Polyglot persistence is about using multiple databases in concert with one another as part of a larger datastore ecosystem. The advantage is that your database layer uses a set of specialized tools to deliver overall value and functionality while simplifying data modeling by separating command and query responsibilities. The arrival of MongoDB and it’s flexible schemas further increases the possibilities of polyglot architectures.

1. Polyglot Persistence
{ Name: ‘Bryan Reinero’,
Title: ‘Developer Advocate’,
Twitter: ‘@blimpyacht’,
Email: ‘bryan@mongdb.com’ }

2. What is the Polyglots?
• Using multiple Database Technologies in a
Given Application
• Using the right tool for the right job

3. What is the Polyglots?
• Using multiple Database Technologies in a
Given Application
• Using the right tool for the right job
Derived from “polyglot programming”.
Applications programmed from a mix of
languages.

4. Why is the Polyglots?
• Relational has been the dominant model
• Higher performance requirements
• Increasingly large datasets
• Use of IaaS and commodity hardware

5. Vertical Scaling

6. Horizontal Scaling

7. 7
Availability
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8. 8
Availability
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Requirements
• Maximize uptime
• Minimize time to recover

9. 9
Availability
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Requirements
• Maximize uptime
• Minimize time to recover
Hardware failures
Network partitions
Data center failures
Maintenance
Operations

10. 10
Availability
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Business critical systems
require automatic fault
detection and fail over

11. 11
Variant Data Models
58842
45647
52320
88237
78932
Key-Value Store
Eratosthenes
Democritus
Hypatia
Shemp
Euripides
ID Name

12. 12
Variant Data Models
Eratosthenes
Democritus
Hypatia
Shemp
Euripides
Graph Databases

13. 13
Variant Data Models
Document Databases
{
maker : ”Agusta",
type : sportbike,
rake : 7,
trail : 3.93,
engine : {
type : "internal combustion",
layout : "inline"
cylinders : 4,
displacement : 750,
},
transmission : {
type : "cassette",
speeds : 6,
pattern : "sequential”,
ratios : [ 2.7, 1.94, 1.34, 1, 0.83, 0.64
]
}
}

14. Polyglot Persistence
Application
Servers MongoDB
RDBMS
Key /
Value
Session Data,
Shopping Carts
Product Catalog,
User Accounts,
Domain Objects
Payment
Systems,
Reporting
Graph
Social Data,
Recommendations

15. Polyglot Persistence
Application
Servers MongoDB
RDBMS
Key /
Value
Session Data,
Shopping Carts
Product Catalog,
User Accounts,
Domain Objects
Payment
Systems,
Reporting
Graph
Social Data,
Recommendations

17. What are your requirements?
• Availability
• Scalability
• Performance
• Access Patterns
• Data Model

18. 18
Key Value Stores
58842
45647
52320
88237
78932
Used for
• Session data
• Cookies
• Shopping carts
Eratosthenes
Democritus
Hypatia
Shemp
Euripides
ID Name

19. 19
Key Value Stores
58842
45647
52320
88237
78932
• Fast, if in memory
• Single access pattern
• Complex data parsed
in client
Eratosthenes
Democritus
Hypatia
Shemp
Euripides
ID Name

20. Key Value Store
“{
maker : ‘Agusta’,
type : sportbike,
rake : 7,
trail : 3.93,
engine : {
type : ‘internal combustion’,
layout : ‘inline’,
cylinders : 4,
displacement : 750,
},
transmission : {
type : ‘cassette’,
speeds : 6,
pattern : ‘sequential’,
ratios : [ 2.7, 1.94, 1.34, 1, 0.83, 0.64 ]
}
}”

21. Data Model
RDBMS MongoDB
Table, View ➜ Collection
Row ➜ Document
Index ➜ Index
Join ➜ Embedded Document
Foreign Key ➜ Reference
Partition ➜ Shard

