DISQUS Continuous Deployment Everything David Cramer @zeeg Friday, April 29, 2011
DISQUSContinuous Deployment Everything
David Cramer@zeeg
Friday, April 29, 2011
Disclaimer: We don’t use Pylons
Friday, April 29, 2011
Shipping new code as soon as it’s ready
(It’s really just super awesome buildbots)
Continuous Deployment
Friday, April 29, 2011
Workflow
1. Developer commits code
2.CI server runs tests automatically
2.1.Build passes, code deploys
2.2.Build fails, block deploy
3.Developer tests feature on production
Friday, April 29, 2011
Pros
• Develop features incrementally
• Release frequently
• Less QA! (maybe)
Cons
• Culture Shock• Stability depends on
test coverage
• Initial time investment
We mostly just care about iteration and stability
Friday, April 29, 2011
Painless Development
Friday, April 29, 2011
Development
• Production > Staging > CI > Dev
• Automate testing of complicated processes and architecture
• Simple is arguably better than complete
• Especially for local development
• python setup.py {develop,test}• Use Puppet/Chef or build a simple
bootstrap.{py,sh}
Friday, April 29, 2011
Production Staging
CI Server Macbook
• PostgreSQL• Memcache• Redis• Solr• Apache• Nginx• RabbitMQ
• PostgreSQL• Memcache• Redis• Solr• Apache • Nginx• RabbitMQ
• Memcache• PostgreSQL• Redis• Solr• Apache• Nginx• RabbitMQ
• PostgreSQL• Apache• Memcache• Redis• Solr• Nginx• RabbitMQ
Friday, April 29, 2011
Bootstrapping Local
• Simplify local setup
• git clone dcramer@disqus:disqus.git• python setup.py develop• python manage.py runserver
• Need to test dependancies?
• virtualbox + vagrant up
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“Under Construction”
from gargoyle import gargoyle
def my_view(request): if gargoyle.is_active('awesome', request): return 'new happy version :D' else: return 'old sad version :('
• Iterate quickly by hiding features
• Early adopters are free QA
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Gargoyle
Being users of our product, we actively use early versions of features before public release
Deploy features to portions of a user base at a time to ensure smooth, measurable releases
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Integration
(or as we like to call it)
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Integration is Required
Deploy only when things wont break
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Reporting is Critical
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CI Requirements
✓ Developers must know when they’ve broken something
✓ IRC, Email, IM
✓ Support proper reporting
✓ XUnit, Pylint, Coverage.py
✓ Painless setup
✓ apt-get install jenkins
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Shortcomings
• False positives lower awareness
• Reporting isn't accurate
• Services fail
• Bad Tests
• Not enough code coverage
• Regressions on untested code
• Test suite takes too long
• Integration tests vs Unit tests
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Fixing False Positives
• Re-run tests several times on a failure
• Report continually failing tests
• Fix continually failing tests
• Rely less on 3rd parties
• Mock/Dingus
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Maintaining Coverage
• Raise awareness with reporting
• Fail/alert when coverage drops on a build
• Commit tests with code
• Drive it into your culture
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Speeding Up Tests
• Write true unit tests
• vs slower integration tests
• Mock 3rd party APIs
• Distributed and parallel testing
• http://github.com/disqus/mule
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Mule
• Unstable (we’re working on it)
• Mostly Django right now
• Generic interfaces for unittest2• Works with multi-processing and Celery
• More complex than normal Celery usage
• Full XUnit integration
• Simple workflow
• mule test --runner="python manage.py mule --worker $TEST"
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Deploy (finally)
Friday, April 29, 2011
How DISQUS Does It
• Incremental deploy with Fabric
• Drop server from pool
• Pull in requirements on each server
• Isolated virtualenv’s built on each server
• Push server back online
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Challenges
• PyPi works on server A, but not B
• Scale, or lack of
• CPU cost per server
• Schema changes, data model changes
• Backwards compatibility
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PyPi is Down
• http://github.com/disqus/chishop
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Help, we have 100 servers!
• Incremental (ours) vs Fanout
• Push vs Pull
• Twitter uses BitTorrent
• Isolation vs Packaging (Complexity)
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1. Add column (NULLable)2. Add app code to fill column3. Deploy4. Backfill column5. Add app code to read column6. Deploy
SQL Schema Changes
Cached Data Changes
• Have a global version number• Have a data model cache version
• maybe md5(cls.__dict__)?
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References
• Mule (distributed test runner)http://github.com/disqus/mule
• Gargoyle (feature switches)https://github.com/disqus/gargoyle
• Jenkins CIhttp://jenkins-ci.org/
code.disqus.com
Friday, April 29, 2011