Video and slides synchronized, mp3 and slide download available at http://bit.ly/18a23ES. John Esser and Russell Barnett discuss Ancestry.com’s SOA implementation capable of supporting continuous delivery, architectural standards used, and how continuous delivery works for them. Filmed at qconsf.com. John Esser is currently the Director of Engineering Productivity and Agile Development at Ancestry.com. He is the architect of Ancestry’s transformation to Agile development and continuous delivery. Russell Barnett has worked as Chief Architect for Ancestry.com for the last ten years. His passion is for building highly scalable, available, and agile architectures.

1. Architecting for Continuous Delivery
Russ Barnett, Chief Architect
John Esser, Director of Engineering Productivity
Ancestry.com

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http://www.infoq.com/presentations
/ancestry-SOA-continuous-delivery

3. Presented at QCon San Francisco
www.qconsf.com
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Strategy
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Highlights
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4. Background
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5. Growth at Ancestry.com
• Subscribers have doubled in the past 3 years
– (~1M to > 2M)
• Page views have doubled in the past 3 years
– (~25M/day to ~50M/day)
• Development head count has tripled
– (100 to 300)
• Feature throughput has dramatically increased
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6. Continuous Delivery
is consistently and reliably
releasing business value increments
fast
through automated build, test, configuration and
deployment.
What is the value of going fast?
A LOT!

7. Evolution to Continuous Delivery
Agile
Boot Up
(Scrum)
Enterprise
Agile
Framework
Agile
Architecture
Standards
Continuous
Delivery
Adoption
Future –
“Agile v2”
Two year period (2010 – present)

8. Typical Impediments to Continuous Delivery
• Cultural
• Technical practices
• Quality ownership
• Infrastructure
• Architectural

9. Limiting Factors
• Pipeline serialized at integration
– Errors that occurred in this stage
stalled the pipeline
• Stalls in integration induced
additional problems
• Increasing frequency of stalls
– As number of development
teams grew, frequency of stalls
increased
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Integration
Source Control
Build
Unit Testing
System Testing
Deployment
Team A Team B Team C
error
causes
stall

10. Everything was coupled!
(Aka, large batch size)
(It became known as the “big blob!”)
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11. 9

12. Little’s Law
𝑊𝑎𝑖𝑡 𝑇𝑖𝑚𝑒 =
𝑄𝑢𝑒𝑢𝑒 𝑠𝑖𝑧𝑒
𝑃𝑟𝑜𝑐𝑒𝑠𝑠𝑖𝑛𝑔 𝑅𝑎𝑡𝑒
𝑄𝑢𝑒𝑢𝑒 𝑠𝑖𝑧𝑒 ∝ 𝐵𝑎𝑡𝑐ℎ 𝑠𝑖𝑧𝑒
We can reduce wait time (cycle time)
by reducing batch size
without changing demand or capacity.
10

13. Problems with Large Batches
• Increases cycle time
• Increases variability non-linearly as 2n
• Increases risk
• Reduces efficiency
• Limited by its worst element.
from Principles of Product Development Flow, Don Reinertsen
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14. Answer?
Utilize small batches.
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15. Fluidity Principle
Loose coupling
between product systems
enables small batches
“Once a product developer realizes that small batches are
desirable, they start adopting product architectures that permit
work to flow in small, decoupled batches.”
from Principles of Product Development Flow, Don Reinertsen
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16. Fluidity Principle
Loose coupling
between product systems
enables small batches
“Once a product developer realizes that small batches are
desirable, they start adopting product architectures that permit
work to flow in small, decoupled batches.”
from Principles of Product Development Flow, Don Reinertsen
14

17. Creating an Architecture for Agility
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18. Architectural Impediments
• Cross-Component Coupling
– Creates groups of systems that must be deployed together
• Insufficient Rollback Capability
– Causes teams to resort to cascading rollback
• Poor Testing and Monitoring
– Requires a long testing period
– Lengthens feedback cycle
– Allows quality problems to escape to and affect customers
16

19. Architectural Methods for Removing Impediments
• Partition into small single-responsibility components
“There should only be one reason for a [component] to change”
- Robert Martin
• Decouple deployment of components
– Separately deploy components
– Remove order dependent deployment
• Support Independent Rollback
– Enforce strict backward and forward compatibility
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20. Backward and Forward Compatibility
Client
Version 1
Service
Version 1
Service
Version 2
Client
Version 2
Deployment
Rollback
Deployment
Rollback
Backward
Compatibility
Backward
Compatibility
Forward
Compatibility
Forward
Compatibility
• Server Backward Compatibility
– Newer servers work with clients
written to old interface
• Server Forward Compatibility
– Existing servers work with clients
written to newer interface
– Supports early client deployment
• Client Backward Compatibility
– Newer clients work with servers
that implement old interface
– Supports server rollback
• Client Forward Compatibility
– Old clients work with servers that
implement new interface

21. Enforcing Decoupled Components
• Implementing standards is insufficient
– Independent deployment forces some decoupling
– High rate of deployment issues indicate remaining coupling
• Improve integration testing
– Verify backward and forward compatibility
– Identify breaking changes quickly
– Make writing integration tests easier
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22. Improving Interface Verification
20
Server
Client
In Process
Client/Server
Server
Client
• Remember when you could run your
entire application in one process?
• How do we get better interface
verification with services?
In Process
Thrift
SOAP/WSDL
REST
Verifiable
Decoupled
Google
Protocol Buffers

23. Interface Verification using Proxies and Stubs
21
Stub
Proxy
Client
Server
Client/Server
with
Proxy/Stub
• Verifies interface at compile time
• Isolates code from versioning issues
• Easier to provide mock implementations
• Can test backward and forward
compatibility

24. Managing a Complex Network of Services
• Ancestry Scale
– About 40 different teams
– Over 300 separate application or service systems
– Stack is 5+ levels deep
• Historical Diagnostics
– Presented a client centric or top down view
– Insufficient for identifying problems in a network of services
• Solution: Deep Status Check
– Components provide dynamic status information for each
client and dependency
– Report traverses dependencies up to a given depth
22

25. Ancestry Deep Status Check
23
Service
1
Service
A
Service
C
Service
B
App 2
Service
2
GetStatus
Monitor App 1
Unidentified
Client
Unregistered
Client
Connection Error
Database
File
System
Level 1
Level 2
Level 3
Level 4

26. < 2 s
95%
< 4 s
fail
99.9%
SLA
Service Level Agreements
• Understand Business Expectations
– Each application and service establishes a contract with each
client specifying the expected performance characteristics
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• Methodology
– Use percentile
buckets rather than
an average
– Performance is a
component of
availability

27. Verify Client Identity
Check Circuit Breaker
Track Each Incoming Request
Report Compliance
Incoming Requests
Check Circuit Breaker
Provide Fallback Mechanism
Track Each Outgoing Request
Report Compliance
Outgoing Requests
Service Level Agreement System
• Compare incoming and outgoing SLA compliance
Service

28. Conclusion
• Architecture affects agility and continuous delivery
capability as much or more than other factors.
• Process and tool improvements alone are insufficient.
• Good architecture techniques enable effective
continuous delivery at large scale.
– Partition to single-responsibility components.
– Decouple deployment
– Support independent rollback
– Improve testing and monitoring infrastructure
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29. • Questions?
Contact info:
jesser@ancestry.com.
rbarnett@ancestry.com.
Ancestry is hiring in San Francisco and Utah
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