The document outlines a workshop on MongoDB led by Lauren Schaefer and Ken Alger, covering key topics including MongoDB terms, CRUD operations, and the advantages of a free MongoDB Atlas cluster. It emphasizes the flexible schema of MongoDB, how data is modeled differently compared to SQL, and the significance of indexing for query efficiency. The workshop also promises insights and tips to optimize MongoDB usage.

1. Back to Basics
Lauren Schaefer Ken Alger
@Lauren_Schaefer @KenWAlger
Bonus points for
following us on
Twitter

2. Parks and Recreation, Season 6, Episode 14

6. Parks and Recreation, Season 6, Episode 14

7. Back to Basics
Lauren Schaefer Ken Alger
@Lauren_Schaefer @KenWAlger
Slides are
available on our
Twitter pages

8. #BackToBasics #MongoDB @KenWAlger @Lauren_Schaefer
The story of this workshop is that
it’s about MongoDB
#MongoDB @KenWAlger @Lauren_Schaefer
1. MongoDB terms & concepts
2. Free MongoDB Atlas Cluster
3. CRUD operations
4. Tips & tricks
5. $100 Atlas credits

9. #BackToBasics #MongoDB @KenWAlger @Lauren_Schaefer
The story of this workshop is that
it’s about MongoDB
#MongoDB @KenWAlger @Lauren_Schaefer
1. MongoDB terms & concepts
2. Free MongoDB Atlas Cluster
3. CRUD operations
4. Tips & tricks
5. $100 Atlas credits

10. #BackToBasics #MongoDB @KenWAlger @Lauren_Schaefer
MongoDB stores data in documents

11. #BackToBasics #MongoDB @KenWAlger @Lauren_Schaefer
MongoDB stores data in documents
{
first_name: "Paul",
surname: "Miller",
cell: "447557505611",
city: "London",
location: [45.123,47.232],
profession: ["banking", "finance", "trader"],
cars: [
{
model: "Bentley",
year: 1973
},
{
model: "Rolls Royce",
year: 1965
}
]
}

12. #BackToBasics #MongoDB @KenWAlger @Lauren_Schaefer
MongoDB stores data in documents
{
first_name: "Paul",
surname: "Miller",
cell: "447557505611",
city: "London",
location: [45.123,47.232],
profession: ["banking", "finance", "trader"],
cars: [
{
model: "Bentley",
year: 1973
},
{
model: "Rolls Royce",
year: 1965
}
]
}

13. #BackToBasics #MongoDB @KenWAlger @Lauren_Schaefer
MongoDB stores data in documents
{
first_name: "Paul",
surname: "Miller",
cell: "447557505611",
city: "London",
location: [45.123,47.232],
profession: ["banking", "finance", "trader"],
cars: [
{
model: "Bentley",
year: 1973
},
{
model: "Rolls Royce",
year: 1965
}
]
}

14. #BackToBasics #MongoDB @KenWAlger @Lauren_Schaefer
MongoDB stores data in documents
{
first_name: "Paul",
surname: "Miller",
cell: "447557505611",
city: "London",
location: [45.123,47.232],
profession: ["banking", "finance", "trader"],
cars: [
{
model: "Bentley",
year: 1973
},
{
model: "Rolls Royce",
year: 1965
}
]
}

15. #BackToBasics #MongoDB @KenWAlger @Lauren_Schaefer
MongoDB stores data in documents
{
first_name: "Paul",
surname: "Miller",
cell: "447557505611",
city: "London",
location: [45.123,47.232],
profession: ["banking", "finance", "trader"],
cars: [
{
model: "Bentley",
year: 1973
},
{
model: "Rolls Royce",
year: 1965
}
]
}

16. #BackToBasics #MongoDB @KenWAlger @Lauren_Schaefer
MongoDB stores data in documents
{
first_name: "Paul",
surname: "Miller",
cell: "447557505611",
city: "London",
location: [45.123,47.232],
profession: ["banking", "finance", "trader"],
cars: [
{
model: "Bentley",
year: 1973
},
{
model: "Rolls Royce",
year: 1965
}
]
}

17. #BackToBasics #MongoDB @KenWAlger @Lauren_Schaefer
MongoDB stores data in documents
{
first_name: "Paul",
surname: "Miller",
cell: "447557505611",
city: "London",
location: [45.123,47.232],
profession: ["banking", "finance", "trader"],
cars: [
{
model: "Bentley",
year: 1973
},
{
model: "Rolls Royce",
year: 1965
}
]
}

