Database Migrations with Gradle and Liquibase

Database Migrationswith Gradle and Liquibase

Dan StineCopyright Clearance Centerwww.copyright.com

Gradle SummitJune 12, 2015

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About Me

• Software Architect

• Library & Framework Developer

• Platform Engineering Lead & Product Owner

• Gradle User Since 2011

• Enemy of Inefficiency & Needless Inconsistency

dstine at copyright.comsw at stinemail.comgithub.com/dstine

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About Copyright Clearance Center

• Global licensing solutions that make © work for everyone

– Get, share and manage content

– Rights broker for the world’s most sought-after materials

– Global company (US, Europe, Asia) – HQ in Danvers, MA

• Industry-specific software systems

– Internal and external user base

– Applications, services, databases

– Organic growth over many years

• In 2011, CCC adopted a Product Platform strategy for growing its software portfolio

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Agenda

• Context

• Liquibase

• Development Process

• Deploy Time

• Extensibility

• Wrap Up

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CONTEXT

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Database Migrations

• Database structure changes

– Tables, constraints, indexes, etc.

– Schema changes (DDL, not DML)

• Reference data

– List of countries, user types, order status, etc.

– Set of allowed values

• Database logic

– Functions, procedures, triggers

– (Very little of this)

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Our Historical Approach

• DB migrations handled in relatively ad-hoc fashion

• Various flavors of “standard” practice

– Framework copied and modified from project to project

– Framework not always used (“small” projects)

• Development teams shared a DEV database

– Conflicts between code and database

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Development Pain Points

• Intra-team collaboration was difficult

• Forced synchronous updates within development team

• Learn variations when switching between projects

• Project startup was costly

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Deployment Pain Points

• Manual process

– Where are the scripts for this app?

– Which scripts should be run and how?

• Recurring difficulties

– Hours spent resolving mismatches between app and database

– Testing activities frequently delayed or even restarted

• Impossible to automate

– Too many variations

• Self-service deployment was a pipe dream

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Standard Software Platform

• Started platform definition in 2011

– Homogenous by default

• Tools

– Java, Spring, Tomcat, Postgres

– Git / GitHub, Gradle, Jenkins, Artifactory, Liquibase, Chef

• Process

– Standard development workflow

– Standard application shape & operational profile

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Vision for Database Script Management

• Integrated into developer workflow

• Feeds cleanly into deployment workflow

• Developer commits scripts and the process takes over

– Just like with application code

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A Plan For Pain Relief

• Manage scripts as first-class citizens

– Same repo as application code

– Standard location in source tree

• Standard execution engine

– No more variations

– Automatic tracking of applied migrations

• Prevent conflicts and mismatches

– Introduce developer workstation databases (LOCAL )

– Dedicated sandbox

– Commit database and associated application change together

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A Plan For Pain Relief

• Liquibase

– Database described as code

– Execution engine & migration tracking

• Gradle

– Provide conventions

– Tasks for invoking Liquibase

– Already familiar to developers from existing build process

– Flexibility to integrate into deployment process

– Flexibility to handle emergent requirements

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LIQUIBASE

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Liquibase Basics

• Provides vocabulary of database changes

– Create Table, Add PK, Add FK, Add Column, Add Index, …

– Drop Table, Drop PK, Drop FK, Drop Column, Drop Index, …

– Insert, Update, Delete, …

• Changes are grouped into changesets

– Change(s) that should be applied atomically

• Changesets are grouped into changelogs

– Files managed in version control

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Liquibase Basics

• Changesets uniquely identified by [Author, ID, File]

– Liquibase tracks changeset execution in a special table

– Lock table to prevent concurrent Liquibase invocations

– Modified changesets are detected via checksums

• Supported databases

– MySQL, PostgreSQL, Oracle, SQL Server, …

• Groovy DSL

– Liquibase v2 supported only XML

– https://github.com/tlberglund/groovy-liquibase

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Example ChangesetchangeSet(id: '2015-01-23', author: 'John Doe <jdoe@copyright.com>') {

createTable(schemaName: 'apps', tableName: 'myapp_version', tablespace: 'ccc_data') { column(name: 'version_uid', type: 'VARCHAR(128)') column(name: 'type', type: 'VARCHAR(10)') column(name: 'owner_uid', type: 'VARCHAR(128)') column(name: 'version', type: 'VARCHAR(20)') column(name: 'start_date', type: 'TIMESTAMPTZ') column(name: 'end_date', type: 'TIMESTAMPTZ') } addPrimaryKey(constraintName: 'PK_myapp_version', schemaName: 'apps', tableName: 'myapp_version', tablespace: 'ccc_index', columnNames: 'version_uid') addForeignKeyConstraint(constraintName: 'FK_myapp_version_2_owner',

baseTableSchemaName: 'apps', baseTableName: 'myapp_version', baseColumnNames: 'owner_uid', referencedTableSchemaName: 'apps', referencedTableName: 'myapp_owner', referencedColumnNames: 'owner_uid')}

