MongoDB .local Houston 2019: Jumpstart: From SQL to NoSQL -- Changing Your Mindset [MongoDB]

From SQL to NoSQL—
Changing Your Mindset
@Lauren_Schaefer, Developer Advocate, MongoDB

Parks and Recreation, Season 6, Episode 14

Lauren Schaefer
Developer Advocate, MongoDB
@Lauren_Schaefer

From SQL to NoSQL—
Changing Your Mindset

Make the leap from SQL to MongoDB
1. Map terms & concepts from SQL to MongoDB
2. Discover the 4 humongous advantages of MongoDB
3. Change your mindset in 3 key ways

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
}
]
}

{
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

{
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

{
first_name: "Paul",
surname: "Miller",
cell: "447557505611",
city: "London",
location: [45.123,47.232],
cars: [
{
model: "Bentley",
year: 1973
},
{
year: 1965
}
]
}
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
Don’t panic!
Use schema validation.

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

Scale cheaper!
As the size of your database grows, scale horizontally.

SQLMongoDB
Query faster!
Stop doing expensive joins to get your data.
ID a ...
1 b ...
2 ... ...
3 ... ...
... ... ...
... ... ...
... ... ...
... ... ...
... ... ...
... ... ...
{
...
a: “b”,
...
c: {
d: “e”
...
},
...
}

SQLMongoDB
Pivot easier!
Easily change the shape of your data as your app evolves.
{
a: “b”,
c: “one”,
e: “f”
}
{
a: “b”,
c: 1,
new: “no biggee”
}
ALTER TABLE `mydb`.`letters_table`
DROP COLUMN `e`,
ADD COLUMN `New` VARCHAR(45) NULL AFTER `C`,
CHANGE COLUMN `C` `C` INT NULL DEFAULT NULL ;

Program faster!
Documents map to data structures in most popular languages.
Update Your Profile

Program faster!
{
first_name: "Paul",
surname: "Miller",
cell: "447557505611",
city: "London",
location: [45.123,47.232],
}
Users
10 1 banking
11 1 finance
12 1 trader
Professions

Program faster!
import pymongo
from pymongo import MongoClient
# CONNECT TO THE DB
client = MongoClient()
client =
pymongo.MongoClient("mongodb+srv://
username:password@cluster0nsdia.
mongodb.net/test?retryWrites=true")
db = client.fabapp
# THE ID OF THE USER WHOSE PROFILE WE
# WILL BE RETRIEVING AND UPDATING
userId = 1
import mysql.connector
# CONNECT TO THE DB
mydb = mysql.connector.connect(
host="localhost",
user=”username",
passwd=”password",
database=”fabapp”
)
mycursor = mydb.cursor()
# THE ID OF THE USER WHOSE PROFILE WE
# WILL BE RETRIEVING AND UPDATING
userId = 1

Program faster!
# GET THE USER'S PROFILE INFORMATION
## We can pull all of the info from
## the same document since we used
## embedding
user =
db['Users'].find_one({"_id":userId})
### Pull the info from the Users table
### & put it in the user dictionary
sql = "Select * FROM Users WHERE
Users.ID=%s”
values = (userId,)
mycursor.execute(sql, values)
result = mycursor.fetchone()
user = {
"first_name": result[1],
"surname": result[2],
"cell": result[3],
"city": result[4],
"location_x": result[5],
"location_y": result[6]
}
### Pull the info from the Professions
### table & put it in the user dict.
sql = "Select * FROM Professions WHERE
Professions.user_id=%s”
values = (userId,)
results = mycursor.fetchall()
professions = []
for result in results:
professions.append(result[2])
user["professions"] = professions

Program faster!
# UPDATE THE USER DICTIONARY BASED ON
# USER INPUT IN THE APP
### We'll update the user dictionary
### manually for simplicity
user = {
"first_name": "NewFirst",
"surname": "NewSurname",
"cell": "123-456-7890",
"city": "NewCity",
"location": [40.762, -73.979],
"professions": ["Manager",
"Engineer"]
}
# UPDATE THE USER DICTIONARY BASED ON
# USER INPUT IN THE APP
### We'll update the user dictionary
### manually for simplicity
user = {
"first_name": "NewFirst",
"surname": "NewSurname",
"cell": "123-456-7890",
"city": "NewCity",
"location_x": 40.762,
"location_y": 73.979,
"professions": ["Manager",
"Engineer"]
}

