Enabling DevOps and Continuous Integration with data virtualization

1. Accelerating DevOps with Virtual Data
1
http://kylehailey.com
kyle@delphix.com
Tim Gorman
tim@delphix.com

2. Accelerating the tempo of application development
Fortune 1 Retailer #1 Social Network #1 Financial Services #1 Network Equipment #1 Cable
#1 Wholesale #1 Food Service Co. #1 Office Supplies #1 Apparel & Footwear #1 Chip Manufacturing
#1 Pharma #1 Auction Marketplace #1 Total Healthcare #1 Aerospace #1 Computer Access
#1 CPG #1 ETL Software #1 Availability Service #1 Mutual Life Ins. #1 Satellite TV
#1 State Gov #1 Cruise Line #1 Retirement Fund #1 IT Services #1 Game Software
© 2014 Delphix. All Rights Reserved Private and confidential 2

3. Are you too busy to Innovate?
Inertia

4. What is DevOps = tools + culture
• Culture :
– Empathy
– Collaboration
– Bridging silos, avoid blame
• Tools :
– Automation
– Measurement
– Self-service
4

5. Note: DevOps > Tools + Culture
DevOps= optimizing flow from Dev to Ops to Pro
5
“Do not seek to follow in the footsteps of the wise.
Seek what they sought”
- Matsuo Bashō
Goal = company’s bottom line

6. The Goal : Theory of Constraints
Improvement
not made
at the constraint
is an illusion
factory floor optimization

7. Factory floor

8. Factory floor
constraint
Not a relay race

9. Tune before constraint
constraint
Tuning here
Stock piling

10. Tune after constraint
constraint
Tuning here
Starvation

11. Factory floor : straight forward
constraint
Goal: find constraint
optimize it

12. Theory of Constraints work for IT ?
• Goals Clarify
• Metrics Define
• Constraints Identify
• Priorities Set
• Iterations Fast
• CI
• Cloud
• Agile
• Kanban
• Kata
“IT is the factory floor of this century”

13. The Phoenix Project
What is the
constraint
in IT ?

14. What are the top 5 constraints in IT?
1. Dev environments setup
2. QA setup
3. Code Architecture
4. Development
5. Product management
“One of the most powerful
things that organizations
can do is to enable development
and testing to get
environment they need when
they need it“
- Gene Kim

15. Data is the constraint
CIO Magazine Survey:
60% Projects Over Schedule
85% delayed waiting for data
Data is the Constraint
only getting worse
Gartner: Data Doomsday, by 2017 1/3rd IT in crisis

16. In this presentation :
• Data Constraint
• Solution
• Use Cases

17. • Data Constraint
• Solution
• Use Cases

18. moving data is hard
– Storage & Systems
– Personnel
– Time

19. Typical Architecture
Production
Instance
Database
File system

20. Typical Architecture
Production
Instance
Backup
Database
File system
Database
File system

21. Typical Architecture
Production
Instance
Reporting Backup
Database
File system
Instance
Database
File system
Database
File system

22. Typical Architecture
Production
Instance
Database
File system
Triple Tax
Dev, QA, UAT Reporting Backup
Instance
Instance
Instance
Instance
Database
Database
File system
Database
File system
File system
Database
File system
Database
File system

23. Typical Architecture
Production
Instance
Database
File system
Instance
Instance
Instance
Instance
Database
Database
File system
Database
File system
File system
Database
File system
Database
File system

24. Data floods infrastructure
92% of the cost of business,
in financial services business , is “data”
www.wsta.org/resources/industry-articles
Most companies average
5% IT spending , ½ on “data”
http://uclue.com/?xq=1133

25. Four Areas hit by data constraint
1. IT Capital resources $
2. IT Operations personnel $
3. Application Development $$$
4. Business $$$$$$$

26. 1. Hardware – copies take up space
–Servers
–Storage
–Network
–Data center floor space, power, cooling

