Exercise 1

MongoDB Atlas & Users

At the end of this Exercise 1 you should be able to...

• Deploy a MongoDB Atlas Cluster (free-tier M0 or paid M10+)
• Understand basics of creating users and controls in MongoDB Atlas
• Know the difference between Atlas users and MongoDB Users

Deploy M0 on Azure

Users

db.createUser()

Exercise 2

MongoDB Compass & CRUD

At the end of this Exercise 2 you should be able to...

• Launch Compass (tool to explore, visualize, optomize, and modify MongoDB data) and connect to that cluster
• Use Compass to create a database, create a collection, and import a data set into that collection
• Use Compass to create, read, update, and delete (CRUD) documents in a collection

Connecting to Atlas with Compass

Connecting to Atlas with Compass

Creating a DB & Collection

Importing Data

Dataset: l.gsky.us#!moviedataset

Connection Strings

If your cluster works, use it, but...

M001 CRUD: Let's Create...

db.movies.insertOne({})

Schema & Data Types

M001 CRUD: Let's Read...

db.movies.find()

M001 CRUD: Let's Update...

db.movies.updateOne({},{$set:{}})

M001 CRUD: Let's Delete...

db.movies.deleteOne()

M001 CRUD: Find me movies...

{year:1987}

{genre:"Comedy"}

{genres:["Comedy"]}

{genres:{$in:["Comedy", "Drama"]}}

{ genres: { $all: ["Comedy", "Drama"] } }

{"imdb.rating" : {$gt: 8.0}, rated:"PG"}

{title: {$regex: '^Dr. Strangelove'}}

Exercise 3

Indexes & Explain Plan

At the end of this Exercise 3 you should be able to...

• Identify how a query was run using the Explain Plan
• Create an index on a collection
• See how an index affects query plans

Explain Plan

Explain Plan

.find({"address.zipcode": {$gt: '5000'}, cuisine: 'Sushi'}).sort({stars:-1})

• Not a good result...
• ColScan and In-Memory Sort
• We need indexes

What Index To Use?

.find({"address.zipcode": {$gt: '5000'}, cuisine: 'Sushi'}).sort({stars:-1})

• Naive approach would be

  db.createIndex({"address.zipcode":1, "cuisine":1, "stars":1})

• While better since it does an index scan, still doing an in-memory sort since this is a range query, not equality
• Add the index above and compare

  .find({"address.zipcode": {$gt: '5000'}, cuisine: 'Sushi'}).sort({stars:-1})

  versus

  .find({"address.zipcode": '5000', cuisine: 'Sushi'}).sort({stars:-1})

Adding Index

ESR Rule

• Great rule of thumb
• Remove your last index
• Remember Equality, Sort, Range

• db.createIndex({"cuisine":1, "stars":1, "address.zipcode":1})

Exercise 4

Aggregation Framework

At the end of this Exercise 4 you should be able to...

• Have a basic understanding of what the aggregation framework is
• Write some basic queries using the aggregation framework

Aggregation Framework

University M121

Aggregation Framework

db.col.aggregate([{stage1},{stage2},{stagen}])

Agg Workshop

$match {genres:"Comedy"}

$match {rated:{$exists:true}}

$project {
    _id:0,
    title:1,
    yearsOld: { $subtract: [2018,"$year"]}
}
                                    

1 Degree of Kevin Bacon

$project {
    title:1, actors:1,_id:0
}
                                    

$graphLookup {
    from: 'movies',
    startWith: "$actors",
    connectFromField: 'actors',
    connectToField: 'actors',
    as: 'foo',
    maxDepth: 1,
    depthField: 'degrees',
    restrictSearchWithMatch: {actors:"Kevin Bacon"}
}
                                    

$project {
    title:1,actors:1,_id:0,"foo.title":1,"foo.actors":1,jumps:{$size:"$foo"}
}
                                    

$match {
    jumps:{$gt:0}
  }
                                        

Exercise 5

BI Connector

At the end of this Exercise 5 you should be able to...

• Enable BI Connector on a cluster
• Connect to a MongoDB cluster using a SQL CLI or BI tool like PowerBI

Enable BI Connector

https://docs.mongodb.com/bi-connector/current/connect/powerbi/

Now We Talk SQL

Eww

Configuring ODBC

https://docs.mongodb.com/bi-connector/current/connect/powerbi/

Configuring ODBC

https://docs.mongodb.com/bi-connector/current/connect/powerbi/

Let's Make Some Dashboards

Use your cluster with Movies DB

Or my cluster has lots of choices...

Exercise 6

Stitch & Integrations

At the end of this Exercise 6 you should be able to...

• Understand MongoDB Stitch serverless platform capabilities
• Create Stitch triggers on Change Streams, functions, etc

Stitch Components

Serverless Scenarios

"Instagram" App

Uploading

"Instagram" App

Browsing

Exercise 7

Big Data

At the end of this Exercise 7 you should be able to...

http://l.gsky.us#!msftspark

• Register the MongoDB Spark Connector on Azure Databricks
• Write data to MongoDB
• Load data from a MongoDB collection
• Load data from an aggregation framework pipeline
• Run Spark SQL

Download HTML Code

http://l.gsky.us#!msftspark