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Introduction to	

Elasticsearch
27th May 2014 - BigData Meetup
Eric Rodriguez	

@wavyx
About Me
Eric Rodriguez
Founder of data.be
!
• Web entrepreneur
• Data addict
• Multi-Language: PHP, Java/
Groovy/Grails, .Net, …
be.linkedin.com/in/erodriguez
!
github.com/wavyx
!
@wavyx
Elasticsearch - Company
• Founded in 2012 => http://www.elasticsearch.com	

• Professional services	

• Training	

• Consultancy / Development support	

• Production support subscription (3 levels of SLAs)
Enterprises using Elasticsearch
(M)ELK Stack
• Elasticsearch - Search server based on Lucene	

• Logstash -Tool for managing events and logs	

• Kibana -Visualize logs and time-stamped data	

• Marvel - Monitor your cluster’s heartbeat
You Know, for Search…
Logstash
• Collect, parse, index, and search logs
Kibana
• A versatile dashboard to see and interact with your data
Marvel
• Monitor the health of your cluster

cluster-wide metrics, overview of all nodes and
indices and events (master election, new nodes)
real time, 	

search and 	

analytics engine	

open-source
Lucene
JSON
schema 	

free	

document

store
RESTful
API
documentation
scalability
high 	

availability	

distributed	

multi 	

tenancy
per-operation

persistence
Use Cases
• Full-Text Search	

• Data Store	

• Analytics	

• Alerts	

• Ads	

• …
Copyright 2014 Elasticsearch Inc / Elasticsearch BV.All rights reserved. Content used with permission from Elasticsearch.
Copyright 2014 Elasticsearch Inc / Elasticsearch BV.All rights reserved. Content used with permission from Elasticsearch.
Copyright 2014 Elasticsearch Inc / Elasticsearch BV.All rights reserved. Content used with permission from Elasticsearch.
Copyright 2014 Elasticsearch Inc / Elasticsearch BV.All rights reserved. Content used with permission from Elasticsearch.
Copyright 2014 Elasticsearch Inc / Elasticsearch BV.All rights reserved. Content used with permission from Elasticsearch.
Copyright 2014 Elasticsearch Inc / Elasticsearch BV.All rights reserved. Content used with permission from Elasticsearch.
Copyright 2014 Elasticsearch Inc / Elasticsearch BV.All rights reserved. Content used with permission from Elasticsearch.
Elasticsearch core
• Apache Lucene is a high-performance, full-featured text search engine library
written entirely in Java	

• Elasticsearch added value: “Simple is best”
• Simple API (with documentation)	

• JSON & RESTful	

• Sharding & Replication	

• Extensibility: plugins and scripts	

• Interoperability: clients and integrations
Terms for DBAs
• Index	

• Type	

• Document	

• Fields	

• Mapping
ElasticsearchRDBMs
• Database	

• Table	

• Row	

• Column	

• Schema
Plug & Play
• Zero configuration	

• 4 LoC to get started ;)
Alive !
=> http://localhost:9200/?pretty
REST
• Check your cluster, node, and index health, status, and statistics	

• Administer your cluster, node, and index data and metadata	

• Perform CRUD (Create, Read, Update, and Delete) and
search operations against your indexes	

• Execute advanced search operations such as paging, sorting,
ltering, scripting, faceting, aggregations, and many others
Basic Operations 1/3
• Add a document
• Create index
Basic Operations 2/3
• Modify/Replace a document
• Delete a document
• Delete index
Basic Operations 3/3
• Update a document
Mapping 1/2
• Define how a document should be mapped
(similar to schema): searchable elds, tokenization,
storage, ..	

