This document provides an overview and examples of using MongoDB aggregations. It begins with introductions to MongoDB basics like documents, collections, and simple queries. The bulk of the document focuses on the MongoDB aggregation pipeline, explaining what it is, common stages like $match, $group, $lookup, and examples of queries using those stages like filtering orders by postcode, grouping orders by type, joining products and brands with $lookup. It provides examples of more advanced techniques like lookups with sub-pipelines, grouping, dates, and exercises for readers to try themselves.

1. MongoDB Aggregations
Mikhail Asavkin
@limenutt

2. Soup du jour
● My company
● Business context
● Aggregation basics
● Coding!
● Take home task

4. We all interact with property every
day.
We build, sell, buy, rent, lease,
own, use and admire it.

5. At Skychute
We improve experiences people
have with property.

6. We use technology to provide
tenants, owners, investors, agents,
developers and builders with
greater experience and outcomes.

7. At the heart of our company,
our greatest asset are
brave engineers
passionate about property.

8. Join
our cause

9. Meet the data!
@limenutt

10. @limenutt

11. MS L

12. M LS

13. M LS

14. M LS
The Product

15. M LS
The Product
$29
$29
$29

16. The Variant
Product: T-Shirt #42
Colour: Yolk
Size: S
S @limenutt

17. S
The Variant
Product: T-Shirt #42
Colour: Snow
Size: S
@limenutt

18. M
The Variant
Product: T-Shirt #42
Colour: Snow
Size: M
@limenutt

19. M
The Variant
Product: T-Shirt #42
Colour: Yolk
Size: M
@limenutt

20. @limenutt

21. Categories
S/S Tee
(T-Shirt)
@limenutt

22. Categories
S/S Tee
(T-Shirt)
S/S Polo
@limenutt

23. Categories
S/S Tee
(T-Shirt)
S/S Polo S/S Shirt
@limenutt

24. Categories
S/S Tee
(T-Shirt)
S/S Polo S/S Shirt L/S Shirt
@limenutt

25. @limenutt

26. Crew neck V-neck
@limenutt

27. Tags
Crew neck V-neck
@limenutt

28. Tags
Crew neck Slim V-neck Regular
@limenutt

29. Tags
Crew neck Slim
Plain
V-neck Regular
Plain @limenutt

30. Tags
Crew neck Slim
CasualPlain
V-neck Regular
Smart-CasualPlain @limenutt

31. Tag buckets
Crew neck Slim
CasualPlain
V-neck Regular
Smart-CasualPlain @limenutt

32. Tag buckets
Crew neck Slim
CasualPlain
V-neck Regular
Smart-CasualPlain @limenutt

33. Tag buckets
Crew neck Slim
CasualPlain
V-neck Regular
Smart-CasualPlain @limenutt

34. Tag buckets
Crew neck Slim
CasualPlain
V-neck Regular
Smart-CasualPlain @limenutt

35. Order
Order
customer
owner
salesperson
variant 1
variant 2
variant 3 @limenutt

36. Product
Category bucket
variants
Variant
category
brand
tags
Brand
Category
categories
Tag bucket
tagBuckets
Tag
tags
Order
variants
customer
owner User
Customer
Colour
product
supplierColours
equivalent
Schema
@limenutt

37. Questions
about the data?
@limenutt

38. Querying data
from MongoDB
@limenutt

39. MongoDB Basics
● MongoDB = Document-oriented database
● Document = record
● Document structure is almost JSON
● Documents belong to a Collection
● Database has many Collections
@limenutt

40. MongoDB Basics: Sample document
@limenutt

41. MongoDB Basics: Simple query
@limenutt

42. MongoDB Basics: Simple query
@limenutt

43. MongoDB Basics: Simple query
@limenutt

44. MongoDB Basics: Simple query
@limenutt

45. MongoDB Basics: Simple query
@limenutt

46. MongoDB Basics: Simple query
@limenutt

47. MongoDB Basics: Simple query
@limenutt

48. MongoDB
Aggregation Pipeline
@limenutt

49. ● FAST: 10x faster than doing the same in the backend
● EASY TO USE: Build a complex report in a few minutes
Why use Aggregation Pipeline
@limenutt

50. ● Aggregation is a list of operations
What is Aggregation Pipeline
@limenutt

51. ● Aggregation is a list of operations
● Operations performed in order
What is Aggregation Pipeline
@limenutt

