A Practical Guide to Observability in DevOps and How It Boosts Software Development

In the Waterfall era, everything was pretty siloed. Nobody really knew what other teams were up to in the software development life cycle. While developers worked on creating new features, the operations and QA teams tested the product in their own ways.

As a result, there was a big communication and collaboration gap. Development teams had little control over quality checks and monitoring, and since they didn't fully understand infrastructure dependencies and chaos engineering, the only goal was the success of the product.

But complexity makes things more unpredictable. In both on-premise and cloud environments, development teams today must deal with hundreds of different technologies. Continuously monitoring these mission-critical apps and systems is necessary to make sure that they are available round-the-clock.

Any slow-performing request needs an immediate response, and it must be clear which component of the application design is to blame for any system lag or failure.

As a result, technological innovators are constantly searching for fast, yet safe iteration techniques.

Enters observability, which gives you the opportunity to understand and analyze your dynamic environments in almost real time while also moving more rapidly and confidently.

According to Gartner, by 2024, 30% of businesses using distributed system architectures will have observability solutions in place to boost the performance of their digital business services, up from less than 10% in 2020.

So, what is data observability? What benefits does it offer and under what circumstances? Are there any challenges? And how does it support DevOps? - This article will deal with it all.

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What is Observability?

Observability in DevOps refers to the ability to gain insights into the internal state of a system based on its external outputs. It involves collecting, analyzing, and utilizing multiple data (logs, metrics, traces, and events) to understand the behavior and performance of a software application in real time.

Here's an amazing definition of observability I recently come across on Reddit:

“Observability is the board you see at an amusement park that shows you the wait time and whether or not a ride is open. It is all about knowing your systems so you know what is happening under the hood.”

So, observability is the process of equipping your system with the tools needed to gather meaningful data so you can identify when errors happen but, more importantly, why they do so. The ability to see every stage of the development lifecycle is one way that modern observability tools help you iterate more quickly.

It is a technique for optimizing a multilayer architecture that focuses on figuring out bugs and what you need to do to improve performance. DevOps and SRE teams can now actively debug their systems and explore system patterns and functionalities to assess their health and any unpredictable behavior.

Previously, dedicated software development team used to compile predefined sets of system-related metrics or logs, but it wasn't effective enough to identify failure causes in advance. As cloud environments are becoming more complex and finding the potential root causes for a failure or anomaly is more challenging now, data observability makes it possible to swiftly identify and fix problems, ensuring systems are flawlessly working and up to the mark, all in real time.

How Does Observability Fit into DevOps?

Understanding production systems and managing complexities are the responsibilities of DevOps and SRE teams. So, it completely makes sense that they should care about the observability of a system that they design and maintain.

In DevOps software development, developers are in charge of managing their production code and streamlining services through cross-functional techniques. Competent DevOps teams measure any potential sign of user pain points and utilize observability tools to dive deep into the problem rather than starting with a long list of known failures.

So, instead of wasting most of your time responding to hundreds of false alarms that have no bearable impact on the system's performance, you can concentrate on methodically eliminating each hypothesis and coming up with fixing strategies for true system failures.

But there is more.

Apart from such break/fix use cases, great DevOps teams also make the best use of observability engineering methods like incident analysis, continuous verification, and feature flagging. Observability facilitates modern DevOps practice cases because it provides the data needed to implement them successfully.

As per the 2022 Observability Forecast report,

• 82% of DevOps organizations have used 4+ observability tools.
• 27% of them have achieved full-stack observability.
• 5% have mature observability practices in place.

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What is Observability in Container and Microservices?

In order to help developers discover and fix performance issues more effectively, observability in containers and microservices exposes the status of software in production.

Microservices and Container as a Service, like Kubernetes and Docker, decrease the risk of downtime and other problems associated with a monolithic architecture, in which any change to the single codebase has an impact on the entire software and its dependencies. Using containers and microservices, developers can break the software into multiple individual modules and modify a specific service without reloading the entire software.

A container-based architecture, however, comes with its own set of difficulties. Interdependent microservices are usually distributed across multiple hosts, and as the infrastructure grows, so does the number of microservices.

As a result, it practically becomes next to impossible for developers to understand what is actually running in production, resulting in longer deployment cycles, system downtime, and more.