• Explicit mapping is defined on an index/type level	

• A default mapping is automatically created
Mapping 2/2
• Core types: string, integer/long, float/double, boolean, and null	

• Other types:Array, Object, Nested, IP, GeoPoint, GeoShape,
Attachment	

• Example
Search API 1/2
• Multi-index, Multi-type
• Uri search - Google like

Operators (AND/OR), fields, sort, paging, wildcards, …
Search API 2/2
• Paging & Sort	

• Fields: selection, scripts	

• Post filter	

• Highlighting	

• Rescoring	

• Explain	

• …
Query DSL
• “SQL” for elasticsearch	

• Queries should be used	

• for full text search	

• where the result depends on a relevance score	

• Filters should be used	

• for binary yes/no searches	

• for queries on exact values
Basic Queries
Basic Filters
Analysis 1/2
• Analysis is extracting “terms” from a given text
• Processing natural language to make it computer
searchable	

• Configurable registry of Analyzers that can be used	

• to break indexed (analyzed) fields when a
document is indexed 	

• to process query strings
Analysis 2/2
• Analyzers are composed of 	

• a singleTokenizer (may be preceded by one or
more CharFilters)	

• zero or moreTokenFilters	

• Default Analyzers

standard, pattern, whitespace, language, snowball
Copyright 2014 Elasticsearch Inc / Elasticsearch BV.All rights reserved. Content used with permission from Elasticsearch.
Analytics
• Aggregation of information: similar to “group by”	

• Facets	

• Aggregated data based on a search query 	

• One-dimensional results	

• Ex:“term facets” return facetcounts for various values for a specific field 

Think color, tag, category, …	

• Aggregations (ES 1.0+)	

• Nested Facets	

• Basic Stats: mean, min, max, std dev, term counts	

• SignificantTerms, Percentiles, Cardinality estimations
Facets
• not yet deprecated, but use aggregations!
• Various Facets

terms, range, histogram, date,
statistical, geo distance, …
Aggregations
• A generic powerful framework that can be divided into 2 main families:	

• Bucketing

Each bucket is associated with a key and a document criterion

The aggregation process provides a list of buckets - each one with a set of
documents that "belong" to it.	

• Metric

Aggregations that keep track and compute metrics over a set of
documents.	

• Aggregations can be nested !
Bucket Aggregators
• global	

• filter	

• missing	

• terms	

• range	

• date range	

• ip range
• histogram	

• date histogram	

• geo distance	

• geohash grid	

• nested	

• reverse nested	

• top hits (version 1.3)
Metrics Aggregators
• count	

• stats	

• extended stats	

• cardinality	

• percentiles
• min	

• max	

• sum	

• avg
Search for end users
• Suggesters - “Did you mean”

Terms, Phrases, Completion, Context	

• “More like this”

Find documents that are "like" provided text by
running it against one or more elds
Percolator
• Classic ES 	

1. Add & Index documents	

2. Search with queries
3. Retrieve matching documents	

• Percolator
1. Add & Index queries	

2. Percolate documents
3. Retrieve matching queries
Why Percolate ?!
• Alerts: social media mentions, weather forecast, news alerts	

• Automatic Monitoring: price monitoring, stock alerts, logs	

• Ads: display targeted ads based on user’s search queries	

• Enrich: percolate new documents, then add query matches
as document tags
High Availability 1/2
• Sharding - Write Scalability
• Split logical data over multiple machines & Control data flows	

• Each index has a fixed number of shards	

• Improve indexing performance	

• Replication - Read Scalability
• Each shard can have 0-many replicas (dynamic setup)	

• Removing SPOF (Single Point Of Failure)	

• Improve search performance
High Availability 2/2
• Zen Discovery
• Automatic discovery of nodes within a cluster
and electing a master node	

• Useful for failover and replication	

• Specific modules:Amazon EC2, Microsoft
Azure, Google Compute Engine	

• Snapshot & Restore module
Cluster Management
• Marvel - http://www.elasticsearch.org/overview/marvel/ 	

• BigDesk - http://bigdesk.org/	

• Paramedic - https://github.com/karmi/elasticsearch-
paramedic	

• KOPF - https://github.com/lmenezes/elasticsearch-kopf/	

• Elastic HQ - http://www.elastichq.org/
Clients & Integration
• Ecosystem: Kibana, Logstash, Marvel, Hadoop integration	