27. $ Never enough environments

28. $ IT Operations – copying data takes people time
• People 1000s hours per year just for DBAs
– DBAs
– SYS Admin
– Storage Admin
– Backup Admin
– Network Admin
• $100s Millions for data center modernizations

29. $ Application Development – wait for copies
• Inefficient QA: Higher costs of QA
• QA Delays : Greater re-work of code
• Sharing DB Environments : Bottlenecks
• Using DB Subsets: More bugs in Prod
• Slow Environment Builds: Delays

30. $ Business – decisions depend on data access
Ability to capture revenue
• Business Applications
– Delays cause lost revenue
• Business Intelligence
– Old data = less intelligence

31. companies unaware

32. companies unaware
Boss, Storage Admin, DBA Developer or Analyst

33. companies unaware
Metrics
– Time
– Old Data
– Storage
Other
– Analysts
–Audits

34. What Problems does Data Constraint Cause
1. Bottlenecks
2. Waiting for environments
3. Waiting to check in code
4. Production Bugs
5. Expensive Slow QA

35. Your Project
Available
Resources

37. Development : bottlenecks
Frustration Waiting

38. Development : Bugs
Old Unrepresentative Data

39. Development : subsets
False Negatives
False Positives
Bugs in Production

40. Production Wall
40

41. Development : silos

42. QA : Long Build times
X Bug
70
60
50
40
30
20
10
0
1 2 3 4 5 6 7
Delay in Fixing the bug
Cost
To
Correct
Software Engineering Economics
– Barry Boehm (1981)

43. DevOps and Data : Impossible?
Life with Waterfall
Dream of Agile & CI

44. Waterfall vs Agile
44

45. Missed !
Goal
Agile & CI vsWaterfall
Agile & CI Achieved !

46. bugs
time
Missed !
Goal
Agile & CI Achieved !
Bugs

47. profit
time
Missed !
Goal
Agile & CI Achieved !
Profit

48. Missed !
Goal
Cost per Deployment
Agile & CI Achieved !
Cost
Per
Deployment time

49. DevOps and Data : Impossible?
• Data & DevOps : Impossible ?
• 20 copies of production a day for CI
• Each copy is like

50. In this presentation :
• Data Constraint
• Solution
• Use Cases

51. 99% of blocks are identical
Clone 1 Clone 2 Clone 3

52. Solution

53. Thin Clone
Clone 1 Clone 2 Clone 3

54. Technology Core : file system snapshots
• EMC
– 16 snapshots on Symmetrix
– Write performance impact
– No snapshots of snapshots
• Netapp
– 255 snapshots
• ZFS
– Compression
– Unlimited snapshots
– Snapshots of Snapshots
• DxFS
– “”
– Storage agnostic
– Shared cache in memory
Also check out new SSD storage such as:
Pure Storage, EMC XtremIO

55. Fuel not equal car
Challenges
1. Technical
2. Bureaucracy

56. Bureaucracy
Developer Asks for DB Get Access
Manager approves
DBA Request
system
Setup DB
System
Admin
Request
storage
Setup
machine
Storage
Admin
Allocate
storage
(take snapshot)

57. 1hour
9 days
1 day
Why are hand offs so expensive?
Bureaucracy

58. Technical Challenge
Production Filer
Database
Luns
Target A
Target B
Target C
snapshot
clones
InsIntsatannccee
InInssttaanncece
InInssttaannccee
InInssttaannccee
Instance
Source

59. Development Filer
Production Filer clones
Database
LUNs
snapshot
Technical Challenge
Instance
Target A
InInssttaannccee
Target B
InInssttaanncece
Target C
InInssttaannccee
Instance

60. Technical Challenge
Production
Copy
Time Flow
Purge
Storage Development
File System Instance
1 2 3
Clone (snapshot)
Compress
Share Cache
Provision
Mount, recover, rename
Self Service, Roles & Security
Instance