System observability efficiently addresses these issues by giving teams the needed visibility into distributed infrastructure and enabling them to better understand the performance and availability of the software. And in case of an error, it offers complete control to quickly identify, analyze, and resolve the error.

Observability vs. Monitoring: What is the Difference?

Although monitoring and observability are two different concepts, there are subtle yet crucial differences that you should know about.

• Monitoring is a task you carry out to make your system more observable.
• A property of that system is observability, just like testability or functionality.

While monitoring lets you keep tabs on the general health of a software product using observability metrics like resource use and network traffic, observability is the system attribute that offers developers visibility and awareness of the things going on inside the system.

When compared to observability, which can explain why something is happening and provide in-depth, insights, monitoring is a more limited DevOps technique. It is a part of observability and broadens its scope.

On the other hand, the primary goal of observability is to provide context, debugging tools, and deep, granular insights. Monitoring, in contrast, is not for in-depth root cause and analysis.

Observability in software gathers monitoring data and transforms it into enhanced and visually appealing information. In comparison, monitoring does not give you the answers and information you need to make things right.

Observability vs. Monitoring vs. Telemetry: How are They Different?

Since we discussed the distinction between monitoring and observability, there’s another similar concept that you should know about, along with how it is different from them – telemetry.

• Continuous system observation is the goal of monitoring in order to spot and warn about unusual behavior. It makes sure the system is operating properly and implementing the required action.
• Observability is the ability to determine a system's internal state by examining its outputs. the aim is to decipher what is occurring within the system and predict how it will function in the future.
• Telemetry is the automatic gathering and delivery of data from distant sources. It focuses on knowing what is happening on the ground and frequently uses sensors and other tools to gather information from challenging or difficult-to-reach environments.

What is the Importance of Observability? Why Do You Need It?

Beyond general IT use cases, observability is far more important than it seems. You can see how your digital services are impacting your organization when you acquire and analyze observability metrics and data. You can also ensure that new software releases support your company's objectives and make business decisions according to what is most important.

To give you an idea, here are some scenarios where observability can make or break a software project:

• It might be difficult to figure out what went wrong when a problem occurs among hundreds or even thousands of microservices connected with one another. To ensure that user requests are properly processed without interfering with the workflow, you need to deploy data observability in action.
• You cannot afford to overlook any data supplied as an output when your software performs a high amount of work and is crucial. Any firm that is creating and delivering products/services cannot afford to disappoint its clients. For this reason, observability in DevOps is essential to be able to see everything that is happening inside the system through a single pane of glass.
• When using a Continuous Integration and Continuous Delivery pipeline to deploy frequent updates, observability is a must since we need to know whether the most recent update is working as intended or if there are any problems that require us to roll back to a prior stage.
• If your system experiences dynamic traffic spikes, you must integrate software observability to ensure uninterrupted workflow without any downtimes.

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What are the Three Main Pillars of Observability?

Implementing on-premise or cloud observability doesn't have to be difficult, despite how complicated it may appear. Start by focusing on the following three crucial pillars that make observability successful:

Logs

Logs are granular text records of certain events that took place over a specific period of time. Structured logging is a must-have for complex ecosystems with multiple components, like Kubernetes services as it enables developers to spot unpredict behavior in a system.

There are three different types of event logs:

• Binary: Binary logs are in the pflog, MySQL binlogs, or the protobuf format.
• Structured: These are JSON-formatted logs that can be sent in a structured way.
• Plain text: Plain text is the data that represents only characters of readable material.

Metrics

Metrics is another key pillar of application observability that defines a numerical representation of data that you can compile over a timeframe. There can be various sources of metrics, including the infrastructure, hosts, and cloud platforms.

For example, metrics provide information on the number of requests processed by a service every second or how much memory a particular pipeline consumes.

Graphite and Prometheus are two of the best tools used to collect metrics.

Tracing

Tracing represents activities of a request or transaction going on in a software product. You can identify bugs in the system and determine their root causes by capturing the traces of requests and figuring out what is happening across the request chain.

Since Tracing enables you to determine the true cause of each problem and define the amount of work executed at each component, it is the most crucial pillar of data observability.

What are the Four Key Components of Observability?

Apart from the pillars, you must also develop a set of apps and tools that can gather the telemetry data in the form of logs, metrics, or traces in order to make your IT systems observable.

