Multi-Cluster Observability Part 2: Developing The Right Strategy

Andreas bluecircle
Andreas PrinsCEO
7 min read

This is the second of a three-part blog series. Prior to reading this, be sure to check out Part 1, Benefiting from multi-cluster setups requires familiarity with common variations.

In your Kubernetes journey, it's highly likely that you'll encounter the need to manage multiple clusters simultaneously. Whether they serve production or development environments — or are configured per engineer — the demand for effective management of these clusters will continue to expand, along with new challenges in gaining visibility into the overall health of each one.  For this reason, a centralized monitoring solution is imperative. 

Tackling observability in a multi-cluster environment is no easy feat, but it's a crucial element of your IT infrastructure. So, how do you address the challenges and still build an effective multi-cluster observability strategy? From achieving a unified view across clusters to untangling network complexities and ensuring scalable tools, let's walk through the key elements that form the foundation of a robust observability framework.

7 features of a strong multi-cluster observability strategy

Each of these seven essential requirements comes with a unique challenge that needs to be solved by your multi-cluster observability framework.

1) Unified View Across Clusters: When providing a platform spanning multiple clusters, it's important to have a central full stack observability platform that can aggregate and visualize data from all clusters. This unified view helps in identifying issues that might span across clusters and provides a holistic view of the entire system's health.

Challenge: Implementing and managing observability across multiple clusters can be complex. Different clusters, especially in a multi-cloud or hybrid environment, might have inconsistent configurations, policies and technologies. Integrating these into a cohesive observability strategy requires careful planning and expertise.

2) Consistent Monitoring and Logging: Implementing consistent monitoring and logging practices across all clusters is just as important. This implies using the same tools, metrics and log formats whenever possible to create a consistency that makes it easier to correlate data and more easily spot trends or anomalies across your clusters.

Challenge: Achieving uniformity in tools and practices across different clusters can be daunting. Each cluster might have its own set of solutions and formats, making it hard to standardize monitoring and logging. This requires negotiation and possibly reconfiguration of existing systems, which can be unpopular, time-consuming and resource-intensive.

3) Handling Network Latency and Complexity: Multi-cluster setups often involve communications over networks, which can introduce latency, complexity and, often, a higher cost. Monitoring network performance and inter-cluster communication is important to make sure there are no bottlenecks affecting an application's performance.

Challenge: Keeping an eye on network performance across dispersed clusters, especially in different geographic locations or across various cloud providers, can bring about challenges related to latency and complexity. Furthermore, accurately monitoring, measuring and managing these aspects to guarantee optimal app performance can prove to be difficult.

4) Scalability of Observability Tools: Your observability tools must scale as you add more clusters — able to handle the increasing volume of data without significant performance degradation.

Challenge: Ensuring that observability tools can scale effectively with the addition of more clusters is no easy task. You’ll want to automatically collect all observability data, show dependencies across services and guide users to the fastest path to remediation regardless of how many clusters you’re running or where they are located.

5) Granularity and Filtering: Given the large volume of data, the ability to filter and drill down to specific clusters, services or issues is an absolute necessity. This granularity helps in quickly identifying problems within a specific cluster while avoiding being overwhelmed by the noise from other issues and alerts.

Challenge: With the vast amount of data generated, providing detailed filtering and granularity while maintaining performance is tough. It requires advanced data processing capabilities and, often, a balance must be struck between the depth of data collected and the performance of the observability system.

6) Alerting and Anomaly Detection: Automated alerts and anomaly detection must have a level of sophistication that allows them to understand the norms specific to each cluster and adapt to changes accordingly. This capability provides early detection of potential issues before they have the chance to escalate.

Challenge: Designing an alerting system that is both sensitive to anomalies and specific enough to avoid false positives is challenging in a multi-cluster environment; alert fatigue is always around the corner. This requires sophisticated algorithms and, often, machine learning models that can adjust to the changing norms of each cluster.

7) Security and Compliance: It pays to be certain that observability tools comply with security policies and data governance, especially in environments where clusters might span different geographical locations or regulatory jurisdictions.

Challenge: With so many choices in the open-source space, engineering teams quickly find a solution to almost every issue. However, looking through the lens of security and compliance, an open-source solution might not necessarily check the governance box. These requirements, which can be vastly different based on industry and region, must be taken into account.

Multi-cluster observability made easy

Being able to observe and diagnose the performance and behavior of a Kubernetes application or cluster(s) can improve performance, troubleshooting, reliability, security and visibility — all while minimizing downtime and optimizing cost management.

Yet, the dynamic nature of Kubernetes environments presents challenges to observability. Organizations looking for a smooth and secure experience with Kubernetes clusters can opt for a free trial of StackState observability or explore the solution in our secure playground!

UP NEXT: We discuss rolling out a multi-cluster observability approach in seven key steps in Part 3 of our blog series.