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Would you like to inspect the original subtitles? These are the user uploaded subtitles that are being translated: 1 00:00:06,840 --> 00:00:11,940 So before we start playing with our shiny new elastic search server, let's go over some basics of elastic 2 00:00:11,940 --> 00:00:16,800 search first. We'll understand the concepts of how it works, what it's all about, how it's architected 3 00:00:17,070 --> 00:00:21,270 and when we're done with that, we'll have a quick little quiz to reinforce what you learned. 4 00:00:21,270 --> 00:00:22,830 After that, we'll start messing around with it. 5 00:00:26,190 --> 00:00:29,850 So there are two main logical concepts behind elastic search. 6 00:00:29,850 --> 00:00:31,720 The first is the document. 7 00:00:31,920 --> 00:00:37,110 So if you're used to thinking of things in terms of databases, a document it's a lot like a row in a 8 00:00:37,110 --> 00:00:41,220 database that represents a given entity, something that you're searching for, 9 00:00:41,220 --> 00:00:46,630 and remember, in elastic search, it's not just about text, any structure data can work. 10 00:00:47,010 --> 00:00:50,160 Now elastic search works on top of Json formatted data. 11 00:00:50,820 --> 00:00:55,590 If you're familiar with Json, it's basically just a way of encoding structured data that may contain 12 00:00:55,590 --> 00:00:58,400 strings or numbers or dates or what have you, 13 00:00:58,500 --> 00:01:03,660 in a way that you can cleanly transmit across the web, and you'll see a ton of examples of this throughout 14 00:01:03,660 --> 00:01:04,050 the course, 15 00:01:04,050 --> 00:01:06,550 so it'll make more sense later on. 16 00:01:06,840 --> 00:01:12,270 Now every document can have a unique I.D. and you can either explicitly assign a unique I.D. to it yourself, 17 00:01:12,540 --> 00:01:15,940 or allow elastic search to assign it for you. 18 00:01:15,960 --> 00:01:22,200 The second concept is the Index. An index is the highest level entity that you can query against an elastic 19 00:01:22,200 --> 00:01:26,090 search, and it can contain a collection of documents. 20 00:01:26,130 --> 00:01:31,500 So again bringing this back to an analogy of a database, you can think of an index as a database table 21 00:01:31,830 --> 00:01:34,370 and a document as a row in that table. 22 00:01:34,590 --> 00:01:39,190 The scheme that defines the data types in your documents also belongs to the index, 23 00:01:39,240 --> 00:01:44,460 you can only have one type of document within a single index and elastic search. 24 00:01:44,460 --> 00:01:49,490 So if you're used to the world of databases, you'll find elastic search to have similar concepts. 25 00:01:49,530 --> 00:01:49,860 Think of 26 00:01:49,860 --> 00:01:51,580 your cluster as a database, 27 00:01:51,630 --> 00:01:55,890 indices as tables, and documents as rows in those tables. 28 00:01:55,950 --> 00:02:00,990 It's just different terminology, but as you'll soon see, even though the concepts are similar, how elastic 29 00:02:00,990 --> 00:02:04,080 search works under the hood is very different from a traditional database. 3155

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