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AppDynamics not-so-secret-sauce

How does AppDynamics go "wide" and "deep" across an entire distributed application-without introducing excess overhead, and without requiring any configuration? The secret sauce is our two patent-pending technologies Dynamics Flow MappingTM and Deep-on-Demand DiagnosticsTM.

Dynamic Flow MappingTM

Dynamic Flow Mapping is the technology that allows us to automatically detect and track the application services and tiers, the business transactions, and the end-to-end flow of each business transaction across multiple services.
  1. Through automatic Byte Code Injection, we inject instrumentation at the appropriate places in the application as needed.
  2. Our Business Transaction matching algorithms identify and bucket user requests into Business Transactions in real-time. We do this by examining the typical entry points in the code, such as servlets or web service end points, and by inspecting the payload as necessary.
  3. Our auto-resolution algorithm resolves and identifies the multiple tiers and services participating in an end-to-end transaction using a reverse-lookup scheme.
  4. When the transaction exits a tier, we decorate the application protocol header and follow it to the next tier-a kind of a "tag and follow" approach.

Deep-on-Demand DiagnosticsTM

Deep-on-Demand Diagnostics is the technology that enables deep visibility automatically as needed without adding excess overhead, and thus making us industry's first true application performance management solution suitable for high-volume production applications.
  1. Our Performance Pattern Analysis algorithm uses heuristic analysis and dynamic baselining to determine when problems are happening, and what diagnostic data is needed to resolve the problem.
  2. Our Call Stack Analysis algorithm analyzes request flow paths in real-time to automatically determine the appropriate instrumentation, and provides deep visibility at the code level without adding much overhead.
Want to know more? Please contact us for more information.

 
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