DevOps and Cloud Computing
How to Implement DevOps Observability and Why It is Important?
In today’s fast-paced business world, launching an app is just the beginning. Maintaining the app and making it successful is a long and complex journey that requires both time and effort. Deploying code quickly is no longer the ultimate and final aim; it’s really just the first step. The real important thing here is how your app behaves when active and interacting with users. In the old days, you’d require monitoring dashboards to check how the app was performing and what the metrics were. Nowadays, teams require deep and actionable insights into the behavior, dependencies, and user interactions of an app. This is where DevOps Observability comes in to serve the needs of teams.
With DevOps Observability, teams don’t have to sit back and watch what unfolds in front of them but can take proactive measures and detect problems early, troubleshoot faster, and make continuous improvements. DevOps observability turns raw data into powerful insights, allowing engineers to deploy frequently and experiment safely with full confidence. This guide covers everything you need to know about DevOps Observability, from what exactly it is, how you can implement it, what challenges you might face, and what practices are useful when employing it.
What is DevOps Observability?
As the name suggests, DevOps Observability is the ability to understand the internal state of an operating system by analyzing its data and how it’s produced, including logs, metrics, and other traces. This represents a significant upgrade compared to conventional monitoring, which alerts you only when something breaks. Observability in DevOps enables you to observe data and ask questions about your system, eliminating the need to rely on predefined fail-safes in case of a failure scenario.
If you’re struggling to still grasp the concept of DevOps observability, consider the following example. In case of a CPU usage spiking, monitoring would alert you of this, but observability dives deeper. It helps you understand why it spiked, and whether it’s due to a new code release, a memory leak, or something external.
What is the importance of Observability in DevOps?
DevOps observability has its benefits in general, as shown with the example above, but it becomes even more significant when taking into context how complex today’s systems have become. Simply knowing when something breaks is good, but it isn’t enough; you also require insights to improve systems and keep them more reliable and resilient. Here are some of the major reasons why DevOps observability matters so much:
Faster Detection
Downtime for any company is a huge blow to both their reliability and reputation, not to mention the financial loss they’d incur in case of such an event. Industry reports suggest that even a few minutes of downtime can cost companies thousands of dollars. Observability allows engineers to intervene and take the required action in case any anomalies are detected. These early-stage detection signals can come in the form of unusual response times, error rate spikes, or degraded service(s). Rather than waiting for a full-blown outage and having angry customers complaining about it, engineers can use these detection signals to intervene and take corrective action before users notice.
Improved Incident Response
When something does go wrong, there is a significant difference between how monitoring and observability work. Conventional monitoring may raise a red light or an alarm indicating “something’s wrong”, but on the other hand, observability tells you why something is wrong. DevOps teams have access to a system of unified logs and traces, using which teams can quickly correlate issues across microservices, APIs, and infrastructure.
DevOps observability speeds up the Mean Time to Resolution (MTTR) by tenfold as compared to traditional monitoring. Engineers can resolve issues in minutes instead of digging for hours trying to find the root cause.
Better Collaboration
DevOps thrives in a culture where collaboration is maximum, but in practice, both parts of DevOps view and work on problems differently. Developers tend to focus on smaller-scale application bugs while ops teams focus on keeping infrastructure stable and secure. Now, observability provides both sides with a shared understanding in the form of concrete data, which both teams analyze and work on together, thereby accelerating the problem-solving process.
Continuous Delivery and Innovation
One of the main things DevOps brings to the table is faster and more frequent releases. This speed can potentially come at a cost, though, with the risk of newer code introducing bugs and regressive performance. At the same time, DevOps isn’t like conventional monitoring, and the risk of a bug going undetected or being detected late is relatively low. This effectively sets off the risks that come with faster releases.
Faster and continuous delivery is a huge advantage for organizations. With DevOps observability, they don’t have to worry about any hidden issues that won’t be detected. They can roll out newer code, preferably incrementally, observe it in real-time, and then roll back quickly in case of any issues.
User Experience
Ultimately, none of those above things matter if customer retention and acquisition aren’t improved or at least maintained. Any application that is slow or buggy is annoying to use and won’t leave a good impression on its users, as well as potentially prompting them to move to a direct competitor. Observability prioritizes user experience by tracking metrics like latency, availability, and error rates, all of which directly relate to customer satisfaction.
How to implement DevOps Observability?
There are a lot of benefits to DevOps observability, but it can be hard to implement this system in practical life. Implementing DevOps Observability isn’t just about buying a tool or tools; it’s about setting up a culture and process of transparency, both in your systems as well as the teams. Here’s a practical roadmap of how you can do so:
1. Define your Goals
The first step in setting up an efficient system of DevOps observability is to ask yourself questions and what you want to observe. The desired goals may differ for individuals and businesses, but some common goals, which everyone desires, are the following:
Identifying performance bottlenecks.
Tracking deployment and its impact.
Understanding user experience.
