Learn how AppDynamics is changing the APM market with major enhancements making it easier to configure and extract data from live transactions. AppDynamics next-gen architecture allows for configuration & instrumentation at scale with a new model for configuration management. See how Live Transaction Detection helps identify and extract commonly Instrumented & unInstrumented entry points in Java (ie. Servlets). Deep dive into new ways to identify business transaction latency, introducing features such as thread contention analysis and garbage collection performance visibility for JVM monitoring. The content covered will be applicable and of great value for not only Java APM users, but also anyone managing application configuration when scaling AppDynamics. Key takeaways: o Learn about the next-generation of transaction configuration & instrumentation for scaling your business monitoring with AppDynamics o Go deep into Java diagnostics with new contention analysis and Garbage Collection visibility features in the agent For more information, go to: www.appdynamics.com

1. Next-Gen Configuration and Java
Performance Diagnostics
Aakrit Prasad, Principal Product Manager, APM
Vinay Srini, Sr. Director of Engineering, APM

2. APPDYNAMICS CONFIDENTIAL AND PROPRIETARY 2
Notice
The information and materials included in this presentation (collectively, the
“Materials”) are the proprietary information of AppDynamics, Inc. (“AppDynamics” or
the “Company”). No part of the Materials may be reproduced, distributed,
communicated or displayed in any form or by any means, or used to make any
derivative work, without prior written permission from AppDynamics.
The Materials may contain product roadmap information of AppDynamics.
AppDynamics reserves the right to change any product roadmap information at any
time, for any reason and without notice. This information is intended to outline
AppDynamics' general product direction, it is not a guarantee of future product
features, and it should not be relied on in making a purchasing decision. The
development, release, and timing of any features or functionality described for
AppDynamics' products remains at AppDynamics' sole discretion. AppDynamics
reserves the right to change any planned features at any time before making them
generally available as well as never making them generally available.
All third-party trademarks, including names, logos and brands, referenced by
AppDynamics in this presentation are property of their respective owners. All
references to third-party trademarks are for identification purposes only and shall be
considered nominative fair use under trademark law. © 2016 AppDynamics, Inc. All
rights reserved.

3. Agenda
• @Scale Configuration
– Flexible Model
– Live Traffic Introspection
– DEMO
• JVM Performance Diagnostics
– Contention Analysis
– Garbage Collection
• Q & A

4. APM vision

5. Our approach
We understand transactions from the start

6. Our approach
We understand transactions from the start

7. Copyright © 2015 AppDynamics. All rights reserved. 7
Continued growth in
application monitoring requires
a scalable configuration

8. Scaling your business
with AppDynamics

9. Software architectural evolution
2000s
Layered Monolithic
2010s
Microservices
1990s
Spaghetti Code

10. Evolving software services
• Growth from monolithic 
modular- micro- services
• AppDynamics Enterprise
growth
– 10 100  1,000 of
applications
• Capability today must evolve to
scale for tomorrow
Web
App
Db

11. Configuration for scale
• Challenges
– Hierarchical
– No Grouping of Services
– Redundant Rules
• Solution
– Flexible
– Groupable Services

12. Hierarchical configuration
Copyright © 2015 AppDynamics. All rights reserved. 12
Web Back MyTier
My Application
/prod/check
What if I need a new rule for only “My Tier”?
Web Back MyTier
● One set of rules applied everywhere
● Simple deployment

13. Hierarchical configuration
Copyright © 2015 AppDynamics. All rights reserved. 13
My Application
/prod/check
My Tier
/prod/check/special
COPY
At scale, this is unreliable and error prone.
3 operations to modify any rule!
1. Modify the original rule
2. Remember what are duplicates
3. Modify the rule that was copied

14. Copyright © 2015 AppDynamics. All rights reserved. 14
Introducing “Scope”
My TierMy Application
Web BackMyTier
Default Scope Special Scope
Scopes can hold combination of Application and Tiers

15. Copyright © 2015 AppDynamics. All rights reserved. 15
Attach your rules to scopes
My TierMy Application
Web BackMyTier
/check /prod /special
No Duplication
Nothing to remember
No more errors
Default Scope Special Scope

16. DEMO : Scope Configuration Model
16

17. Effective configuration
• Flexible configuration != effective configuration
• Challenges today
– Matching & splitting conditions not trivial
– Mis-configured rules (common for Pojo)
– Masked transaction detection
– Uninstrumented traffic Auto rule BT
Custom Rule BT
(*Masked*)