22. MongoDB
{ _id: 78234974,
maker : ”Agusta",
type : sportbike,
rake : 7,
trail : 3.93,
engine : {
type : "internal combustion",
layout : "inline"
cylinders : 4,
displacement : 750,
},
transmission : {
type : "cassette",
speeds : 6,
pattern : "sequential”,
ratios : [ 2.7, 1.94, 1.34, 1, 0.83, 0.64 ]
}
}
Self Defining Schema

23. MongoDB
{ _id: 78234974,
maker : ”Agusta",
type : sportbike,
rake : 7,
trail : 3.93,
engine : {
type : "internal combustion",
layout : "inline"
cylinders : 4,
displacement : 750,
},
transmission : {
type : "cassette",
speeds : 6,
pattern : "sequential”,
ratios : [ 2.7, 1.94, 1.34, 1, 0.83, 0.64 ]
}
}
Self Defining Schema
Nested Objects

24. MongoDB
{ _id: 78234974,
maker : ”Agusta",
type : sportbike,
rake : 7,
trail : 3.93,
engine : {
type : "internal combustion",
layout : "inline"
cylinders : 4,
displacement : 750,
},
transmission : {
type : "cassette",
speeds : 6,
pattern : "sequential”,
ratios : [ 2.7, 1.94, 1.34, 1, 0.83, 0.64 ]
}
}
Self Defining Schema
Nested Objects
Array types

25. MongoDB
{ _id: 78234974,
maker : ”Agusta",
type : sportbike,
rake : 7,
trail : 3.93,
engine : {
type : "internal combustion",
layout : "inline"
cylinders : 4,
displacement : 750,
},
transmission : {
type : "cassette",
speeds : 6,
pattern : "sequential”,
ratios : [ 2.7, 1.94, 1.34, 1, 0.83, 0.64 ]
}
}
Primary Key,
Auto indexed

26. Multiple Access Patterns
{ _id: 78234974,
maker : ”Agusta",
type : sportbike,
rake : 7,
trail : 3.93,
engine : {
type : "internal combustion",
layout : "inline"
cylinders : 4,
displacement : 750,
},
transmission : {
type : "cassette",
speeds : 6,
pattern : "sequential”,
ratios : [ 2.7, 1.94, 1.34, 1, 0.83, 0.64 ]
}
}
Secondary
indexes

27. Projections
{ _id: 78234974,
maker : ”Agusta",
type : sportbike,
rake : 7,
trail : 3.93,
engine : {
type : "internal combustion",
layout : "inline"
cylinders : 4,
displacement : 750,
},
transmission : {
type : "cassette",
speeds : 6,
pattern : "sequential”,
ratios : [ 2.7, 1.94, 1.34, 1, 0.83, 0.64 ]
}
}
Projections
db.vehicles.find (
{_id:78234974 },
{ engine:1,_id:0 }
)

28. Flexible Schemas

29. Flexible Schemas
{ maker : "M.V. Agusta",
type : sportsbike,
engine : {
type : ”internal combustion",
cylinders: 4,
displacement : 750
},
rake : 7,
trail : 3.93
}
{ maker : "M.V. Agusta",
type : Helicopter
engine : {
type : "turboshaft"
layout : "axial”,
massflow : 1318
},
Blades : 4
undercarriage : "fixed"
}

30. Flexible Schemas
Discriminator column
{ maker : "M.V. Agusta",
type : sportsbike,
engine : {
type : ”internal
combustion",
cylinders: 4,
displacement : 750
},
rake : 7,
trail : 3.93
}
{ maker : "M.V. Agusta",
type : Helicopter
engine : {
type : "turboshaft"
layout : "axial”,
massflow : 1318
},
Blades : 4
undercarriage : "fixed"
}

31. Flexible Schemas
Shared indexing strategy
{ maker : "M.V. Agusta",
type : sportsbike,
engine : {
type : ”internal
combustion",
cylinders: 4,
displacement : 750
},
rake : 7,
trail : 3.93
}
{ maker : "M.V. Agusta",
type : Helicopter
engine : {
type : "turboshaft"
layout : "axial”,
massflow : 1318
},
Blades : 4
undercarriage : "fixed"
}

