Download - Frequent Releases Reduce Risk
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Releasing Frequently Reduces Risk
Owen RogersTwitter: @exortech
http://exortech.com/blog
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1 year
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48 releases
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~ 1 release / week
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10+/day
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50+/day
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continuous deployment
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crazy
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sea change
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competitive advantage
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evolve software
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resolve problems
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faster than the competition
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capability
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respond to change
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▪ Individuals and interactions over processes and tools
▪ Working software over comprehensive documentation
▪ Customer collaboration over contract negotiation
▪ Responding to change over following a plan
Agile Manifesto
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proposition
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increase riskfrequent releases
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increase riskreduce
frequent releases
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1) expose
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2) mitigate
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?
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can you deploy daily?
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“we can’t do that...”
(3 minutes, 3 reasons)
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3 common excuses
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1. not enough time to test
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1. not enough time to test
2. disruptive to users
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1. not enough time to test
2. disruptive to users
3. too much overhead
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1. not enough time to test
2. disruptive to users
3. too much overhead
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1. not enough time to test
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1. not enough time to test
Risk: cost of bugs getting into production
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1. not enough time to test
Risk: cost of bugs getting into production
Assumptions:
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1. not enough time to test
Risk: cost of bugs getting into production
Assumptions:• we know what our customers want
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1. not enough time to test
Risk: cost of bugs getting into production
Assumptions:• we know what our customers want• bugs are expensive
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1. not enough time to test
Risk: cost of bugs getting into production
Assumptions:• we know what our customers want• bugs are expensive• testing takes a long time
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1. not enough time to test
Risk: cost of bugs getting into production
Assumptions:• we know what our customers want• bugs are expensive• testing takes a long time• all bugs can be found in test
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1. not enough time to test
Risk: cost of bugs getting into production
Assumptions:• we know what our customers want• bugs are expensive• testing takes a long time• all bugs can be found in test
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1. not enough time to test
Assume: we know what our customers want
testing: did we build it right?
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1. not enough time to test
Assume: we know what our customers want
risk: did we build the right thing?
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1. not enough time to test
Assume: we know what our customers want
$O
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1. not enough time to test
Assume: we know what our customers want
$O(well, negative $ actually)
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1. not enough time to test
Assume: we know what our customers want
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1. not enough time to test
Assume: we know what our customers want
• we understand our customer’s needs
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1. not enough time to test
Assume: we know what our customers want
• we understand our customer’s needs• our customers know what they want
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1. not enough time to test
Assume: we know what our customers want
• we understand our customer’s needs• our customers know what they want• our customers will still want what we have when we give to them
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1. not enough time to test
Assume: we know what our customers want
validated learning about customers
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1. not enough time to test
Assume: we know what our customers want
simplest solution to validate hypothesis
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1. not enough time to test
Assume: we know what our customers want
split test
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1. not enough time to test
Assume: we know what our customers want
“deliver fast enough that the customer doesn’t have time
to change their mind”
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1. not enough time to test
Risk: cost of bugs getting into production
Assumptions:• we know what our customers want• bugs are expensive• testing takes a long time• all bugs can be found in test
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1. not enough time to test
Assumption: bugs are expensive
$ million bug
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1. not enough time to test
Assumption: bugs are expensive
= $10 billion
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1. not enough time to test
Assumption: bugs are expensive
= 120M users/day
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1. not enough time to test
Assumption: bugs are expensive
bugs are inevitable
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1. not enough time to test
Assumption: bugs are expensive
speed of response
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1. not enough time to test
Assumption: bugs are expensive
continuous monitoring
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1. not enough time to test
Assumption: bugs are expensive
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1. not enough time to test
Assumption: bugs are expensive
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1. not enough time to test
Assumption: bugs are expensive
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1. not enough time to test
Assumption: bugs are expensive
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1. not enough time to test
Risk: cost of bugs getting into production
Assumptions:• we know what our customers want• bugs are expensive• testing takes a long time• all bugs can be found in test
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1. not enough time to test
Assumption: testing takes a long time
small changes
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1. not enough time to test
Assumption: testing takes a long time
continuous integration
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1. not enough time to test
Assumption: testing takes a long time
continuous testing
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1. not enough time to test
Assumption: testing takes a long time
test automation
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1. not enough time to test
Assumption: testing takes a long time
always releaseable
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1. not enough time to test
Risk: cost of bugs getting into production
Assumptions:• we know what our customers want• bugs are expensive• testing takes a long time• all bugs can be found in test
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1. not enough time to test
Assumption: all bugs can be found in test
= 40,000 photos/sec
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1. not enough time to test
Assumption: all bugs can be found in test
= 6 Pb of storage
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1. not enough time to test
Assumption: all bugs can be found in test
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1. not enough time to test
2. disruptive to users
3. too much overhead
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2. disruptive to users
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2. disruptive to users
Risk: new releases will annoy users
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2. disruptive to users
Risk: new releases will annoy users
Assumptions:
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2. disruptive to users
Risk: new releases will annoy users
Assumptions:• releases incur downtime
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2. disruptive to users
Risk: new releases will annoy users
Assumptions:• releases incur downtime• users will notice changes
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2. disruptive to users
Risk: new releases will annoy users
Assumptions:• releases incur downtime• users will notice changes• changes are visible to all users
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2. disruptive to users
Risk: new releases will annoy users
Assumptions:• releases incur downtime• users will notice changes• changes are visible to all users
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2. disruptive to users
Assumption: releases incur downtime
patch releases?
