6 min read time

What’s new in OpenText Core Application Observability

by   in IT Operations Cloud

Observability helps cloud operations teams, site reliability engineers (SRE), and central IT teams detect and manage application performance issues quickly and efficiently. This 24.4 release of OpenTextTM Core Application Observability  introduces the enhanced Application Overview page, Trace Explorer enhancements and Metric Explorer enhancements along with integrations with OpenText Infrastructure Observability and Cloud Network Observability.

Summary of changes

OpenText Core Application Observability

  • Enhanced integration with OpenText Core Infrastructure Observability
  • Integration with OpenText Core Cloud Network Observability
  • Application Overview page
  • Trace Explorer enhancements
  • Metric Explorer enhancements

Let us dive into the details of the new features in this release

In today's complex IT environments, achieving seamless observability is crucial for maintaining system health and performance. And this must be done across the applications, infrastructure and network.  Let's look into the details of integration of OpenText Core Application Observability with OpenText Core Infrastructure Observability and OpenText Core Cloud Network Observability and how this integrated observability solutions can bridge gaps

See the figure below showing how you can isolate an issue using the root cause analysis using OpenText Core Application Observability.

Figure 1: Root cause analysis within OpenText Core Application Observability

You can now integrate OpenText Core Infrastructure Observability and OpenText Core Cloud Network Observability with OpenText Core Application Observability to proactively monitor and manage your IT environment with ease. This integration allows you to quickly identify the problem owner and improve the application uptime by:

  • Speed up your MTTR with holistic insight: In today's fast-paced digital landscape, minimizing mean time to repair (MTTR) is crucial for maintaining seamless operations and customer satisfaction. When an application error occurs, it's essential to quickly identify the root cause. By leveraging comprehensive monitoring tools, you can drill down from the error directly to any potential cause, whether it's related to infrastructure or network issues. This holistic insight allows for faster diagnosis and resolution, reducing downtime and improving overall efficiency.
  • Delegate with confidence: One of the common challenges in incident management is determining the ownership of an issue. By linking application trace issues to system and network infrastructure problems, you can delegate tasks with confidence. This approach avoids lengthy debates over ownership and ensures that the right team addresses the problem promptly.

Integration with OpenText Core Infrastructure Observability

You can now integrate Core Application Observability with OpenText Infrastructure Observability, specifically with its Hyperscale Observability (HSO) capability.  HSO enables the discovery and monitoring of AWS, GCP, and Azure services, along with Kubernetes objects and VMware resources. It allows for the collection of metrics, setting of thresholds, and continuous monitoring for any breaches. When a threshold is breached, an event is automatically generated. With this integration, you can access your EKS and EC2 Hyperscale Observability dashboards from Application Observability. The below figure, where you try to isolate or find the root cause of an unusual metric for the application that is integrated with OpenText Core Infrastructure Observability and OpenText Core Application Observability

While monitoring an application, you may observe that despite the application being healthy, there is an abnormal increase in errors and latency for a specific service. OpenText Core Application Observability captures detailed logs, metrics and traces that highlight the errors in the trace explorer. By analyzing the errors in the trace explorer, you can determine whether the issue is related to infrastructure or other causes. The span details show the cluster, node related details. From here, we can cross launch the K8S Dashboard to view the infrastructure health. From the span details, you will be able to cross launch the database (AWS RDS) dashboard to check for any potential issues with infrastructure.

In short, you can drill down from the error directly to any potential cause, whether it's related to infrastructure, application, or network.

Figure 2: Root cause analysis leading to the K8S dashboard to analyze the infrastructure health

For details see integration with Infrastructure Observability

Integration with OpenText Core Cloud Network Observability

OpenTextTm Core Cloud Network Observability allows you to discover and visualize network elements and applications, as well as monitor system performance metrics.

In Core Application Observability, you can track your applications, view traces, and spans. When viewing span information, you can access the Cloud Network Observability dashboard for details on the network elements used, providing a comprehensive system view for informed decision-making.

Figure 3: Root cause analysis leading to the Network Test dashboard to analyze the network health

For more information about Cloud Network Observability configurations, see Cloud Network Observability

Updated - Applications landing page

The enhanced landing page is now more actionable, offering a high-definition view of the application. It includes information on applications, trace groups, the number of traces, and a bar chart that represents the volume of each trace group. This comprehensive overview helps you to quickly understand and analyze the application’s performance allowing to dive deep into further root cause analysis.

Figure 4: Application overview page

A specific application can be selected to view the details of the application

Figure 5: Selected Application detail page

The visual tools like bar charts can be insightful and this is available on the application landing page for the Trace Groups by volume. You can hover over individual trace groups to get information on the key metrics error rate, latency and number of requests. Additionally, clicking on the “Inspect Traces” in the tooltip, takes you to the Trace Explorer, where the context is automatically set to the selected trace group. 

Figure 6: Trace Group Volume in the Application Overview page

Trace Explorer Enhancements

The Trace Explorer now samples data over a 12-hour period, providing a smaller, more manageable dataset. This prevents the system from becoming overwhelmed and potentially crashing, enhancing both stability and ease of use. Trace Explorer now has a 10,000 trace limit to prevent performance issues by displaying sample data when filters are too broad.

Figure 7: Trace Explorer for last 12 Hours

The new diversified sampler works against our trace group code, offering a varied set of traces. This approach ensures that users see a representative sample of traces, rather than just the most frequent or “noisy” ones.

Improved Filters

The filter section has been revamped to adopt the filter component used in the metrics tab. Significant improvements have been made to the attribute and attribute value drop downs. Attributes are now filtered by application and service, providing more specific and relevant options.

The filters support derived attributes. New derived attributes, such as trace ID, have been added. These can be used for upcoming integrations, offering more flexibility and functionality.

Easier selection via loading subset of values

The Trace Explorer now supports filtering by attributes with a high number of possible values. Previously, selecting an attribute or value would load all possible options, which was not scalable. Now you can filter the traces using attributes and this change significantly improves performance and user experience.

Metric Explorer: Enhanced conditions and aggregation types

You can now perform more detailed data analysis by leveraging the enhanced conditions and the new 'Average' aggregation type

Enhanced conditions

Metric Explorer now offers a wider range of conditions, such as 'none of', 'greater than', and more. These conditions allow you to refine your data queries with greater precision. Additionally, you can create conditional groups to logically combine multiple conditions, enabling more sophisticated data filtering and analysis.

New aggregation type

Previously, Metric Explorer supported two aggregation types: None and Rate of. With this release, a new aggregation type, 'Average', has been introduced. This addition provides a more comprehensive way to analyze your data, allowing you to calculate the average value of your metrics over a specified period.

 

More OpenText Core Application Observability 24.4 release-related details are provided in the AI Ops and Observability (previously: Operations Bridge) Release Readiness Webinar. The slides and the recording are available on our Community page here.

We encourage you to try out our new features and enhancements! For further information on our offerings, visit the OpenText Core Application Observability product page and check out our blogs.

If you have feedback or suggestions, don’t hesitate to comment on this article below.

Explore the full capabilities of OpenText Core Application Observability by looking at the online documentation on our Practitioner Portal: OpenText Core Application Observability.

Labels:

Operations Bridge