• API Clients: Java, Javascript, Groovy, PHP, Perl, Python, .Net,
Ruby, Scala, Clojure, Go, Erlang, …	

• Integrations: Grails, Django, Play!, Symfony2, Carrot2, Spring,
Drupal,Wordpress, …	

• Rivers: CouchDB, JDBC, MongoDB, Neo4j, Redis, RabbitMQ,
ActiveMQ,Amazon SQS, File System,Twitter,Wikipedia, RSS, …
Fast & Furious Evolution
Version 1.1

March 25, 2014
• Cardinality Agg	

• Percentiles Agg	

• SignificantTerms
Agg	

• SearchTemplates	

• Cross fields search	

• Alias for indices &
templates
Version 1.2

May 22, 2014
• Java 7	

• Indexing & Merging
performance 	

• Aggregations
performance	

• Context suggester	

• Deep scrolling	

• Field value factor
Benchmark API coming in 1.3
Version 1.0

Feb 12, 2014
• Aggregations	

• Snapshot & Restore	

• Distributed
Percolator	

• Cat API	

• Federated search	

• Doc values	

• Circuit breaker
Resources
• http://www.elasticsearch.org/guide/	

• http://www.elasticsearch.org/videos/	

• http://www.elasticsearchtutorial.com/	

• http://exploringelasticsearch.com/	

• http://joelabrahamsson.com/elasticsearch-101/	

• http://belczyk.com/2014/01/elasticsearch-recomended-learning-materials/	

• http://www.elasticsearch.org/guide/en/elasticsearch/reference/1.x/modules-
plugins.html
Books
• Elasticsearch Server

http://www.packtpub.com/
elasticsearch-server-2e/book
• Elasticsearch in Action

http://www.manning.com/
hinman/
Books
• Elasticsearch Cookbook

http://www.packtpub.com/
elasticsearch-cookbook/book
• Mastering Elasticsearch

http://www.packtpub.com/
mastering-elasticsearch-
querying-and-data-handling/
book
Books
• Elasticsearch -The Definitive Guide

http://www.elasticsearch.org/blog/elasticsearch-denitive-guide/
Thank you!
eric@data.be - @wavyx
be.linkedin.com/in/erodriguez - github.com/wavyx
http://www.meetup.com/ElasticSearch-User-Group-Belux-Belgium-Luxembourg/

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Elasticsearch Introduction at BigData meetup

