Working with Data in MongoDB

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来自 MongoDB Inc. 的课程

Introduction to MongoDB

85 个评分

MongoDB Inc.

Introduction to MongoDB

85 个评分

This course will get you up and running with MongoDB quickly, and teach you how to leverage its power for data analytics. We'll start by mastering the fundamentals of MongoDB, including MongoDB’s Document data model, importing data into a cluster, working with our CRUD API and Aggregation Framework. These topics will be taught through a demo application which will give you a great first encounter of how simple and practical it can be to build applications with MongoDB. In addition to these essential topics, you will also learn and work with useful MongoDB tools and services. You will work with Atlas, MongoDB's database as a service, MongoDB Compass, a schema visualization tool, as well as many other useful command-line utilities.

从本节课中

Week 1

MongoDB Document Model5:15

Creating an Atlas Free-Tier Cluster4:44

Installing MongoDB on Windows12:10

Installing MongoDB on OSX12:19

Getting Data Into MongoDB4:12

Using Compass to View Data in an Atlas Cluster3:37

Working with Data in MongoDB4:12

How to Connect to Your Atlas Cluster from Python3:21

Intro to the Aggregation Framework3:23

Your First Aggregation (Using $group)7:01

Incremental Improvements with $sort and $sortByCount1:24

Wowza! You can do that? ($facet)8:42

Filtering on Scalar Fields ($match, find(), and Compass)4:52

与讲师见面

Norberto Leite
Lead Engineer
Engineering
Kirby Kohlmorgen
Senior Curriculum Engineer
Education
Shannon Bradshaw
Vice President
Education

There are a number of ways we can work with data in MongoDB.

As we've just seen, one way is through Compass.

In addition to simply viewing a collection, we can also filter and

perform a number of other operations.

Here, our filter for the movie,

The Big Short Note that just two documents from this collection match this filter.

In fact, this is actually evidence of some dirty data because

one of these is a duplicate.

So we can delete the dupe.

We can also update an individual document by editing its fields.

Here, I'll update this doc with the runtime.

From a google search, I happen to know that it's 130 minutes.

So compass provides a convenient means of exploring data and it's visual.

So that helps immensely in investigating many types of questions you might have

about a data set.

Especially as you first begin to work with it.

But the bulk of the work will be in manipulating and analyzing data.

For that, we want to use a combination of the Mongodb aggregation framework and

query language.

In this chapter, I'll provide you with an introduction to both, as we work to

reshape and clean up the movies data set, and integrate it with other data sources

that will set us up to build an application and analytics infrastructure.

We'll do all of this from Python, using the MongoDB driver, PyMongo.

The same principles would apply across Java, Node, C Sharp, R or

any of the other programming languages MongoDB supports.

First however, it's valuable to discuss how all the pieces work together.

And I wouldn't be doing my job as an instructor if I didn't throw at least one

architectural diagram your way.

As you know, your MongoDB free tier cluster is running in Atlas.

Atlas is a convenient platform for running MongoDB which is why we're using it for

this class.

You could just as easily run MongoDB from your local computer but Atlas requires

very little set up on your part and all the administration is managed for you.

Depending on where you are in the world, your cluster is probably running on

servers physically located in an Amazon AWS data center.

This means its in the cloud.

I've drawn your MongoDB cluster as three servers because that's what it is.

It's a replica set, meaning that three servers are working together to remain in

sync, each maintaining a redundant copy of your data.

One member of your replica set is always primary, meaning that it's the one that

you were communicating with when writing data, and usually when reading data.

If the primary stops functioning or loses it's internet connection,

another member of the replica set will step in to serve as primary.

Since it holds a complete copy of your data,

you probably won't even notice if this happens.

By default, both Compass and PyMongo are designed to direct the request to

whichever node in a replica set is primary, even if the primary changes.

For a course like this, it's less important but

in production environments this type of high availability is essential.

The free tier option in Atlas supports this because a three member replica set is

the default minimum deployment for all clusters in this platform.

Now, you downloaded Compass to your local computer, so it's running there and

connecting to your Atlas cluster through an internet connection.

We will also be developing scripts and

applications written in Python that will use your free tier cluster.

In order to connect to MongoDB and make use of the aggregation framework and

the MongoDB query language, we need to use PyMongo.

PyMongo is available in PyPi.

And, you can install it using Pip or easy install.

PyMongo is just a Python, library like many of the other you are constantly

using, such as namPy, Sci fi, or pandas.

We'll also be making extensive use of python Jupiter notebooks in this class,

to enable you to work through exercises right in your web browser.

This will also make use of PyMongo.

But that will all be handled behind the scenes in the Coursera platform.

And that about wraps it up for the big moving parts of the scripts and

applications we'll be using in this class.

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