Microsoft cloud workshop - automated cloud service for MongoDB on Microsoft Azure

MONGODB ATLAS & MICROSOFTMONGODB ATLAS & MICROSOFT
AZUREAZURE
HANDS-ON EXERCISESHANDS-ON EXERCISES
Solutions Architect @ MongoDB |
Deck & Content |
Registration Link |
chris.grabosky@mongodb.com
l.gsky.us/#!mdbms 2018
l.gsky.us/#!mdbms 2018reg
Click Here For Printable Version
Speaker notes
On demand lab - Automated cloud service for MongoDB on Microsoft Azure - 14/11/2018 New York is currently scheduled
and ready for use on Wednesday, November 14, 2018.
The lab activation details are as follows:
Sign-Up Link (For All): http://bit.ly/2RBaRj6
Support Link (For Instructors / Proctors): http://bit.ly/mcw-support
It is recommended that you share the sign-up link with participants only during the specified lab time 09:00 AM (UTC-
05:00) Eastern Time (US & Canada) on Wednesday, November 14, 2018.
This information is shared with:
Chris Grabosky
Eugene Kang
Valmik Mehta
Mohan Das
Please share this information with other instructors / proctors, who were not tagged with this lab request.

TERMINOLOGY CHECKTERMINOLOGY CHECK

EXERCISE 1EXERCISE 1
MONGODB ATLAS & USERSMONGODB ATLAS & USERS

AT THE END OF THISAT THE END OF THIS EXERCISE 1EXERCISE 1
YOU SHOULD BE ABLE TO...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 AZUREDEPLOY M0 ON AZURE

USERSUSERS
DB.CREATEUSER()DB.CREATEUSER()

MONGODB COMPASS & CRUDMONGODB COMPASS & CRUD

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 WITHCONNECTING TO ATLAS WITH
COMPASSCOMPASS

CREATING A DB & COLLECTIONCREATING A DB & COLLECTION

IMPORTING DATAIMPORTING DATA
DATASET:DATASET: l.gsky.us#!moviedatasetl.gsky.us#!moviedataset

CONNECTION STRINGSCONNECTION STRINGS
IF YOUR CLUSTER WORKS, USE IT, BUT...IF YOUR CLUSTER WORKS, USE IT, BUT...
Key Value
Server mdbazuretraining-v9iwf.azure.mongodb.net
SRV Record Yes on Switch
Authentication Username/Password
Username azureacct
Password azurepasswd
Auth DB admin

M001M001 CCRUD: LET'S CREATE...RUD: LET'S CREATE...
DB.MOVIES.INSERTONE({})DB.MOVIES.INSERTONE({})

0:00 / 1:20
Speaker notes
title: Ghostbusters
Year: 1984 Change Int32
Rated: PG
Runtime: 105 Change Int32
Type: movie
Genres: comedy, action Change Array
Director: Ivan Reitman
Writers: dan aykroyd, Harold ramis Change Array
DO NOT DO BELOW AS THAT IS DONE ON UPDATE:
imdb Change Object
imdb.id: tt0087332
imdb.rating: 7.8 Change Double
imdb.votes: 312,798 Change Int32

SCHEMA & DATA TYPESSCHEMA & DATA TYPES

M001 CM001 CRRUD: LET'S READ...UD: LET'S READ...
DB.MOVIES.FIND()DB.MOVIES.FIND()

M001 CRM001 CRUUD: LET'S UPDATE...D: LET'S UPDATE...
DB.MOVIES.UPDATEONE({},{$SET:{}})DB.MOVIES.UPDATEONE({},{$SET:{}})
Speaker notes
title: Ghostbusters
Year: 1984 Change Int32
Rated: PG
Runtime: 105 Change Int32
Type: movie
Genres: comedy, action Change Array
Director: Ivan Reitman
Writers: dan aykroyd, Harold ramis Change Array
ABOVE IS DONE, DO BELOW ON UPDATE:
imdb Change Object
imdb.id: tt0087332
imdb.rating: 7.8 Change Double
imdb.votes: 312,798 Change Int32