18. #BackToBasics #MongoDB @KenWAlger @Lauren_Schaefer
MongoDB stores data in documents
{
first_name: "Paul",
surname: "Miller",
cell: "447557505611",
city: "London",
location: [45.123,47.232],
profession: ["banking", "finance", "trader"],
cars: [
{
model: "Bentley",
year: 1973
},
{
model: "Rolls Royce",
year: 1965
}
]
}

19. #BackToBasics #MongoDB @KenWAlger @Lauren_Schaefer
Modeling data in MongoDB vs SQL
{
first_name: "Paul",
surname: "Miller",
cell: "447557505611",
city: "London",
location: [45.123,47.232],
profession: ["banking", "finance", "trader"],
cars: [
{
model: "Bentley",
year: 1973
},
{
model: "Rolls Royce",
year: 1965
}
]
}

20. #BackToBasics #MongoDB @KenWAlger @Lauren_Schaefer
Modeling data in MongoDB vs SQL
{
first_name: "Paul",
surname: "Miller",
cell: "447557505611",
city: "London",
location: [45.123,47.232],
profession: ["banking", "finance", "trader"],
cars: [
{
model: "Bentley",
year: 1973
},
{
model: "Rolls Royce",
year: 1965
}
]
}
ID first_name surname cell city location_x location_y
1 Paul Miller 447557505611 London 45.123 47.232
Users

21. #BackToBasics #MongoDB @KenWAlger @Lauren_Schaefer
Modeling data in MongoDB vs SQL
{
first_name: "Paul",
surname: "Miller",
cell: "447557505611",
city: "London",
location: [45.123,47.232],
profession: ["banking", "finance", "trader"],
cars: [
{
model: "Bentley",
year: 1973
},
{
model: "Rolls Royce",
year: 1965
}
]
}
ID first_name surname cell city location_x location_y
1 Paul Miller 447557505611 London 45.123 47.232
Users
ID user_id profession
10 1 banking
11 1 finance
12 1 trader
Professions

22. #BackToBasics #MongoDB @KenWAlger @Lauren_Schaefer
Modeling data in MongoDB vs SQL
{
first_name: "Paul",
surname: "Miller",
cell: "447557505611",
city: "London",
location: [45.123,47.232],
profession: ["banking", "finance", "trader"],
cars: [
{
model: "Bentley",
year: 1973
},
{
model: "Rolls Royce",
year: 1965
}
]
}
ID user_id profession
10 1 banking
11 1 finance
12 1 trader
Professions
ID user_id model year
20 1 Bentley 1973
21 1 Rolls Royce 1965
Cars
ID first_name surname cell city location_x location_y
1 Paul Miller 447557505611 London 45.123 47.232
Users

23. #BackToBasics #MongoDB @KenWAlger @Lauren_Schaefer
Modeling data in MongoDB vs SQL
{
first_name: "Paul",
surname: "Miller",
cell: "447557505611",
city: "London",
location: [45.123,47.232],
profession: ["banking", "finance", "trader"],
cars: [
{
model: "Bentley",
year: 1973
},
{
model: "Rolls Royce",
year: 1965
}
]
}
ID user_id profession
10 1 banking
11 1 finance
12 1 trader
Professions
ID user_id model year
20 1 Bentley 1973
21 1 Rolls Royce 1965
Cars
ID first_name surname cell city location_x location_y
1 Paul Miller 447557505611 London 45.123 47.232
Users

24. #BackToBasics #MongoDB @KenWAlger @Lauren_Schaefer
Collections vs Tables
{
first_name: "Paul",
surname: "Miller",
cell: "447557505611",
city: "London",
location: [45.123,47.232],
profession: ["banking", "finance", "trader"],
cars: [
{
model: "Bentley",
year: 1973
},
{
model: "Rolls Royce",
year: 1965
}
]
}
{
first_name: ”Lauren",
surname: ”Schaefer",
cell: ”1235552222",
city: ”Lancaster",
profession: [”software engineer", ”developer advocate"],
}
{
first_name: ”Sydney",
surname: ”Schaefer",
city: ”Lancaster",
school: ”Daisy’s Daycare”
}
ID first_name surname cell city location_x location_y
1 Paul Miller 447557505611 London 45.123 47.232
2 Lauren Schaefer 1235552222 Lancaster NULL NULL
3 Sydney Schaefer NULL Lancaster NULL NULL
UsersUsers

