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Storing content
Finding & retrieving content
data is stored within a cluster, i.e. a collection of one or more servers
each server in the cluster is a node
within a cluster, there are indices (analogy: databases), which can be split into multiple shards, potentially across nodes
within an index, we can define multiple types (analogy: tables) *
within the types, we will store documents (analogy: rows)
documents contain fields (analogy: columns)
so how do we get these documents back?
how does search work in Elasticsearch?
Postman -> Search
Exercise
Add a request to the Index folder that indexes data for one book (it can be invented data)
Add a request to the Search folder that retrieves your document (the query type is your choice)
What is Elasticsearch?
... so let's see how this works!
Postman -> Index
&& Postman -> Info / Diagnostics
what happened to our documents?
they went through analysis, which converted them into tokens
the tokens were stored in the search engine, along with the original text
open-source search engine built on top of Apache Lucene, a fulltext search engine library.
central functions of a search engine: storing, finding, and retrieving content.
Queries for me?
/_search?q= ...
Fig 1 (of 1): book index
But how were the documents stored?
at the core of a search engine there is a data structure called the inverted index.
the inverted index is composed of a terms dictionary and a postings list.
Documents
0. One dragon, two dragon, the black dragon, the red dragon.
1. The black dragon is the best dragon.
2. The best queen is the red one.
Postings list
0 => [1, 2]
1 => [0, 1]
2 => [0, 1]
3 => [1, 2]
4 => [0, 2]
5 => [2]
6 => [0, 2]
7 => [0, 1, 2]
8 => [0]
Term dictionary
0 => best
1 => black
2 => dragon
3 => is
4 => one
5 => queen
6 => red
7 => the
8 => two
Elasticsearch Basics:
You Know, for Search
... and let's break up some text!
Postman -> Analysis