M001 CRUM001 CRUDD: LET'S DELETE...: LET'S DELETE...
DB.MOVIES.DELETEONE()DB.MOVIES.DELETEONE()

M001 CM001 CRRUD: FIND ME MOVIES...UD: FIND ME MOVIES...
Question Answer
From 1987
"Comedy" as one of their genres
"Comedy" as only genre
"Comedy" or "Drama"
"Comedy" and "Drama
IMDB Rating > 8.0 and PG Rating
Whole title of "Dr. Strangelove"

Question Answer
From 1987
"Comedy" or "Drama"
"Comedy" and "Drama
{year:1987}

Question Answer
From 1987
"Comedy" or "Drama"
"Comedy" and "Drama
{year:1987}
{genre:"Comedy"}

Question Answer
From 1987
"Comedy" or "Drama"
"Comedy" and "Drama
{year:1987}
{genre:"Comedy"}
{genres:["Comedy"]}

Question Answer
From 1987
"Comedy" or "Drama"
"Comedy" and "Drama
{year:1987}
{genre:"Comedy"}
{genres:["Comedy"]}
{genres:{$in:["Comedy", "Drama"]}}

Question Answer
From 1987
"Comedy" or "Drama"
"Comedy" and "Drama
{year:1987}
{genre:"Comedy"}
{genres:["Comedy"]}
{ genres: { $all: ["Comedy", "Drama"] } }

Question Answer
From 1987
"Comedy" or "Drama"
"Comedy" and "Drama
{year:1987}
{genre:"Comedy"}
{genres:["Comedy"]}
{"imdb.rating" : {$gt: 8.0}, rated:"PG"}

Question Answer
From 1987
"Comedy" or "Drama"
"Comedy" and "Drama
{year:1987}
{genre:"Comedy"}
{genres:["Comedy"]}
{"imdb.rating" : {$gt: 8.0}, rated:"PG"}
{title: {$regex: '^Dr. Strangelove'}}

INDEXES & EXPLAIN PLANINDEXES & EXPLAIN PLAN

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

EXPLAIN PLANEXPLAIN PLAN
.FIND({"ADDRESS.ZIPCODE": {$GT: '5000'}, CUISINE:.FIND({"ADDRESS.ZIPCODE": {$GT: '5000'}, CUISINE:
'SUSHI'}).SORT({STARS:-1})'SUSHI'}).SORT({STARS:-1})
Not a good result...
ColScan and In-Memory Sort
We need indexes

WHAT INDEX TO USE?WHAT INDEX TO USE?

Naive approach would be
db.createIndex({"address.zipcode":1, "cuisine":1, "stars":1})

While better since it does an index scan, still doing an
memory sort since this is a range query, not equality

While better since it does an index scan, still doing an
memory sort since this is a range query, not equality
Add the index above and compare
versus
.find({"address.zipcode": {$gt: '5000'}, cuisine: 'Sushi'}).sort
.find({"address.zipcode": '5000', cuisine: 'Sushi'}).sort({stars

ADDING INDEXADDING INDEX
ESR RULEESR RULE
Great rule of thumb
Remove your last index
Remember Equality, Sort, Range

ADDING INDEXADDING INDEX
ESR RULEESR RULE
Great rule of thumb
Remove your last index
Remember Equality, Sort, Range
db.createIndex({"cuisine":1, "stars":1, "address.zipcode":1})

AGGREGATION FRAMEWORKAGGREGATION FRAMEWORK

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

UNIVERSITY M121UNIVERSITY M121

DB.COL.AGGREGATE([{STAGE1},{STAGE2},{STAGEN}])DB.COL.AGGREGATE([{STAGE1},{STAGE2},{STAGEN}])

AGG WORKSHOPAGG WORKSHOP
Question Answer
That Are Comedies
Have a rating
Just the title and how many years old

