5 Data Sources That Turn APM Data Into Application Performance Analytics
Author — Bill Emmett
Applications are critical to the success of any organization. But are you doing everything you can to optimize them? Here are five data sources that can help you deliver better applications in less time. Check out the Essential Guide to Machine Data: User and Application Machine Data for even more.
APM Tool Logs
Examples: Logs from Dynatrace, New Relic, AppDynamics, Pulseway, LogicMonitor, Stackify, Boomerang.js, Jmeter, CA Technologies, Idera, Ipswitch
If you already have an APM tool, you can get great insights into end user monitoring, byte code instrumentation and on-page errors. These logs can show infrastructure problems and bottlenecks that aren’t visible when looking at each system individually, such as slow DNS resolution, causing a complex web app to bog down as it tries to access content and modules on many different systems. And when you monitor these logs, you can get early warnings to application problems so you can remediate issues before users experience them.
Custom App and Debug Logs
Example: Custom applications
For developers, these are often the most sought-after data sources because they provide minute details of application state, variables and error conditions or exceptions. Analysis of these logs can help expose the cause of application crashes, memory leaks, performance degradation and security holes. With custom applications, the exact type of sources differ depending on the app.
CRM, ERP and Other Business Applications
Examples: SAP, SFDC, Oracle, Microsoft Exchange, Microsoft Dynamics
Many of your applications integrate with Messaging CRM and ERP platforms, so getting insights into the use and performance of these solutions may give you insights into your critical applications as well. CRM can provide a complete record of information and events leading up to a customer escalation. When combined with other data sources, CRM can provide indicators of deeper issues. Like other application records, ERP logs are needed when debugging performance and reliability problems due to the complex interactions between many systems in an ERP implementation. They’re also useful in capacity planning.
Automation, Configuration, Deployment Tools (Platform)
Examples: Puppet Enterprise, Ansible Tower, Chef, SaltStack, Rundeck, machine data ingested through APIs, webhooks or run logs
These data sources are key because automation tools help you understand when new releases are brought into production. Monitoring, analyzing and managing this data gives you the ability to compare before/after performance of an application update, and usage and availability for a particular release.
Test Coverage Tools
Examples: Static Analysis & Unit Testing logs (SonarQube, Tox, PyTest, RubyGem MiniTest, Bacon, Go Testing), build server logs and performance metrics
Test coverage data monitoring can help you understand:
- How much technical debt and issues are being resolved
- How ready is your next release
- How many unit tests were performed per hour and which tests are being run
If test coverage data is combined with build data, you can start monitoring build and release performance and start understanding the release quality. You can understand the trends in error percentage and make decisions on whether the build is ready for production. Understanding code quality can also help support teams get prepared for any additional volume of calls or any particular issues that may arise. For example, CSAA uses the data from production information to determine which user responses they want to provide to perform the most in-depth testing.
There are more data sources that can help you improve your APM. Get the Essential Guide to Machine Data: User and Application Machine Data for more recommendations.
Originally published at www.splunk.com on June 28, 2017.