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DevOps observability: What is it and how to implement it?

Aruna Pattabiraman
Aruna Pattabiraman
Published on
March 1, 2023

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DevOps is no more a buzzword. In recent years, it has emerged as the most sought-after software development methodology in the digital world. IT organizations across the globe have promptly embarked on the DevOps journey to strike the balance between development and operations teams for faster product delivery.

There’s no question that many businesses have successfully adopted DevOps practices to develop and deliver high-quality software, swiftly and securely. However, there are many more businesses that have failed to realize the true potential of the DevOps methodology. In fact, a Gartner research revealed that 75% of DevOps initiatives fail to meet full expectations in 2022. Why? There may be many reasons for DevOps failure, but overlooking observability is considered to be the prime culprit.

So, what is observability in DevOps, and how businesses can leverage it to tap optimal DevOps potential? Let’s dig deep…

What is observability in DevOps?

In general, observability is described as the ability of a business to gain valuable insights about the internal state or condition of a system just by analyzing data from its external outputs. If a system is said to be highly observable then it means that businesses can promptly analyze the root cause of an identified performance issue, without any need for testing or coding.

In DevOps, observability is referred to the software tools and methodologies that help Dev and Ops teams to log, collect, correlate, and analyze massive amounts of performance data from a distributed application and glean real-time insights. This empowers teams to effectively monitor, revamp, and enhance the application to deliver a better customer experience.

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Why DevOps observability is the future and why your organization needs it

In the past, IT businesses have used Application Performance Monitoring (APM) to monitor and enhance application performance. It collects and analyzes telemetry data of the applications and systems and provides valuable insights for teams to address and prevent abnormal conditions. However, APM is the right fit only for monolithic applications or traditional distributed applications. It is because in those applications, the new code is released regularly and workflows and dependencies are well-known and easy to identify.

But in the present world, the highly distributed nature of the applications has made APM obsolete. Organizations today are leveraging DevOps practices, including agile development, continuous integration & continuous deployment (CI/CD), to deliver applications faster than ever. And, APM can't stay abreast. The DevOps ecosystem required high-quality telemetry data to create accurate, context-rich, fully-correlated information of every application. Therefore, organizations need DevOps observability to gain high visibility of their complex application ecosystem, understand any change (planned or unplanned), and stay ahead of the curve.

Observability vs Monitoring: What are the differences

Though observability has gained huge prominence in recent times, there is a lot more to it than meets the eye. Observability is often used interchangeably with monitoring. In reality, both concepts differ from each other. Let's find out how they are different:

Monitoring enables IT teams to gain a comprehensive picture of an application's behavior and performance, with metrics such as network traffic, resource utilization, and trends. It also notifies the teams when an issue arises.

Observability, on the other hand, offers deep visibility and awareness of what is happening within an application. It collects application data and converts it into enriched, visualized information and actionable insights, enabling DevOps teams to see what is happening and address issues promptly. In contrast, monitoring doesn’t facilitate enhanced data and solutions to fix glitches.

Observability is intended for deep and granular insights, context, and debugging capabilities. Whereas, monitoring is not for deep root cause analysis. In fact, observability goes beyond monitoring methods to better address the increasingly distributed and dynamic nature of present-day applications. Observability doesn’t displace monitoring, rather it facilitates better monitoring.

Observability vs Monitoring vs Telemetry: What should you choose?

Apart from monitoring, there is another buzzword that is being used very often in the DevOps ecosystem. It is telemetry. Let’s dig into the details:

Telemetry is a mechanism for collecting actionable data from monitoring. This telemetry data when deployed into the production environment enables DevOps teams to automate feedback process and monitor applications in real-time.

Going the extra mile, observability gleans valuable insights from the application telemetry data and leads teams directly to the root cause of any issue and address it quickly. With observability, businesses can intelligently troubleshoot and correlate application issues, no matter the complexity of that app.

Organizations must take due cognizance of the fact that observability helps stay on top of any application issue throughout the software development lifecycle. It provides insights into the infrastructure and systems, while offering high visibility into their health in real-time. Simply put, observability goes beyond monitoring and telemetry to solve the problems and ultimately create a better customer experience. These enticing features make observability the need of the hour in the current speed-driven software world.

