This document provides an overview of Elasticsearch, including what it is, how it works, and how to perform basic operations like indexing, updating, and searching documents. It explains that Elasticsearch allows for advanced search across large amounts of data by making documents searchable and scaling easily. It also demonstrates how to index, update, search for, and retrieve documents through RESTful API calls. Faceted search, aggregations, and cluster architecture are also summarized.

2. Loïc Bertron
Director of Research & Development @Cedrom-SNI
!
Working on Big Data for Cedrom-SNI : social media, tv & radio aggregation
Introduced Elasticsearch at Cedrom-Sni
!
Cedrom-Sni
!
10k+ different sources, 750k+ new docs/days
Our job : Ingesting, enriching, extracting analytics and intelligence from docs
loic.bertron@cedrom-sni.com
linkedin.com/in/loicbertron
@loicbertron
Who am I ?

3. ElasticSearch is offering advanced search features to any application or
website easily, scaling on a large amount of data.
«
»
ElasticSearch

4. Simple : Plug & Play - Schema free - RESTful API
!
Elastic : Automatically discover all others instances
!
Strong : Replication & Load balancing - Scales massively - Lucene based
!
Fast : Requests executed in parallel - Real Time
!
Full featured : Search, Analytics, Facets, Percolator, Geo search, Suggest, Plugins …
What is ElasticSearch ?

5. Document as JSON
• Object representing your data
• Grouped in an index
• One index can have multiples types of documents
{
"message": "Introducing #ElasticSearch",
"post_date": "2014-03-12T18:30:00",
"author": {
"first_name" : "Loïc",
"email" : "loic.bertron@cedrom-sni.com"
},
"employee_at_Cedrom" : true,
"Tags" : ["Meetup","Montreal"]
}

6. • API REST : http://host:port/[index]/[type]/[_action/id]
HTTP Methods: GET, POST, PUT, DELETE
• Documents
• http://node1:9200/twitter/tweet/1 (POST)
• http://node1:9200/twitter/tweet/1 (GET)
• http://node1:9200/twitter/tweet/1 (DELETE)
• Search
• http://node1:9200/twitter/tweet/_search (GET)
• http://node1:9200/twitter/_search (GET)
• http://node1:9200/_search (GET)
• Metadata
• http://node1:9200/twitter/_status (GET)
• http://node1:9200/_shutdown (POST)
API

7. Index a document
$ curl -X PUT http://node1:9200/twitter/tweet/1 -d '{
"user": "loicbertron",
"post_date": "2014-03-12T18:30:00",
"message": "Introducing #ElasticSearch"
}'

8. {
"ok":true,
"_index":"twitter",
"_type":"tweet",
"_id":"1"
"_version":"1"
}
Index a document

9. Update a document
$ curl -X PUT http://node1:9200/twitter/tweet/1 -d '{
"user": "loicbertron",
"post_date": "2014-03-12T18:40:00",
"message": "Introducing #ElasticSearch to the #Community"
}'

10. {
"ok":true,
"_index":"twitter",
"_type":"tweet",
"_id":"1"
"_version":"2"
}
Update a document

11. $ curl -XGET http://node1:9200/twitter/tweet/_search -d '{
"query": {
"term": { "message": "ElasticSearch" }
}
}'
Search for documents
$ curl -XGET http://node1:9200/twitter/tweet/_search?q=elasticsearch

12. Search for documents
{
"took" : 24,
"timed_out" : false,
"_shards" : { "total" : 2, "successful" : 2, "failed" : 0 },
"hits" : {
"total" : 1,
"max_score" : 0.227,
"hits" : [ {
"_index" : "twitter",
"_type" : "tweet",
"_id" : "1",
"_score" : 0.227, "_source" : {
"user": "loicbertron",
"post_date": "2014-03-12T18:40:00",
"message": "Introducing #ElasticSearch to the #Community"
}
} ]
}
}

13. Search for documents
{
"took" : 24,
"timed_out" : false,
"_shards" : { "total" : 2, "successful" : 2, "failed" : 0 },
"hits" : {
"total" : 1,
"max_score" : 0.227,
"hits" : [ {
"_index" : "twitter",
"_type" : "tweet",
"_id" : "1",
"_score" : 0.227, "_source" : {
"user": "loicbertron",
"post_date": "2014-03-12T18:40:00",
"message": "Introducing #ElasticSearch to the #Community"
}
} ]
}
}
Execution
time