  • 1. Introduction to Elasticsearch 27th May 2014 - BigData Meetup Eric Rodriguez @wavyx
  • 2. About Me Eric Rodriguez Founder of data.be ! • Web entrepreneur • Data addict • Multi-Language: PHP, Java/ Groovy/Grails, .Net, … be.linkedin.com/in/erodriguez ! github.com/wavyx ! @wavyx
  • 3. Elasticsearch - Company • Founded in 2012 => http://www.elasticsearch.com • Professional services • Training • Consultancy / Development support • Production support subscription (3 levels of SLAs)
  • 5. (M)ELK Stack • Elasticsearch - Search server based on Lucene • Logstash -Tool for managing events and logs • Kibana -Visualize logs and time-stamped data • Marvel - Monitor your cluster’s heartbeat You Know, for Search…
  • 6. Logstash • Collect, parse, index, and search logs
  • 7. Kibana • A versatile dashboard to see and interact with your data
  • 8. Marvel • Monitor the health of your cluster
 cluster-wide metrics, overview of all nodes and indices and events (master election, new nodes)
  • 9. real time, search and analytics engine open-source Lucene JSON schema free document
 store RESTful API documentation scalability high availability distributed multi tenancy per-operation
 persistence
  • 10. Use Cases • Full-Text Search • Data Store • Analytics • Alerts • Ads • …
  • 11. Copyright 2014 Elasticsearch Inc / Elasticsearch BV.All rights reserved. Content used with permission from Elasticsearch.
  • 12. Copyright 2014 Elasticsearch Inc / Elasticsearch BV.All rights reserved. Content used with permission from Elasticsearch.
  • 13. Copyright 2014 Elasticsearch Inc / Elasticsearch BV.All rights reserved. Content used with permission from Elasticsearch.
  • 14. Copyright 2014 Elasticsearch Inc / Elasticsearch BV.All rights reserved. Content used with permission from Elasticsearch.
  • 15. Copyright 2014 Elasticsearch Inc / Elasticsearch BV.All rights reserved. Content used with permission from Elasticsearch.
  • 16. Copyright 2014 Elasticsearch Inc / Elasticsearch BV.All rights reserved. Content used with permission from Elasticsearch.
  • 17. Copyright 2014 Elasticsearch Inc / Elasticsearch BV.All rights reserved. Content used with permission from Elasticsearch.
  • 18. Elasticsearch core • Apache Lucene is a high-performance, full-featured text search engine library written entirely in Java • Elasticsearch added value: “Simple is best” • Simple API (with documentation) • JSON & RESTful • Sharding & Replication • Extensibility: plugins and scripts • Interoperability: clients and integrations
  • 19. Terms for DBAs • Index • Type • Document • Fields • Mapping ElasticsearchRDBMs • Database • Table • Row • Column • Schema
  • 20. Plug & Play • Zero conguration • 4 LoC to get started ;)
  • 22. REST • Check your cluster, node, and index health, status, and statistics • Administer your cluster, node, and index data and metadata • Perform CRUD (Create, Read, Update, and Delete) and search operations against your indexes • Execute advanced search operations such as paging, sorting, ltering, scripting, faceting, aggregations, and many others
  • 23. Basic Operations 1/3 • Add a document • Create index
  • 24. Basic Operations 2/3 • Modify/Replace a document • Delete a document • Delete index
  • 25. Basic Operations 3/3 • Update a document
  • 26. Mapping 1/2 • Dene how a document should be mapped (similar to schema): searchable elds, tokenization, storage, .. • Explicit mapping is dened on an index/type level • A default mapping is automatically created
  • 27. Mapping 2/2 • Core types: string, integer/long, float/double, boolean, and null • Other types:Array, Object, Nested, IP, GeoPoint, GeoShape, Attachment • Example
  • 28. Search API 1/2 • Multi-index, Multi-type • Uri search - Google like
 Operators (AND/OR), elds, sort, paging, wildcards, …
  • 29. Search API 2/2 • Paging & Sort • Fields: selection, scripts • Post lter • Highlighting • Rescoring • Explain • …
  • 30. Query DSL • “SQL” for elasticsearch • Queries should be used • for full text search • where the result depends on a relevance score • Filters should be used • for binary yes/no searches • for queries on exact values
  • 33. Analysis 1/2 • Analysis is extracting “terms” from a given text • Processing natural language to make it computer searchable • Congurable registry of Analyzers that can be used • to break indexed (analyzed) elds when a document is indexed • to process query strings
  • 34. Analysis 2/2 • Analyzers are composed of • a singleTokenizer (may be preceded by one or more CharFilters) • zero or moreTokenFilters • Default Analyzers
 standard, pattern, whitespace, language, snowball