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Liquibase @ CCC

• Learning curve

– Team needs to understand the underlying model

– Don’t edit changesets once they’ve been applied

• Our standards

– Schema name and tablespace are required

– Parameterize schema name and tablespace

createTable( schemaName: dbAppsSchema, tableName: 'myapp_version', tablespace: dbDataTablespace)

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DEVELOPMENT PROCESS

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Development Workflow

• Gradle is our SCM hub

– Workstation builds

– LOCAL app servers via command line

– IDE integration

– CI and release builds on Jenkins

• Maintain Gradle-centric workflow

– Integrated database development

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Standard Project Structure

• Single Git repo with multi-project Gradle build

myapp myapp-db myapp-rest myapp-service myapp-ui

group = com.copyright.myapp

• UI and REST service published as WARs

• DB published as JAR

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Custom Gradle Plugin

• Created custom plugin: ccc-postgres

• Standard script location

– Main source set: src/main/liquibase

– Package: com.copyright.myapp.db

• Standard versions

– Liquibase itself

– Postgres JDBC driver

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Plugin Extension

• Custom DSL via Gradle extension

cccPostgres { mainChangelog = 'com/copyright/myapp/db/main.groovy'

}

• Main changelog includes other changelogs

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Development Lifecycle Tasks

• Provided by ccc-postgres

• Easy to manage LOCAL development database

– Isolated from other developers and deployments

– Pull in new schema changes run a task

• Built on Gradle Liquibase plugin

https://github.com/tlberglund/gradle-liquibase-plugin

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Development Lifecycle Tasks

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Development Lifecycle Tasks

• Typical developer loop

– gradlew update

– gradlew tomcatRun and/or IDE

• Not just for product development teams

– Simple to run any app

– Architects, QA, Platform Engineering

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Development Lifecycle Tasks

Task Runs As Description

createDatabase postgres

Creates ccc user and databaseCreates data and index tablespaces

createSchema ccc Creates apps schema

update ccc Runs main changelog

dropDatabase postgres

Drops ccc user and database

resetBaseChangelog

postgres

Truncates postgres.public.databasechangelog

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• resetBaseChangelog

– Must clear all traces of Liquibase to start over

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Plugin Configuration

• Override default library versions

cccPostgres.standardDependencies.postgresDriver

• Defaults point to LOCAL development database

– Can override property values

dbHost, dbPort, dbName

dbUsername, dbPassword

dbDataTablespace, dbIndexTablespace

dbBaseUsername, dbBasePassword

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Standardization and Compliance

• So all our teams are authoring DB code

• But Liquibase is new to many

• And we have company standards

• Let’s automate!

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Static Analysis

• CodeNarc

– Static analysis of Groovy code

– Allows custom rule sets

• Created a set of custom CodeNarc rules

– Analyze our Liquibase Groovy DSL changelogs

• Apply to our db projects via the Gradle codenarc plugin

– Fail build if violations are found

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Static Analysis – Required Attributes

• Our rule categorizes all change attributes

– Required by Liquibase• createTable requires tableName

– Required by CCC• createTable requires schemaName and tablespace

– Optional

• Unintended positive consequence!

– Catches typos that otherwise would not be detected until farther downstream

– constrainttName or tablspace

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Static Analysis – Required Parameterization

• Ensure that schemaName & tablespace are parameterized for future flexibility

@Overridevoid visitMapExpression(MapExpression mapExpression) {

mapExpression.mapEntryExpressions .findAll { it.keyExpression instanceof ConstantExpression }

.findAll { ['schemaName', 'tablespace'] .contains(it.keyExpression.value) } .findAll { it.valueExpression instanceof ConstantExpression }

.each { addViolation(it, "${it.keyExpression.value} should not be hard-coded") }

super.visitMapExpression(mapExpression)}

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Schema Spy

• Generates visual representation of database structure

– Requires running database instance

– Requires GraphViz installation

• Custom task runSchemaSpy

– By default, points at LOCAL database

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Continuous Integration for DB Scripts

• Compile Groovy

– Catches basic syntax errors

• CodeNarc analysis

– Catches policy and DSL violations

• Integration tests

– Apply Liquibase scripts to H2 in-memory database

– Catches additional classes of error

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Release Build

• Publish JAR

– Liquibase Groovy scripts from src/main/liquibase

• META-INF/MANIFEST.MF contains entry point

Name: ccc-postgres MainChangelog: com/copyright/myapp/db/main.groovy

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DEPLOY TIME

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Deployment Automation

• Early efforts focused on applications themselves

– Jenkins orchestrating Chef runs

– Initial transition from prose instructions to Infrastructure as Code

• Database deployments remained manual

– Better than ad-hoc approach

– But still error prone and time-consuming

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Automated Application Deployments

• Chef environment file

– Cookbook versions: which instructions are used

• Chef data bags

– Configuration values for each environment

– Encrypted data bags for (e.g.) database credentials

• Jenkins deploy jobs (a.k.a “the button”)

– Parameters = environment, application version

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Initial Delivery Pipeline

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ManualDeploy

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Initial Delivery Pipeline (DB Deployments)

