Intro to MongoDB (Extended Session)

Intro to MongoDB
Lauren Schaefer Ken Alger
@Lauren_Schaefer @KenWAlger
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Parks and Recreation, Season 6, Episode 14

Intro to MongoDB
Lauren Schaefer Ken Alger
@Lauren_Schaefer @KenWAlger

#AllThingsOpen #MongoDB @KenWAlger @Lauren_Schaefer
The story of this workshop is that
it’s about MongoDB
1. Create a MongoDB cluster
2. Map terms & concepts from
SQL to MongoDB
3. Load sample data
4. Execute the CRUD operations
5. Tips & tricks

Sign up for MongoDB Atlas
http://bit.ly/MDB_Atlas

Build a cluster

Add discount code: ATOPEN100

The story of this workshop is that
it’s about MongoDB
1. Create a MongoDB cluster
2. Map terms & concepts from
SQL to MongoDB
3. Load sample data
5. Tips & tricks

MongoDB stores data in documents

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
}
]
}

Modeling data in MongoDB vs
SQL
{
first_name: "Paul",
surname: "Miller",
cell: "447557505611",
city: "London",
location: [45.123,47.232],
cars: [
{
model: "Bentley",
year: 1973
},
{
year: 1965
}
]
}

SQL
{
first_name: "Paul",
surname: "Miller",
cell: "447557505611",
city: "London",
location: [45.123,47.232],
cars: [
{
model: "Bentley",
year: 1973
},
{
year: 1965
}
]
}
ID first_name surname cell city location_x location_y
1 Paul Miller 447557505611 London 45.123 47.232
Users

SQL
{
first_name: "Paul",
surname: "Miller",
cell: "447557505611",
city: "London",
location: [45.123,47.232],
cars: [
{
model: "Bentley",
year: 1973
},
{
year: 1965
}
]
}
Users
ID user_id profession
10 1 banking
11 1 finance
12 1 trader
Professions

SQL
{
first_name: "Paul",
surname: "Miller",
cell: "447557505611",
city: "London",
location: [45.123,47.232],
cars: [
{
model: "Bentley",
year: 1973
},
{
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
Users

Collections vs Tables
{
first_name: "Paul",
surname: "Miller",
cell: "447557505611",
city: "London",
location: [45.123,47.232],
cars: [
{
model: "Bentley",
year: 1973
},
{
year: 1965
}
]
}
{
first_name: ”Lauren",
surname: ”Schaefer",
cell: ”1235552222",
city: ”Lancaster",
profession: [”software engineer", ”developer advocate"],
}
{
first_name: ”Sydney",
school: ”Daisy’s Daycare”
}
2 Lauren Schaefer 1235552222 Lancaster NULL NULL
3 Sydney Schaefer NULL Lancaster NULL NULL
UsersUsers

{
first_name: "Paul",
surname: "Miller",
cell: "447557505611",
city: "London",
location: [45.123,47.232],
cars: [
{
model: "Bentley",
year: 1973
},
{
year: 1965
}
]
}
{
cell: ”1235552222",
}
{
}
UsersUsers

Schemaless
database

Schemaless
database
Don’t panic!
Use schema validation.

Load the sample dataset

Document Row
{
...
a: “b”
...
}
ID a ...
1 b ...
2 ... ...
3 ... ...

Document Row(s)
{
...
a: “b”
...
}
ID a ...
1 b ...
2 ... ...
3 ... ...
... ... ...
... ... ...
... ... ...
... ... ...
... ... ...
... ... ...

Field Column
ID a ...
1 b ...
2 c ...
3 ... ...
{
...
a: “b”
...
}
{
...
a: “c”
...
}

Collection Table
{
...
}
... ... ...
... ... ...
... ... ...
... ... ...
{
...
}
{
...
}

Database Database
... ... ...
... ... ...
... ... ...
... ... ...
{
...
}
{
...
}
{
...
}
{
...
}
{
...
}
{
...
}
{
...
}
... ... ...
... ... ...
... ... ...
... ... ...
... ... ...

