HAProxy is one of the world’s most innovative and highest-performing load balancing solutions. The load balancer is critical for enabling high availability and supporting the dynamic scaling of infrastructure within modern applications. Because of its importance, engineers need tools that can quickly and effectively diagnose any problems with the load balancer if they arise.

Red Hat OpenShift is an open source container application platform that incorporates a collection of software that enables developers the ability to run an entire Kubernetes environment. It includes streamlined workflows to help teams get to production faster and is tested with dozens of technologies while providing a robust tightly-integrated platform supported over a 9-year lifecycle.

Anyone tracking the evolution of the IT industry is probably familiar with the concept of Industry 4.0. Essentially, it describes the process by which traditional industrial tasks become both digitized and continually managed in an IT-like fashion via modern technologies like cloud computing, digital twins, Internet of Things (IoT) sensorization, and artificial intelligence/machine learning.

MEAN is evolving as a popular web stack for developing cloud native applications because of its scalability, ease of extension, and high reliability. Each component in MEAN is built on JavaScript, contributing to a cohesive development platform. In this post, we take you through the log management options that are available for each component of the MEAN stack framework and their respective limitations – limitations that are addressable with a refined log management solution like observIQ.

People’s involvement has been inevitable with log management despite advancements in ITOps. Log management at a high level collects and indexes all your application and system log files so that you can search through them quickly. It also lets you define rules based on log patterns so that you can get alerts when an anomaly occurs. Log management analytics solution leveraging RDA has been able to detect anomalies and aid predictive models over a machine learning layer.

First of all, don’t ask this! Instead of asking what to log, we should start by asking “what questions do we want to answer?” Then, we can determine which data needs to be logged in order to best answer these questions. Once a question comes up, we can answer it using only the data and knowledge that we have on hand. In emergent situations such as an unforeseen system failure, we cannot change the system to log new data to answer questions about the current state of the system.

It sounds like a wild claim, considering that billion dollar companies like Splunk, Datadog, New Relic, and Solarwinds are consistently making national headlines, for both good and bad reasons. Observability leaders are anything but invisible, so how can the perfect solution be different? Are they that far off?