Opening the Pandora Box with Observability - Blog Series

The Five Pillars of OpenShift Observability

Red Hat OpenShift Observability aims at turning your data into answers. Not only are we working toward facilitating metrics, traces, and logs collection/storage, but also to aggregate them, normalize them, transport them in a flexible way - as well as to assess the health of your cluster(s) with intuitive and efficient user interfaces. A summary of our current offering is provided in the diagram below. 

With the OpenShift Monitoring 4.12 and Logging 5.6 releases, we continuously work toward delivering a unified, consistent, and simplified Observability experience across any footprint: cloud, on-prem, and edge - putting equal emphasis on all the five pillars of Red Hat OpenShift Observability: (i) Data Collection, (ii) Data Storage, (iii) Data Delivery, (iv) Data Visualization, and (v) Data Analytics. In the next section, let’s take a closer look at the features included in the upcoming releases and what, as users, you can expect next.


What are the problems you can now solve with Observability?

From a Data Collection point of view, with Logging 5.6, Vector - as an alternate collector to Fluentd - is now GA. For an overview of the benefits coming from adopting Vector, take a look at our Logging 5.5 release blog. Vector further consolidates our OpenShift Logging Architecture, providing users with great flexibility (see flowchart below). Given its scalability, vendor-neutrality, flexible configuration options, and less memory consumption, Vector makes collecting, transforming, and sending logs, metrics, and events very easy.

From a Monitoring standpoint, to improve data consistency in the collection phase, Thanos Querier is now being employed as backend to get metrics for the prometheus-adapter (e.g., prometheus-adapter CPU and RAM time series). Prior to this, Platform Prometheus was used. Because of this specific design, users would receive metrics from two different Prometheus instances, which, not having replicated data, might have provided different results. 


In regard to Data Storage, with Logging 5.6 we have exposed stream-based retention capabilities in the Loki Stack. For stream-based retention, retention in Loki has always been global for a cluster and deferred to the underlying object store. With Loki v2.3.0, Loki can do retention through the Loki Compactor, and can enable retention configuration per tenant and per stream.

With OpenShift Monitoring 4.12, users have now the option to specify topology spread constraints for Prometheus, Alertmanager, and Thanos Ruler - improving the overall Data Storage experience. By having better control on how pods are spread across users’ clusters, the protection against node or zonal failures is strengthened. Pod topology spread constraints are suitable for controlling pod scheduling within hierarchical topologies where nodes are spread across different infrastructure levels, such as regions and zones within those regions. Additionally, by being able to schedule pods in different zones, you can improve network latency in certain scenarios. 

Along with this feature, admins can now also create new alerting rules based on platform metrics, improving the management of alert rules. With OpenShift Monitoring 4.12, this feature is available in Tech Preview.

What about data aggregation, normalization, and transportation? With the Logging 5.6 release, you can now benefit from the support provided for forwarding logs to Splunk. In 2022, not only have we provided support for forwarding logs to Google Cloud Logging, but also included CloudWatch log forwarding add-on, supporting STS installations. These features make the overall Logging Data Delivery experience more customizable, thus more versatile for users and their use cases. 

From a Data Visualization perspective, with OpenShift 4.12, logs can now also be explored via the Developer Perspective of the OpenShift Web Console. Under the Observe > Aggregated Logs tab, you can easily search, filter, and visualize logs by severity and quickly identify issues related to your cluster(s). Predefined filters, such as pod and container, have also been added to facilitate the search of logs. Take a look at the demo for a snapshot of these new features.

HubSpot Video


Along with these new logging capabilities, in the OpenShift Web Console we now also support Alertmanager’s negative matchers for silencing alerts. This new feature provides greater flexibility to Web Console users when it comes to managing silences. The demo below provides an overview of this functionality in the OpenShift Web Console.

HubSpot Video


With every release, we also keep working toward improving user navigation. With OpenShift 4.12, Web Console users can now easily make use of runbooks URLs for alerts via the Alerting UI. This means that if an alert includes a runbook_url label, users will be able to access the runbook information by clicking on the alert and rapidly address issues. Briefly, this new feature helps reduce the time spent on debugging activities - facilitating interactive Data Analytics. Moreover, from a logging perspective, you can now also add the OpenShift cluster ID to log records to uniquely identify clusters in aggregated logs, increasing the control that you have on the data.

What are we planning next for Observability?

We are working on emphasizing all five pillars of Red Hat Observability and gradually moving away from siloed observability pillars. As we acknowledge the importance of providing a customizable Observability experience, our magic formula continues to be flexibility. That is why we are expanding third party integration opportunities. 

Along with establishing partnerships, we aim at extracting value from all observability signals. To achieve this, we are working on a series of initiatives, including providing log-based alerts and enhancing the Dashboards UI in the OpenShift Web Console. Briefly, we are planning to deliver a 360-degree OpenShift Observability experience, where all observability pillars are complementary to one another.

Are you curious to learn more about our roadmap? Do you need more information on how to open the Pandora Box with Observability? Interested in learning more about Data Visualization and Analytics opportunities? Do not hesitate to get in touch with Red Hat’s Field Organization for more information and support or drop us an email for any questions you may have on the upcoming features. Stay tuned for more blogs and welcome aboard to our new OpenShift Observability journey!


OpenShift Commons, OpenShift 4, Data Science, Prometheus, Monitoring, observability, Red Hat Data Science Model, OpenShift 4.12

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