25. #BackToBasics #MongoDB @KenWAlger @Lauren_Schaefer
ID first_name surname cell city location_x location_y
1 Paul Miller 447557505611 London 45.123 47.232
2 Lauren Schaefer 1235552222 Lancaster NULL NULL
3 Sydney Schaefer NULL Lancaster NULL NULL
Collections vs Tables
{
first_name: "Paul",
surname: "Miller",
cell: "447557505611",
city: "London",
location: [45.123,47.232],
profession: ["banking", "finance", "trader"],
cars: [
{
model: "Bentley",
year: 1973
},
{
model: "Rolls Royce",
year: 1965
}
]
}
{
first_name: ”Lauren",
surname: ”Schaefer",
cell: ”1235552222",
city: ”Lancaster",
profession: [”software engineer", ”developer advocate"],
}
{
first_name: ”Sydney",
surname: ”Schaefer",
city: ”Lancaster",
school: ”Daisy’s Daycare”
}
UsersUsers

26. #BackToBasics #MongoDB @KenWAlger @Lauren_Schaefer
Collections vs Tables
ID first_name surname cell city location_x location_y
1 Paul Miller 447557505611 London 45.123 47.232
2 Lauren Schaefer 1235552222 Lancaster NULL NULL
3 Sydney Schaefer NULL Lancaster NULL NULL
{
first_name: "Paul",
surname: "Miller",
cell: "447557505611",
city: "London",
location: [45.123,47.232],
profession: ["banking", "finance", "trader"],
cars: [
{
model: "Bentley",
year: 1973
},
{
model: "Rolls Royce",
year: 1965
}
]
}
{
first_name: ”Lauren",
surname: ”Schaefer",
cell: ”1235552222",
city: ”Lancaster",
profession: [”software engineer", ”developer advocate"],
}
{
first_name: ”Sydney",
surname: ”Schaefer",
city: ”Lancaster",
school: ”Daisy’s Daycare”
}
UsersUsers

27. #BackToBasics #MongoDB @KenWAlger @Lauren_Schaefer
Flexible Schema

28. Don’t panic!
Use schema validation.
#BackToBasics #MongoDB @KenWAlger @Lauren_Schaefer
Flexible Schema

29. Document Row
{
...
a: “b”
...
}
ID a ...
1 b ...
2 ... ...
3 ... ...
#BackToBasics #MongoDB @KenWAlger @Lauren_Schaefer

30. Document Row(s)
{
...
a: “b”
...
}
ID a ...
1 b ...
2 ... ...
3 ... ...
... ... ...
... ... ...
... ... ...
... ... ...
... ... ...
... ... ...
#BackToBasics #MongoDB @KenWAlger @Lauren_Schaefer

31. Field Column
ID a ...
1 b ...
2 c ...
3 ... ...
{
...
a: “b”
...
}
{
...
a: “c”
...
}
#MongoDB @KenWAlger @Lauren_Schaefer#BackToBasics

32. Collection Table
{
...
}
... ... ...
... ... ...
... ... ...
... ... ...
{
...
}
{
...
}
#MongoDB @KenWAlger @Lauren_Schaefer#BackToBasics

33. Database Database
... ... ...
... ... ...
... ... ...
... ... ...
{
...
}
{
...
}
{
...
}
{
...
}
{
...
}
{
...
}
{
...
}
... ... ...
... ... ...
... ... ...
... ... ...
... ... ...
#MongoDB @KenWAlger @Lauren_Schaefer#BackToBasics

34. Index Index
{
...
}
{
...
}
{
...
}
{
...
}
... ... ...
... ... ...
... ... ...
... ... ...
... ... ...
#MongoDB @KenWAlger @Lauren_Schaefer#BackToBasics

35. View View
{
...
}
... ... ...
... ... ...
... ... ...
... ... ...
{
...
}
{
...
}
#MongoDB @KenWAlger @Lauren_Schaefer#BackToBasics

36. Embedding Join
{
...
a: “b”,
...
c: {
d: “e”
...
},
...
}
ID a ...
1 b ...
2 ... ...
3 ... ...
... d ...
1 e ...
... ... ...
#MongoDB @KenWAlger @Lauren_Schaefer#BackToBasics