32. Flexible Schemas
Polymorphic Attributes
{ maker : "M.V. Agusta",
type : sportsbike,
engine : {
type : ”internal
combustion",
cylinders: 4,
displacement : 750
},
rake : 7,
trail : 3.93
}
{ maker : "M.V. Agusta",
type : Helicopter,
engine : {
type : "turboshaft”,
layout : "axial”,
massflow : 1318
},
Blades : 4,
undercarriage : "fixed"
}

33. Tao of MongoDB
• Model data for use, not storage
• Avoid ad-hoc queries
• Index effectively, index efficiently

34. Whattabout
the
Real World?

35. 35
Systems of Engagement
• Context rich and User
Relevant Interactions
• Integrates data from many
systems
• Integrates Analytics

36. 36

37. 37
Customer Single View
• Understand customer
relationships
• Improves customer experience
• Develops effective customer
marketing
• Improves product

38. Requirements
• High performance requirements
• Increasingly large datasets
• High Availability

39. 39
Architecture
Systems of Engagement
DataServices
Systems of Record
Master Data
Raw Data
Integrated Data
…
ETL
record
record
record

40. 40
Aggregating a Single View
Single customer
VIEW

41. 41
Aggregating a Single View
Common Data
Source Metadata
Source Data A
Source Data B

42. 42
Aggregating a Single View
Common Data
Source Metadata
Source Data A
Source Data B
{
_id: <hash>,
address: {
num: 860,
street: “Grove”,
city: “San Francisco”,
state: “CA”,
zip:
}
}

43. 43
Aggregating a Single View
Common Data
Source Metadata
Source Data A
Source Data B
{
sources: [
{
source: “URI”,
updated: ISODate(),
},
…
]
}

44. 44
Aggregating a Single View
Common Data
Source Metadata
Source Data A
Source Data B
Shopping Cart,
Purchase history,
Prescriptions,
Medical History,

45. Strong Consistency
vs.
Eventual Consistency

46. Availability

47. Availablity

48. Fail-over

49. Fail-over

50. Strong vs. Eventual Consistency

51. Analytics

52. 52
Hadoop
A framework for distributed processing of large data sets
• Terabyte and petabyte datasets
• Data warehousing
• Advanced analytics
• No indexes
• Batch processing

53. 53
Use Cases
• Behavioral analytics
• Segmentation
• Fraud detection
• Prediction
• Pricing analytics
• Sales analytics

54. 54
Typical Implementations
Application Server

55. 55
MongoDB as an Operational Store
Application Server

56. 56
Data Flows
Hadoop
Connector
BSON Files
MapReduce & HDFS

57. 57
Cluster
MONGOS
SHARD A
SHARDB
SHARD C
SHARD D
MONGOS Client

58. 58

59. 59
Hadoop / Spark Trade-offs
Plus
• Access to Analytics
Libraries
• Processes unstructured
data
• Handles petabyte data
sets
Minus
• Overhead of a separate
distributed system
• Writing MapReduce not
for the faint of heart
• Designed for batch
oriented processing

60. 60
Relational for Reporting & Business Intelligence
Plus
• Existing ecosystem of BI
tools
• Lower overhead than
Hadoop clusters
• Large pool of expertise
and talent

61. 61
Capture Data Changes
Systems of Engagement
DataServices
Data Processing
Integration, Analytics, etc.
Systems of Record
Master Data
Raw Data
Integrated Data
…
ETL
Bus
Apache Kafka
record
record
record

62. Integrations & ETL
RDBMSPrimary

63. RDBMSPrimary ETL
Oplog
Replication

64. LucenePrimary
Mongo
Connector
Oplog
Replication
Integrations with Search Solutions

65. Considerations
• Increased system
complexity
• Operations overhead
• Increased expertise

66. Thanks!
{ Name: ‘Bryan Reinero’,
Title: ‘Developer Advocate’,
Twitter: ‘@blimpyacht’,
Email: ‘bryan@mongdb.com’ }