DevOps culture is underpinned by a desire for constant improvement: both the improvement of software applications and the processes behind them.
DevOps’ iterative approach to software development both encourages and facilitates continuous enhancement in the way teams work. Feedback loops are short and small changes can be made often, allowing developers to make minor tweaks without too much disruption; tweaks that could make a big difference to the effectiveness of the pipeline.
But as business management guru Peter Drucker famously said: If you can’t measure it, you can’t improve it.
So let’s talk monitoring and observability in DevOps: what the difference is, why they’re so important, and how you can use them to optimise your DevOps practice.
Monitoring vs Observability: What’s the Difference?
Monitoring and observability are central to driving high performance in DevOps teams. While they’re often grouped together, monitoring and observability are two distinct practices with distinct goals. They can be executed separately with distinct tools, though many modern DevOps platforms monitoring and observability can manage both.
So what are they and how exactly are they different?
Here are the definitions of each solution according to Google’s DevOps Research and Assessment (DORA):
Monitoring is tooling or a technical solution that allows teams to watch and understand the state of their systems. Monitoring is based on gathering predefined sets of metrics or logs.
Observability is tooling or a technical solution that allows teams to actively debug their system. Observability is based on exploring properties and patterns not defined in advance.
Monitoring has long been a concept in software development, and the practice of collecting information generated by system behaviour into logs is a common one. However, relying on traditional monitoring alone presents some challenges.
Firstly, monitoring systems don’t log everything. They don’t cover all event types, and they’re typically set up to scan for known or expected issues—things you specifically tell it to look for. This means that new concerns or events that are “unknown” to the system get missed.
Secondly, just because metrics are logged doesn’t mean they’re being aggregated or analysed in a meaningful way. Useful insights can sit idle in logging solutions and never provide anything valuable if they’re not able to be digested and viewed in context by those that use the systems they’re monitoring.
That’s where observability comes in.
Observability is about being able to quickly analyse system data, discern valuable information from it, and action that information to rectify any issues.
Observability tools and platforms deliver a more accessible, insightful, and comprehensive picture of what’s going on in an environment. While monitoring tools are built to collect and log data, observability goes a step further, taking context into account and aggregating, analysing, and surfacing key insights from the huge quantities of data it collects. It also automatically identifies new sources of data, without them having to be pre-defined.
These tools give teams the information they need to hone in on the source of a problem fast. Legacy monitoring systems tell you there’s a problem; observability tells you what the problem is, as well as where it’s coming from, and helps you fix it. The key difference is that monitoring is reactive, and observability is proactive.
To deliver the efficiency and high performance you’d expect from a DevOps approach, both monitoring and observability strategies are needed so that teams can properly track application performance, address issues quickly, and create a better product.
Why Monitoring and Observability are so important in DevOps
If you want to do things fast and effectually, you need to be able to test, amend, and try again in short cycles. The faster the feedback is gathered, the sooner a working product can be delivered.
Monitoring and observability provide the data needed to identify and fix immediate problems. This data allows DevOps teams to find and drill down on issues with their applications, so they can be corrected and the product enhanced for both the customer and the end user.
It also allows teams to track longer-term progress against KPIs to learn what’s working and how the DevOps process can be made more productive and efficient.
Basic metrics that monitoring solutions can track include things like resource utilisation, application response times, uptime, and error rates. But one of the key metrics that observability can help DevOps teams improve, for example, is mean time to resolution (MTTR). Mean time to resolution refers to how long it takes for a team to fully fix a problem. Shorter MTTR results in reduced downtime, better performance, and ultimately, happier customers.
Best Practices for Monitoring and Observability
Harvesting useful information from monitoring and observability tools requires platforms to be well-implemented, configured to your specific needs, and operated properly. Parameters for monitoring can and should be tweaked over time, but following some basic best practices around monitoring and observability will help you get off to a flying start.
#1 Define your goals
The first step to achieving optimum monitoring and observability is defining what exactly it is you want to monitor and observe.
Think about what goals you’re looking to achieve with your DevOps practice right now, and what’s most important to your business.
#2 Choose the right tools
Apply your due diligence and investigate a wide range of DevOps monitoring and observability tools. Though basic features like logging, reporting, and alert generation can be found across the board, each platform will have unique functionality and limitations, so make sure the tools you choose meet all your needs today, and in the future. You should also keep integration with your wider tech stack in mind.
#3 Don’t do too much, too fast
Your system and your processes generate an unfathomable amount of data. Don’t try and monitor it all. Be selective and focus on monitoring the critical path and most important infrastructure; the things that will cause problems if they fail.
Keeping your data ingestion light and manageable also makes for faster analysis and more timely alerts, so that you can rectify issues quickly.
It’s not about making things perfect—start by making them more reliable. You can always add in additional logs or new streams of data as your DevOps processes evolve, but trying to observe too much will get you bogged down, and obscure the valuable insights you could be gaining.
#4 Get personal with your dashboards
Default dashboards are great for getting you familiar with your monitoring and observability platform’s UI and what kind of reports it can generate. But getting the most out of this practice means tailoring it to fit your team and your goals.
Custom goals require custom reports, so once you know your way around the off-the-shelf dashboards, start building ones that highlight the metrics you’ve decided to focus on.
Your dashboards should be accessible and easy to interpret for all on your DevOps team, so that everyone can spot potential issues and get stuck into fixing them. Keep dashboards simple, and don’t overwhelm your users with information they don’t need to do their jobs better.
#5 Automate your incident response
Many of the issues that might be identified by monitoring can be responded to using automation. Since automation is central to the efficiency of your DevOps pipeline, you should automate as much of your incident management and remediation process as possible to keep things moving along swiftly.
Automated remediation solutions can get ahead of incidents or events by autonomously responding in a variety of ways, such as toggling feature flags, rolling back deployments, restarting processes, or allocating resources. Tackling common, low-level issues like patching and resource utilisation are good places to start.
#6 Use data to be more proactive
The data logged by monitoring and observability platforms is great for spotting problems and managing incidents, but the real end goal of getting to know more about how your system and applications are operating is so you can improve your products.
Use the insights and learnings you gather from monitoring platforms to address common issues in the development and testing stage, so that the same bugs don’t occur and get flagged by your monitoring and observability tools in future. This process is often considered the last stage of the DevOps pipeline: continuous monitoring.
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