How Visualization Can Be Used In Observability

Visualization can be an extremely powerful tool for observing the state of a system. Observables can be used to analyze and monitor performance changes, and a number of different technologies make this possible. This article discusses the use of observability and visualization with the Maistra Service Mesh.

Observable

Observable’s visualization tools are free for anyone to use, but some advanced coding skills are needed. There are many ways to visualize data with Observable, including its own visualization website, plugging your own data into a database, or creating a custom visualization. The company is aiming to become an easy-to-use destination for curious consumers.

Visualization in observability is a great tool for understanding how your application is performing. By looking at the way a request is processed, you can determine where the bottleneck or breakdown is happening. But remember that using a visualization tool doesn’t mean you’ll be able to decipher the metrics yourself. You’ll need different tools and approaches to make sense of the data.

Traditional dashboards are often built on bar plots, line charts, and pies. While these tools are useful for understanding the health of a single application, it’s important to use a multi-dimensional view of the application environment to improve decision-making and reaction times. This is important for observing application performance in real-time, as it helps to identify issues early on.

While the buzzword “observability” may seem like a buzzword, it is an important strategy to adopt to make sure your business’s systems can adapt to the changes in technology. It allows users to monitor and analyze data across multiple layers of IT architecture, simplifying root cause analysis. This method enables organizations to identify problems in real-time and improve security and data management.

As the volume of data grows, the need for data observability will be more critical than ever for businesses of all sizes. The quality of data is the backbone of effective decision-making. Without this, businesses risk putting valuable information at risk.

Log visualizations

Log visualizations can help identify and analyze issues. Modern systems are complicated and have many variables, so a logical and visual approach to log data analysis is necessary. Log visualization tools are becoming easier to use and more intuitive. They can help engineers and developers make sense of log data and identify issues.

Log visualization tools allow users to drill down to a specific log entry in the logging system. A user can click on a time series in a chart to navigate to its corresponding log entry in the logging system. These visualizations can be filtered by time range, filtering, and user permission.

Observability provides a real-time view of a system and can correlate data from many different systems. Tools like Coralogix can aggregate data from many different sources and provide a native Kibana view as well as plugins for other tools. However, observing data is not necessarily easier to interpret than logs.

Observability also relies on traces, which provide context for the other components of observability. Traces can help identify the most important metrics and logs to monitor. They can also highlight inefficient or problematic code and performance bottlenecks. The combination of metrics, traces, and logs can provide a comprehensive picture of a system’s health and availability.

Observability has numerous benefits for management teams and users. In addition to providing insights into the entire infrastructure, it also makes it easy to analyze and query data. This helps teams to provide meaningful alerts and troubleshooting. This knowledge allows them to improve performance, minimize downtime, and increase user satisfaction.

Trace visualizations

The observability paradigm emphasizes the use of traces to visualize logs, metrics, and other events. Traces can help you find and understand which metrics are valuable, which are unimportant, and which are performance bottlenecks. They can also help you debug distributed systems. These traces provide a holistic view of how your application works and can help you determine the cause of problems.

To create an observability system, you must understand your business priorities and your enterprise’s technology stack. Once you’ve established these, you can begin documenting your data formats, structures, and metadata. This step will help you create a unified view of your data and avoid any misunderstandings later on. This is especially important for large organizations where different teams tend to work in silos.

While metrics and logs are great for understanding individual systems, traces help you track the complete lifecycle of a request through a distributed system. A single trace can represent each request across all components and provide insight into the cause of a breakdown or bottleneck. Trace visualizations can also be overlaid with metrics and logs to see how the requests flow through the system. By combining these tools, you can create a unified view of the entire system and focus on high-value areas for optimization.

Using a unified view of your application’s trace data is crucial for troubleshooting performance issues in microservice-based applications. Without visibility into the state of every individual microservice, it can be impossible to pinpoint the root cause of performance problems. Distributed tracing provides a central overview of your distributed system, and helps you identify any bottlenecks and find ways to resolve performance problems.

Observable with Maistra Service Mesh

You can use Maistra Service Mesh to visualize and control your microservices. Maistra Service Mesh uses the Istio open-source project to manage its components. You must install both the Maistra and Istio components in the proper order. The first step is to log in to the OpenShift Container Platform web console as the cluster-admin role.

The Observable component of Maistra Service Mesh can be configured by amending the default resource or by creating a custom resource. The default resource is called ServiceMeshControlPlane. You can also extend the API by creating your own custom resources. The istio-system project is used throughout the documentation.

The Observable component allows you to monitor your application in real-time by storing and analyzing the metrics it produces. It allows you to easily filter your metrics by application, workload, and service. Moreover, you can customize charts by selecting specific metrics, presenting metrics by source, and overlaying trace spans.

To create an observable with Maistra Service Mesh visualization, you must have the Jaeger custom resource and Red Hat OpenShift distributed tracing platform. Jaeger is the default tracing component of Maistra, but you can also install it by a privileged user.

The Observable with Maistra Service Mesh management console Kiali provides a rich set of observable information for your application. It provides you with detailed traffic topology, health grades, powerful dashboards, and correlated views of metrics. In addition, it offers strong validations to detect configuration problems.

Observable with Maistra Service Mesh can be used with Red Hat OpenShift Dedicated. However, it is necessary to install and manage the service mesh operators before using it.

Observable with Prometheus

Prometheus is a monitoring solution for the Linux platform. It also offers a number of visualization tools, such as Grafana. You can use the sample NServiceBus data to capture metrics from an NServiceBus instance and store them in Prometheus. To run the sample, you’ll need Prometheus and Grafana.

Prometheus supports OpenTelemetry metrics. It can be used to measure a wide variety of metrics such as import speed, request latencies, and memory usage. Its GlobalView feature enables unified querying of all instances under a single username. This makes it possible to compare metrics from different services.

Unlike other monitoring systems, Prometheus provides an expression language for metrics. You can add labels to metrics in your Prometheus data collection. But, remember that aggregating metrics will reduce data fidelity. For example, if you create a new metric, you’ll need to convert its name and label. Then, you can use the new metric as the source of a new time series.

Prometheus supports Grafana as a visualization tool. This dashboard allows you to explore data from multiple applications using Prometheus. You can also create custom visualizations using Grafana. The dashboard can also be imported into other applications. By using Prometheus and Grafana, you can create custom charts and combine multiple metrics from different sources.

You can configure RabbitMQ to expose metrics to Prometheus on a dedicated TCP port. The port is 15692. This should be set in the Prometheus configuration for RabbitMQ. Several ways to configure Prometheus are described in the documentation. You can also refer to the first steps in the Prometheus guide to getting started.