5 Tools to implement observability in the Kubernetes environment
Tags: kubernetes, tooling, serverless, cloud
Published: 2022-10-31 18:04:44

Sometimes all you need is a helmsman to steer your business to success! Don’t like riddles? We all love some clarity, whether it’s life or application development. Kubernetes(Helmsman in greek) has been the pinnacle of app development clarity. 

Containerization brings several benefits of portability, process isolation, and higher flexibility. However, you need a platform to manage containers, and Kubernetes makes it happen effectively.


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According to [Builtwith](https://trends.builtwith.com/framework/Kubernetes), more than 60,412 websites use Kubernetes, including more than 7800 websites in the US alone. In addition, the [Cloud Native Computing Foundation (CNCF) survey](https://www.cncf.io/wp-content/uploads/2022/02/CNCF-AR_FINAL-edits-15.2.21.pdf) in 2021 indicated that 96% of organizations were either evaluating or using Kubernetes. 

So, there is no denying that Kubernetes has seen rapid adoption among businesses. However, Kubernetes is not perfect, and there are flaws. Monitoring these flaws may help you improve Kubernetes performance, but observing the system’s current state takes the game a notch higher. 

Kubernetes helps in the orchestration of containers and their management across cloud services. However, it adds complexity to the system with new layers of abstraction. Observability allows organizations to leverage metrics, logs, and traces to overcome Kubernetes flaws. So let’s first understand observability.

##What is Kubernetes observability?

Observability term refers to a process of measuring the internal state of systems. Kubernetes observability means measuring the resilience and stability of the system. There are three pillars of [Kubernetes observability](https://www.simform.com/blog/kubernetes-observability/),

- **Logs** are files that store data of errors, events, and warning flags that occur in a software environment. In addition, it contains contextual data for events, like specific time of occurrence or which endpoint is connected to the user’s device.
- **Metrics** are the results of measurements of specific indicators pre-defined by organizations. It helps businesses determine system health. The numerical aspect of metrics makes it more effective than logs, as you can quickly analyze them and make changes in the system.
- **Traces** are events representing patterns businesses can use to understand the request path. Teams can follow the request flow patterns, troubleshoot system performance issues, and reduce dependencies. 

Implementing observability requires tools to help businesses access, analyze and integrate data from multiple sources. In addition, these tools must provide logs, metrics, and traces for a system to improve performance. 

##Best tools to implement Kubernetes observability

Kubernetes monitoring tools are popular among businesses due to their multiple features. However, observability implementation needs specific tracking and tracing tools. Here are some of the tools you can use.

**1.Fluentd**

Fluentd is an open-source tool that you can use to collect data on unified logging layers. It relies on tags to route events. Each event has a tag that provides Fluentd information on where it needs routing. The primary purpose of Fluentd tools is to capture log events which you can use to implement observability in Kubernetes.

Pros 

- Events are routed based on tags.
- More than 500 plugins
- Less memory consumption
- The highly configurable buffering system 
- Enterprise support

Cons

- It does not provide the capability to track metrics without plugins
- Higher latency
- Inability to keep data for a long time

**2.Prometheus**

Prometheus is an open-source tool that helps you monitor microservices and containers. It enables you to run flexible queries and configure real-time alerts for the system. You can use Prometheus to improve visibility for containerized workloads, APIs, and distributed apps. 

It uses a pull-based system that sends HTTP requests known as scrape. Each scrape is created according to the pre-defined config instruction in your deployment files. Every response to scrape is parsed and stored in a repository. A single repository can handle massive data enabling Prometheus to monitor several machines simultaneously.

Pros

- Works seamlessly with Kubernetes 
- Provides APIs for accessing metric data
- Offers extensive libraries to collect metrics
- Pull-based approach to collecting time series data

Cons

- Data collected through Prometheus lacks context.
- Lacks in-depth data as exporters provide information periodically
- Does not monitor legacy infrastructure
- Lack of authentication and encryption 

**3.Jaeger**

[Jaeger](https://www.jaegertracing.io/docs/1.22/frontend-ui/) is an end-to-end distributed and open-source tracing tool. It was open-sourced by Uber engineering and is in incubation with the CNCF. Jaeger allows you to implement observability in Kubernetes through transaction monitoring. If there is a microservice failing, determining how requests flow from one service to another is difficult. 

Jaeger uses a tracing approach to find the path of request flow, analyze the reasons for failure, and monitor transactions.

Pros

- Easy to install
- Feature-rich interface 
- Support for CNCF projects
- Good documentations
- Highly flexible and distributed architecture


Cons

- Still in incubation with CNCF
- It uses GO language instead of Java

**4.Elastic Stack(ELK)**

ELK stack is another open-source tool that enterprises can use for log management. It includes three tools, Elastic search, Logstash, Kibana, and Beats, and it can handle a large number of Kubernetes logs and help manage them. 

- Elasticsearch acts as a NoSQL database and analytics engine
- Logstash captures and processes logs
- Kibana provides visualization of the data through a dashboard component
- Beats is a data collection agent

ELK provides auto-discovery capabilities through Beats and helps in monitoring system-level performance.

Pros

- Multiple hosting options
- Centralized logging
- Real-time data visualizations


Cons

- Complex management requirements
- Stability and uptime issues 

**5.Kubewatch**

It is a tool that allows you to track specific Kubernetes events and sends alerts to collaboration tools like Slack. Kubewatch monitors changes in pods, DaemonSets, deployments, Replicasets, configuration maps, and services. 

Pros

- Supports multiple endpoints for tracking changes
- Easy to deploy on your system
 
Cons

- It does not have actionable features

##Conclusion

Observability is essential if you want to improve Kubernetes’ performance. There are many aspects of Kubernetes optimization that need fine-tuning of the system. Tracking and tracing sudden changes or events that disrupt system performance becomes vital; that is where you need tools to implement observability. Here we have discussed different tools with pros and cons. However, which one to choose will depend on your specific requirements.