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What gives me the right ??
• Programmer/DBA/IT Technologies knowledge since the last century • APM Specialist since the last decade
Agenda
• Intro • What is Dynatrace AppMon and how does it work • Metrics driven pipeline (or What don’t I know when testing my apps)
• Using AppMon for Memory and CPU analysis • Dynatrace AppMon and application Optimization
What does Dynatrace AppMon do?
3rd parties
Akamai
Cloudfront
Synthetic
Apache
IIS
Node.js
nginx
Java
.NET
PHP
IBM
WMQ
ESBs
MongoDB
Hbase
Cassandra
CICs
IMS
ORACLE
MSSQL
MySQL
DB2
Mobile
Collector
Plugins
Dynatrace Server
Hosts
Session Storage Performance Warehouse
Splunk
Elasticsearch
Solr
Rich Client
Web Interface
Web
Biz/Ops
Locate a User
Biz/Ops
Inspect Users
Biz/Ops
Dev
Biz/App
App/Ops
Dev/Arch
Method Level Hotspots
+ Exceptions, Logs, Memory Allocation, Threads, Actual Code ...
Perf Eng.
CPU metric without a local agent installed
Find out which operations are taking the most time
Perf Eng.
Retrieve Execution Plan from any SQL Statement
Find out if the performance of a SQL Statement could be
improved by using a different index
Perf Eng.
Dev/CI/CD
#1: Analyzing every Unit, Integration & REST API test
#2: Key Architectural Metrics for each test
#3: Detecting regression based on measure per Checkin
Build-by-Build Quality View
Quality Overview by Build In Dynatrace
Or pulled into your Build Server, e.g: Jenkins, Bamboo …
Dev/CI/CD
Biz/Ops Biz/App
Dev Dev/CI/CD
Export & Share
Biz/Ops Biz/App
Dev Dev/CI/CD
Demo Time
Metrics Driven pipeline
Early 2015: Monolith Under Pressure
Can‘t scale vertically endlessly!
May: 2.68s 94.09% CPU Bound
April: 0.52s
From Monolith to Services in a Hybrid-Cloud
Front End to Cloud
Scale Backend in Containers!
26.7s Load Time
5kB Payload
33! Service Calls
99kB - 3kB for each call!
171! Total SQL Count
Architecture Violation Direct access to DB from frontend service
Single search query end-to-end
The fixed end-to-end use case “Re-architect” vs. “Migrate” to Service-Orientation
2.5s (vs 26.7) 5kB
Payload
1! (vs 33!) Service Call
5kB (vs 99) Payload!
3! (vs 177) Total
SQL Count
From 0 to DevOps in 80 days Lessons learnt from shifting an on-prem to a cloud culture
Bernd Greifeneder, CTO
http://dynatrace.com/trial
Webinar: http://ow.ly/cEYo305kFEy Podcast: http://bit.ly/pureperf
2 major releases/year
customers deploy & operate on-prem
26 major releases/year
170 prod deployments/day self-service online sales SaaS & Managed
2011 2016
believe in the mission impossible
6 months major/minor release + intermediate fix-packs
+ weeks to months
rollout delay
sprint releases (continuous-delivery)
1h : code to production
„Always seek to Increase Flow“
„Understand and Respond to Outcome“
„Culture on Continual Experimentation“
„Always seek to Increase Flow“
Testing: Ensure Success in The First Way
Removing Bottlenecks
Eliminating Technical Debt
Enable Successful Cloud & Miroservices Migration
Shift-Left Quality
Reduce Code Complexity
It‘s not about blind automation of pushing more bad code on new stacks through a pipeline
It‘s not about blindly adding new features on top
of existing withouth measuring its success
You measure it! from Dev (to) Ops
35
What you currently measure
What you should measure
Quality Metrics
in your pipeline # Test Failures
Overall Duration
Execution Time per test
# calls to API
# executed SQL statements
# Web Service Calls
# JMS Messages
# Objects Allocated
# Exceptions
# Log Messages
# HTTP 4xx/5xx
Request/Response Size
Page Load/Rendering Time
…
Build 17 testNewsAlert OK
testSearch OK
Build # Use Case Stat # API Calls # SQL Payload CPU
1 5 2kb 70ms
1 3 5kb 120ms
Use Case Tests and Monitors Service & App Metrics
Build 26 testNewsAlert OK
testSearch OK
Build 25 testNewsAlert OK
testSearch OK
1 4 1kb 60ms
34 171 104kb 550ms
Ops
#ServInst Usage RT
1 0.5% 7.2s
1 63% 5.2s
1 4 1kb 60ms
2 3 10kb 150ms
1 0.6% 4.2s
5 75% 2.5s
Build 35 testNewsAlert -
testSearch OK
- - - -
2 3 10kb 150ms
- - -
8 80% 2.0s
Metrics from and for Dev(to)Ops
Re-architecture -> Performance Fixes
Scenario: Monolithic App with 2 Key Features
Reduce Lead Time: Stop 80% of Performance Issues in your Integration Phase
CI/CD: Test Automation (Selenium, Appium, Cucumber, Silk, ...) to detect functional and
architectural (performance, scalabilty) regressions
Perf: Performance Test (JMeter, LoadRunner, Neotys, Silk, ...) to
detect tough performance issues
your tool of choice
#SQL, #Threads, Bytes Sent, # Connections WPO Metrics, Objects Allocated, ...
