The document discusses the use of analytics in Gerrit code review and its integration with continuous delivery processes. It highlights the challenges of managing extensive logs and the importance of collecting and analyzing data to improve performance and efficiency in software development. Additionally, it emphasizes the use of Gerrit plugins for real-time event generation and data aggregation to enhance the analytics pipeline.

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Gerrit Analytics
Uncover the hidden value of your Gerrit Code Review
Luca Milanesio
luca@gerritforge.com

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• Founded in the UK
• HQ in London, Offices in the UK and Italy
• Committed to OpenSource
About GerritForge

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• Co-founder and Director of
GerritForge
• over 20 years in Agile
Development and ALM
• Contributor to many OpenSource
projects
• Mind and hands of @gitenterprise
and @gerritreview Twitter feeds
About Luca Milanesio

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NOT a lecture on Continuous Delivery
NOT a lecture on Code Review
NOT a lecture on BigData
What NOT to expect?

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SHOW
a Continuous Delivery scenario

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LEARN
together from its problems

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EXPERIMENT
a new approach using BigData

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DISCUSS
results together and iterate

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Continuous Delivery Pipeline
Push Build
Unit Tests
Push Build
Unit Tests
Automated
Tests
Push Build
Unit Tests
Automated
Tests
Beta Users
Tests
User's feedback Publish to all Users
Failed
Failed

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Code Review & CD Validation
A1
refs/for/master
master
C2 C2*
A1 C2 (local)
master
C2*
push
Verified: -1
(build failed)
push
Verified: +1
(build OK)
Reviewed: +2
CI Build: OK
C2*
CI Build: OK
amend

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Git/Gerrit
(Code Review)
Components
Jenkins
(Continuous
Integration & Delivery)

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We use micro-services
–100+ Components
–200+ Repositories
–300+ People and 100,000+ of review comments
–Cross-components [implicit] dependencies
–Lots of build chains
–10,000+ of builds
Now feel the pain :-(

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Problem #1 : Tons of logs slows us down
"Build was so fast a week ago … why is it getting so
slow now?"
100Ks of logs are tough for Jenkins

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"It was working until 5 mins ago … then someone
changed something"
 Rings any bell?
Problem #2 : Ripple effect

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• Collect all review events
• Collect all logs
• Channel them to a central store
• Crunch and Crunch continuously
• Never delete
• Process, inspect and learn
BigData to the rescue

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Continuous Delivery – Analytics Pipeline
Data
Aggregation
Jobs

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Gerrit plugins to extract analytics data
https://gerrit.googlesource.com/plugins/analytics/

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Gerrit plugins to generate real-time events on Kafka
https://gerrit.googlesource.com/plugins/kafka/

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Continuous Delivery – Analytics Dimensions
Projects Commits
People Reviews
System Metrics

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Continuous Delivery – Tools Dimensions
SCM / Code Review
CI / CD

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DEMO
Continuous Delivery Analytics

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• Collect more data (System, JVM, Review scores)
• Real-time statistics
• Events correlation
• Feedback loop to Code-Review
• Production data harvest
• Collect Issue-Tracker logs for End-2-End cycle time
Future directions

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