Building a Modern Enterprise SOA at LinkedIn

Agenda
 Building Code at LinkedIn
 Product Development with Multiproduct
 Build Automation with Gradle
 A Peek at the Future
©2013 LinkedIn Corporation. All Rights Reserved. 2

Building Code at LinkedIn

In the beginning there was Network
 Single, relatively homogenous code base
– Build from source, little dependency management
 Java, Spring, Ant, …
 JavaScript, HTML, JSPs, CSS, …
 JARs, WARs, Jetty, Tomcat, …

Then everything went exponential
Number of developers, programming languages, build
systems, frameworks, lines of code, servers, users, page views, …
… and pretty much everything else

Hypergrowth isn’t all fun and games
 Acquisitions came in with their own technology and processes
 Releases became more and more painful
 Productivity and stability suffered

Scaling a software development organization
 Requires sensible code and dependency management
– Source code APIs
– Service APIs
– Versioned dependencies
 Performance is king
– Iterative improvements
 Divide and conquer
– Split and isolate failures

Network Trunk Development
 All development in Network shifted to trunk
– No branches, no merging
 Continuous releases from trunk
– Deploy multiple times per day
 Work started on break up and clean up
– Migrating build logic to Gradle

Product Development with Multiproduct
©2013 LinkedIn Corporation. All Rights Reserved.

Traditional Software Development
 Use a well-established technology stack
– Homogeneity => Simplicity
 To adopt a new technology:
– Requires “out of the box” thinking and effort
– Do a proof-of-concept implementation
– Present to decision makers to demonstrate ROI => get approval
 Slow-moving by design
– New technology integration expensive
– Top-down management decisions used as barrier

LinkedIn Software Development
 We don’t ever want to be in the box
– Technical experimentation and diversity encouraged
– Living on the bleeding edge, often defining it
 The Need for Speed
– Pace of iteration
– Automation (the human is slow)
– Continuous delivery
 Build versus buy

Multiproduct
 Toolset that is architected for a heterogeneous technology world
 Agnostic to version control, build system and programming stack
– Future-proof
 Abstracts common software tasks
– For example: “build”, “test”, “release”
 Provide a default implementation, allow users to override
– i.e. Gradle w/ LI plug-ins

Key Concepts
 Elevate tooling from artifact to product level
 Metadata ties the tooling together
– Ivy
– Version and Build specification
 Pluggable implementation of subsystems
 Version management
 Continuous automated delivery

Source
Control
Build System Deployment

Version Management
 End-of-life dates
– Graceful deprecation and upgrades
 Push version upgrades to consumers
 Dependency Reports
– What products depends on me
– What products do I depend on

Push My Upgrade

Tracking Upgrades

Continuous Delivery in Multiproduct
 Automated pipeline triggered on developer change
– No other developer action needed
 Publishing 10,000+ artifacts per day for 300+ products
– Mean time for a good commit: ~10 minutes
– Mean time counting failures: ~25 minutes

Continuous Delivery

Build Automation with Gradle

Why LinkedIn uses Gradle
 Dependency resolution engine
 Rich plug-in system w/ real programming language
– DSL has high learning curve, but powerful
 Visions align
– Automation
– Continuous delivery

LinkedIn Gradle plug-ins
 Customize built-in plug-ins for LinkedIn’s environment
– i.e. Java, Scala, War, FindBugs, Cobertura
 Add custom artifact types
– For example database patches, static content, and Hadoop workflows
 Create metadata for publishing and deployment tooling to consume
 Elevate concepts from artifact to product level

Dependency Graph powered by Gradle

End-of-life enforcement

Our own gradlew: ligradle
 Our own custom Gradle wrapper
 Provisions Gradle and plug-ins
– Allows each product to define versions it uses
 Provides lifecycle management for Gradle and plug-ins
– End-of-life
 Usage data
– Used to track usage and discover problems

Usage Data

Source vs Binary Dependencies
 Source offers incremental updates and flexibility  intra-product
 Binary offers stability and speed inter-product
There’s no right answer, but there are plenty of wrong answers!

Network migration to Gradle
 3,600 build.xml files to convert
– Many of them with custom logic
 300 developers to train
 Performance targets
– 2x speed-up for clean builds
– 5x speed-up for incremental builds

Proof of Concept
 Migrated 1,100 modules
 Tested single large build versus isolated
segments
– Single large build simpler
 Scale feasible
– Requires scalability and performance
work in Gradle core

Gradle features required for migration
 Configuration on demand
– Only configure the task graph you need
 Refactored cache logic for performance
– Task history
– Dependency descriptors
 Candidate performance improvements
– Parallel configuration
– Daemon stores project model
– Daemon performs continuous up-to-date checks

Daemon Heap Usage

Project Timeline – 1 year
 Q1: Proof-of-concept and prep work
 Q2: Implementation 
 Q3: Roll-out
 Q4: Clean-up

A Peek at the Future

Gradle Features
 Faster
– Use the daemon effectively in development and CI
– Intra-project parallel execution
 More scalable
– Heap usage
 Ease of use
– IDE integration

More Multiproduct Intelligence
 Analytics
– Common exceptions
– Usage and error patterns
 Verification suite
– CheckStyle, FindBugs and Cobertura
 Automation and Integration
 Ease of use

Distributed Build Automation
 Distribute build and testing on a cluster
 Automatic provisioning
 Artifact sharing at scale

Building a Modern Enterprise SOA at LinkedIn

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