Introduction to Indexes

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Del curso dictado por Johns Hopkins University

Ruby on Rails Web Services and Integration with MongoDB

283 calificaciones

Johns Hopkins University

Ruby on Rails Web Services and Integration with MongoDB

283 calificaciones

Curso 3 de 6 en SpecializationRuby on Rails Web Development

In this course, we will explore MongoDB, a very popular NoSQL database and Web Services concepts and integrate them both with Ruby on Rails. MongoDB is a used to handle documents with a pre-defined schema which will give the developers an ability to store, process and use data using it’s rich API. The modules will go in-depth from installation to CRUD operations, aggregation, indexing, GridFS and various other topics where we continuously integrate MongoDB with RailsRuby. We will be covering the interface to MongoDB using the Mongo Ruby API and the Mongoid ORM framework (the MongoDB access counterpart to RDBMS/ActiveRecord within Rails). The last portion of the course will focus on Web Services with emphasis on REST, its architectural style and integration of Web Services with Rails. Core concepts of Web Services like request/response, filters, data representation (XML/JSON), web linking and best practices will covered in depth. This course is ideal for students and professionals who have some programming experience and a working knowledge of databases.

De la lección

Aggregation Framework, Performance, and Advanced MongoDB

In this module, we’re going to explore the fundamentals of the Aggregation framework in MongoDB. We will work on examples where you will process data records and return computed results. You will learn about and get to work on hands-on examples where you will be grouping values from multiple documents and performing a variety of operations on the grouped data to get a single result. We will look at a number of aggregation commands and paging. We will explore some advanced concepts like full text search, GridFS, Geospatial API, and wrap up the module with detailed demonstrations of all the capabilities presented in this module.

Introduction to Indexes8:59

Creating Indexes9:19

Listing & Deleting Indexes3:34

Unique, Sparse & TTL Indexes6:38

Conoce a los instructores

Kiran Chittargi
Adjunct Professor, Graduate Computer Science
Whiting School of Engineering

[MUSIC]

>> Hello and welcome back.

In this lecture, let's look at indexes, and what are indexes, and

how do they impact our queries?

So basically indexes are needed for efficient execution of queries in MongoDB.

So what happens without indexes is that MongoDB will

basically perform what we call a collection scan which is nothing but

scanning every single document in a collection.

So let's say you have 30,000 documents in a collection, and

when you look for data, a piece of data, say you're looking for

the second document, right, or the third document.

Whatever document you're looking for, MongoDB actually goes and

does a scan of every single document in the collection.

So that it goes through 30,000, it's fairly fast, but still,

it has to go through every single document, right?

So this is where indexes will come handy.

Indexes actually store a small portion of the collection's data set

in a easy to traverse form, right?

So it actually makes a smaller subset of a larger data set and we'll look

at an example and that should help you understand indexes much nicer and cleaner.

So when you actually create an index,

it all it does is it stores the values of a specific field.

So if you have ten fields in a document, and if you are trying to index, say

the third field, right, say a state in our example, or a city in our zips example.

Then it'll actually go and create a index and

stores only the value of a specific field.

And you can create a multi-field index as well which we'll talk about in

the upcoming lectures.

So it'll create based on the one or many fields that you pass, and

it also stores the data.

In an orderly fashion so ascending or descending order, right?

So let's see an example.

So here what we have is the list of scores.

This is an example taken directly from the Mongo site, right?

So we have say, a bunch of scores.

So here I think we have approximately nine documents in a collection.

But this could be thousands and thousands of scores of students in a University.

So there's a bunch of other information, name, first name, last name, and

all that, let's say.

But really all we care about is this one field called score,

which has a numeric value between min to max.

Min to max, let's say it's 100, so 0 to 100.

So this student has 25, this student has 56, 45, 75, sort of

random order in the way the documents came into the collection or however these

documents were added to the collection so we have a bunch of values here.

Now when you create an index so this is saying that go and

create an index on score.

