When your users are encountering errors or high latency in your application, drilling down to view the logs from a problematic request can reveal exactly what went wrong. By pulling together all the logs pertaining to a given request, you can see in rich detail how it was handled from beginning to end so you can quickly diagnose the issue.
Even in an era where container, serverless, and cloud-computing technologies garner considerable attention, many companies continue to run a sizeable share of their mission-critical applications on highly resilient, fault-tolerant systems such as IBM AIX on Power-series hardware. AIX, one of the most popular Unix-based operating systems, is trusted by large companies that process critical data such as medical health records and banking transactions.
The ELK Stack is a great open-source stack for log aggregation and analytics. It stands for Elasticsearch (a NoSQL database and search server), Logstash (a log shipping and parsing service), and Kibana (a web interface that connects users with the Elasticsearch database and enables visualization and search options for system operation users). With a large open-source community, ELK has become quite popular, and it is a pleasure to work with.
“Citrix is slow” is one of the most common complaints Citrix admins hear on a daily basis. Somewhat ironically, Citrix itself is rarely the problem and the actual cause of the Citrix slow performance issues lies elsewhere. This can make troubleshooting “Citrix is slow” issues extraordinarily difficult to do.
When managing cloud-native applications, it’s essential to have end-to-end visibility into what’s happening at any given time. This is especially true because of the distributed and dynamic nature of cloud-native apps, which are often deployed using ephemeral technologies like containers and serverless functions.
Containers have rapidly become a necessary part of the modern data center. Containers can be built on any of a number of operating system foundations — so how do you choose one over another?
At the end of Part 1, the end result was a set of polygons that represented each Cassandra node in a Kubernetes statefulset. The cAdvisor-based metrics CPU/Memory/Disk utilization are scraped by Prometheus. This second tutorial will focus on a rollup of multiple Cassandra clusters running inside Kubernetes. We will end up with three dashboards tied together to provide an overview of our Cassandra clusters.
Mattermost 5.10 includes several new features that will help your team work better together.
Here's why AIOps, which combines big data, machine learning and visualization to deliver greater insights, should be on every enterprise's shopping list.
Today's resource managers and container orchestrators allow us to describe and deploy workloads in a consistent and repeatable fashion. So why are our workloads growing ever more complex?