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2. disruptive to users
Assumption: releases incur downtime
good will
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2. disruptive to users
Assumption: releases incur downtime
zero-downtime deployment
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2. disruptive to users
Assumption: releases incur downtime
redundancy
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2. disruptive to users
Risk: new releases will annoy users
Assumptions:• releases incur downtime• users will notice changes• changes are visible to all users
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2. disruptive to users
Assumption: users will notice changes
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2. disruptive to users
Assumption: users will notice changes
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2. disruptive to users
Assumption: users will notice changes
version?
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2. disruptive to users
Risk: new releases will annoy users
Assumptions:• releases incur downtime• users will notice changes• changes are visible to all users
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2. disruptive to users
Assumption: changes are visible to all users
big-bang release
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2. disruptive to users
Assumption: changes are visible to all users
options
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2. disruptive to users
Assumption: changes are visible to all users
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2. disruptive to users
Assumption: changes are visible to all usersOptions:
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2. disruptive to users
Assumption: changes are visible to all usersOptions:
• release to user subset (private beta)
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2. disruptive to users
Assumption: changes are visible to all usersOptions:
• release to user subset (private beta)• rollout to some servers
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2. disruptive to users
Assumption: changes are visible to all usersOptions:
• release to user subset (private beta)• rollout to some servers• downgrade feature
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2. disruptive to users
Assumption: changes are visible to all usersOptions:
• release to user subset (private beta)• rollout to some servers• downgrade feature• disable feature (feature darkmode)
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2. disruptive to users
Assumption: changes are visible to all usersOptions:
• release to user subset (private beta)• rollout to some servers• downgrade feature• disable feature (feature darkmode)• defer commit
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2. disruptive to users
Assumption: changes are visible to all usersOptions:
• release to user subset (private beta)• rollout to some servers• downgrade feature• disable feature (feature darkmode)• defer commit• defer release
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2. disruptive to users
Assumption: changes are visible to all users
options = $$$
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2. disruptive to users
Assumption: changes are visible to all users
“release is a marketing decision”
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2. disruptive to users
Assumption: changes are visible to all users
bonus:
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1. not enough time to test
2. disruptive to users
3. too much overhead
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3. too much overhead
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3. too much overhead
Risk: cost of a release is greater than the benefit of its changes
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3. too much overhead
Risk: cost of a release is greater than the benefit of its changes
Assumptions:
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3. too much overhead
Risk: cost of a release is greater than the benefit of its changes
Assumptions:• high coordination overhead
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3. too much overhead
Risk: cost of a release is greater than the benefit of its changes
Assumptions:• high coordination overhead• releases are risky
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3. too much overhead
Risk: cost of a release is greater than the benefit of its changes
Assumptions:• high coordination overhead• releases are risky• deployment takes a long time
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3. too much overhead
Risk: cost of a release is greater than the benefit of its changes
Assumptions:• high coordination overhead• releases are risky• deployment takes a long time
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3. too much overhead
Assumption: high coordination overhead
functional silos
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3. too much overhead
Assumption: high coordination overhead
dev vs. ops
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3. too much overhead
Assumption: high coordination overhead
devs don’t know prod
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3. too much overhead
Assumption: high coordination overhead
ops don’t know code
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3. too much overhead
Assumption: high coordination overhead
shared accountability
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3. too much overhead
Assumption: high coordination overhead
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3. too much overhead
Assumption: high coordination overhead
shared accountability
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3. too much overhead
Risk: cost of a release is greater than the benefit of its changes
Assumptions:• high coordination overhead• releases are risky• deployment takes a long time
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3. too much overhead
Assumption: releases are risky
“if it hurts, do it more often”
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3. too much overhead
Assumption: releases are risky
incremental change
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3. too much overhead
Assumption: releases are risky
more or less same system
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3. too much overhead
Assumption: releases are risky
practice makes perfect
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3. too much overhead
Risk: cost of a release is greater than the benefit of its changes
Assumptions:• high coordination overhead• releases are risky• deployment takes a long time
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3. too much overhead
Assumption: deployment takes a long time
risk: manual steps
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3. too much overhead
Assumption: deployment takes a long time
automated deployment
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3. too much overhead
Assumption: deployment takes a long time
one-step process
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Summary
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?
Frequent Releases?
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concerns
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1. not enough time to test
2. disruptive to users
3. too much overhead
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risks
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1. bugs getting into production
2. new releases will annoy users
3. cost of a release is greater than the benefit of its changes
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localized risk
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1) expose
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underlying risks
Risks:• do we know what customers want?• can we respond fast enough to problems?• can we detect problems when they happen? • can we limit the impact of changes?• can we deploy without downtime?• can we build production-ready software?
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2) mitigate
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mitigation strategies
Strategies:• validated learning about customers• split testing• continuous monitoring• continuous integration • test automation • zero-down time deployment• deployment options• operation levers• automated deployment
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Thank You!
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Shameless Plug
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![Page 151: Frequent Releases Reduce Risk](https://reader033.vdocuments.net/reader033/viewer/2022061300/54c764a54a7959154e8b457b/html5/thumbnails/151.jpg)
Nov 2 - 5• Eric Evans
• Michael Feathers
• Martin Fowler
• Jeff Patton
• Mary Poppendieck
• Michael Nygard
• Linda Rising
• Johanna Rothmann
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Releasing Frequently Reduces Risk
Owen RogersTwitter: @exortech
http://exortech.com/blog
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1. not enough time to test
2. disruptive to users
3. too much overhead
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1. bugs getting into production
2. new releases will annoy users
3. cost of a release is greater than the benefit of its changes