Question Answer
That Are Comedies
Have a rating
$match {genres:"Comedy"}

Question Answer
That Are Comedies
Have a rating
$match {rated:{$exists:true}}

Question Answer
That Are Comedies
Have a rating
$match {rated:{$exists:true}}
$project {
_id:0,
title:1,
yearsOld: { $subtract: [2018,"$year"]}
}

1 DEGREE OF KEVIN BACON1 DEGREE OF KEVIN BACON
Stage 1
Stage 2
Stage 3
Stage 4

Stage 1
Stage 2
Stage 3
Stage 4
$project {
title:1, actors:1,_id:0
}

Stage 1
Stage 2
Stage 3
Stage 4
$project {
}
$graphLookup {
from: 'movies',
startWith: "$actors",
connectFromField: 'actors',
connectToField: 'actors',
as: 'foo',
maxDepth: 1,
depthField: 'degrees',
restrictSearchWithMatch: {actors:"Kevin Bacon"}
}

Stage 1
Stage 2
Stage 3
Stage 4
$project {
}
$graphLookup {
from: 'movies',
as: 'foo',
maxDepth: 1,
}
$project {
title:1,actors:1,_id:0,"foo.title":1,"foo.actors":1,jumps:{$size:"$foo"}
}

Stage 1
Stage 2
Stage 3
Stage 4
$project {
}
$graphLookup {
from: 'movies',
as: 'foo',
maxDepth: 1,
}
$project {
title:1,actors:1,_id:0,"foo.title":1,"foo.actors":1,jumps:{$size:"$foo"}
}
$match {
jumps:{$gt:0}
}

BI CONNECTORBI CONNECTOR

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

ENABLE BI CONNECTORENABLE BI CONNECTOR
HTTPS://DOCS.MONGODB.COM/BI-CONNECTOR/CURRENT/CONNECT/POWERBI/HTTPS://DOCS.MONGODB.COM/BI-CONNECTOR/CURRENT/CONNECT/POWERBI/

NOW WE TALK SQLNOW WE TALK SQL

NOW WE TALK SQLNOW WE TALK SQL
EWWEWW

CONFIGURING ODBCCONFIGURING ODBC
HTTPS://DOCS.MONGODB.COM/BI-CONNECTOR/CURRENT/CONNECT/POWERBI/HTTPS://DOCS.MONGODB.COM/BI-CONNECTOR/CURRENT/CONNECT/POWERBI/

LET'S MAKE SOME DASHBOARDSLET'S MAKE SOME DASHBOARDS
USE YOUR CLUSTER WITH MOVIES DBUSE YOUR CLUSTER WITH MOVIES DB
OR MY CLUSTER HAS LOTS OF CHOICES...OR MY CLUSTER HAS LOTS OF CHOICES...

STITCH & INTEGRATIONSSTITCH & INTEGRATIONS

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

STITCH COMPONENTSSTITCH COMPONENTS

SERVERLESS SCENARIOSSERVERLESS SCENARIOS
App Atlas Tech Azure Tech Link
Bowling
Tracker
Query Anywhere
WebHooks
Trigger
Functions
Web App l.gsky.us/#!bowlsrc
"Instagram" Query Anywhere
WebHooks
Trigger
Functions
Web App
Azure Storage
Xamarin App
Cognitive
Vision
l.gsky.us/#!igsrc
"Tiny URL" WebHooks Web App l.gsky.us/#!linksrc

"INSTAGRAM" APP"INSTAGRAM" APP
UPLOADINGUPLOADING

"INSTAGRAM" APP"INSTAGRAM" APP
BROWSINGBROWSING

BIG DATABIG DATA

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
http://l.gsky.us#!ms sparkhttp://l.gsky.us#!ms spark

DOWNLOAD HTML CODEDOWNLOAD HTML CODE
http://l.gsky.us#!ms sparkhttp://l.gsky.us#!ms spark

THANK YOUTHANK YOU
CLOUD.MONGODB.COMCLOUD.MONGODB.COM
AZURE.MICROSOFT.COMAZURE.MICROSOFT.COM

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