Presented by Andrew Erlichson, Vice President, Engineering, Developer Experience, MongoDB Audience level: Beginner MongoDB’s basic unit of storage is a document. Documents can represent rich, schema-free data structures, meaning that we have several viable alternatives to the normalized, relational model. In this talk, we’ll discuss the tradeoff of various data modeling strategies in MongoDB. You will learn: - How to work with documents - How to evolve your schema - Common schema design patterns

1. Schema Design
(and its performance implications)
Andrew Erlichson
VP, Engineering
andrew@mongodb.com
@erlichson

2. 2
Agenda
1. Example Problem
2. MongoDB Schema Design vs. Relational
3. Modeling Relationships
4. Schema Design and Performance

3. Example Problem

4. 4
Medical Records
• Collects all patient information in a central repository
• Provide central point of access for
– Patients
– Care providers: physicians, nurses, etc.
– Billing
– Insurance reconciliation
• Hospitals, physicians, patients, procedures, records
Patient
Records
Medications
Lab Results
Procedures
Hospital
Records
Physicians
Patients
Nurses
Billing

5. 5
Medical Record Data
• Hospitals
– have physicians
• Physicians
– Have patients
– Perform procedures
– Belong to hospitals
• Patients
– Have physicians
– Are the subject of procedures
• Procedures
– Associated with a patient
– Associated with a physician
– Have a record
– Variable meta data
• Records
– Associated with a procedure
– Binary data
– Variable fields

6. 6
Lot of Variability

7. Relational View

8. Schema Design:
MongoDB vs. Relational

9. MongoDB Relational
Collections Tables
Documents Rows
Data Use Data Storage
What questions do I have? What answers do I have?
MongoDB versus Relational

10. Complex Normalized Schemas

11. Complex Normalized Schemas

12. 13
Documents are Rich Data Structures
{
first_name: ‘Paul’,
surname: ‘Miller’,
cell: ‘+447557505611’
city: ‘London’,
location: [45.123,47.232],
Profession: [banking, finance, trader],
cars: [
{ model: ‘Bentley’,
year: 1973,
value: 100000, … },
{ model: ‘Rolls Royce’,
year: 1965,
value: 330000, … }
]
}
Fields can contain an array of sub-documents
Fields
Typed field values
Fields can contain arrays

13. Relationships

14. Modeling One-to-One Relationships

15. 16
Referencing
Procedure
• patient
• date
• type
• physician
• type
Results
• dataType
• size
• content: {…}
Use two collections with a reference
Similar to relational

16. 17
Procedure
• patient
• date
• type
• results
• equipmentId
• data1
• data2
• physician
• Results
• type
• size
• content: {…}
Embedding
Document Schema

17. 18
Referencing (1:1)
Procedure
{
"_id" : 333,
"date" : "2003-02-09T05:00:00"),
"hospital" : “County Hills”,
"patient" : “John Doe”,
"physician" : “Stephen Smith”,
"type" : ”Chest X-ray",
”result" : 134
}
Results
{
“_id” : 134
"type" : "txt",
"size" : NumberInt(12),
"content" : {
value1: 343,
value2: “abc”,
…
}
}

18. 19
Embedding (1:1)
Procedure
{
"_id" : 333,
"date" : "2003-02-09T05:00:00"),
"hospital" : “County Hills”,
"patient" : “John Doe”,
"physician" : “Stephen Smith”,
"type" : ”Chest X-ray",
”result" : {
"type" : "txt",
"size" : NumberInt(12),
"content" : {
value1: 343,
value2: “abc”,
…
}
}
}

19. 20
Embedding (1:1)
• Advantages
– Retrieve all relevant information in a single query/document
– Avoid implementing joins in application code
– Update related information as a single atomic operation
• MongoDB doesn’t offer multi-document transactions
• Limitations
– Large documents mean more overhead if most fields are not relevant
– 16 MB document size limit

20. 23
Referencing (1:1)
• Advantages
– Smaller documents
– Less likely to reach 16 MB document limit
– Infrequently accessed information not accessed on every query
• Limitations
– Two queries required to retrieve information
– Cannot update related information atomically