In every ecosystem, you can typically implement observability software by combining these four elements:

Open Instrumentation

Open instrumentation refers to collecting, tracking, and measuring telemetry data moving through the software by using code or agents. Metrics, Events, Logs, and Traces, aka MELT, are examples of telemetry data. And containers, applications, hosts, and services are a few examples of entities.

Data Correlation and Context

It's important to have a broad perspective, especially for large-scale enterprise software development that generates vast quantities of raw telemetry data. Hence, in order to understand any patterns or abnormalities in the acquired telemetry data, correlations, and context analysis are vital components of observability engineering.

Incident Management

Incident management is a suite of procedures, methods, and techniques that collectively allows you to identify, look into, and respond to incidents. By rapidly resolving risks and vulnerabilities, IT teams can now minimize the overall impact of incidents and maintain the desired functionality of systems and services as per data compliance standards.

AIOps Tools

Accelerating incident response is necessary if you want to ensure that your deployment infrastructure is constantly accessible with reduced false alarms, active identification of problems, and shorter mean time to resolution (MTTR). To automate IT operations including aggregating, correlating, and prioritizing incident data, AIOps tools leverage machine learning models.

What are the Benefits of Observability?

You mean apart from gaining quick and easy access to data that simply shows how your system is operating? Not a problem! Here are some more reasons for you to adopt enterprise observability;

• Reliable and Faster Development

Observability leaders do much better than newcomers in terms of software quality metrics and KPIs. They are able to push products more quickly, with fewer issues, and with dramatically reduced downtime. And they can deal with problems more quickly when they arise.

According to the 2022 State of Observability Report by Splunk, 41% of organizations can identify issues in internally built apps within minutes. leading to a 37% improvement in the mean time to detection (MTTD).

Additionally, twice as many companies can resolve unpredicted downtime disruption in mission-critical software in four hours or less. As a result, the mean time to resolution (MTTR) has reportedly improved by 69%.

• Avoiding Unnecessary Downtime

Although it may seem like a broad category, organizations that practice full-stack observability are less likely to experience downtime and related expenses, both direct and indirect.

On average, they decrease the cost of downtime by over 90%. This fact is indeed a very persuasive vote for the power of observability. It streamlines the finding and debugging process for a software product since developers can observe the end-to-end journey of a request with contextualized data about a specific error.

• Digital Innovation

CIOs, CTOs, and other C-suite executives continue to place a high priority on achieving digital transformation. But if outages, downtimes, and other service issues continue to push back development teams, they will be unable to move forward.

Thus, helping teams relieve themselves of tedious tasks so they can be productive is one of the main objectives of tech leaders. And needless to say, the firms that have used observability tools are also the most creative.

As a matter of fact, 60% of DevOps organizations following observability have launched more products or created more revenue streams than beginners.

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• Confidence in Developers

The two main advantages of observability are the ability to see what is happening and the capacity to fix problems more quickly, including before they break. Without a doubt, maintaining visibility in a complicated cloud environment is a unique and important issue. The fact that observability adoption is increasing at the same time that cloud usage is increasing may not come as a surprise.

Hence, system and data observability offer greater advantages as the infrastructure becomes more complicated. A developed observability practice increases developers'’ trust in their skills to provide an effective and ground-breaking digital experience.

• Organizational Agility

Observability helps businesses to view their entire IT infrastructure from a wider and more comprehensive perspective. It should not be surprising that this DevOps approach helps cross-functional teams in the company have an in-depth understanding and holistic point of view of the larger picture.

With the help of cloud observability, you can find issues you might not have known to look for or are aware of. Then you can track how they relate to certain performance problems, and get the context you need to quickly identify the causes of problems. For such a transparent and clear approach, there is a growing focus on collaboration and teamwork across the entire organization.

What are the Common Challenges of Observability?

Although practicing observability has always been difficult, the rapid advancement of cloud technology and the complexity of microservices architecture have made it a burning issue for enterprises to address.

Take a look at a few of the observability challenges organizations usually run into:

• Manual Configurational and Instrumentation

When developers have to manually create and update code for every new type of agent and component, they spend the majority of their time trying to configure observability tools rather than focusing based on the observability data.

• Siloed Data

It is challenging to understand the interdependencies across various digital channels, different cloud platforms, and applications due to the huge number of siloed monitoring tools, different data sources, and agents.