• Clone Git repo and checkout tag

• Manually configure & run Gradle task from ccc-postgres

gradlew update -PdbHost=testdb.copyright.com -PdbPort=5432 -PdbDatabase=ccc-PdbUsername=ccc -PdbPassword=******

• Many apps xmany versions xmultiple environments =

TIME & EFFORT & ERROR

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Target Delivery Pipeline

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Full StackAutomatedDeploy

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Target Delivery Pipeline

• Automated process should also update database

– Single Jenkins job for both apps and database scripts

• Maintain data-driven design

– Environment file lists database artifacts

– Controlled flow down the pipeline

• Gradle database deployment task

– Retrieve scripts from Artifactory

– Harvest information already in Chef data bags (URL, password)

– Execute Liquibase

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Automated Database Deployment

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Jenkins Deploy Job

• One job per application group, per set of deployers

– E.g. myapp.qa allows QA to deploy to environments they own

– Typically contains multiple deployables (apps, db artifacts)

– Typical deployer sets = DEV, QA, OPS

• Executes Liquibase via Gradle for database deployments

– Invokes deployDbArtifact task for each db artifact

• (Executes Chef for application deployments)

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Gradle deployDbArtifact Task

• Parameterized via Gradle project properties

– appGroup = myapp

– artifactName = myapp-db

– artifactVersion = 2.1.12

– environment = TEST

• Downloads JAR from Artifactory

– com.copyright.myapp:myapp-db:2.1.12

– Extract MainChangelog value from manifest

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Gradle deployDbArtifact Task

• Retrieves DB URL from Chef data bag item for TEST

"myapp.db.url": "jdbc:postgresql://testdb:5432/ccc"

• Retrieves password from encrypted Chef data bag

– myapp.db.password

• Executes Liquibase

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Data Bag Access

• Built on top of Chef Java bindings from jclouds

• No support for encrypted data bags

• Java Cryptography Extensions and the following libs:compile 'org.apache.jclouds.api:chef:1.7.2'compile 'org.apache.jclouds.provider:enterprisechef:1.7.2'

compile 'commons-codec:commons-codec:1.9'

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Push-Button Deploys

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Deploy History

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DEV TEST PROD

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Automated Deployments By Role

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QA Rising

QAOvertakes

OPSOPS

Falling

InitialRollout

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EXTENSIBILITY

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Additional Scenarios

• Framework originally design to handle migrations for schema owned by each application

• Achieved additional ROI by managing additional database deployment types with low effort

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Roles and Permissions

• An application that manages user roles and permissions (RP) for all other applications

– Has rp-db project to manage its schema, of course

– But every consuming app (e.g. myapp) needs to manage the particular roles and permissions known to it

– Reference data that lives in tables owned by another app

• myapp now has multiple db projects

– myapp-db to manage its schema

– myapp-rp-db to manage its RP reference data

– Both are deployed with new versions of myapp

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Roles and Permissions

• Minor addition of conditional logic

if (artifactName.endsWith('-rp-db')) { // e.g. myapp-rp-db // deploy to RP database

} else { // e.g. myapp-db // deploy to application's own database }

• Easy to implement because … Gradle & Groovy

• Conceptual integrity of framework is maintained

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WRAP UP

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Observations

• Power of convention and consistency

– Once first schemas were automated, dominoes toppled quickly

• Power of flexible tools and building blocks

– Handle legacy complexities, special cases, acquisitions, strategy changes, evolving business conditions

– New database project types fell easily into place

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Observations

• Know your tools

– Knowledge (how) has to propagate through the organization

– Ideally the underlying model (why)

• Schema changes no longer restrained by process

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“If it hurts, do it more often”

“If it’s easy, do it more often”

“If it hurts, do it more often”

Reduced technical debt

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Dirty Work …

• Database development and deployment processes are often considered to be unexciting

• But sometimes you need to roll up your sleeves and do the dirty work to realize a vision

• And relational databases are still the bedrock of most of today’s information systems

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Dirty Work … Can Be Exciting!

• Efficient processes

• Reliable and extensible automation

• CONTINUOUS DELIVERY

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Full Stack Automated Self-Service Deployments

• Reduced workload of Operations team

• Safely empowered individual product teams

• Significantly reduced the DEV-to-TEST time delay

• Reinvested the recouped bandwidth

– More reliable & frequent software releases

– Additional high-value initiatives

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Resources

• Liquibase

http://www.liquibase.org

https://github.com/tlberglund/groovy-liquibase

https://github.com/tlberglund/gradle-liquibase-plugin

• Refactoring Databases: Evolutionary Database Design Ambler and Sadalage (2006)

• Jenkins and Chef:Infrastructure CI and Application Deployment

http://www.slideshare.net/dstine4/jenkins-and-chef-infrastructure-ci-and-automated-deployment

https://www.youtube.com/watch?v=PQ6KTRgAeMU

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The word and design marks which appear in this presentation are the trademarks of their respective companies.

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Thank You:

Copyright Clearance Center Engineering Team

Gradle Summit Organizers