ElasticSearch Basics

Why we need SEARCH ENGINE!!!
 Work Life without Google ?
 Amazon,Flipkart,Facebook,Twitter,GitHub,
StackOverflow,Zomato don’t have a search capability ?

You know, for search …
 Lot of DATA around us but less INFORMATION.
 Make our life easier.
 Find relevant stuff.
 Find it faster.
 For research,to shop,for entertainment etc

Elasticsearch !!!
 Real-time(near real time) distributed search & analytics engine
 Runs on top of Apache Lucene, written in Java,supports REST API
 Search Engine Software
 Private search engine service (like a Bing or a Google) but with
say, private, sensitive, or confidential data/documents that you
don’t want on the public web
 Developed by Shay Banon

ElasticSearch Clients
 Git Hub
 StackOverflow
 Wikipedia
 The Guardian
 SoundCloud
 McGraw-Hill

Relational db vs Elasticsearch

ElasticSearch Concepts
 Document : JSON document stored in ES. Like row in table in
Relational DB
 Id : Uniquely identifies a document
 Field : key-value pairs. Like column in Relational DB
- Simple value like string ,integer, date
- Array or an object
 Type : Like a table in realational DB.Has list of fields.

 Near RealTime(nrt) : Slight time lag between index a document
and being searchable.
 Shard : Low level worker unit,Single lucene instance
- Primary Shard (Physically stored document)
- Replica Shard (Copy of primary shard)
 Index : Like Database in relational db,Logical namespace which
maps to primary and replica shard
 Node : Running instance of elasticsearch

 Cluster : Collection of one or more nodes
- Facilitates indexing
- Search capabilities across nodes

ElasticSearch Getting Started …
 Recent version of Java
 elasticsearch.org/download
 Latest version of any browser
 Marvel & Sense
 Marvel : monitoring and management tool
 Sense : interactive console

Talking to ElasticSearch
 RESTful api json over http
 A request to Elasticsearch consists of the same parts as
any HTTP request:
curl -X<VERB> '<PROTOCOL>://<HOST>/<PATH>' -d '<BODY>‘
curl -X<VERB> '<PROTOCOL>://<HOST>/<PATH>?<QUERY_STRING>'

Document Oriented
 Stores entire objects or documents. It not only stores them, but
also indexes the contents of each document in order to make them
searchable
 Elasticsearch uses JavaScript Object Notation, or JSON,
{
"email": "john@smith.com",
"first_name": "John",
"last_name": "Smith",
"info": {
"bio": "Eco-warrior and defender of the weak",
"age": 25,
"interests": [ "dolphins", "whales" ]
},
"join_date": "2014/05/01"
}

Create Index,Insert Data …
 Cluster : myelasticsearch
Node : The Dark Knight
Index : Megacorp
Type : Employee
 A request to Elasticsearch consists of the same parts as any HTTP request(Using Sense):
<VERB> '<PROTOCOL>://<HOST>/<INDEX>/<TYPE>/ID ' '<BODY>‘
<VERB> '<PROTOCOL>://<HOST>/<PATH><INDEX>/<TYPE>/ID /?<QUERY_STRING>'
 Create Index Example :
PUT localhost:9201/megacorp/employee/1
{
"first_name" : "John",
"last_name" : "Smith",
"age" : 25,
"about" : "I love to go rock climbing",
"interests": [ "sports", "music" ]
}

What is Inverted Index?
 It allows very fast full text search.
 Doc_1 : The quick brown fox jumped over the lazy dog
Doc_2 : Quick brown foxes leap over lazy dogs in summer.

Inverted Index cont
 Search “quick brown”
 Both docs match here.
Doc_1 is more relevant.

Analysis and Analyzers
 Character filters A character filter could be used to strip out
HTML, or to convert &characters to the word and.
 Tokenizer Next, the string is tokenized into individual terms by
a tokenizer.
 Token filters Last, each term is passed through any token
filters in turn, which can change terms (for example,
lowercasing Quick), remove terms (for example, stopwords such
as a, and, the) or add terms (for example, synonyms
like jump and leap).
 Examples: Standard Analyzer,Simple Analyzer,Whitespace
analyzer,Language Analyzers etc

Inverted Index After Analysis
 Doc_1 : The quick brown fox jumped over the lazy dog
Doc_2 : Quick brown foxes leap over lazy dogs in summer.
 Quick can be lowercased to become quick.
 foxes can be stemmed--reduced to its root form—to
Become fox. Similarly, dogs could be stemmed to dog.
 jumped and leap are synonyms and can be indexed as just
the single term jump.

Retrieve Using Query String (SearchLite)…
 Retrieve Example :
GET localhost:9201/megacorp/employee/1
 Query String Search Example :
GET /megacorp/employee/_search?q=last_name:Smith

Retrieve Using DSL …
 Query DSL Search
Build complicated and robust queries.
 The domain-specific language (DSL) is specified using a JSON request body.
 Example :
GET /megacorp/employee/_search
{
"query": {
"match": {
"last_name": "Smith"
}
}
}

Querying
First part is the range
Filter.
Second Part is the
Match query

Full Text Search
 Elasticsearch can search within full-text fields and return the most relevant results
first. This concept of relevance is important to Elasticsearch, and is a concept that is
completely foreign to traditional relational databases, in which a record either
matches or it doesn’t.

Phrase Search
 Matching Exact sequence of words or phrases

Highlight Searches
 Highlight snippets of text from each search result.

ElasticSearch Basics

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