With the continued maturing of DevOps, organizations are constantly trying to improve the performance of their software and put out the best quality of product in the market. And when it comes to modern-day DevOps conversations, Kubernetes has taken center stage with a whole ecosystem built around it. This makes complete sense given that Kubernetes and DevOps are a natural fit where the former helps streamline building, testing, and deploying pipelines, while the latter enables this at the infrastructure level.
Monitoring vs. observability and the golden triangle
The difference between monitoring and observability is that the latter is an inherent property of a system while the former is what someone does to the system. The developer or operations team is solely responsible for observability, whereas monitoring can be done by anyone. In cloud-native polyglot architectures, it is difficult to identify and solve performance issues. This is why there is a need for observability.
The golden triangle
The golden triangle represents the three pillars of monitoring and observability.
- Metrics: They represent numeric values that change over time. They are a key data structure used in monitoring and they give an insight into the health of a system.
- Logs: They represent discrete events and contain metadata. They are the largest volume of data that is needed to be stored.
- Traces: They trace information about specific application operations.
And lastly, Instruments aren’t necessarily part of the golden triangle, but they are just as important. Instruments are the parts of a code used to generate metrics and logs, and traces.
The top five tools enabling monitoring and observability in Kubernetes
Kubernetes is the most popular container orchestration software, and one of the reasons for this is its modular architecture. It doesn’t come with features such as built-in monitoring; it expects the admin or user to configure it using readily available tooling. Monitoring and observability are essential to analyze numerous containers running in Kubernetes to ensure all the services are up and running perfectly. So, the development of various open-source monitoring tools for Kubernetes isn’t surprising.
Before we look at some of these tools, it’s worth mentioning that Prometheus is the leading monitoring tool for Kubernetes. However, we’ve tried to look beyond it to up-and-coming projects that are blazing new trails with monitoring. In fact, some of these tools serve to enhance the default functionality of Prometheus. Let’s look at five such tools that are essential for monitoring and observability in Kubernetes.
Cortex is an open-source Prometheus-as-a-service system platform providing a complex, secure multi-tenant Prometheus experience. It is essentially a monitoring system that offers limitless horizontal scalability for apps and microservices. It runs across multiple machines in a cluster which improves its storage and enables it to push metrics and run globally aggregated data in a single place.
Cortex has no single point of failure since it can replicate data between machines when run in a cluster and support multiple cloud computing platforms. It provides long-term storage for customized metrics that enable search history with charts and graphs. This enables DevOps teams to customize visualization of real-time network traffic and monitor runtime events. In addition to having a fast query response, Cortex provides multi-tenancy as a first-class capability to large organizations with many teams.
Thanos is an open-source monitoring system that accumulates data from various Prometheus deployments based on metrics and provides a centralized global query view. It leverages the Prometheus 2.0 storage format to store unlimited, long-term, historical data. Thanos provides downsampling — a reduction in the sampling rate of a signal - to aid in speeding up and keeping queries responsive when querying large time ranges. It is extendable across multiple Kubernetes clusters and has an easy backup for metrics.
Monitoring data is collected when two Prometheus instances are running in the same Kubernetes cluster. Thanos helps pull data from the replicas and deduplicates and stores it, thus preventing downtime. It easily integrates with all Prometheus setups, and its operations are simple with a negligible baseline cost.
OpenTelemetry is an open-source observability framework and collection of observability tools for cloud-native software. While not a monitoring tool in itself, it enables various monitoring tools such as Jaeger, Prometheus, or any commercial monitoring tool that you’d like to send data to.
OpenTelemetry offers a readily available package of tooling to generate, collect and export data from cloud-native apps to analyze and understand their performance and health. OpenTelemtry standardizes the process of data collection by providing a common format of instrumentation across services. In layman’s terms, OpenTelemetry is all about shipping off your observability data to a place where you can use it.
The data collected by OpenTelemetry is a type of data known as telemetry data. This data is in the form of metrics, logs, and traces and is analyzed to give a view of dependencies within a distributed system. OpenTelemetry defines how to collect this telemetry data, so it becomes useful for teams to instrument observability in application from the get-go.
At the heart of OpenTelemetry are its collectors. They are like agents installed in the system that needs to be observed, and from here, they are responsible for collecting and transporting the data to a destination for consumption. How this works is the software tells system components which metrics to gather using the API to instrument the code and then pools this data to transfer it for processing. It then breaks down the data and filters it to eliminate errors before converting and exporting it. The analysis of this data makes it easier to observe the behavior of multi-layered systems.
4. Chaos Mesh
Chaos Mesh is an open-source cloud-native chaos engineering platform that is capable of orchestrating fault scenarios. Chaos engineering is essentially an approach that helps identify failures before they cause an issue to build resilient systems. So, Chaos Mesh is a versatile chaos engineering solution that works by simulating abnormalities that could occur in reality for complex systems on Kubernetes. It also provides visualization to help design custom chaos experiment scenarios and has multiple layers of security control.
The highly scalable platform has two major components — the chaos operator, which is the fully open-source core component, and the chaos dashboard, which is a web UI for designing and monitoring chaos experiments. As Chaos Mesh is specifically designed for Kubernetes, it requires no special dependencies and can be directly deployed on Kubernetes clusters. It injects chaos into the Kubernetes infrastructure in a manageable way to provide automatic orchestration.
Every application creates logs, and being able to analyze these logs is important for organizations to track their performance. Although there are many log aggregation tools out there, Loki stands out due to its unique approach. It only indexes metadata (such as labels of the Kubernetes pods) from every logline. Additionally, it stores unstructured compressed logs, and the actual log content is stored in object stores, thus simplifying log aggregation and making it simpler to operate. Loki can easily select and search time-series logs where the logs are stored stably.
It is horizontally scalable, multi-tenant, and can be easily plugged into Prometheus.
Collecting logs in Kubernetes is a huge task since it has a microservice-based architecture with hundreds of pods and even more containers. This is where Loki comes in and facilitates the collection of logs from Kubernetes pods efficiently.
Power of many for Kubernetes monitoring and observability
There are multiple tools that each have their unique solutions that focus on one or two specific observability problems. So, since there is no all-in-one solution to cover monitoring and observability among all environments of the Kubernetes system, implementing problem-specific tools can prove too much work for the DevOps team. However, implementing a smart combination of a few of these tools after analyzing the requirements might just do the trick.
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