61. How to get a Data Virtualization?
2 1
– EMC + SRDF
– Netapp 2 + SMO
1
– Oracle EM 12c + data guard + Netapp /ZFS
– Actifio - hardware
– Delphix - software
3 1 2
Source
sync
Deploy
automation
Storage
snapshots
1 2 3

62. Goal : virtualize, govern, deliver
62
• Masking: Masking
• Security: Chain of custody
• Self Service: Logins
• Developer: Versioning , branching
• Audit: Live Archive
Data Supply Chain
Data Virtualization
Thin Cloning
Snap Shots

63. Dev
Production Time Flow
Prod
2.6
Dev finishes a sprint or point
release and QA forks off a clone
virtual database from Dev
database

64. Dev
QA
Production Time Flow
Prod
2.6
Continuous integration
Nightly or hourly regressions

65. Dev
QA
Production Time Flow
Prod
2.6
Dev finishes a sprint or point
release and QA forks off a clone
virtual database from Dev
database

66. Dev
QA
Production Time Flow
Prod
2.6
Dev finishes a sprint or point
release and QA forks off a clone
virtual database from Dev
database
UAT

67. Prod
Dev
2.7
QA
UAT
Production Time Flow
UAT
QA
Dev
2.6

68. Intel hardware
DB2
Data
File Systems
Binaries
Install Delphix on x86 hardware

69. Allocate Any Storage to Delphix
Allocate Storage
Any type
Pure Storage + Delphix
Better Performance for
1/10 the cost

70. One time backup of source database
Production
InsIIntnsasttanannccceee
Database
File system

71. DxFS (Delphix) Compress Data
Production
InsIIntnsasttanannccceee
Database
Data is
compressed
typically 1/3
size
File system

72. Incremental forever change collection
Production
Database
File system
Changes
Time Window
• Collected incrementally forever
• Old data purged
InsIIntnsasttanannccceee

73. Snapshot 1 – full backup once only at link time
Jonathan Lewis © 2013
Virtual DB
73 / 30
a b c d e f g h i
We start with a full backup - analogous to a level 0 rman backup. Includes
the archived redo log files needed for recovery. Run in archivelog mode.

74. Snapshot 2 (from SCN)
a b c d e f g h i
b' c'
The "backup from SCN" is analogous to a level 1
incremental backup (which includes the relevant
archived redo logs). Sensible to enable BCT.
Jonathan Lewis © 2013
Delphix executes
standard rman scripts

75. Apply Snapshot 2
a b b' c c' d e f g h i
The Delphix appliance unpacks the rman backup and "overwrites" the
initial backup with the changed blocks - but DxFS makes new copies of
the blocks
Jonathan Lewis © 2013

76. Drop Snapshot 1
a b' c' d e f g h i
The call to rman leaves us with a new level 0 backup, waiting for recovery.
But we can pick the snapshot root block. We have EVERY level 0 backup
Jonathan Lewis © 2013

77. Creating a vDB
a b' c' d e f g h i
The first step in creating a vDB is to take a snapshot of the filesystem as at
the backup you want (then roll it forward)
Jonathan Lewis © 2013
My vDB
(filesystem)
Your vDB
(filesystem)

78. Creating a vDB
a b' c' d e f g h i
The first step in creating a vDB is to take a snapshot of the filesystem as at
the backup you want (then roll it forward)
Jonathan Lewis
© 2013
My vDB
(filesystem)
Your vDB
(filesystem)
b'' c'' ff ii i’

79. Database Virtualization

80. Three Physical Copies
Three Virtual Copies
Data
Virtualization
Appliance

81. Before Virtual Data
Production Dev, QA, UAT
Instance
Reporting Backup
Database
File system
Instance
Instance
Instance
Instance
Database
Database
File system
Database
File system
File system
Database
File system
Database
File system
“triple data
tax”

82. With Virtual Data
Production
Instance
Dev & QA
Instance
InInssttaannccee
InInssttaannccee
Database
Reporting
Instance
Database
Backup
Database
Instance Instance Instance
Database
Database
Database
File system
Data
Virtualization
Appliance