37. Database References Join
ID ... ...
1 ... ...
2 ... ...
3 ... ...
... ... ...
1 ... ...
... ... ...
{
...
}
{
...
}
{
...
}
{
...
}
{
...
}
{
...
}
{
...
}
#MongoDB @KenWAlger @Lauren_Schaefer#BackToBasics

38. $lookup
(Aggregation Pipeline)
Left Outer Join
ID ... ...
1 ... ...
2 ... ...
3 ... ...
... ... ...
1 ... ...
4 ... ...
{
...
}
{
...
}
{
...
}
{
...
}
{
...
}
{
...
}
{
...
}
#MongoDB @KenWAlger @Lauren_Schaefer#BackToBasics

39. $graphLookup
(Aggregation Pipeline)
Recursive Common
Table Expressions
{
...
}
... ... ...
... ... ...
... ... ...
... ... ...
{
...
}
{
...
}
#MongoDB @KenWAlger @Lauren_Schaefer#BackToBasics

40. Multi-Document ACID
Transaction
Multi-Record ACID
Transaction
{
...
}
{
...
}
{
...
}
{
...
}
{
...
}
{
...
}
{
...
}
... ... ...
... ... ...
... ... ...
... ... ...
... ... ...
... ... ...
... ... ...
#MongoDB @KenWAlger @Lauren_Schaefer#BackToBasics

41. #BackToBasics #MongoDB @KenWAlger @Lauren_Schaefer
Term mapping summary
x
Row Column Table Database Index Join Join Left Outer
Join
Recursive
Common Table
Expressions
View Transaction
Document Field Collection Database Index Embedding
Database
References
$lookup $graphLookup View Transaction

42. #BackToBasics #MongoDB @KenWAlger @Lauren_Schaefer
The story of this workshop is that
it’s about MongoDB
1. MongoDB terms & concepts
2. Free MongoDB Atlas Cluster
3. CRUD operations
4. Tips & tricks
5. $100 Atlas credits

45. #BackToBasics #MongoDB @KenWAlger @Lauren_Schaefer
The story of this workshop is that
it’s about MongoDB
1. MongoDB terms & concepts
2. Free MongoDB Atlas Cluster
3. CRUD operations
4. Tips & tricks
5. $100 Atlas credits

48. #BackToBasics #MongoDB @KenWAlger @Lauren_Schaefer
The story of this workshop is that
it’s about MongoDB
1. MongoDB terms & concepts
2. Free MongoDB Atlas Cluster
3. CRUD operations
4. Tips & tricks
5. $100 Atlas credits

51. #BackToBasics #MongoDB @KenWAlger @Lauren_Schaefer
Use Indexes for Read Speed
• Very important for reads.
• However, they come with overhead.
• New in MongoDB 4.2, Wildcard Indexes

52. #BackToBasics #MongoDB @KenWAlger @Lauren_Schaefer
Indexes support the efficient
execution of queries in MongoDB.
Use Indexes for Read Speed

53. #BackToBasics #MongoDB @KenWAlger @Lauren_Schaefer
Index Types in MongoDB
Single Field { karma: 1}
Compound Field { karma: 1, user_id: -1 }
Multikey { “address.postal_code”: 1 }
Geospatial
Text
Hashed
Wildcard

54. #BackToBasics #MongoDB @KenWAlger @Lauren_Schaefer
Model Data Using Schema Design Patterns
• Different way of modeling from the legacy database
paradigm.
• Schema Design is important.

55. #BackToBasics #MongoDB @KenWAlger @Lauren_Schaefer
Why Do We Create Models?
Ensure:
• Good performance
• Scalability
despite constraints
Hardware
• RAM faster than Disk
• Disk cheaper than RAM
• Network latency
• Reduce costs $$$
Database Server
• Maximum size for a document
Data set
• Size of data

56. #BackToBasics #MongoDB @KenWAlger @Lauren_Schaefer
• Frequency of Access
• Subset
• Approximation
• Extended Reference
Patterns by Category
• Grouping
• Computed
• Bucket
• Outlier
• Representation
• Attribute
• Schema Versioning
• Document Versioning
• Tree
• Polymorphism
• Pre-Allocation

57. #BackToBasics #MongoDB @KenWAlger @Lauren_Schaefer
Add a field to track the
schema version number, per
document
Does not have to exist for
version 1
Pattern: Schema Versioning

58. #BackToBasics #MongoDB @KenWAlger @Lauren_Schaefer
Problem:
Updating the schema of a database is:
• Not atomic
• Long operation
• May not want to update all documents, only do it on updates
Schema Versioning Pattern
Use cases:
Practically any database that will go to production