The three pillars of observability

Observability focuses on three types of telemetry data, that are widely known as the 'three pillars of observability'. These telemetry data types are separate data types with their dashboards. Here's a quick breakdown of the three pillars of observability:

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1) Logs

Logs (or log messages) are the lines of text produced by an application or service when execution reaches a defined stage in the code. It indicates that something has occurred in the application at a specific time, such as an error occurred, a query that took too long, or a database has started. Logs can be structured or unstructured lines and can be one line or span multiple lines. Though logs are easily generated, it is quite expensive to store them and challenging to glean meaningful insights.

Ø  Structured log example: - frank [10/Oct/2000:13:55:36 -0700] "GET /apache_pb.gif HTTP/1.0" 200 2326

Ø  Unstructured log example:

"thing happened"

Ø  Single-liner log example:

2017-07-31 13:36:42.585 CEST [4974] LOG: database system was shut down at 2017-06-17 16:58:04 CEST

Ø  Multi-liner log example:

2017-07-31 13:36:43.557 CEST [4983] postgres@postgres LOG: duration: 37.118 ms statement: SELECT d.datname as "Name",

pg_catalog.pg_get_userbyid(d.datdba) as "Owner",

 pg_catalog.pg_encoding_to_char(d.encoding) as "Encoding",

 d.datcollate as "Collate",

 d.datctype as "Ctype",

 pg_catalog.array_to_string(d.datacl, E'\n') AS "Access privileges"

 FROM pg_catalog.pg_database d


2) Metrics

Metrics are numeric values that indicate the behavior and characteristic of a system. Typically, metrics come in various formats, including counters that increment whenever something happens in the system and accumulators. They can also be aggregated or measured over a time period. One can glean useful information from metrics such as how much memory is used by a process or the number of requests handled by a service. (Also read: 13 DevOps KPIs every leader should track)

At this juncture, businesses must take heed of the fact that log data can include metrics. So, one must ensure that the observability solution can seamlessly extract metrics from the log data without losing the contextual information.

3) Traces

Traces outline the activity of the requests and the path they take through an application. They can show a trace for a single operation or a distributed trace of an entire transaction across multiple services. Traces can be represented as a waterfall view or a service map.

Traces are the prime component of observability, as they are the first stop when debugging issues or outages. They help define which metrics or logs might be relevant to review during a particular situation or issue.

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How observability platforms combine & correlate telemetry data

The observability platform collects the telemetry data continuously by integrating with the instrumentation built into application and infrastructure components. Once this telemetry data is collected, the platform combines and correlates it in real-time to facilitate the DevOps teams with deep visibility into any event that could indicate, cause, or address an application performance issue.

Implementing DevOps observability

Implementing observability is easier said than done. You need a robust implementation strategy. Every organization has unique observability needs based on the size and scope of their business. So, the approach for your observability strategy will also be unique. Below are the common aspects businesses must take into consideration to hammer out a comprehensive observability strategy:

1) Your observability platform must be future-ready

The DevOps ecosystem is evolving incessantly. So, an observability platform that may suit your current business needs and infrastructure most likely won't be able to keep pace with future needs. Therefore, you must take into consideration the future business needs and trends, and choose an observability platform that best fits that long-term strategy. Whether it is an open-source tool or a commercial one, you must take into account your future infrastructure and select a platform that addresses your future needs, without taking a hit on your expenses.

2) Your observability platform must collect and automate data

Your observability platform must be able to capture data from all the systems and components and store it in a centralized resource. This makes the information more accessible, user-friendly, and provides a comprehensive picture of the system's health. The collection of telemetry data, including metrics, logs, and traces, should be as dynamic as your application ecosystem. If you are running your applications on VMs, make sure that the observability platform you choose can automatically include and monitor any VMs that spin up automatically. If you are running in containers, ensure to configure the monitoring so that any containers that spin up automatically will get included. The platform should be able to provide the insights need to determine whether the problems are programmatic or environmental.

3) Your observability platform must analyze the telemetry data for actionable insights

Logs, metrics, and traces are completely different from each other. So, your observability solution must be able to analyze 100% of the telemetry data and provide actionable insights. It must provide intuitive data visualization and navigation, so that you can interact with your data easily and flexibly. It must also enable you to filter and identify the logs for a specific application at a specific time. Simply put, you must be able to quickly develop custom metric aggregations to research and resolve a performance issue immediately.

