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Manage Databases like Codebases

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Presenters

Tim O'Brien• Analyst, CTO• Gleanster Research

Yaniv Yehuda @yanivyehuda • Co-Founder & CTO at DBmaestro• Presenter at world-wide conferences: ODTUG, ilOUG

Kyle Hailey @kylehhailey• Technical Evangelist at Delphix• Oracle ACE, member of the OakTable Network

About Delphix

• Founded in 2008, launched in 2010• CEO Jedidiah Yueh (founder of Avamar: >$1B revenue))• Based in Silicon Valley, Global Operations• 10% of Fortune 500

About DBmaestro

• Founded in 2008, launched in 2010• Founded by Yariv Tabac and Yaniv Yehuda• Headquartered in Israel, Global Operations

Manage Databases like Codebases

Kyle Hailey & Yaniv Yehuda

The Business Need

Copyright@2010, Gartner RAS Core Research

80% of unplanned downtime is due to Change

50% More than

of this, half is due to human errors

40% of changes FAIL Copyright@2008, Juniper Networks, Inc.

Code, Databases, and Agility in 2014May 20, 2014

Tim O’BrienAnalyst, CTOGleanster Research

Industry Trends

Code in 2014

Distributed & Asynchronous New Normal

Self-service Critical to Productivity

Increasing Variety (from Javascript, Java, .NET, Ruby, Python + everything)

Everything is Automated

Industry Trends

Databases in 2014

More than just Relational Increasing Variety (Oracle, Mongo)

Management? Not as Mature as Code

Oddity

Big Data marginally more manageable than traditional RDBMS

“We’re still waiting for the database…”

Code is King

Modern Enterprise

Fully distributed development

Continuous Deployment Pipelines

Everything Automated! - DevOps, PaaS, IaaS

“Every Company is a Software Company”

How did we get here?

Scale Development

Open Source @ Scale Google Chrome, Mozilla Firefox Android, Apache httpd Linux*, FreeBSD

Characteristics Rapid Iterations, Frequent Releases

Numerous Experiments (or Branches)

Thousands of developers

OSS @ Scale established best practices now in use.

Scale Development

Organizations @ Scale Facebook, Wal-Mart, eBay, Salesforce, Intuit

Goldman Sachs, Apple, Amazon, Yahoo!

Google, SSA, Ford, Toyota

Characteristics Rapid Iterations, Frequent Releases

Numerous Experiments (or Branches)

Thousands of developers, Many Teams

@ ScaleEverything isContinuous

Continuous integration and deployment

Modern set of Tools Integrated with:

Source Control – Perforce, Clearcase, Git

Infrastructure-as-a-Service – Openstack

Platform-as-a-Service – AWS, Heroku

Databases? - Still an Integration Nightmare

Case in Point

Example Fortune 500 Relaunch Re-architecture: More distributed,

Move to a PaaS Scope: Everything, 2 Year Migration

Project Size: 1500 Developers, Several

Continents, $100M+

Results? Code: Distributed Projects,

Continuous Deployment Infrastructure: Self-service, On-

demand, Agile Database: Delays, Manual Builds,

Cost Overruns

Root Cause Analysis

Code is Agile Developers move FAST. Branches are fungible, disposable, personal. Deployments managed with DevOps tools

(immediately)

Traditional Databases are Not Costly to Setup Often require Dedicated Hardware Little automation. No Change

Management

“The rest of the department is Virtual but our Databases

are stuck in 1998.”

Worst Practices(we’re all used to)

Lack of Database Change Management:

Shared Developer Databases Results in Contention

Costly, Slow Environment Provisioning

Testing and Staging Not Synchronized with Production

Drag on Productivity and Efficiency

Opportunity lost

“We’ve accepted that the databases causes delays”

What We’ve Seen

1. QA: Expensive, Slow 2. Development: Bottlenecks & bugs3. Provisioning: Delays

1. QA: Expensive, Slow : Long Build times

Build Time

96% of QA time was building environment$.04/$1.00 actual testing vs. setup

Build

Build Time

QA Test

QA Test

Build

1. QA: Expensive, Slow: bugs found late

Build QA Env QA Build QA Env QA

Sprint 1 Sprint 2 Sprint 3

Bug CodeX

1 2 3 4 5 6 70

10203040506070

Delay in Fixing the bug

Cost ToCorrect

Software Engineering Economics – Barry Boehm (1981)

Build QA Env QA

2. Development: Bottlenecks & bugs: Bottlenecks

Frustration Waiting

2. Development: Bottlenecks & bugs: subsets cause bugs

2. Development: Bottlenecks & bugs: subsets cause bugs

3. Provisioning Delays : weeks to months

Management

DBA

System Admin

Storage Admin

Developers Submit Request

Disk Capacity?