59. #BackToBasics #MongoDB @KenWAlger @Lauren_Schaefer
Solution:
Have a field keeping track of the schema version
Schema Versioning Pattern –
Solution
Benefits:
Don't need to update all the documents at once
May not have to update documents until their next modification

60. #BackToBasics #MongoDB @KenWAlger @Lauren_Schaefer
Reduce Aggravations with the
Aggregation Framework
• Use whenever possible
• Operations are done server-side
• Order of stages matters

61. #BackToBasics #MongoDB @KenWAlger @Lauren_Schaefer
Aggregation

62. #BackToBasics #MongoDB @KenWAlger @Lauren_Schaefer
PIPELINE
ps ax | grep mongod| head 1
*nix command line pipe

63. #BackToBasics #MongoDB @KenWAlger @Lauren_Schaefer
PIPELINE
$match $group | $sort|
Input stream {}
{} {} {}
Result {} {}
...
MongoDB document pipeline

64. #BackToBasics #MongoDB @KenWAlger @Lauren_Schaefer
The story of this workshop is that
it’s about MongoDB
1. MongoDB terms & concepts
2. Free MongoDB Atlas Cluster
3. CRUD operations
4. Tips & tricks
5. $100 Atlas credits

67. #BackToBasics #MongoDB @KenWAlger @Lauren_Schaefer
Sign up for MongoDB Atlas
http://bit.ly/MDB_Atlas

68. #BackToBasics #MongoDB @KenWAlger @Lauren_Schaefer
Add discount code: BACKTOBASICS2020

69. #BackToBasics #MongoDB @KenWAlger @Lauren_Schaefer
Add discount code: BACKTOBASICS2020

70. #BackToBasics #MongoDB @KenWAlger @Lauren_Schaefer
Add discount code: BACKTOBASICS2020

71. #BackToBasics #MongoDB @KenWAlger @Lauren_Schaefer
The story of this workshop is that
it’s about MongoDB
1. MongoDB terms & concepts
2. Free MongoDB Atlas Cluster
3. CRUD operations
4. Tips & tricks
5. $100 Atlas credits

72. #BackToBasics #MongoDB @KenWAlger @Lauren_Schaefer
1. Map terms from SQL to MongoDB
x
Row Column Table Database Index Join Join Left Outer
Join
Recursive
Common Table
Expressions
View Transaction
Document Field Collection Database Index Embedding
Database
References
$lookup $graphLookup View Transaction

73. #BackToBasics #MongoDB @KenWAlger @Lauren_Schaefer
2. Free Atlas cluster

74. #BackToBasics #MongoDB @KenWAlger @Lauren_Schaefer
3. The CRUD operations

75. #BackToBasics #MongoDB @KenWAlger @Lauren_Schaefer
4. Tips & tricks
• Use Indexes for Read Speed
• Model Data Using Schema Design Patterns
• Reduce Aggravation with the Aggregation Pipeline

76. #BackToBasics #MongoDB @KenWAlger @Lauren_Schaefer
5. $100 Atlas credits with code:
BACKTOBASICS2020

77. Don’t be Ron Swanson
(in this particular case)
#MongoDB @KenWAlger @Lauren_Schaefer#BackToBasics

78. Change your mindset &
get the full value of MongoDB
Don’t be Ron Swanson
#MongoDB @KenWAlger @Lauren_Schaefer#BackToBasics

79. #BackToBasics #MongoDB @KenWAlger @Lauren_Schaefer
Additional resources on data modeling
patterns
• Advanced Schema Design Patterns (webinar)
• Building with Patterns: A Summary (blog series)
• M320: Data Modeling (MongoDB University Course –
brand new!)

80. #BackToBasics #MongoDB @KenWAlger @Lauren_Schaefer
Additional resources
• The MongoDB Docs
• Quick Start blog series in a variety of programming
languages
• JSON Schema Validation – Locking down your model the
smart way
• JSON Schema Validation - Checking Your Arrays
• M121: The MongoDB Aggregation Framework

81. Don’t be Ron Swanson
(in this particular case)
Change your mindset and get the
full value of MongoDB
Change your mindset &
get the full value of MongoDB
Get the slides on our Twitter pages:
@KenWAlger
@Lauren_Schaefer
#MongoDB @KenWAlger @Lauren_Schaefer#BackToBasics