There’s no doubt that Kubernetes has seen an increase in adoption over the last few years. However, so has the complexity of working and managing them as you scale, especially monitoring and logging post-deployments. One needs to adopt new strategies for Kubernetes monitoring while working with limited tools available today, which can be quite a conundrum.
As per a recent study in 2021, it was found that more than 96% of respondents are already using or plan to use Kubernetes in the near future. The reason is simple: Kubernetes always has some benefits for both large enterprises and startups. It improves the efficiency of the developers, reduces unnecessary costs, and results in a better experience for the end users. However, we all know these benefits. Where we are lost, however, is handling the complexities associated with using Kubernetes once you scale. On that front, we have tried listing down the top Kubernetes monitoring and logging tools used by DevOps teams globally with our inputs that can help you choose what can be best for your use case.
1. Kubernetes Dashboard
Kubernetes dashboard is an add-on for Kubernetes Clusters. It’s fairly straightforward to manage, monitor, or troubleshoot your environment with the Kubernetes dashboard. It lets you view basic statistics like memory and CPU usage of your nodes to monitor the health of your workloads. It can easily be installed with ready-to-use YAML files.
One of the main advantages of the Kubernetes dashboard is that users can get an overall view of applications running on remote clusters. Along with monitoring services and logs, it helps detect anomalies as well.
The drawback of using the Kubernetes dashboard is that, though it is standalone for visualizing and debugging Kubernetes objects, it offers no functionality for CI/CD & governance. You can, in principle, deploy apps through the wizards, but there are no approvals and advanced metrics available, which is key for any Kubernetes monitoring tool.
2. Prometheus
Prometheus is one of the most popular open-source tools being widely used by developers everywhere. Developed by SoundCloud to monitor Kubernetes, it was later included in the CNCF (Cloud Native Computing Foundation) ecosystem.
Prometheus is widely used to collect metrics from services that run around the clock and allow the metric data to be accessed via HTTP endpoints. It gathers, organizes, and stores metrics along with unique identifiers and timestamps.
The fact that makes Prometheus different from the rest is its dynamic multidimensional data model and the flexibility of its query language (PromQL). Moreover, it has a pull model and an in-built real-time alert mechanism. As an open-source tool, it has created a large community of users who focus on innovation.
One of the major advantages of using Prometheus is that users don’t need to take any extra effort to install Prometheus. It has all the services and analysis from Prometheus endpoints and has in-built monitoring as well as instant alerts.
The main disadvantages of using Prometheus are related to its installation process and storage capacity. Each Kubernetes cluster will need to have Prometheus installed which is a major obstacle if you want to standardize your deployments. Someone from the operations team like an SRE would need to install it for every cluster in case you want the metrics exported.
Prometheus also has limited server storage which becomes an issue with its monitoring capabilities. Additionally, due to the lack of an in-built dashboard, users would need Grafana to complement Prometheus as a visualization tool. It also does not hold long-term data or has features like anomaly detection, user management, or horizontal scaling.
3. Grafana
Grafana can be considered an abstraction tool that connects to multiple data sources to provide feature-rich visualizations on one single dashboard. This helps to easily interpret and understand the data.
When compared to the other visualization tools, Grafana stands out because it connects to most of the databases. For Kubernetes Monitoring, Grafana usually sits on top of Prometheus. However, along with Prometheus, Grafana as a graph editor also connects with InfluxDB, Graphite, ElasticSearch, InfluxDB, PostgreSQL, and the likes. You can create comprehensive Kubernetes-ready monitoring dashboards with the graphs of your choice like heatmaps, bar graphs, histograms, or geomaps.
It is super easy to set up and use as Grafana has relatively straightforward deployment requirements. One of the best aspects of Grafana is that it can analyze data from varied data sources that span multiple use cases and its portable dashboard makes it convenient for teams to access them anytime anywhere.
However, with all these advantages, the main disadvantage of Grafana relates to its complex UI as some of the settings are not easy to configure and it does not have log analysis when users want to use visualization.
4. Thanos
Thanos is an open-source tool that lets developers overcome the main disadvantage of Prometheus: To scale its setup with long-term storage options. It’s easy to integrate with Prometheus using a sidecar that runs on the same pod or same host as the Prometheus server. Unlike Prometheus, Thanos is not tied to Kubernetes in particular.