21. 24
One to One: General Recommendations
• Embed
– No additional data duplication
– Can query or index on
embedded field
• e.g., “result.type”
• Exceptional cases…
• Embedding results in large
documents
• Set of infrequently access
fields
{
"_id" : 333,
"date" : "2003-02-09T05:00:00"),
"hospital" : “County Hills”,
"patient" : “John Doe”,
"physician" : “Stephen Smith”,
"type" : ”Chest X-ray",
”result" : {
"type" : "txt",
"size" : NumberInt(12),
"content" : {
value1: 343,
value2: “abc”,
…
}
}
}

22. Modeling One-to-Many Relationships

23. 26
{
_id: 2,
first: “Joe”,
last: “Patient”,
addr: { …},
procedures: [
{
id: 12345,
date: 2015-02-15,
type: “Cat scan”,
…},
{
id: 12346,
date: 2015-02-15,
type: “blood test”,
…}]
}
Patients
Embed
One-to-Many Relationships
Modeled in 2 possible ways
{
_id: 2,
first: “Joe”,
last: “Patient”,
addr: { …},
procedures: [12345, 12346]}
{
_id: 12345,
date: 2015-02-15,
type: “Cat scan”,
…}
{
_id: 12346,
date: 2015-02-15,
type: “blood test”,
…}
Patients
Reference
Procedures

24. 27
One to Many: General Recommendations
• Embed, when possible
– Access all information in a single query
– Take advantage of update atomicity
– No additional data duplication
– Can query or index on any field
• e.g., { “phones.type”: “mobile” }
• Exceptional cases:
– 16 MB document size
– Large number of infrequently accessed fields
{
_id: 2,
first: “Joe”,
last: “Patient”,
addr: { …},
procedures: [
{
id: 12345,
date: 2015-02-15,
type: “Cat scan”,
…},
{
id: 12346,
date: 2015-02-15,
type: “blood test”,
…}]
}

25. Modeling Many-to-Many Relationships

26. 29
Many to Many
Traditional Relational Association
Join table
Physicians
name
specialty
phone
Hospitals
name
HosPhysicanRel
hospitalId
physicianId
X
Use arrays instead

27. 30
{
_id: 1,
name: “Oak Valley Hospital”,
city: “New York”,
beds: 131,
physicians: [
{
id: 12345,
name: “Joe Doctor”,
address: {…},
…},
{
id: 12346,
name: “Mary Well”,
address: {…},
…}]
}
Many-to-Many Relationships
Embedding physicians in hospitals collection
{
_id: 2,
name: “Plainmont Hospital”,
city: “Omaha”,
beds: 85,
physicians: [
{
id: 63633,
name: “Harold Green”,
address: {…},
…},
{
id: 12345,
name: “Joe Doctor”,
address: {…},
…}]
}
Data Duplication

28. 31
{
_id: 1,
name: “Oak Valley Hospital”,
city: “New York”,
beds: 131,
physicians: [12345, 12346]
}
Many-to-Many Relationships
Referencing
{
id: 63633,
name: “Harold Green”,
address: {…},
…}
Hospitals
{
_id: 2,
name: “Plainmont Hospital”,
city: “Omaha”,
beds: 85,
physicians: [63633, 12345]
}
Physicians
{
id: 12345,
name: “Joe Doctor”,
address: {…},
…}
{
id: 12346,
name: “Mary Well”,
address: {…},
…}

29. 32
Many to Many
General Recommendation
• Use case determines whether to reference or
embed:
1. Data Duplication
• Embedding may result in data duplication
• Duplication may be okay if reads
dominate updates
2. Referencing may be required if many
related items
3. Hybrid approach
• Potentially do both
{
_id: 2,
name: “Oak Valley Hospital”,
city: “New York”,
beds: 131,
physicians: [12345, 12346]}
{
_id: 12345,
name: “Joe Doctor”,
address: {…},
…}
{
_id: 12346,
name: “Mary Well”,
address: {…},
…}
Hospitals
Reference
Physicians

