Introduction to Elasticsearch with basics of Lucene
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Introduction to Elasticsearchwith basics of Lucene
May 2014 Meetup
Rahul Jain
@rahuldausa@http://www.meetup.com/Hyderabad-Apache-Solr-Lucene-Group/
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Who am I Software Engineer
7 years of software development experience
Built a platform to search logs in Near real time with volume of 1TB/day#
Worked on a Solr search based SEO/SEM software with 40 billion records/month (Topic of next talk?)
Areas of expertise/interest High traffic web applications JAVA/J2EE Big data, NoSQL Information-Retrieval, Machine learning
# http://www.slideshare.net/lucenerevolution/building-a-near-real-time-search-engine-analytics-for-logs-using-solr
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Agenda
• IR Overview
• Basic Concepts
• Lucene
• Elasticsearch
• Logstash & Kibana - Short Introduction
• Q&A
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Information Retrieval (IR)
”Information retrieval is the activity of obtaining information resources (in the form of documents) relevant to an information need from a collection of information resources. Searches can be based on metadata or on full-text (or other content-based) indexing”
- Wikipedia
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Basic Concepts
• Term t : a noun or compound word used in a specific context
• tf (t in d) : term frequency in a document • measure of how often a term appears in the document• the number of times term t appears in the currently scored document d
• idf (t) : inverse document frequency • measure of whether the term is common or rare across all documents, i.e.
how often the term appears across the index• obtained by dividing the total number of documents by the number of
documents containing the term, and then taking the logarithm of that quotient.
• boost (index) : boost of the field at index-time
• boost (query) : boost of the field at query-time
Basic ConceptsTF - IDF
TF - IDF = Term Frequency X Inverse Document Frequency
Credit: http://http://whatisgraphsearch.com/
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Apache Lucene
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Apache Lucene
• Fast, high performance, scalable search/IR library• Open source• Initially developed by Doug Cutting (Also author
of Hadoop)• Indexing and Searching• Inverted Index of documents• Provides advanced Search options like synonyms,
stopwords, based on similarity, proximity.• http://lucene.apache.org/
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Lucene Internals - Inverted Index
Credit: https://developer.apple.com/library/mac/documentation/userexperience/conceptual/SearchKitConcepts/searchKit_basics/searchKit_basics.html
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Lucene Internals (Contd.)
• Defines documents Model
• Index contains documents.
• Each document consist of fields.
• Each Field has attributes.– What is the data type (FieldType)
– How to handle the content (Analyzers, Filters)
– Is it a stored field (stored="true") or Index field (indexed="true")
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Indexing Pipeline
• Analyzer : create tokens using a Tokenizer and/or applying Filters (Token Filters)
• Each field can define an Analyzer at index time/query time or the both at same time.
Credit : http://www.slideshare.net/otisg/lucene-introduction
Analysis Process - Tokenizer
WhitespaceAnalyzerSimplest built-in analyzer
The quick brown fox jumps over the lazy dog.
[The] [quick] [brown] [fox] [jumps] [over] [the] [lazy] [dog.]
Tokens
Analysis Process - Tokenizer
SimpleAnalyzerLowercases, split at non-letter boundaries
The quick brown fox jumps over the lazy dog.
[the] [quick] [brown] [fox] [jumps] [over] [the] [lazy] [dog]
Tokens
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Elasticsearch
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Introduction
• Enterprise Search platform for Apache Lucene
• Open source
• Highly reliable, scalable, fault tolerant
• Support distributed Indexing, Replication, and load
balanced querying
• http://www.elasticsearch.org/
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Elasticsearch - Features
• Distributed RESTful search server
• Document oriented
• Domain Driven
• Schema less
• Restful
• Easy to scale horizontally
Elasticsearch - Features
• Highlighting• Spelling Suggestions• Facets (Group by)• Query DSL
– based on JSON to define queries
• Automatic shard replication, routing• Zen discovery
– Unicast– Multicast
• Master Election– Re-election if Master Node fails
APIs
• HTTP RESTful Api
• Java Api
• Clients
– perl, python, php, ruby, .net etc
• All APIs perform automatic node
operation rerouting.
How to startIt’s this Easy.