The advantages of using Thanos is that it has high availability, easy data access, backup for metrics, and the ability to retain these metrics for a longer time. For users running Kubernetes workloads across multiple clusters, Thanos helps save time with a centralized view.
As Thanos, when loosely defined, is a highly available counterpart of Prometheus, there are no specific disadvantages as such. It simply integrates with existing Prometheus setups to provide a global view across connected Prometheus servers, and deduplicates and merges the metrics.
5. OpenTelemetry
OpenTelemetry is known for its observability and its ability to collect logs, metrics, and more in order to analyze the performance or infrastructure to give you a full picture of what’s happening with the deployment. It’s an open-source tool that’s vendor-neutral and being used by the biggest players in the industry.
OpenTelemetry helps trace and extract data across platforms for modern cloud-native applications where the data is distributed. This is helpful if you’re trying to compile all the data you need from various sources into a single database.
The tool helps trace and extract data cross-platform for modern cloud-native applications and distributed data. This is helpful if you’re trying to compile all the data you need from various sources into a single database.
The main advantage of using OpenTelemetry is that utilizes telemetry data in a unified manner much like container orchestration. Due to its functionalities, it gives a complete picture to the users of the app’s performance rather than being limited to basic monitoring.
The main disadvantage of Telemetry is that it has no visualization features and requires more time to set it up. Post setup, it has been reported to be rather resource-heavy, courtesy of the OpenTelemetry (Otel) Collector, which is used side-by-side almost all the time.
6. Jaeger
It is an open-source tool that makes distributed computing and microservice-based architectures easier to monitor and manage. Known for its abilities in distributed tracing, Jaeger gives engineers a chance to monitor and troubleshoot easily. It is used by startups as well as established enterprises typically on a massive scale. Example – Uber.
Jaeger is used for optimizing deployment verifications by analyzing latency and performance, especially root cause analysis and research service dependencies. Its often seen that Prometheus and Jaeger are used together to identify issues in infrastructure or spikes in latency for microservices.
The tool is super easy to install and has a superior web-based UI that can easily be deployed and extended, complemented by pretty extensive documentation. The new architecture, though complex, is high on performance, reliability, and scalability.
However, being relatively new in the market, users would need to familiarise themselves with Go which is far less popular than Java in order to work with Jaeger. Furthermore, it has a few in-memory storage issues and the new architecture can get a bit complicated to work with.
7. New Relic
A monitoring tool with Kubernetes integration that gives developers an outline of the servers, applications, and services. It can capture metadata and data for pods, nodes, replica sets, and deployments. It has next-level search capabilities as well as tag-driven dashboarding. The cluster explorer gives a multi-dimensional view of a Kubernetes cluster, troubleshooting failures, and other abnormal behavior across Kubernetes environments.
It is feature-rich, with the capability to integrate easily with APM and other tools. Users can create custom queries against the data collected. With its next level Dashboard, performance report users are always on top of their application performance.
Although it has the above capabilities, its interface is a little tricky to understand as it has a lot of options to navigate around. While the tool does report on a variety of stats, for example, the load time of a web app, it becomes difficult to track the route cause of an issue, in case loading times are high. Another con of New Relic would be that the alerts cannot be turned off and users will have to manually start or stop them.
Conclusion
Kubernetes adoption is increasing which implies that the need to monitor Kubernetes deployments has become a priority for many DevOps teams. There are many closed and open source tools available out there which have their own capabilities which suit varied requirements as explained in this blog. However, no matter which Kubernetes monitoring tool you choose, it simply adds to your existing DevOps toolchains. Manually orchestrating these will take away the efficiency of most DevOps teams.
This is where, for efficient Kubernetes monitoring, you would need one unified DevOps platform that can help you onboard your existing monitoring tools along with others. Ozone supports the tools mentioned above along with many others across every phase of CI/CD. This lets teams onboard the tools of their choice and automates DevOps end-to-end. It implies that you get to extract the maximum out of your DevOps toolchains while saying no to manual orchestrations, introduce security in your workflows, improve the quality of your deployments, and as all data is generated and available on one platform, paves the way for efficient governance.