Program faster!
# UPDATE THE USER'S PROFILE IN THE DB
### Since the user's data is stored in
### a single document, we only have to
### make one update
result = db['Users'].update_one(
{"_id": userId}, {"$set": user})
# UPDATE THE USER'S PROFILE IN THE DB
### First update what is stored in the
### Users table
sql = "UPDATE Users SET first_name=%s,
surname=%s, cell=%s, city=%s,
location_x=%s, location_y=%s
WHERE (ID=%s)"
values = (
user["first_name"],
user["surname"],
user["cell"],
user["city"],
user["location_x"],
user["location_y"],
userId)
mydb.commit()
### Delete existing records in
### Professions table and add new ones
sql = "DELETE FROM Professions WHERE
user_id=%s”
values = (userId,)
mydb.commit()
if(len(user["professions"]) > 0):
sql = "INSERT INTO Professions
(user_id,profession) VALUES
(%s, %s)”
values = []
for profession in user["professions"]:
values.append((userId, profession))
mycursor.executemany(sql,values)
mydb.commit()

Program faster!
30 lines of code 73 lines of code

Embrace document diversity
The Polymorphic Pattern
{
first_name: "Paul",
surname: "Miller",
cell: "447557505611",
city: "London",
location: [45.123,47.232],
cars: [
{
model: "Bentley",
year: 1973
},
{
year: 1965
}
]
}
{
cell: ”1235552222",
}
{
}

Embrace document diversity
The Outlier Pattern
{
_id: ”Lauren_Schaefer",
displayName: ”Lauren Schaefer",
numFollowers: 1310
followers: [
“naomi_pen”,
“kenwalger”,
“mylynn”
...
]
}
{
_id: ”Nick_Offerman",
displayName: ”Nick Offerman",
numFollowers: 1730332
followers: [
“c_hotaling”,
“IAmJerdog”,
“ChloeCondon”
...
],
has_extras: true
}
{
_id: ”Nick_Offerman_1",
twitter_id: “Nick_Offerman”,
is_overflow: true,
followers: [
“StephenAtHome”,
“TheEllenShow”,
“hulu”
...
]
}

Data that is accessed together should
be stored together

0
20000
40000
60000
80000
100000
120000
1985 2017
Storage vs Developer Costs
Storage Cost per GB Developer Salary
be stored together

Don’t normalize your data for the sake of normalizing it.
{
a: “b”,
c: {
d: “e”
...
},
f: [“g”, “h”, “i”],
j: [
{
k: “l”
},
{
m: “n”
}
]
}
be stored together

Tread carefully with transactions
Relying on transactions is a bad design smell.
{
...
}
{
...
}
{
...
}
{
...
}
{
...
}
{
...
}
{
...
}

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

1. Scale cheaper

1. Scale cheaper
2. Program faster
## We can pull all of the info from
## the same document since we used
## embedding
user =
db['Users'].find_one({"_id":userId})
### Pull the info from the Users table ### &
put it in the user dictionary
sql = "Select * FROM Users WHERE
Users.ID=%s”
values = (userId,)
result = mycursor.fetchone()
user = {
"first_name": result[1],
"surname": result[2],
"cell": result[3],
"city": result[4],
"location_x": result[5],
"location_y": result[6]
}

1. Scale cheaper
2. Program faster
3. Query faster

1. Scale cheaper
2. Program faster
3. Query faster
4. Pivot easier

1. Embrace document diversity

2. Data that is accessed together should be stored together

2. Data that is accessed together should be stored together
3. Tread carefully with transactions

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 my Twitter page:
@Lauren_Schaefer

MongoDB .local Houston 2019: Jumpstart: From SQL to NoSQL -- Changing Your Mindset [MongoDB]

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