• Extreme Troubleshooting

Teams from multiple departments gather to try to pinpoint and troubleshoot the source of issues, wasting valuable time making guesses, attempting to make sense of the telemetry data, and coming up with solutions.

• Environment Complexity

In large-scale cloud platforms like AWS, Azure, and Google Cloud Platform, the massive amount of raw data created from all the components makes it next to impossible to figure out the solution. this also includes containers and Kubernetes that can quickly scale up and down.

Who Uses Observability?

Observability benefits various roles within an organization, including software developers and engineers, operations teams, and business stakeholders.

Developers can benefit from observability by gaining a deeper understanding of how their code is performing in production. With access to data from logs, metrics, traces, and events, developers can quickly identify issues, debug code, and optimize performance.

Operations teams can benefit from observability by gaining visibility into the status and performance of their systems. They can identify issues, troubleshoot problems, and quickly resolve incidents by collecting and evaluating data from various sources.

Business stakeholders can benefit from observability by gaining insights into the behavior and performance of their applications and services. By monitoring key business metrics such as user engagement, conversion rates, and revenue, stakeholders can make data-driven decisions and optimize the performance of their systems.

In summary, observability benefits all stakeholders in an organization by providing visibility into the health and performance of their applications and services, allowing them to make data-driven decisions and improve the overall customer experience.

How Do You Implement Observability?

There always should be a holistic approach to implementing the practices of observability engineering. And from our experience as a DevOps pioneer, here is how you should do it:

1. Define Your Goals

To efficiently track application health, performance metrics, and deployment cycles, you must set a clear set of objectives. In order to make informed decisions regarding their DevOps processes, teams must establish accurate observability metrics that they to keep a track of.

2. Understand the Contextual Topography

In highly dynamic, multi-cloud setups with potentially millions of interconnected components, this step includes instrumenting in a way that generates an understanding of interactions between every interdependency. Real-time topology maps are the results of rich context metadata, which gives a definition of causal dependencies both horizontally across hosts, processes, and services, as well as vertically within the stack.

3. Continuously Automate Processes

When it comes to DevOps observability and monitoring, automation is highly critical because human checks are ineffective and prone to mistakes. With automation in place, teams can promptly identify errors, enhance performance KPIs, and make sure that all environments adhere to code quality and coding standards.

4. Foster Cross-Platform Collaboration

The involvement of multiple teams from development, operations, infrastructure, security, support, and other areas is necessary for monitoring and observability. Businesses can make sure that everyone has access to the same data points required for observability by encouraging collaboration between various departments and routinely sharing information.

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5. Consider External Factors

APIs, network speed, and third-party services are a few examples of external factors that companies should assess since they could have a great impact on app performance monitoring. Evaluating external factors empower teams to find out any possible bottlenecks and proactively address them before they become a major issue.

6. Use KPIs and SLAs to Measure Success

Organizations should establish key performance indicators (KPIs) that track system performance as a whole and service level agreements (SLAs) that monitor software uptime in order to evaluate the success of their data observability initiative. Teams can use this to pinpoint areas for improvement and then modify their procedures as necessary.

7. Enable Data Logging

If at all possible, create standardized data logging using the Simple Network Management Protocol (SNMP) or other methods. In tools used to aggregate and collect data, you can occasionally enable data logging, but you should do it if it is not already.

Also, connectors have the ability to convert data into a standard format, which makes it simpler for you to comprehend the meaning of the data patterns. In this situation, you can use a variety of data aggregation tools, like Alteryx, Stata, Google Locker, GoSpotCheck, Integrate.io, etc.

Best Practices to Successfully Implement Observability

The main objective of DevOps observability is to provide correct visibility into your design and infrastructure. Nonetheless, there are several basic challenges you must overcome while establishing observability in your ecosystem.

Below is a list of the best practices you should follow to see observability in action for your development teams:

• Do not store all logs and data
• Do not monitor each and everything
• Enables alerts only for critical events
• Monitor only the things you need to fix
• Do not use default graphs
• Create a custom graph as per user needs
• Filter data to the point of creation
• Store the logs that provide information about critical events
• Enable alerts based on Grafana or Prometheus metrics
• Integrate automatic remediation systems with observability tools

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