to analyze. A unified platform can also ensure data is collected and stored consistently for easier access. Consider choosing a platform that also offers pre-built integrations, bringing in data from other sources and simplifying it.

Example: When selecting your DevOps tool, you choose a unified solution to pull all required metrics, logs, and traces from your system.

DevOps observability isn't just about the tools you use; it's about the culture you create. By fostering a learning culture that welcomes experimentation, you can explore new approaches and techniques within the observability process. When a team member experiments and fails, focus on treating the situation as a learning experience and identifying how they can predict negative results in the future.

Example: Host lunch and learns or other events introducing your team to new ideas, tools, and concepts for potential growth.

Ensuring you equip your team with the knowledge it needs to succeed is critical within the DevOps methodology. Make time for training and development opportunities to mitigate skill gaps rather than transfer the work to another team or individual. Keep your team engaged in the latest DevOps observability trends, helping them connect the metrics they receive to real-world results.

Example: Help new team members grow by pairing them up with more experienced DevOps developers for mini-mentorship opportunities.

Start with smaller metrics and iterate over time. Choose improvements with achievable goals and evaluate them over time based on observability results. By picking smaller, more manageable goals, you can keep a closer eye on results and avoid overwhelming your team with excessive data. As your team becomes more familiar with the process, you can expand your DevOps observability approach, focusing on additional metrics and goals.

Example: Start with a critical metric your team is already familiar with and add additional data sources and complexity as time evolves.

The Pluralsight team utilizes the DevOps observability process to optimize its system and deliver the best results to users. Here are answers to frequently asked questions we hear about DevOps observability practices.

Software engineering key performance indicators (KPIs) help determine a system's reliability, performance, utilization, and cost within the DevOps observability practice. Such metrics can also track overall customer satisfaction. These are some of the most common DevOps observability KPIs engineers rely on:

• Cost per transaction

• Cost per user

• CPU and memory usage

• Customer satisfaction

• Error rate

• Latency

• Mean time between failure (MTBF)

• Mean time to repair (MTTR)

• Network traffic

• Page load time

• Response time

• Throughput

• Uptime

The four golden signals of DevOps are metrics that help provide better insight into a system's health and performance. By monitoring these signals, your team can better proactively address possible risks or challenges. The four golden signals of DevOps include:

• Errors: The rate and type of errors that occur within a system
• Latency: The time it takes for a request to be processed and returned
• Saturation: The times when a system reaches its capacity
• Throughput: The number of requests a system can handle

In observability, the acronym APM stands for application performance monitoring; it's a subset of the observability practice that focuses on the performance of applications rather than a system in its entirety. Factors examined within APM may include an application's response time, error rates, and overall resource utilization. Monitoring APM allows developers to identify bottlenecks and potential issues.

Use the features within Pluralsight Flow to kick-start your DevOps observability process. Flow visualizations highlight bottlenecks in your development process, and provided insights can identify areas of team collaboration that may require improvement. Demo Pluralsight Flow today.