Why Observability in DevOps Fails




Wissen Team


December 18, 2023

DevOps is rising in popularity as it delivers the capacity to develop and deploy software solutions, initiate changes, and implement upgrades and updates quickly. DevOps practices increase developer productivity, enable clear communication and collaboration, and allow enterprises to release high-quality, secure, and reliable software.

Observability is one of the pillars that provides insights into system performance during the development cycle. Developers can then address bugs and software defects proactively before they impact the larger system. 

Observability helps in enhancing service-level metrics such as performance monitoring and empowers QA teams to observe, adjust, and revamp the application for better customer experiences.  

The Importance of DevOps Observability

Observability in DevOps provides the feedback needed to ensure the continuous delivery of projects without compromising quality. Observability delivers insights into aspects that impact performance and changes that can potentially break the application.

Observability can often be confused with monitoring. However, while monitoring identifies the errors in the system, observability provides insight into the internal state of the system and identifies the point of error. This becomes vital as applications become more distributed and pinpointing the point of error becomes complex.

Why Does Observability in DevOps Fail?

The importance of observability is undisputed, but DevOps teams can still be found struggling to prevent, detect, and resolve production issues.

Data shows that while “mean time to recovery (MTTR), a key metric for observability efficiency has been increasing across organizations…only 14% of respondents are satisfied with their current MTTR, indicating a strong need for improvement”.

Organizations have to alleviate some key challenges to prevent the failure of observability in DevOps. Some of these are:

Managing the Data Chasm

One of the main objectives of observability is to provide the right insights that increase the MTTR (mean time to recovery). The longer the MTTR, the longer the downtime and the greater the impact on customer experiences. One of the key challenges to resolve here is that of data siloes and navigating the explosion of data.

While the volume of data is important for informed and insightful engineering, the just huge volumes of data and its increasing complexity can be challenging if the data resides in silos. 

IT departments have to prevent the irregular distribution of data and ensure that it is accessible to all developers across the organizations to accelerate debugging and that information is spread evenly across the developer network.

Inappropriate Tools

Effective observability needs data integration from every aspect of the application. Most organizations employ a plethora of tools to gain visibility around logs, metrics, and traces. This tool sprawl has a silo effect on the data and makes it hard to correlate the data and extract the insights needed to drive system performance.

It also becomes hard to achieve end-to-end observability and support in a modern environment, especially if the tools are open source or the third-party vendors do not provide the support to unify data and address all the related data requirements. Observability tools need telemetry data features to provide access to the right data needed for observability and to deliver robust insights.

Environment Complexity

The growing complexity of the IT environment can also make observability hard to achieve. The rising adoption of cloud-native technologies and Kubernetes that generate abundant and complex data makes observability challenging.

Kubernetes adoption has increased as it allows organizations to dynamically scale their infrastructure as needed. Kubernetes gets its power from containerization where the applications are hosted in containers. The complexity and abstraction layers introduced by Kubernetes become hard to navigate without expert intervention. 

It is also challenging for engineers to understand the root cause of any incident that spans different microservices, and different K8s clusters in different regions since they are more disconnected from the infrastructure.

Organizations, as such, need observability solutions that not only deliver more capabilities but also allow engineers to cope with the rising complexity of their environments.

Unreliable Telemetry Pipelines

Observability needs a reliable and high-performance pipeline for telemetry data. Most organizations rely on open-source platforms to monitor and troubleshoot performance problems in their data pipelines.

Designing reliable and high-performance pipelines for telemetry data is critical as this data of lower quality can impair observability.

Identifying data pipeline software and infrastructure, isolating issues proactively, automating issue scenarios, and applying AI and ML to these pipelines to distribute tracing effectively to isolate incidents and understand performance issues are vital for observability success.

Observing Everything

DevOps observability also fails when teams try to observe everything. It is necessary to identify what needs observability, set and collect the data essential for the same, and concentrate on examining the critical things and mitigating them promptly.

Gathering all logs and data access, alerts for everything, etc. can be counterproductive and have developers go on a wild goose chase. DevOps teams need mechanisms that connect the right dots and generate data graphs effortlessly to enhance the usability of the information across the teams.

Poor Tracing

Developers and QA engineers need the capabilities to navigate the exact workflow that resulted in a defective output through observability. Teams need to establish traceability across all their transactional workflows to understand how the data and insights are moving between different systems when a transactional request is processed.

When DevOps teams fail to set up traceability, observability suffers since it impedes the capacity of the teams to pinpoint the services or systems causing erroneous outputs and poor performance.

Lack of Automated Reporting

Just identifying the problem is not enough. When IT teams have to wait for information or if issues need to be mapped and transferred manually then application productivity and performance suffer. Observability works effectively when the right team is intimated of the issues at the right time. 

Automated reporting becomes a crucial component for reaping the benefits of observability. It reports the health and state of all business systems as observed by end users and is crucial for prompt action.

In Conclusion

Observability plays an important role in DevOps success and allows teams to deliver seamless and consistent experiences. Implementing observability successfully requires a clear understanding of the nuances of the modern digital environment, architecture, and technologies at work such as serverless, IaaS, etc.

A technology partner like Wissen makes it easier to establish robust DevOps practices and build observability using the right tools and technologies.