One of the ways we strive to deliver relevant insights is to only create Traces around interesting requests. Some examples of these request types that are interesting would be faster than normal, slower than normal, new web requests, requests introducing new SQL, requests introducing new exceptions, or abnormal Satisfaction scores. Under performing requests will be profiled more often than fast …
Release Notes: October 2016
Here are some of the highlights from the October 2016 release: APM Tracked Functions RabbitMQ Montoring APM Tracked Functions for Linux This release introduces Tracked Functions, which allows you to track a specific portion of a request independently from the rest of the request. Support for Windows will be coming soon. More information on how to configure Tracked Functions can be found …
Performance: Tracked Functions
Tracked Functions essentially allow you to track a sub-transaction or block of code within a larger transaction. For example, they can be implemented for tracking various Elasticsearch queries to uniquely identify them. First you will need to add the StackifyLib dependency to your app: PM> Install-Package StackifyLib Once you have the StackifyLib installed you need to modify your code like …
Custom Profiling: Configure for Java
The custom configuration file needs to be named ‘stackify-apm.json’ and be located in the classpath. You can configure additional classes and methods that you want to instrument. Example: [{ “Class”: “com.stackify.example.util.ClassToBeInstrumented”, “Method”: “methodToBeInstrumented” }, { “Class”: “com.stackify.example.util.ClassToBeInstrumented”, “Method”: “anotherMethodToBeInstrumented” }] You can also add custom instrumentation to classes by using @Trace Annotation. See more information on our Github page here: …