Index Index
{
...
}
{
...
}
{
...
}
{
...
}
... ... ...
... ... ...
... ... ...
... ... ...
... ... ...

View View
{
...
}
... ... ...
... ... ...
... ... ...
... ... ...
{
...
}
{
...
}

Embedding Join
{
...
a: “b”,
...
c: {
d: “e”
...
},
...
}
ID a ...
1 b ...
2 ... ...
3 ... ...
... d ...
1 e ...
... ... ...

Database References Join
ID ... ...
1 ... ...
2 ... ...
3 ... ...
... ... ...
1 ... ...
... ... ...
{
...
}
{
...
}
{
...
}
{
...
}
{
...
}
{
...
}
{
...
}

$lookup
(Aggregation Pipeline)
Left Outer Join
ID ... ...
1 ... ...
2 ... ...
3 ... ...
... ... ...
1 ... ...
4 ... ...
{
...
}
{
...
}
{
...
}
{
...
}
{
...
}
{
...
}
{
...
}

$graphLookup
(Aggregation Pipeline)
Recursive Common
Table Expressions
{
...
}
... ... ...
... ... ...
... ... ...
... ... ...
{
...
}
{
...
}

Multi-Document ACID
Transaction
Multi-Record ACID
Transaction
{
...
}
{
...
}
{
...
}
{
...
}
{
...
}
{
...
}
{
...
}
... ... ...
... ... ...
... ... ...
... ... ...
... ... ...
... ... ...
... ... ...

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

3. Navigate to https://jupyter.org/try
4. Click “Try JupyterLab”
5. Import the notebook you just downloaded
from GitHub
6. Execute all steps in “Set up”
Prepare for CRUD
1. Navigate to
http://bit.ly/ATO_MongoDB_Notebook
2. Save the file with pynb extension (NOT
txt)

Use Indexes for Read Speed
• Very important for reads.
• However, they come with overhead.
• New in MongoDB 4.2, Wildcard Indexes

Indexes support the efficient
execution of queries in MongoDB.
Use Indexes for Read Speed

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

Model Data Using Schema Design
Patterns
• Different way of modeling from the legacy database
paradigm.
• Schema Design is important.

Why Do We CreateModels?
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

• Frequency of Access
• Subset
• Approximation
• Extended Reference
Patterns byCategory
• Grouping
• Computed
• Bucket
• Outlier
• Representation
• Attribute
• Schema Versioning
• Document Versioning
• Tree
• Polymorphism
• Pre-Allocation

Add a field to track the
schema version number, per
document
Does not have to exist for
version 1
Pattern:SchemaVersioning

Problem:
Updating the schema of a database is:
• Not atomic
• Long operation
• May not want to update all documents, only do it on updates
SchemaVersioning Pattern
Use cases:
Practically any database that will go to production

Solution:
Have a field keeping track of the schema version
SchemaVersioning Pattern –
Solution
Benefits:
Don't need to update all the documents at once
May not have to update documents until their next modification

Reduce Aggravations with the
Aggregation Framework
• Use whenever possible
• Operations are done server-side
• Order of stages matters

Aggregation

PIPELINE
ps ax | grep mongod | head 1
*nix command line pipe

PIPELINE
$match $group | $sort|
Input stream {} {} {} {} Result {} {} ...
MongoDB document pipeline

1. Create a MongoDB cluster using
Atlas

2. 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

3. Load sample data

5. Tips & tricks
• Use Indexes for Read Speed
• Model Data Using Schema Design Patterns
• Reduce Aggravation with the Aggregation Pipeline

Don’t be Ron Swanson
(in this particular case)

Change your mindset &
get the full value of MongoDB

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!)

Additional resources
• The MongoDB Docs
• JSON Schema Validation – Locking down your model
the smart way
• JSON Schema Validation - Checking Your Arrays
• M121: The MongoDB Aggregation Framework

(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
Please rate this
session in the
app!
Check out the
workshop at
2:15 today!

Intro to MongoDB (Extended Session)

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