MongoDB Days UK: Jumpstart: Schema Design
Download as PPTX, PDF8 likes1,565 views
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
![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](https://image.slidesharecdn.com/schemadesign05nov2015v121-151117173502-lva1-app6891/85/MongoDB-Days-UK-Jumpstart-Schema-Design-12-320.jpg)
![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](https://image.slidesharecdn.com/schemadesign05nov2015v121-151117173502-lva1-app6891/85/MongoDB-Days-UK-Jumpstart-Schema-Design-23-320.jpg)
![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”,
…}]
}](https://image.slidesharecdn.com/schemadesign05nov2015v121-151117173502-lva1-app6891/85/MongoDB-Days-UK-Jumpstart-Schema-Design-24-320.jpg)
![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](https://image.slidesharecdn.com/schemadesign05nov2015v121-151117173502-lva1-app6891/85/MongoDB-Days-UK-Jumpstart-Schema-Design-27-320.jpg)
![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: {…},
…}](https://image.slidesharecdn.com/schemadesign05nov2015v121-151117173502-lva1-app6891/85/MongoDB-Days-UK-Jumpstart-Schema-Design-28-320.jpg)
![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](https://image.slidesharecdn.com/schemadesign05nov2015v121-151117173502-lva1-app6891/85/MongoDB-Days-UK-Jumpstart-Schema-Design-29-320.jpg)
![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](https://image.slidesharecdn.com/schemadesign05nov2015v121-151117173502-lva1-app6891/85/MongoDB-Days-UK-Jumpstart-Schema-Design-35-320.jpg)
![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](https://image.slidesharecdn.com/schemadesign05nov2015v121-151117173502-lva1-app6891/85/MongoDB-Days-UK-Jumpstart-Schema-Design-36-320.jpg)
![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](https://image.slidesharecdn.com/schemadesign05nov2015v121-151117173502-lva1-app6891/85/MongoDB-Days-UK-Jumpstart-Schema-Design-40-320.jpg)
![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](https://image.slidesharecdn.com/schemadesign05nov2015v121-151117173502-lva1-app6891/85/MongoDB-Days-UK-Jumpstart-Schema-Design-41-320.jpg)
Ad
More Related Content
What's hot (19)
Viewers also liked (20)
Ad
Similar to MongoDB Days UK: Jumpstart: Schema Design (20)
Ad
More from MongoDB (20)
![MongoDB .local San Francisco 2020: Powering the new age data demands [Infosys]](https://cdn.slidesharecdn.com/ss_thumbnails/315pminfosysfinalsfoversionvocalpart1-200120221508-thumbnail.jpg?width=560&fit=bounds)
Recently uploaded (20)
MongoDB Days UK: Jumpstart: Schema Design
- 1. Schema Design (and its performance implications) Andrew Erlichson VP, Engineering [email protected] @erlichson
- 2. 2 Agenda 1. Example Problem 2. MongoDB Schema Design vs. Relational 3. Modeling Relationships 4. Schema Design and Performance
- 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
- 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
- 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
- 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”, … } } }
- 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”, …}] }
- 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
- 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