Approve Request $$ (2 Weeks)

Approve Request $$

(1 Week)

RequestAdditional Storage?

ProvisionCapacity

File SystemConfigured?

Configure LUNS & Build File System

Coordinate Replication w/ Infrastructure

Re-Parameterize & Configure DB

Mount Recovery DB to

Specific PIT

Begin Work

Approve Request $$ (2 Weeks)

(3 Days)

(3 Days)

(2 Days)

(3 Days)

(3 Days)

…….1-2 Weeks of Approvals, Delays, and Provisioning……

24

Developer Asks for DB Get Access

Manager approves

DBA Request system

Setup DB

System Admin

Requeststorage

Setup machine

Storage Admin

Allocate storage (take snapshot)

3. Provisioning Delays : culture of no

What We’ve Seen

1. QA: Higher costs more code re-work2. Development: Bottlenecks & bugs3. Provisioning: Delays

Three Physical Copies Three Virtual Copies

Install Delphix on Commodity Intel Hardware

Intel hardware

Allocate Any Storage to Delphix

Allocate StorageAny type

One time backup of source database

Database

Production

Instance

File system

File system

DxFS (Delphix) Compress Data

Database

Production

Instance

Data is compressed typically 1/3 size

File system

Incremental forever change collection

Database

Production

Instance

File system

Changes

• Collected incrementally forever• Old data purged

Time Window

File system

Typical Architecture

Production

Instance

Development

Instance

QA

Instance

UAT

Instance

Database

File systemFile system

Database

File systemFile system

Database

File systemFile system

Database

File systemFile system

With Delphix

Database

File system

Production

Instance

Database

Development

Instance

Database

QA

Instance

Database

UAT

Instance

Parallel environments

Instance

Instance

Instance

Instance

Source

What We’ve Seen With Delphix

1. QA: Low cost, fast bug detection 2. Development: Parallelized, less bugs3. Provisioning: Fast, Culture of Yes

1. QA Efficient : Lower cost

Build Time

QA Test

1% of QA time was building environment$.99/$1.00 actual testing vs. setup

Build Time

QA Test

Build

1. QA Fast : bugs found fast and fixed

Sprint 1 Sprint 2 Sprint 3

Bug CodeX

QA QA

Build QA Env

QA

Build QA Env

QA

Sprint 1 Sprint 2 Sprint 3

Bug Code

X

2. Development : Parallelize

gif by Steve Karam

2. Development: Eliminate bugs

3. Provisioning: Fast, Efficient, Self Service, Culture of Yes!

What We’ve Seen With Delphix

1. QA: Low cost, fast bug detection 2. Development: Parallelized, less bugs3. Provisioning: Fast, Culture of Yes

But …

How do we manage database changes as code changes and automate deployment of changes?

Dealing with Risk Smaller and more focused changes are easier to manage (Agile…) Automation of repeating tasks lowers risk of (human) error Development and Operations should work in synergy (DevOps)

45

• More rapid changes• Reacting quickly to market needs• Getting ahead of competition

• Fewer changes backed out• Better collaboration• Fewer defects

• Ultimately better service • Happy customers • Profitability

Why Continuous Delivery?

46

• Team and process• Version everything• Automate your tests• Fix it, properly (no out of process changes!)

• Automate your deployments

Database Continuous Delivery?

47

A quick poll

48

• The database holds your essential information

• Changes can impact the entire system• Need to be synchronized with other

changes• Often overlooked

Database is a Key Component

49

• There is more to a database than SQL scripts– Schema structure– Code– Content and meta-content– Internal dependencies– …

• Ensure that changes are not made without authorization

• Ensure no out-of-process changes

Reaching Inside the Database

50

• Old adage but true• The database is often neglected and therefore

can become the weakest link• Essential from a compliance point of view• Should be the strongest link

The Weakest Link In a Chain

51

Challenges…

Un-coordinated Process

Silos in development

Deployment delays…

Out-of-Process Changes

Errors in production

Lack of confidence in automation

Code overrides

Different methodologies

Lack of history Missing changes

wrong versions

52

Two isolated Processes

Version Control Process Development Process

Check-Out Script

Modify Script

Get updated Script from DB

Check-In Script

Compile Scriptin DB

Debug Scriptin DB

?

