Se ha denunciado esta presentación.
Utilizamos tu perfil de LinkedIn y tus datos de actividad para personalizar los anuncios y mostrarte publicidad más relevante. Puedes cambiar tus preferencias de publicidad en cualquier momento.

MongoDB Schema Design and its Performance Implications

1.599 visualizaciones

Publicado el

MongoDB Schema Design and its Performance Implications by Jay Runkel

Publicado en: Software
  • Inicia sesión para ver los comentarios

MongoDB Schema Design and its Performance Implications

  1. 1. Schema Design (and its performance implications) Jay Runkel Principal Solutions Architect jay.runkel@mongodb.com @jayrunkel
  2. 2. 2 Agenda 1. Today’s Example 2. MongoDB Schema Design vs. Relational 3. Modeling Relationships 4. Schema Design and Performance
  3. 3. Today’s Example
  4. 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. 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. 6 Lot of Variability
  7. 7. Relational View
  8. 8. Schema Design: MongoDB vs. Relational
  9. 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. 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. 11. Complex Normalized Schemas
  12. 12. Complex Normalized Schemas
  13. 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. 14. Relationships
  15. 15. Modeling One-to-One Relationships
  16. 16. 16 Referencing Procedure • patient • date • type • physician • type Results • dataType • size • content: {…} Use two collections with a reference Similar to relational
  17. 17. 17 Procedure • patient • date • type • results • equipmentId • data1 • data2 • physician • Results • type • size • content: {…} Embedding Document Schema
  18. 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. 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. 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. 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. 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. 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. 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. 25. Modeling One-to-Many Relationships
  26. 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. 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. 28. Modeling Many-to-Many Relationships
  29. 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. 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. 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. 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. 33. What If I Want to Store Large Files in MongoDB?
  34. 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. 35. Schema Design and Performance Two Examples
  36. 36. Example 1: Hybrid Approach Embed and Reference
  37. 37. 37 Healthcare Example patients procedures
  38. 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. 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. 40. Example 2: Time Series Data Medical Devices
  41. 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. 42 We have a hospital(s) of devices
  43. 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. 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. 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. 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. 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. 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. 49. Questions? jay.runkel@mongodb.com @jayrunkel

×