Geplaatst: 29 April 2020
New Version 9.5
We have again a major new version of the ADF Performance Monitor available – version 9.5 ! We have added many new valuable features and improvements. Many overview screens have got a facelift and new charts. In several blogs I will write on them.
This blog is on one of those new features, automatic SLA and health KPI warnings. The monitor will automatically interpret the metrics and will show warnings if the ADF application is not meeting the configured SLA thresholds (KPIs). Or if configured JVM and system health thresholds are not met – like JVM garbage collection, JVM CPU load, system CPU load, OS memory, database, webservice, application server, network, and browser. From now on it will be even more fast and simple to interpret the metrics. You do not have to be a performance expert/engineer, the monitor will already show the (type of) problems!
Geplaatst: 8 December 2019
Major New Version 9.0 (Part 1)
I’m very excited to announce that we have a major new version of the ADF Performance Monitor – version 9.0 !
We have added many valuable new features; new metrics that can detect and help explain poor performance, disruptions, hiccups, and help troubleshooting ADF applications. Like operating system metrics: the CPU usage of the ADF application, the total CPU usage of the whole underlying operating system, the total used and free physical (RAM) memory of the whole system, and the Linux load averages. A high CPU usage rate and memory usage may indicate a poorly tuned or designed application. Optimizing the application can lower CPU utilization. Generic APM tools have these kinds of metrics too in some way, but the combination of system metrics with ADF specific metrics of the ADF Performance Monitor makes it even more possible to relate performance problems.
Another reason to pay attention to system metrics is that nowadays more and more applications are deployed on the cloud. Very likely there will be shared virtual machines and resources (CPU, memory, network). Applications and processes could influence each other if frequently other processes have a very high usage of the available CPU or memory capacity.
This blog (part 1) describes the first part of these new features. Part 2 describes the CPU execution time of individual HTTP requests and click actions. It answers the question: “What request/click action in the application is responsible for burning that CPU ? (Lees meer..)