30. What If I Want to Store Large Files in MongoDB?

31. 34
GridFS
Driver
GridFS API
doc.jpg
(meta
data)
doc.jpg
(1)doc.jpg
(1)doc.jpg
(1)
fs.files fs.chunks
doc.jpg
mongofiles utility provides command line GridFS interface

32. Schema Design and Performance
Two Examples

33. Example 1: Hybrid Approach
Embed and Reference

34. 37
Healthcare Example
patients
procedures

35. Tailor Schema to Queries (cont.)
{
"_id" : 593340651,
"first" : "Gregorio",
"last" : "Lang",
"addr" : {
"street" : "623 Flowers Rd",
"city" : "Groton",
"state" : "NH",
"zip" : 3266
},
"physicians" : [10387 33456],
"procedures” : ["551ac”, “343fs”]
}
{
"_id" : "551ac”,
"date" :"2000-04-26”,
"hospital" : 161,
"patient" : 593340651,
"physician" : 10387,
"type" : "Chest X-ray",
"records" : [ “67bc6”]
}
Patient Procedure
Find all patients from NH that
have had chest x-rays

36. Tailor Schema to Queries (cont.)
{
"_id" : 593340651,
"first" : "Gregorio",
"last" : "Lang",
"addr" : {
"street" : "623 Flowers Rd",
"city" : "Groton",
"state" : "NH",
"zip" : 3266
},
"physicians" : [10387 33456],
"procedures” : [
{id : "551ac”,
type : “Chest X-ray”},
{id : “343fs”,
type : “Blood Test”}]
}
{
"_id" : "551ac”,
"date" :"2000-04-26”,
"hospital" : 161,
"patient" : 593340651,
"physician" : 10387,
"type" : "Chest X-ray",
"records" : [ “67bc6”]
}
Patient Procedure
Find all patients from NH that
have had chest x-rays

37. Example 2: Time Series Data
Medical Devices

38. 41
Vital Sign Monitoring Device
Vital Signs Measured:
• Blood Pressure
• Pulse
• Blood Oxygen Levels
Produces data at regular intervals
• Once per minute

39. 42
We have a hospital(s) of devices

40. 43
Data From Vital Signs Monitoring Device
{
deviceId: 123456,
spO2: 88,
pulse: 74,
bp: [128, 80],
ts: ISODate("2013-10-16T22:07:00.000-0500")
}
• One document per minute per device
• Relational approach

41. 44
Document Per Hour (By minute)
{
deviceId: 123456,
spO2: { 0: 88, 1: 90, …, 59: 92},
pulse: { 0: 74, 1: 76, …, 59: 72},
bp: { 0: [122, 80], 1: [126, 84], …, 59: [124, 78]},
ts: ISODate("2013-10-16T22:00:00.000-0500")
}
• Store per-minute data at the hourly level
• Update-driven workload
• 1 document per device per hour

42. 45
Characterizing Write Differences
• Example: data generated every minute
• Recording the data for 1 patient for 1 hour:
Document Per Event
60 inserts
Document Per Hour
1 insert, 59 updates

43. 46
Characterizing Read Differences
• Want to graph 24 hour of vital signs for a patient:
• Read performance is greatly improved
Document Per Event
1440 reads
Document Per Hour
24 reads

44. 47
Characterizing Memory and Storage Differences
Document Per Minute Document Per Hour
Number Documents 52.6 B 876 M
Total Index Size 6364 GB 106 GB
_id index 1468 GB 24.5 GB
{ts: 1, deviceId: 1} 4895 GB 81.6 GB
Document Size 92 Bytes 758 Bytes
Database Size 4503 GB 618 GB
• 100K Devices
• 1 years worth of data
100000 * 365 *
24 * 60
100000 * 365 *
24
100000 * 365 *
24 * 60 * 130
100000 * 365 *
24 * 130
100000 * 365 *
24 * 60 * 92
100000 * 365 *
24 * 758

45. 48
Summary
• Relationships can be modeled by embedding or references
• Decision should be made in context of application data and query workload
– Tailor schema to application workload
• It is okay recommended to violate RDBMS schema design principles
– No duplication of data
– Normalization
• Different schemas may result in dramatically different
– Query performance
– Hardware requirements

46. Questions?
andrew@mongodb.com
@erlichson