The document discusses data modeling and partitioning in Azure Cosmos DB. It begins with an overview of Cosmos DB's scalability and flexibility as a non-relational database. It then walks through modeling common entities like customers, products, orders and optimizing the data model and partitioning strategy. The key aspects covered include choosing a partition key, embedding vs referencing data, denormalizing for performance, and using change feeds to keep data synchronized across partitions.

1. Data modelling and partitioning in Azure
Cosmos DB
(Azure Cosmos DB でのデータ モデリングとパーティション分割)

2. Session's objectives

3. What is Azure Cosmos DB?
Non-relational and horizontally scalable

4. What is Azure Cosmos DB?
horizontally scalable

5. What is Azure Cosmos DB?
non-relational

6. What is Azure Cosmos DB?
non-relational
and
horizontally scalable

7. So is Azure Cosmos DB suitable for relational
workloads?

8. Let's look at a concrete example

9. Identifying the operations we have to serve

10. Now let's implement this model on Azure Cosmos DB!

11. Starting with the Customer entity

12. Starting with the Customer entity

13. To embed or to reference?

14. To embed or to reference?
-
-
-
-
-
-

15. Our first entity: Customer

16. Customer
customers
PK: ?

17. What is partitioning?

18. What is partitioning?
logical partitions

19. What is partitioning?
Andrew
Theo
Mark
TimDeborah Luis

20. What is partitioning?
Max size: 20 GB
Max size: 2 MB

21. What is partitioning?

22. What is partitioning?

23. What is partitioning?

24. What is partitioning?
Andrew TheoMarkTimDeborah Luis
SELECT * FROM c WHERE c.username = 'Mark'
our partition key

25. What is partitioning?
Andrew TheoMarkTimDeborah Luis
SELECT * FROM c WHERE c.favoriteColor = 'orange'
?

26. Choosing a partition key for customers
customers
PK: ?

27. Choosing a partition key for customers
customers
PK: ?

28. Choosing a partition key for customers
customers
PK: id

29. Choosing a partition key for customers
customers
PK: id

30. Next: product categories

31. Product categories

32. Product categories
productCategories
PK: ?

33. Product categories
productCategories
PK: ?
SELECT * FROM c

34. Product categories
productCategories
PK: type

35. Next: product tags

36. Product tags

37. Product tags
productTags
PK: ?

38. Product tags
productTags
PK: ?

39. Product tags
productTags
PK: type

40. Next: products

41. Products

42. Products

43. Products
products
PK: ?

44. Products
products
PK: ?
CategoryA CategoryCCategoryB
SELECT * FROM c WHERE c.categoryId = 'CategoryA'

45. Products
products
PK: categoryId
category name?
tag names?

46. Products: how to return category and tag names?
products
SELECT * FROM c WHERE c.categoryId = 'CategoryA'
productCategories
SELECT c.name FROM c WHERE c.id = 'CategoryA'
productTags
SELECT * FROM c
WHERE c.id IN ('<tagId1>', '<tagId2>', '<tagId3>')

47. Introducing denormalization

48. Products: denormalizing category and tag names
products
PK: categoryId

49. Products: keeping everything in sync
productCategories
productTags
products

50. Cosmos DB's change feed

51. Products: keeping everything in sync
productCategories
productTags
products

52. Next: sales orders

53. Sales orders

54. Sales orders

55. Sales orders
salesOrders
PK: ?

56. Sales orders
salesOrders
PK: ?

57. Sales orders
salesOrders
PK: ?
CustomerA CustomerCCustomerB
SELECT * FROM c WHERE c.customerId = 'CustomerA'

58. Sales orders
salesOrders
PK: customerId

59. Sales orders
salesOrders
PK: customerId
customers
PK: id

60. Mixing entities in the same container?

61. Sales orders
salesOrders
PK: customerId
customers
PK: id

62. Sales orders: mixing with customers
customers
PK: id

63. Sales orders: mixing with customers
customers
PK: customerId

64. Sales orders: mixing with customers
customers
PK: customerId

65. Sales orders: mixing with customers
CustomerA
CustomerC
CustomerB
customer sales orders
customers
PK: customerId

66. Sales orders
customers
PK: customerId
SELECT * FROM c WHERE c.customerId = 'CustomerA'
AND c.type = 'salesOrder'

67. Sales orders
customers
PK: customerId

68. Denormalizing the count of sales orders per customer

69. Denormalizing the count of sales orders per customer

70. Denormalizing the count of sales orders per customer
CustomerA
CustomerC
CustomerB
customer sales orders
customers
PK: customerId

71. Denormalizing the count of sales orders per customer
CustomerA
CustomerC
CustomerB
update the customer add a sales order
customers
PK: customerId

72. Denormalizing the count of sales orders per customer
CustomerA
CustomerC
CustomerB
update the customer add a sales order

73. Sales orders
customers
PK: customerId
SELECT * FROM c WHERE c.type = 'customer'
ORDER BY c.salesOrderCount DESC

74. Our final design
customers
PK: customerId
productCategories
PK: type
productTags
PK: type
products
PK: categoryId

75. Our final design, optimized!
customers
PK: customerId
productMeta
PK: type
products
PK: categoryId

76. Key takeaways

77. Going further
https://docs.microsoft.com/azure/cosmos-db/modeling-data
https://docs.microsoft.com/azure/cosmos-db/how-to-model-partition-example
https://devblogs.microsoft.com/cosmosdb/data-modeling-and-partitioning-for-relational-workloads/
https://github.com/AzureCosmosDB/labs/blob/master/readme.md
https://github.com/AzureCosmosDB/labs/blob/master/decks/Data-Modeling.pptx