Continuous Delivery – Quantity vs. Quality Karsten Flott

Transcription

Continuous Delivery – Quantity vs. Quality Karsten Flott
Continuous Delivery – Quantity vs. Quality
Karsten Flott
BANK
Today
Yesterday
Business Defined Software – requires Continuous Delivery
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Enterprises strived for the ‘single pane of glass’
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But ended up with a ‘single glass of pain’
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Challenge 1: New Ideas = increase
complexity
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Continuous Delivery – Starting here
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Flugstatus
Flugsuche
Bezahlen
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Continuous Delivery – Ending here
Agile
SOA
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Flugstatus
Cloud
Mobile
Flugsuche
Bezahlen
Web
NOSQL
Big data
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Continuous Delivery – Ending here
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Challenges
•  Direct relation of components require testing the
behavior of whole application instead of single functions
•  Tests are more complex and time intensive
•  Becoming more and more less flexible
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Challenge 2: Reduce cost = Near-/OffShoring
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Delivery of functions – no performance context
Transaction: ViewListOfTopSellers
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Challenges
•  Performance is not part of their task (contractual) – just
function driven
•  Risk of less skilled people building solution which has to
run in enterprise environments
•  In my environment is it fast … must be you
–  Mean Time to Innocence – increase delay for go live
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Challenge 3: Adapting Processes
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Deploy of new features … anytime?
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Challenges
•  Deployments need to be well planned
•  Cannot test any situation
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Requirements on Performance Monitoring
in a Continuous Delivery Environment
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Continuous Delivery Aims – Fast & successful Deploys
•  Fasten release cycles through automated build-pipelines
–  Require succeeded tests
•  Any build which reaches the end stage can be put in
production automatically or by pressing a button
•  Requirements:
–  Automated as much as possible
–  Fully reproducible
–  Fast feedback required
à Fast and stable build pipeline and software of high quality
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Continuous Delivery Aims
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Continuous Delivery Aims
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… the biggest problem; TESTING
•  New features come fast and in small increments
à Performance impact small and hard to measure
•  Tools like Jmeter, LoadRunner, Grinder etc. are great in generating load
and measuring Performance outside the App as black box
à But it is insufficient to measure small fluctuations impacted from new
features
•  Application Performance Management Solutions need to be in place in
the whole Application Lifecycle
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à 
à 
à 
In prod = reduce mean time to innocence
In test and dev = find the small increase in method execution - Baseline
Transparency in dependencies through changes
Getting fast feedback through tests
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Application Performance Management in
Production and Test gives you the ….
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karsten.flott@appdynamics.com
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