52. ● Aggregation is a list of operations
● Operations performed in order
● Output of the previous operation is the input of the next
one, hence Pipeline
What is Aggregation Pipeline
@limenutt

53. ● Aggregation is a list of operations
● Operations performed in order
● Output of the previous operation is the input of the next
one, hence Pipeline
● It’s mapreduce on steroids
What is Aggregation Pipeline
@limenutt

54. $match( or )
$group(‘$colour’)
collection(‘shapes’)
@limenutt

55. $match( or )
$group(‘$colour’)
collection(‘shapes’)
@limenutt

56. $match( or )
$group(‘$colour’)
collection(‘shapes’)
@limenutt

57. $match( or )
$group(‘$colour’)
collection(‘shapes’)

59. Aggregation Pipeline
basics
@limenutt

60. Aggregation pipeline:
@limenutt

61. Aggregation pipeline:
Aggregation stage 1
Aggregation stage 2
@limenutt

62. @limenutt

63. Aggregation stage name
@limenutt

64. Aggregation stage name
Aggregation stage body
@limenutt

65. Starter app
@limenutt

66. bit.ly/mongoapp
@limenutt

67. 1. Open the project in your
favourite editor (vscode)
@limenutt

68. 1. Open the project in your
favourite editor (vscode)
2. Open the terminal window
@limenutt

69. 1. Open the project in your
favourite editor (vscode)
2. Open the terminal window
3. Run
npm i && npm run start-w

70. src/app.ts
@limenutt

71. src/app.ts
Your password goes there
@limenutt

72. src/app.ts
Here goes your
aggregation pipeline
@limenutt

73. src/app.ts
@limenutt

74. src/app.ts
This is the collection you are
fetching the data from.
You will need to change it
depending on the task @limenutt

75. Please, keep the docs open:
@limenutt

76. Please, keep the docs open:
● Aggregation Pipeline
Stages
@limenutt

77. Please, keep the docs open:
● Aggregation Pipeline
Stages
● Aggregation Pipeline
Operators @limenutt

78. Let’s code!
@limenutt

79. T: Filtering ($match stage)
@limenutt

80. T: Filtering ($match stage)
E: List all orders with postcode 2000
@limenutt

81. T: Filtering ($match stage)
E: List all orders with postcode 2000
@limenutt

82. T: Filtering ($match stage)
E: List all orders with postcode 2000
@limenutt

83. T: Filtering ($match stage)
E: List all orders with postcode 2000
@limenutt

84. T: Filtering ($match stage)
E: List all orders with postcode 2000
Operator ‘equals’ [a, b]
@limenutt

85. T: Filtering ($match stage)
E: List all orders with postcode 2000
Operator ‘equals’ [a, b]
Reference to the field postcode
@limenutt

86. T: Filtering ($match stage)
E: List all orders with postcode 2000
Operator ‘equals’ [a, b]
Reference to the field postcode
@limenutt

87. T: Filtering ($match stage)
E: List all orders with postcode 2000
Operator ‘equals’ [a, b]
Reference to the field postcode
@limenutt

88. T: Filtering ($match stage)
1. List all orders in NSW
2. List all products under $50 (retailPrice)
3. List all products within $50 — $100
@limenutt

89. T: Sorting ($sort stage)
E: Orders sorted by creation date
@limenutt

90. T: Sorting ($sort stage)
E: Orders sorted by creation date
@limenutt

91. T: Sorting ($sort stage)
1. Products sorted by retail price
ascending
2. Products sorted by retail price
descending
@limenutt

92. T: Limiting ($limit stage)
E: Top 5 most recent orders
@limenutt

93. T: Limiting ($limit stage)
E: Top 5 most recent orders
@limenutt

94. T: Limiting ($limit stage)
E: Top 5 most recent orders
@limenutt

95. T: Limiting ($limit stage)
1. Top 10 most expensive products
@limenutt

96. T: Grouping ($group stage)
E: List all order types
@limenutt

97. T: Grouping ($group stage)
E: List all order types
@limenutt

98. T: Grouping ($group stage)
1. List all order postcodes
@limenutt

99. T: Group accumulator operators
E: Average product retail price
@limenutt

100. T: Group accumulator operators
E: Average product retail price
@limenutt

101. T: Group accumulator operators
E: Average product retail price
Accumulator operator ‘average’
@limenutt

102. T: Group accumulator operators
1. Maximum product price
2. Sum of prices of all products
3. Total number of products
@limenutt