  • 35. Copyright 2014 Elasticsearch Inc / Elasticsearch BV.All rights reserved. Content used with permission from Elasticsearch.
  • 36. Analytics • Aggregation of information: similar to “group by” • Facets • Aggregated data based on a search query • One-dimensional results • Ex:“term facets” return facetcounts for various values for a specic eld 
 Think color, tag, category, … • Aggregations (ES 1.0+) • Nested Facets • Basic Stats: mean, min, max, std dev, term counts • SignicantTerms, Percentiles, Cardinality estimations
  • 37. Facets • not yet deprecated, but use aggregations! • Various Facets
 terms, range, histogram, date, statistical, geo distance, …
  • 38. Aggregations • A generic powerful framework that can be divided into 2 main families: • Bucketing
 Each bucket is associated with a key and a document criterion
 The aggregation process provides a list of buckets - each one with a set of documents that "belong" to it. • Metric
 Aggregations that keep track and compute metrics over a set of documents. • Aggregations can be nested !
  • 39. Bucket Aggregators • global • lter • missing • terms • range • date range • ip range • histogram • date histogram • geo distance • geohash grid • nested • reverse nested • top hits (version 1.3)
  • 40. Metrics Aggregators • count • stats • extended stats • cardinality • percentiles • min • max • sum • avg
  • 41. Search for end users • Suggesters - “Did you mean”
 Terms, Phrases, Completion, Context • “More like this”
 Find documents that are "like" provided text by running it against one or more elds
  • 42. Percolator • Classic ES 1. Add & Index documents 2. Search with queries 3. Retrieve matching documents • Percolator 1. Add & Index queries 2. Percolate documents 3. Retrieve matching queries
  • 43. Why Percolate ?! • Alerts: social media mentions, weather forecast, news alerts • Automatic Monitoring: price monitoring, stock alerts, logs • Ads: display targeted ads based on user’s search queries • Enrich: percolate new documents, then add query matches as document tags
  • 44. High Availability 1/2 • Sharding - Write Scalability • Split logical data over multiple machines & Control data flows • Each index has a xed number of shards • Improve indexing performance • Replication - Read Scalability • Each shard can have 0-many replicas (dynamic setup) • Removing SPOF (Single Point Of Failure) • Improve search performance
  • 45. High Availability 2/2 • Zen Discovery • Automatic discovery of nodes within a cluster and electing a master node • Useful for failover and replication • Specic modules:Amazon EC2, Microsoft Azure, Google Compute Engine • Snapshot & Restore module
  • 46. Cluster Management • Marvel - http://www.elasticsearch.org/overview/marvel/ • BigDesk - http://bigdesk.org/ • Paramedic - https://github.com/karmi/elasticsearch- paramedic • KOPF - https://github.com/lmenezes/elasticsearch-kopf/ • Elastic HQ - http://www.elastichq.org/
  • 47. Clients & Integration • Ecosystem: Kibana, Logstash, Marvel, Hadoop integration • API Clients: Java, Javascript, Groovy, PHP, Perl, Python, .Net, Ruby, Scala, Clojure, Go, Erlang, … • Integrations: Grails, Django, Play!, Symfony2, Carrot2, Spring, Drupal,Wordpress, … • Rivers: CouchDB, JDBC, MongoDB, Neo4j, Redis, RabbitMQ, ActiveMQ,Amazon SQS, File System,Twitter,Wikipedia, RSS, …
  • 48. Fast & Furious Evolution Version 1.1
 March 25, 2014 • Cardinality Agg • Percentiles Agg • SignicantTerms Agg • SearchTemplates • Cross elds search • Alias for indices & templates Version 1.2
 May 22, 2014 • Java 7 • Indexing & Merging performance • Aggregations performance • Context suggester • Deep scrolling • Field value factor Benchmark API coming in 1.3 Version 1.0
 Feb 12, 2014 • Aggregations • Snapshot & Restore • Distributed Percolator • Cat API • Federated search • Doc values • Circuit breaker
  • 49. Resources • http://www.elasticsearch.org/guide/ • http://www.elasticsearch.org/videos/ • http://www.elasticsearchtutorial.com/ • http://exploringelasticsearch.com/ • http://joelabrahamsson.com/elasticsearch-101/ • http://belczyk.com/2014/01/elasticsearch-recomended-learning-materials/ • http://www.elasticsearch.org/guide/en/elasticsearch/reference/1.x/modules- plugins.html
  • 51. Books • Elasticsearch Cookbook
 http://www.packtpub.com/ elasticsearch-cookbook/book • Mastering Elasticsearch
 http://www.packtpub.com/ mastering-elasticsearch- querying-and-data-handling/ book
  • 52. Books • Elasticsearch -The Denitive Guide
 http://www.elasticsearch.org/blog/elasticsearch-denitive-guide/
  • 53. Thank you! eric@data.be - @wavyx be.linkedin.com/in/erodriguez - github.com/wavyx http://www.meetup.com/ElasticSearch-User-Group-Belux-Belgium-Luxembourg/