Elasticsearch is a distributed, open source search and analytics engine. It was created in 2010 by Shay Banon, who previously created Compass in 2004. Elasticsearch is written in Java, runs on Linux, Windows and Mac OS X, and is distributed and highly available. It features real-time search, document oriented data model with no schema, and is built on top of Lucene. The document provides details on setting up Elasticsearch, indexing and retrieving data, and includes examples of using it with Ruby on Rails through the Tire gem for full text search capabilities in web applications.

1. ElasticSearch
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Presented by:
Abuzar Hasan
Arkhitech <http://www.arkhitech.com>

2. History
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Shay Baron
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Compass in 2004
ElasticSearch in 2010
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Written in Java
Cross-platform
Distributed

3. Users
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StumbleUpon
Mozilla
Github
Foursquare

4. Features
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Distributed and Highly Available Search Engine
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Each index is fully sharded with a configurable number of shards
Each shard can have one or more replicas
Read / Search operations performed on either one of the replica
shard
Multi Tenant with Multi Types
Various set of APIs

5. Features
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Document Oriented
No need for upfront schema definition
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(Near) Real Time Search
Per-Operation Persistence
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Single document level operations are atomic, consistent, isolated
and durable

6. Features
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Built on top of Lucene
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Each shard is a fully functional Lucene index
All the power of Lucene easily exposed through simple
configuration / plugins
Independent of file format
Open Source under Apache 2 License

7. Setting Up Elastic Search
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Download ElasticSearch (CentOS)
sudo yum install elasticsearch
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Start ElasticSearch Server
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elasticsearch -f -D
es.config=/usr/local/Cellar/elasticsearch/0.18.5/config/elasticsear
ch.yml

8. Setting up ElasticSearch w/
Ruby
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The ElasticSearch is not ruby specific so 'tire' gem helps ruby
based projects to communicate with ElasticSearch
'tire' gem
https://github.com/karmi/retire
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We then need to require following libraries
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require 'rubygems'
require 'tire'

9. Indexing Records
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Indexing includes both “Create” and “Update” in CRUD.
In order to make index request for new JSON object, we pass
the following URL
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http://localhost:<port>/<index>/<type>/[<id>].

10. Indexing
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Creating an index
curl -XPUT "http://localhost:9200/movies/movie/1" -d'
{
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"title": "The Godfather",
"director": "Francis Ford Coppola",
"year": 1972
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}'

11. Indexing Response
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After executing the request, we get the following response
{
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}
“ok”:
“_index”:
“_type”:
“_id”:
“_version”:
true,
“movies”,
“movie”,
“1”,
1

12. Retrieving Index
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Retrieving the index
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curl -XGET "http://localhost:9200/movies/movie/1" -d''

13. Retrieve Index Response
{
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“_index”:
“_type”:
“_id”:
“_version”:
“exists”:
“_source”:
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}
}
“movies”,
“movie”,
“1”,
1,
true,
{
"title": "The Godfather",
"director": "Francis Ford Coppola",
"year": 1972

14. Delete Index
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Deleting the index
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curl -XDELETE "http://localhost:9200/movies/movie/1" -d''

15. Demo Application
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Demo Application by karmi
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$ rails new searchapp -m
https://raw.github.com/karmi/tire/master/exam les/railsapplication-template.rb

16. Demo: Search App - Model
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Model
include Tire::Model::Search
include Tire::Model::Callbacks
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These two 'tire' models allow your rails application to use tire
gem for searching purposes

17. Demo: Search App
- Controller
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Controller
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@articles = Article.tire.search params[:query]
This line in your search method helps to search using tire gem

18. Demo: Search App - View
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View
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<%= form_tag search_articles_path, method: :get do %>
<%= text_field_tag :q, params[:query] %>
<%= submit_tag :search %>
<% end %>

19. Demo: Search App - Index
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Create an index
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Tire.index 'articles' do
delete
create
store :title => 'One', :tags => ['ruby']
store :title => 'Two', :tags => ['ruby', 'python']
store :title => 'Three', :tags => ['java']
store :title => 'Four', :tags => ['ruby', 'php']
refresh
end

20. Demo: Search App - Index
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For custom mapping
Tire.index 'articles' do
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delete
create :mappings => {
:article => {
:properties => {
:id
=> { :type => 'string', :index => 'not_analyzed', :include_in_all => false },
:title => { :type => 'string', :boost => 2.0, :analyzer => 'snowball' },
:tags => { :type => 'string', :analyzer => 'keyword' },
:content => { :type => 'string', :analyzer => 'snowball' }
}
}
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}
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end

21. Demo: Search App – Bulk
Indexing
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For indexing large amount of data
articles = [
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{ :id => '1', :type => 'article', :title => 'one', :tags => ['ruby'] },
{ :id => '2', :type => 'article', :title => 'two', :tags => ['ruby', 'python'] },
{ :id => '3', :type => 'article', :title => 'three', :tags => ['java'] },
{ :id => '4', :type => 'article', :title => 'four', :tags => ['ruby', 'php'] }
]
Use Import
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Tire.index 'articles' do
import articles
end

22. Displaying Facets
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For displaying facets
s.results.facets['global-tags']['terms'].each do |f|
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puts "#{f['term'].ljust(10)} #{f['count']}"
end
Output
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ruby
python
php
java
3
1
1
1

23. References
• https://github.com/karmi/retire
• http://railscasts.com/episodes/306-elasticsearch-part1
QUESTIONS?