103. T: Filtering + counting
1. Number of orders with type of
Style Lab
2. Number of products over $100
@limenutt

104. T: Grouping + counting
1. Number of orders by type
2. Number of orders by state
3. Number of colours by name
@limenutt

105. T: Adding fields and Expressions
E: Products with margin amounts
@limenutt

106. T: Adding fields and Expressions
E: Products with margin amounts
@limenutt

107. T: Adding fields and Expressions
E: Products with margin amounts
This stage adds new fields to the documents
@limenutt

108. T: Adding fields and Expressions
E: Products with margin amounts
This stage adds new fields to the documents
Field expression
@limenutt

109. T: Adding fields and Expressions
1. Products with margin percentage
2. Top 10 products with the highest
margin percentage
3. Products by number of variants
@limenutt

110. T: 1-1 Lookups
Product
brand (id)
Brand
name
_id
@limenutt

111. T: 1-1 Lookups
Product
brand (id)
Brand
name
_id
Product
brand:
name
_id
$lookup
@limenutt

112. T: 1-1 Lookups ($lookup stage)
E: Products with brand
@limenutt

113. T: 1-1 Lookups ($lookup stage)
E: Products with brand
@limenutt

114. T: 1-1 Lookups ($lookup stage)
E: Products with brand
Collection to look up from
Field to match from current collection
Field to match from another collection
Field for the result
@limenutt

115. T: 1-1 Lookups ($lookup stage)
E: Products with brand
Collection to look up from
Field to match from current collection
Field to match from another collection
Field for the result
The result is always an array
@limenutt

116. T: 1-1 Lookups ($lookup stage)
E: Products with brand
Collection to look up from
Field to match from current collection
Field to match from another collection
Field for the result
The result is always an array
Turn array into object (for now)@limenutt

117. T: 1-1 Lookups ($lookup stage)
1. Variants with product
2. Variants with retail price
@limenutt

118. T: 1-* Lookups
Product
variants [id, id,...id]
Variant
size
_id
@limenutt

119. T: 1-* Lookups
Product
variants [id, id,...id]
Variant
size
_id
Product
variants:
$lookup
size
_id
size
_id
size
_id
size
_id
@limenutt

120. T: 1-* Lookups
E: Products with variants
@limenutt

121. T: 1-* Lookups
E: Products with variants
@limenutt

122. T: 1-* Lookups
1. Orders with variants and customer
@limenutt

123. T: Lookups with grouping
1. Number of products by category
2. Average product price by brand
@limenutt

124. 1. Top 10 most expensive Casual products
2. Top 5 cheapest red products
3. Products with tags joined by commas
T: Lookups with expressions
@limenutt

125. T: Lookups with sub-pipelines
Order
variants [id, id,...id]
Variant
size
_id
customer
@limenutt

126. T: Lookups with sub-pipelines
Order
variants [id, id,...id]
Variant
size
_id
Variant
orders:
$lookup
customer
_id
customer
_id
customer
_id
customer
_id
customer
@limenutt

127. T: Lookups with sub-pipelines
@limenutt

128. T: Lookups with sub-pipelines
@limenutt

129. T: Lookups with sub-pipelines
Variables to be used in sub-pipeline
@limenutt

130. T: Lookups with sub-pipelines
Variables to be used in sub-pipeline
This pipeline is executed in orders collection
@limenutt

131. T: Lookups with sub-pipelines
Variables to be used in sub-pipeline
This pipeline is executed in orders collection
$in [b, a] operator checks if value b is in the array a
Identity comparison used!
@limenutt

132. T: Lookups with sub-pipelines
1. Products with orders
2. Orders with total amounts
@limenutt

133. T: Dates
E: Number of orders by year
@limenutt

134. T: Dates
E: Number of orders by year
@limenutt

135. T: Dates
1. Sales by year
2. Sales by month-year
3. Sales by a salesperson (owner)
by month-year
@limenutt

136. T: Advanced
1. Most popular products
2. Brands by margin percentage
3. Top 5 most profitable categories
4. Variants by base colours
5. Most popular base colours by month
6. Number of products per $50 price
bracket, sorted by max price ascending
@limenutt

137. Thank you!
@limenutt

138. Keep in touch
@limenutt