Original subtitles

So before we start playing with our shiny new elastic search server, let's go over some basics of elastic

search first. We'll understand the concepts of how it works, what it's all about, how it's architected

and when we're done with that, we'll have a quick little quiz to reinforce what you learned.

After that, we'll start messing around with it.

So there are two main logical concepts behind elastic search.

The first is the document.

So if you're used to thinking of things in terms of databases, a document it's a lot like a row in a

database that represents a given entity, something that you're searching for,

and remember, in elastic search, it's not just about text, any structure data can work.

Now elastic search works on top of Json formatted data.

If you're familiar with Json, it's basically just a way of encoding structured data that may contain

strings or numbers or dates or what have you,

in a way that you can cleanly transmit across the web, and you'll see a ton of examples of this throughout

the course,

so it'll make more sense later on.

Now every document can have a unique I.D. and you can either explicitly assign a unique I.D. to it yourself,

or allow elastic search to assign it for you.

The second concept is the Index. An index is the highest level entity that you can query against an elastic

search, and it can contain a collection of documents.

So again bringing this back to an analogy of a database, you can think of an index as a database table

and a document as a row in that table.

The scheme that defines the data types in your documents also belongs to the index,

you can only have one type of document within a single index and elastic search.

So if you're used to the world of databases, you'll find elastic search to have similar concepts.

Think of

your cluster as a database,

indices as tables, and documents as rows in those tables.

It's just different terminology, but as you'll soon see, even though the concepts are similar, how elastic

search works under the hood is very different from a traditional database.