??

?

A

A’

53

X1.11.1.11.11.21.31.41.51.61.7

Build Once Deploy Many

Int QA Stage Prod

Database Deploy Script

Environment

Re-Base (due to defects)

DevDev

DevModel

1.1 1.2

1.2 1.3

1.3 1.4

1.4 1.5

1.5 1.6

1.6 1.7

1.11.11.41.7

1.1 1.2

1.2 1.3

1.3 1.4

1.4 1.5

1.5 1.6

1.6 1.7

1.1 1.2

1.2 1.3

1.3 1.4

1.4 1.5

1.5 1.6

1.6 1.7

Out of Process Change

X

X

X

X

X

? 1.1.1

X

54

Dealing with challenges…

Coordinated Process Traceability

Start in the Beginning

No Out-of-Process Changes

Impact Analysis

Automation

Task Based Development

Well Defined Processes

What is DBmaestro TeamWork

• Database Enforced Change Management+ Database version control+ Plugs into the ALM (change request, tickets & work

items)

+ Database merge & change impact analysis

• DevOps Solution for databases + Baseline aware deployment automation, rollback

& recovery+ Plugs into release management & Continuous

Delivery

How?

• Database version control – Enforced Check Out/In – Labels– Rollback/Undo – Audit trail reports

• Database impact analysis – Utilizes version control repository information – 3-way analysis

• Database deployment automation– API – Baselines – Conflict resolution – Customized business logic

57

Version Control - One Enforced Process

58

1.11.21.31.41.51.61.7

Build & Deploy On Demand

*

Int QA Stage Prod

Database Deploy Script

Environment* Execute the same script being executed at the Stage environment

Re-Base (due to defects)

DevDev

DevModel

1.1 1.2

1.2 1.3

1.3 1.4

1.4 1.5

1.5 1.6

1.6 1.7

1.1 1.4

1.4 1.7

1.1.1 1.7

1.1 1.1 1.11.41.7

File Based Version Control

Out of Process Change

1.1.11.7 1.1.11.7

Safety Net For Deployment Automation

Source vs. Target

Action

= No Action

≠ ?

Source vs. Baseline

Target vs. Baseline

Action

= = No Action

≠ = Deploy Changes

= ≠ Protect Target

≠ ≠ Merge Changes

You do not have all of the information

With Baselines and 3 way analysis the unknown is now known

Simple Compare & Sync Baseline aware Deployment

An index exists in Target (Production) but not in source (QA). What should we do? Drop the index or not?

Benefits - Development

• Database change repository• Follow SCM best practices

(Check-Out/Check-In) • All changes are documented• Manage who can do what, where, when &

why

Benefits - Operations

• Integrated deployment engine• Business level audit• Roles & responsibilities enforcement

Benefits - Management

• Complete visibility into changes in progress• Management reports, Audit reports• No silos

+

Time Window

Instance

Instance

Instance

Virtual DatabaseDev1

Dev2

Trunk

Virtual Database

Virtual Database

Developer 1 modify

Developer 2 modify

DBVC

Merge Dev1 to ForkMerge to dev2

Dev2

Dev1Merge to dev1

Merge Dev2 to Fork

Trunk

Merge Dev1 to Fork

Merge Dev2 to Fork

DBVC

Fork

Fork

Fork

Fork

Safe and Efficient Work Process

• Clone relevant number of virtual copies of the Trunk– Dev Team 1-n, Developer a-z, Hot fix environment, Etc…– Work in parallel, save time

• Rely on enforced change process– Know exactly who did what, when and why

• Deployment automation– Save time, mitigate risk– Continuous integration, Continuous Delivery

• Deal with conflicts & merge them into the Trunk• Test before deploy to production

– Pre-prod clone

Q&A

Kyle Hailey @kylehhaileyDelphix: delphix.com

Yaniv Yehuda @yanivyehudaDBmaestro: dbmaestro.com

Tim O'Brien Gleanster: gleanster.com

Thanks!

Kyle Hailey @kylehhaileyDelphix: delphix.com

Yaniv Yehuda @yanivyehudaDBmaestro: dbmaestro.com

Tim O'Brien Gleanster: gleanster.com