The final goal is to gain end-to-end observability across your application environs with all of your telemetry data. The solution must provide visibility across your infrastructure metrics, through the application, and to the end-user experience. Moreover, it should analyze and correlate telemetry data to your specific needs at any specific time. A robust observability platform enables you to monitor day-to-day application performance, address known issues, and identify unknown issues.

4) Your observability platform must work in near real-time

To enable DevOps teams to react and resolve performance issues, your observability platform must be as close to real-time as possible. With a massive amount of data to handle, your observability platform must be able to keep up and running at intelligent speed and not turn into a bottleneck itself. Gaining telemetry insights and alerts when it matters can save your company from untold expenses in missed SLOs and sourced customer relationships.

Tips to avoid common pitfalls while implementing DevOps Observability

DevOps observability is still an unchartered territory. So, it's common that your DevOps team to experience some pitfalls when implementing observability. Here we bring you some tips for avoiding the common pitfalls during the implementation of observability:

1) Familiarize observability across the organization

In many organizations, DevOps teams are the only ones that gain adequate knowledge of the workings of the observability platform. Restraining observability knowledge to a specific team will inadvertently bog down observability implementation. Thus, organizations must educate all the relevant teams in the firm about the working of observability. This enables businesses to swiftly respond to performance issues as information is spread evenly among employees.

2) Leverage the right tools

Without the right tools at your disposal, it becomes a herculean task to gain visibility across all the system activities. For instance, without the right tools, you fail to glean the right data during monitoring. And improper data often leads to improper data insights and alerts. To prevent this pitfall, businesses must leverage the observability tools that are built on high-quality telemetry.

3) Imbibe good alerting system

In observability, DevOps teams often prioritize symptom-based alerts over cause-based alerts. This is because developers usually write alerts for all the possible issues in the system without taking into account their causes. For example, the reason for slow response can be an overloaded CPU.

Moreover, all the possible alerts are delivered to the team using a single pathway, which leads to overlooking alert calls. To avert this, businesses must leverage a good alerting system that uses different pathways for different alerts.

The right tools for DevOps observability

The success of DevOps observability solely depends on the tools that you choose. You can build your own observability tools using open-source software like Jaeger or Zipkin. Or you can procure the observability tools available in the market. Anyways, selecting the right tools on par with your business needs a bit challenging. To make it easy for you, we have curated some common rules that apply to all observability tools:

1) Simple to integrate

The DevOps observability tools you choose must be simple to integrate. If integration is complex, it can easily bog down your project implementation. For successful observability, you must leverage the tools that are already in use. The tools must support environments involving various languages and frameworks. They must be able to easily integrate with service mesh or container platform and connect with Slack and PagerDuty or the systems you prefer.

2) Easy to use

If the observability tools are difficult to learn and use, it can be challenging to integrate them with the existing processes and workflows. The DevOps teams can feel uncomfortable using the tools during any stressful incidents, which leads to small improvements in the health and reliability of the system.

3) Scalable

The right observability tools are highly scalable. They ingest, process, and analyze the telemetry data without any latency in a cost cost-effective manner. Ultimately, observability tools must enhance customer experience, improve developer velocity, and ensure a more reliable, resilient, and stable system at scale.

4) ROI enabler

The observability platform leverages a massive amount of data, network, and storage. So, costs play a significant role. Observability can become a burden if investments outrun revenue.

There are many observability solutions available in the market with pricing structures that penalize businesses for over scaling or moving too much data across networks. You must ensure that the observability solution you are choosing can scale with your business and handles telemetry in a cost-effective manner.

The aforementioned considerations can help you choose the right DevOps observability tools.

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Opsera’s Insights as your right DevOps observability platform

With Opsera's Insights platform for DevOps observability, everybody, including DevOps leaders, can gain visibility into the entire toolchain. Our Insights platform aggregates software delivery analytics across your CI/CD process into a single and unified view. It provides persona-based dashboards targeting vertical roles, including developers, managers, and executives. Moreover, our platform facilitates contextualized logs across all your platforms.

Want a real-time view of your infrastructure and solve problems at a fast pace? Leverage our Insights Platform. Let’s connect!

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