Operations
INDEX CREATION
curl -XPUT "http://localhost:9200/movies/movie/1" -d‘ {
"title": "The Godfather", "director": "Francis Ford Coppola",
"year": 1972 }'
http://localhost:9200/<index>/<type>/[<id>]
Credit: http://joelabrahamsson.com/elasticsearch-101/
INDEX CREATION RESPONSE
Credit: http://joelabrahamsson.com/elasticsearch-101/
UPDATE
curl -XPUT "http://localhost:9200/movies/movie/1" -d' { "title": "The Godfather", "director": "Francis Ford Coppola", "year": 1972, "genres": ["Crime", "Drama"]
}'
Updated Version
Credit: http://joelabrahamsson.com/elasticsearch-101/
New field
GET
curl -XGET "http://localhost:9200/movies/movie/1" -d''
Credit: http://joelabrahamsson.com/elasticsearch-101/
curl -XDELETE "http://localhost:9200/movies/movie/1" -d''
DELETE
Credit: http://joelabrahamsson.com/elasticsearch-101/
Search across all indexes and all types http://localhost:9200/_search
Search across all types in the movies index. http://localhost:9200/movies/_search
Search explicitly for documents of type movie within the movies index. http://localhost:9200/movies/movie/_search
curl -XPOST "http://localhost:9200/_search" -d'{ "query": { "query_string": { "query": "kill" } }}'
SEARCH
Credit: http://joelabrahamsson.com/elasticsearch-101/
Credit: http://joelabrahamsson.com/elasticsearch-101/
SEARCH RESPONSE
Updating existing Mapping
curl -XPUT "http://localhost:9200/movies/movie/_mapping" -d'{ "movie": { "properties": { "director": { "type": "multi_field", "fields": { "director": {"type": "string"}, "original": {"type" : "string", "index" : "not_analyzed"} } } } }}'
Credit: http://joelabrahamsson.com/elasticsearch-101/
Cluster Architecture
Source: http://www.slideshare.net/DmitriBabaev1/elastic-search-moscow-bigdata-cassandra-sept-2013-meetup
Index Request
Source: http://www.slideshare.net/DmitriBabaev1/elastic-search-moscow-bigdata-cassandra-sept-2013-meetup
Search Request
Source: http://www.slideshare.net/DmitriBabaev1/elastic-search-moscow-bigdata-cassandra-sept-2013-meetup
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Who are using
• Github
• Stumbleupon
• Soundcloud
• Datadog
• Stackoverflow
• Many more…
– http://www.elasticsearch.com/case-studies/
Logstash
Logstash
• Open Source, Apache licensee• Written in JRuby• Part of Elasticsearch family• http://logstash.net/• Current version: 1.4.0• This talk is with 1.3.3
Logstash
• Multiple Input/ Multiple Output• Centralize logs
• Collect• Parse• Forward/Store
Architecture
Source: http://www.infoq.com/articles/review-the-logstash-book
Logstash – life of an event
• Input Filters Output
• Filters are processed in order of config file
• Outputs are processed in order of config file
• Input: Input stream
– File input (tail)
– Log4j
– Redis
– Syslog
– and many more…
• http://logstash.net/docs/1.3.3/
Logstash – life of an event• Codecs : decoding log messages
• Json
• Multiline
• Netflow
• and many more…
• Filters : processing messages
• Date – Date format
• Grok – Regular expression based extraction
• Mutate – Change data type
• and many more…
• Output : storing the structured message
• Elasticsearch
• Mongodb
• Nagios
• and many more…
http://logstash.net/docs/1.3.3/
Quick Start
< 1.3.3 version:java -jar logstash-1.3.3-flatjar.jar agent -f agent.conf – web
1.4 version:bin/logstash agent –f agent.confbin/logstash –web
basic-agent.conf :input {tcp { type => "apache" port => 3333 } }output { stdout { debug => true } elasticsearch { embedded => true }}
Kibana
Source: http://www.slideshare.net/AmazeeAG/2014-0422-loggingwithlogstashbastianwidmercampusbern
Source: http://www.slideshare.net/AmazeeAG/2014-0422-loggingwithlogstashbastianwidmercampusbern
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Analytics
Analytics source : Kibana.org based on ElasticSearch and Logstash Image Source : http://semicomplete.com/presentations/logstash-monitorama-2013/#/8
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Thanks!@rahuldausa on twitter and slideshare
http://www.linkedin.com/in/rahuldausa
Find Interesting ?
Join us @ http://www.meetup.com/Hyderabad-Apache-Solr-Lucene-Group/
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