18. AppDynamics
Rule configuration with live
traffic introspection

19. Uninstrumented code detection
• Find Transactions by Backends
• Complete Stack Trace View
• Monitor Method Data
• Instant Instrumentation

20. Live Instrumentation for Pojo Entry Points
• Validate Method Invocations
– Name
– Type (return)
– Details
• Object Traversal
20

21. Preview for masked transaction
• Live Transactions Detected from Transient Rules
• BT Generated (Unique v. Masked)
• Masking BT(s) Information
– Name
– State
– Load
• Save and View in BT List

22. Introspection via class method browser
• Reflect on all Class & Methods
• Instant Pojo Instrumentation
• See Other Instrument Methods

23. Live instrumentation for servlet entry points
• All HTTP requests
– Headers, Params,
Cookies
– Matching & Splitting
• Aggregated view
– URI
– Headers
– HTTP Params
– Hostname
– Port

24. Live Transaction Instrumentation DEMO
1.Pojo Entry from Uninstrumented Code
2.Splitting High Value Servlet BTs

25. BT performance diagnostics
Copyright © 2015 AppDynamics. All rights reserved. 25
!
Deep Dive Features for Detection & Diagnostics
• Per Transaction : Contention Analysis
• All Transactions : GC Optimization

26. Java APM
Features for Performance
Diagnostics

27. Thread contention 101
Copyright © 2015 AppDynamics. All rights reserved. 27
Multi-threaded enterprise services with many shared resources

28. Contention visibility
Copyright © 2015 AppDynamics. All rights reserved. 28
• Critical for Dev-Ops Monitoring when Scaling Services
• More Common in Microservices Architectures
• Application Performance RCA

29. Thread Contention Analyzer
Copyright © 2015 AppDynamics. All rights reserved. 29

30. BT Call Graph Thread Contention
Copyright © 2015 AppDynamics. All rights reserved. 30

31. Call Graph Element : Block Time
Copyright © 2015 AppDynamics. All rights reserved. 31

32. Service Endpoint List View
Copyright © 2015 AppDynamics. All rights reserved. 32

33. Call Graph Element : Wait Time
Copyright © 2015 AppDynamics. All rights reserved. 33
• Thread Time Waiting to Execute
• JVM Wait States
– Thread.sleep(sleeptime)
– Object.wait(timeout)
– Thread.join(timeout)
• All Wait States Captured

34. Transaction Latency from GC
• Issues can be Systemic and NOT Isolated to one BT
• Common Sources when all Transactions are Latent
– Network Congestion
– Hardware Scale (VM Paging)
– JVM Garbage Collection (App Level)
Copyright © 2015 AppDynamics. All rights reserved. 34

35. AppDynamics JVM GC
Enhancements

36. Garbage collection
• Benefits
– Memory management
– Avoid memory leaks
– Extensively tunable
• Drawbacks
– Tuning needed for performance
– Monitoring
– Ergonomics dependency
Copyright © 2015 AppDynamics. All rights reserved. 36

37. GC visibility today
Copyright © 2015 AppDynamics. All rights reserved. 37
• Young Generation Heap
– Eden
– S1 & S2
• Old Generation Heap
• Perm Generation Heap
• Code Cache

38. Copyright © 2015 AppDynamics. All rights reserved. 38
http://www.oracle.com/technetwork/java/javase/gc-tuning-6-140523.html
• Allocation Rate
• Promotion Rate
• Free Rate
• Live Data Size
GC Performance

39. Allocation rate
• Objects created / [Time]
–Eden Space
–Old Heap
• Tuning Young Heap
• High
–Frequent Young GCs
Copyright © 2015 AppDynamics. All rights reserved. 39
Promotion rate
• Objects Promoted / [Time]
–S2 => Old Heap
• Tuning Young & Old Heap
• High
–Frequent Full GCs
–Premature Promotion

40. Live data
• Lifetime memory
• Release comparison
• Growth impact
– Frequent full GCs
– Longer full GCs
– Higher OOM probability
– Heap fragmentation
Copyright © 2015 AppDynamics. All rights reserved. 40
Free rate
• Objects free / [time]
• GC efficiency
• Tuning impact

41. Summary
Copyright © 2015 AppDynamics. All rights reserved. 41
Configuration for Scale
• Flexible Model
• Live Traffic Introspection
Performance Diagnostics for Low Latency
• Contention Analysis (GA : 4.2.5)
• Tuning the GC

42. Questions?
Questions?

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