Now the actual creation of an index single field, multi field.

You know, something we will do in a few minutes here.

But i'm just telling you that this intact for you to create an index, right.

And one is ascending, and minus one is descending.

Basically this is saying hey, go and create an index on this field, score.

And in that ascending order.

So what Mongo does is that it actually goes and creates and

index with the scores, just on the score, in an ascending fashion.

So you have the minimum here, you have the maximum here.

Notice how this pointer is actually is five,

right, that's the next minimum of all these scores here.

Five isn't the lowest, right?

Followed by 18, and then 30, and 45, and so on.

So it's actually building it in an ascending fashion.

Now, why is this important because the next time when somebody makes a query,

like less than 30, right?

When somebody makes a call, hey give me all the scores less than 30,

this by the way is a Mongo syntax.

Just pure Mongo syntax.

Of course you can do the same thing in Row B, which we will do later on.

So this, what this discusses, this actually goes and since we already have

the data in a nice ascending fashion, it goes and pulls the less than 30s.

In our case we will get five and 18 and whatever else we have.

Maybe just five and 18 and 25, of course, so we will return three documents.

So notice how quick and fast this is,

because you don't have to go through every single document in the collection.

So later on, in an example I'll show you, by using a command called explain.

You can actually see the difference between a command, a call that you make

before indexes and then a call that you will make same indexes.

So to apply the same concept to our zips example right?

So we have 30,000 documents in our collection, and so say,

I'm just going to take a sample of some data.

And we have four cities that belongs to one state.

MD, MD, MD and MD here and they you have a couple of other states in the mix.

I'm taking this on purpose just to show you that we have multiple or

duplicate entries, meaning of the same state.

So we know the problem, every time you make a call,

whether you look for Bethesda in Maryland or you look for For Reston, Virginia.

It has to go and look at all the 30,000 documents

in the collection to give you back the result you are looking for.

Data on the disk is stored in some random order, right?

All right, there is no guarantee that all the Maryland entries are in one

are altogether because it just depends on how the data was inserted.

And it's obviously inefficient because it has to process large volume of data.

So when you create an index, right, and we'll see the syntax in a minute here.

That basically what happens is you will create

an index on State in an ascending order.

State colon one in this example.

So notice what happens is it'll start putting the pointers to

the actual document in a way, like in an ascending fashion.

So you will have California, see in this example of the six documents that I have,

or seven, or however many we have in the previous.

So you have C, you'll have I, M, and V, right, that's how.

So from 6 or from 600 or 6,000 or

30,000 you're narrowing it down into a much smaller set.

So that's basically what indexes are.

They're special data structures that store a small portion of the collections

data set in an easy to traverse form.

Right and

really what all they do is they have a pointer to the document in the collection.

Now there is a data structure called B-Tree which you can go and

read up or look up online or Wikipedia.

So that's what Mongo uses under the covers to

actually do the indexing and searching and sorting and coming back with the data.

So you can go and see the implementation of that if you're interested.

But that's more that's the algorithm or

that's the data structure concept that gets applied when you do indexes.

Now as I said you also have multiple indexes or sorry multiple field indexes

which means you can also pick a state and say a city, and combine those two.

And create an index.

So, that's called multiple fields, or compound index and so on.

So, we'll look at an example of that as well, all right?

So, the bottom line, just to summarize,

indexes support efficient execution of queries in Mongo.

So the whole idea is to reduce the time and you will see that explain command.

That there is a significant change in time.

Now, we have 30,000 documents, but when you look at, you know,

gigabytes of data, that's a huge impact in the time and the performance.

And again, the whole idea is again MongoDB can use the index to

limit the number of documents that it must inspect.

So the whole idea is to make sure that you don't go through every single document,

instead look at a smaller collection.

Smaller set of documents rather.

So we will go into the concepts.

We will actually create some indexes.

And we'll actually see run some examples and see the performance and

see the difference between a pre-index and a post-index.

Okay, thank you.

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