This document discusses schema design in MongoDB and how it differs from relational databases. It provides examples of modeling one-to-one, one-to-many, and many-to-many relationships using embedding and referencing. The document also discusses two examples - a medical records system and time series device data - and how different schema designs can significantly impact performance and hardware requirements. Relationships can be modeled flexibly in MongoDB and the best approach depends on the application's data and queries.

1. Schema Design
(and its performance implications)
Jay Runkel
Principal Solutions Architect
jay.runkel@mongodb.com
@jayrunkel

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

3. Today’s Example

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. Attribute MongoDB Relational
Storage N-dimensional Two-dimensional
Field Values 0, 1, many, or embed Single value
Query Any field or level Any field
Schema Flexible Very structured

11. Complex Normalized Schemas

12. Complex Normalized Schemas

13. 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

14. Relationships

15. Modeling One-to-One Relationships

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

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

18. 18
Referencing
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”,
…
}
}

19. 19
Embedding
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”,
…
}
}
}

20. 20
Embedding
• 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

21. 21
Atomicity
• Document operations are atomic
db.patients.update({_id: 12345},
{$inc : {numProcedures : 1},
$push : {procedures : “proc123”},
$set : {addr.state : “TX”}})
• No multi-document transactions
db.beginTransaction();
db.patients.update({_id: 12345}, …);
db.procedure.insert({_id: “proc123”, …});
db.records.insert({_id: “rec123”, …});
db.endTransaction();

22. 22
Embedding
• 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

23. 23
Referencing
• Advantages
– Smaller documents
– Less likely to reach 16 MB document limit
– Infrequently accessed information not accessed on every query
– No duplication of data
• Limitations
– Two queries required to retrieve information
– Cannot update related information atomically

24. 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”,
…
}
}
}

25. Modeling One-to-Many Relationships

26. 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

27. 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”,
…}]
}

28. Modeling Many-to-Many Relationships

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

30. 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

31. 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: {…},
…}

32. 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

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

34. 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

35. Schema Design and Performance
Two Examples

36. Example 1: Hybrid Approach
Embed and Reference

37. 37
Healthcare Example
patients
procedures

38. 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

39. 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

40. Example 2: Time Series Data
Medical Devices

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

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

43. 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

44. 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

45. 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

46. 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

47. 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

48. 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

49. Questions?
jay.runkel@mongodb.com
@jayrunkel