2. Collect Data
Data here refers to data you want to keep track of. Generally speaking, there are three main “pillars of observability”:
Logs are detailed events that capture what happened.
Metrics, which refer to numerical data points such as CPU usage and response time.
And Traces, which are end-to-end request journeys across different services.
3. Instrument your Applications
It can be a little tricky to have your applications create the right and necessary telemetry. You can add instrumentation to your applications by using libraries and SDKs. OpenTelemetry has become a renowned and standard framework for this purpose.
4. Centralize Data
Data, which is useless and at worst can be misleading, is presented in its raw form. It should be selected and centralized so that teams can look at logs, metrics, traces, and correlate issues, if any.
5. Automate Dashboards
Dashboards should be set up and automated to consistently display Key Performance Indicators (KPIs) and send alerts to the relevant team members when the numbers exceed preset parameters.
6. CI/CD Pipelines Observability
CI/CD, which stands for Continuous Integration and Continuous Deployment, should also have observability built into it, so that every release is automatically monitored.
7. Refining
Observability isn’t a one-time implementation that can be forgotten about later. It’s an iterative process that should be frequently reviewed—what works, what doesn’t, and what’s missing.
What are the key components of DevOps Observability?
DevOps observability is built upon several key components, all of which are essential for its success.
Telemetry Data (Logs, Metrics, Traces)
Often referred to as the “Pillars of Observability”, these are the foundation of observability, and without them, there’s no visibility.
Correlation and Context
Data without any context, in unscattered form, is as good as no data; you need to correlate it, for example, matching an error log with a specific user request trace.
Visualization
Data should be presented in such a form that it’s understandable to users, and clear visualization helps teams understand patterns and links between data.
Alerting
The whole purpose of observability is to detect and correct errors before users notice them. Alerts should be set in place so that issues are detected before impacting user experience.
Automation
Newer observation tools and solutions work in tandem with automation and machine learning to detect any anomalies that humans might miss.
What are the popular tools for DevOps Observability?
There’s no shortage of tools when it comes to DevOps observability; there are plenty of tools that help you build observability. Some of the most widely used and renowned tools include:
Prometheus and Grafana are used for collecting metrics and to help with visualization.
The ELK/EFK Stack (Elasticsearch, Logstash/Fluentd, Kibana) is used to help with log management.
Jaeger/Zipkin are industry favorites for distributed tracing.
Datadog is a SaaS observability platform that also offers full-stack monitoring.
New Relic is focused on monitoring application performance and observability.
Splunk, a San Francisco-based software company, excels in analytics and security integration.
OpenTelemetry, as mentioned before, is the standard for telemetry data collection and its instrumentation.
Common Challenges in DevOps Observability with Solutions
Observability is becoming increasingly important in today’s digital era, but its implementation can bring about certain challenges.
Data Overload
Too much telemetry data can overwhelm teams and, in some cases, have them focus on the wrong metrics. Business-critical KPIs should be paid attention to, and intelligent sampling for traces.
Tool Sprawl
You should have only a singular tool for logs, metrics, and traces. Employing different tools for each can create silos, and this should be avoided by using centralized platforms or integrating tools.
High Costs
There are high costs associated with using commercial observability platforms; instead, open-source solutions like Prometheus, Grafana, and Jaeger should be preferred with SaaS tools.
Cultural Resistance
As with any newer tech, there may be a resistance towards observability too, and teams might consider it as added work on their shoulders rather than embracing the usefulness it brings.
Scaling
There might be scaling issues if the wrong tools are selected and/or if your architecture isn’t sustainable for growth. Tools that scale horizontally should be used, with architecture being reviewed regularly, because what works for 10 servers may fail at 1,000.
Best practices to follow for DevOps Observability
Here are some of the best practices adopted by teams that successfully implemented observability:
Adopt Observation Early
If you’re launching now or are in the early stages of your startup/business, adopt observability into your DevOps pipeline now instead of having to retrofit it later on.
Prioritize End-User Impact
You should focus on what matters the most, and not on irrelevant metrics and information. User experience should be prioritized, with focus being placed on improving end-user experience.
Correlate across systems
In case of any error or bug being detected early, all logs, metrics, and traces should be connected to give a single truth.
Automation
Automation should be promoted with error alerts, dashboards, and even anomaly detection being automated to reduce human effort as well as human error.
Continuous Improvement
Observability should neither be thought of nor treated as a one-time investment or procedure. You should continuously be looking for ways to improve, with refinement of data collection, dashboards, and alerts over time.
To conclude, DevOps observability isn’t just about implementing tools, but it’s about developing a culture and mindset that promotes your teams’ understanding of how their systems behave over time. Investing in observability doesn’t just help with minimizing downtime and firefighting, but it also gives you the confidence to innovate faster and try experiments without any fear.
If you’re starting from scratch, begin by focusing on one critical service and then gradually build from there over time. Give us a call here at Coding Crafts today, and let us help you implement a successful DevOps observability system.