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MongoDB introduction at Google Cloud next Algiers
Codelab
Who are
we ? Who are we ?
Data and storage
● Most apps need storage
● Apps store data
● There is two types of data
○ Structured data
○ Unstructured data
What do we look for in a database ?
Right structure to match our data
Performance and Scale
Features that help developers
Relational
Relational designed for one thing, used for everything
Relational
● Query on any field
● In-place updates
● Two-dimensional storage
● Each field contains a single value
● Very structured schema (table)
● Normalization, process requires many tables, joins, indexes
and poor data locality
Oracle, MSSQL, MySQL, PostgreSQL, DB2
Primary
key
Apps and Data today
Modern apps use unstructured data
Data growing in size and complexity
Agile development
Scalability and distribution
NoSQL
Large volumes of rapidly changing structured,
semi-structured, and unstructured data
Object-oriented programming
Geographically distributed scale-out architecture
(We call this Horizontal scaling)
NoSQL Database Types
Document databases
MongoDB
Graph stores
Neo4J
Key-value stores
Redis
Wide-column stores
Cassandra and HBase
NoSQL or RDBMS for my-app ?
You know the answer now….
MongoDB
Finally we can talk about this “MongoDB”
MongoDB is a NoSQL Document Database
It’s free and open source
What the hell is a document
A Document is not a word or a pdf
document
It’s a JSON Object
JSON == JavaScript
Object
Notation
MongoDB Nexus architecture
Scalability
Consistency
Developers super-powers with MongoDB
Let’s Play
Let’s play some MongoDB now
To your keyboards !
Import data set
mongoimport --db codelab --collection companies --drop --file
~/Desktop/companies.json
find
db.getCollection("companies").find({})
find
db.getCollection("restaurants").find({"borough":"Manhattan"})
find
db.getCollection("restaurants").find({"address.zipcode":"10075"})
find
db.getCollection("restaurants").find({$or: [ {"cuisine":"Italian"},
{"address.zipcode":"10075"} ] } )
find
db.getCollection("restaurants").find({"grades.score": {$gt:30}})
find
db.getCollection("restaurants").find({"grades.score": {$lt:30}})
sort
db.getCollection("restaurants").find().sort({"borough":1,
"address.zipcode":1})
insert
db.getCollection("restaurants").insert({"cuisine":"Algerienne X)"})
update
db.getCollection("restaurants").update({"name":"Juni"}, {$set
:{"cuisine": "American (New)"}})
delete
db.getCollection("restaurants").remove({"borough":"Manhattan"}
)
Aggregate
db.getCollection("restaurants").aggregate(
[
{$match:{"borough":"Queens", "cuisine":"Brazilian"} },
{$group:{"_id":"$address.zipcode", "count":{$sum :1}}
]
)
Index
db.getCollection("restaurants").createIndex({cuisine:1})
So...
db.getCollection("theEnd").insert({greetings:”Thank You !”})
and :p
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MongoDB introduction at Google Cloud next Algiers