83. • Problem in the Industry
• Solution
• Use Cases

84. Use Cases
1. Development and QA
2. Production Support
3. Business

85. Use Cases
1. Development and QA
2. Production Support
3. Business

86. Development: Virtual Data
• Unlimited
• Full size
• Self Service
Development

87. Virtual Data: Easy
Instance
Instance
Instance
Instance
Source
DVA

88. Development Virtual Data: Parallelize
gif by Steve Karam

89. Development Virtual Data: Full size

90. Development Virtual Data: Self Service

91. QA : Virtual Data
• Fast
• Parallel
• Rollback
• A/B testing

92. Dev
QA
QA Virtual Data : Fast
Prod
Instance
DVA
• Low Resource
• Find bugs Fast
Production Time Flow

93. QA with Virtual Data: Rewind
Instance
QA
Prod
Production Time Flow

94. QA with Virtual Data: A/B
Instance
Instance
Instance
Index 1
Index 2
Production Time Flow

95. Data Version Control
Dev
QA
2.1
Dev
QA
2.2
DVA Production Time Flow
2.1 2.2
Prod
Instance
11/11/2014 95

96. Use Cases
1. Development and QA
2. Production Support
3. Business

97. • Backups
• Recovery
• Forensics
• Migration
• Consolidation
Recovery

98. 9TB database 1TB change day
30 day backups storage requirements
98
70
60
50
40
30
20
10
0
week 1
week 2
week 3
week 4
original
Oracle
Delphix

99. Recovery
Source
Instance
Recover VDB
Instance
Drop
DVA Production Time Flow

100. Forensics
Instance
Development
DVA
Source
Production Time Flow

101. Development (the new production)
Instance
Development
DVA
Source
Development
Prod & VDB Time Flow
X

102. Migration

103. Consolidation

104. Use Cases
1. Development and QA
2. Production Support
3. Business Intelligence

105. Business Intelligence
• ETL
• Temporal
• Confidence Testing
• Federated Databases
• Audits

106. Business Intelligence: ETL and Refresh Windows
1pm 10pm 8am
noon

107. Business Intelligence: batch taking too long
1pm 10pm 8am
noon
2011
2012
2013
2014
2015

108. 6am 8am 10pm
10pm 8am noon 9pm
1pm 10pm 8am
noon
2011
2012
2013
2014
2015

109. Business Intelligence: ETL and DW Refreshes
Prod
Instance
DW & BI
Instance

110. Virtual Data: Fast Refreshes
• Collect only Changes
• Refresh in minutes
Prod
Instance
BI and DW
ETL
24x7
DVA
Production Time Flow

111. Temporal Data

112. Confidence testing

113. Modernization: Federated
Source1
Instance
Source2
Instance
DVA
Production Time Flow 1
Production Time Flow 2

114. Modernization: Federated

115. Modernization: Federated
“I looked like a hero”
Tony Young, CIO Informatica

116. Live Archive
Production Time Flow
Audit
Prod
Instance
DVA
11/11/2014 116

117. Use Case Summary
1. Development & QA
2. Production Support
3. Business

118. How expensive is the Data Constraint?
DVA at Fortune 500 :
Dev throughput increase by 2x

119. How expensive is the Data Constraint?
Faster
• Financial Close
• BI refreshes
• Surgical recovery
• Projects

120. Virtual Data Quotes
• Projects “12 months to 6 months.”
– New York Life
• Insurance product “about 50 days ... to about 23 days”
– Presbyterian Health
• “Can't imagine working without it”
– State of California

122. Summary
• Problem: Data is the constraint
• Solution: Virtualize Data
• Results:
• Half the time for projects
• Higher quality
• Increase revenue

123. Thank you!
• Kyle Hailey| Oracle ACE and Technical
Evangelist, Delphix
– Kyle@delphix.com
– kylehailey.com
– slideshare.net/khailey

124. 124