Confidential, Dynatrace, LLC
https://github.com/Dynatrace/Dynatrace-Test-Automation-Samples https://dynatrace.github.io/ufo/
Fail the build!
https://dynatrace.github.io/ufo/
“In Your Face” Data!
Demo Time
Confidential, Dynatrace, LLC
Use application metrics as additional Quality Gates Dev&Test: Personal
License to Stop Bad Code when it gets created!
Tip: Dont leave your IDE!
Continuous Integration: Auto-Stop bad Builds based on AppMetrics from Unit-, Integration, - Perf Tests
Tip: integrate with Jenkins, Bamboo, ...
Prod: Monitor Usage and Runtime Behavior per Service, User Action,
Feature ... Tip: Stream to ELK, Splunk and Co ...
Automated Tests: Identify Non-Functional Problems by looking at App Metrics
Tip: Feed data back into your test tool!
Memory Diagnostics with AppMon
Main Pains for Problem Hunters
Where do millions of short living objects come from?
Component/Layer
Component/Layer
Component/Layer
How can single users create so many objects?
How can certain business transactions create so many objects?
Which collections are growing?
The JVM/CLR contains a lot of objects. How to analyze, especially in a load or
production environment?
What keeps my objects alive?
#1: Do we really have a memory related problem?
#3: Growing “Old Gen” is a good
indicator for a Mem Leak
#4: Heavy GC kicks in when Old Generation is full!
#5: Throughput of Application goes to 0
due to no High GC and resulting Out of
Memory
#1: Eden Space stays constant. Objects being propagated to
Survivor Space
#2: GC Activity in Young Generation ultimately moves objects into Old
Generation
http://apmblog.dynatrace.com/2014/11/25/finding-fixing-memory-leaks-tibco-business-works/
#2: Which classes are growing on the heap?
#3: Who is keeping these objects alive?
Keep Alive Direct Reference
Keep Alive will only show References
that are kept alive due to the referrer The referrer that is solely responsible
for the object not being Garbage Collected
visualize the real object hierarchy, which includes the referring property
5 3
4
2
1 1
2 3
4 5
For THIS object
This is the REFERRER
And this is the REFEREE
Response Time w & w/o Suspension
476ms
Garbage Collector
455ms
21ms
Dynatrace recognizes the suspension by the garbage collector…
…and knows precisely how long the PurePath was interrupted
Thus, we can calculate the duration of the PurePath without suspension
Monitor how GC runs impact PurePath duration
View suspension time in detail
Contribution of garbage collection to response time
Demo Time
• Blog: http://apmblog.dynatrace.com/2016/06/23/automatic-problem-detection-with-dynatrace/
Automatic Optimization Detection based on Patterns
Database Patterns: N+1, Long Running SQL, Chatty ...
Web Service Patterns: N+1, Chatty, Payload
Threading Patterns: # of Threads, Wait, Sync, ...
• Response Time: From Very Fast (< 100ms) to Very Slow (> 4s)
• State: No Errors, Errors, Transaction Failed
• Database: Chatty, Single Long SQL, N+1, Overall DB Time High
• Web Service: High Payload, Chatty, Slow, N+1, High Service Time
• Threading: Normal (1-3), Acceptable (3-6), Heavy (> 6)
• Complexity:
• Normal (< 3 Agents or < 100 Nodes) to
• Complex (> 10 Agents or 1000 Nodes)
• Content Type: Static, Dynamic
• HTTP Response Code: 4xx, 5xx
• Async: No Async, Normal, Heavy (> 10% Nodes executed async)
• Blog: http://apmblog.dynatrace.com/2016/06/23/automatic-problem-detection-with-dynatrace/
All Patterns
Demo Time
• YouTube Channel: http://bit.ly/dttutorials
• Dynatrace Personal: http://bit.ly/dtpersonal
• Contact: [email protected]
• Share Your PurePath: http://bit.ly/sharepurepath
Reminders
Confidential, Dynatrace, LLC