14. {
"took" : 24,
"timed_out" : false,
"_shards" : { "total" : 2, "successful" : 2, "failed" : 0 },
"hits" : {
"total" : 1,
"max_score" : 0.227,
"hits" : [ {
"_index" : "twitter",
"_type" : "tweet",
"_id" : "1",
"_score" : 0.227, "_source" : {
"user": "loicbertron",
"post_date": "2014-03-12T18:40:00",
"message": "Introducing #ElasticSearch to the #Community"
}
} ]
}
}
# of documents
matching
Search for documents

15. {
"took" : 24,
"timed_out" : false,
"_shards" : { "total" : 2, "successful" : 2, "failed" : 0 },
"hits" : {
"total" : 1,
"max_score" : 0.227,
"hits" : [ {
"_index" : "twitter",
"_type" : "tweet",
"_id" : "1",
"_score" : 0.227, "_source" : {
"user": "loicbertron",
"post_date": "2014-03-12T18:40:00",
"message": "Introducing #ElasticSearch to the #Community"
}
} ]
}
}
Infos
Search for documents

16. {
"took" : 24,
"timed_out" : false,
"_shards" : { "total" : 2, "successful" : 2, "failed" : 0 },
"hits" : {
"total" : 1,
"max_score" : 0.227,
"hits" : [ {
"_index" : "twitter",
"_type" : "tweet",
"_id" : "1",
"_score" : 0.227, "_source" : {
"user": "loicbertron",
"post_date": "2014-03-12T18:40:00",
"message": "Introducing #ElasticSearch to the #Community"
}
} ]
}
}
Score
Search for documents

17. {
"took" : 24,
"timed_out" : false,
"_shards" : { "total" : 2, "successful" : 2, "failed" : 0 },
"hits" : {
"total" : 1,
"max_score" : 0.227,
"hits" : [ {
"_index" : "twitter",
"_type" : "tweet",
"_id" : "1",
"_score" : 0.227, "_source" : {
"user": "loicbertron",
"post_date": "2014-03-12T18:40:00",
"message": "Introducing #ElasticSearch to the #Community"
}
} ]
}
}
Document
Search for documents

18. Search operand
Terms quebec
quebec ontario
Phrases "city of montréal"
Proximity "montreal collusion" ~5
Fuzzy schwarzenegger ~0.8
Wildcards queb*
Boosting Quebec^5 montreal
Range [2011/03/12 TO 2014/03/12]
[java to json]
Boolean quebec AND NOT montreal
+quebec -montreal
(quebec OR ottawa) AND NOT toronto
Fields title:montreal^10 OR body:montreal
$ curl -XGET http://node1:9200/twitter/tweet/_search?q=<Your Query>

19. $ curl -XGET http://node1:9200/twitter/tweet/_search -d ‘{
"query": {
"filtered" : {
"query" : {
"bool" : {
!
"must" : {
"match" : {
"author.first_name" : {
"query" : "loic",
"fuzziness" : 0.1
}
}
},
!
"must" : {
"multi_match" : {
"query" : "elasticsearch",
"fields" : ["title^10","body"]
}
}
}
},
!
"filter": {
"and" : [
{"terms" : { "tags" : ["search","scale","store"] } },
{"range" : { "created_at" : {"from": "2013" } } } ,
{"term": { "featured" : true } }
]
}
}
}
}’
Query DSL

20. $ curl -XGET http://node1:9200/twitter/tweet/_search -d ‘{
"query": {
"filtered" : {
"query" : {
"bool" : {
!
"must" : {
"match" : {
"author.first_name" : {
"query" : "loic",
"fuzziness" : 0.1
}
}
},
!
"must" : {
"multi_match" : {
"query" : "elasticsearch",
"fields" : ["title^10","body"]
}
}
}
},
!
"filter": {
"and" : [
{"terms" : { "tags" : ["search","scale","store"] } },
{"range" : { "created_at" : {"from": "2013" } } } ,
{"term": { "featured" : true } }
]
}
}
}
}’
Query DSL
"must" : {
"match" : {
"author.first_name" : {
"query" : "loic",
"fuzziness" : 0.1
}
}

21. $ curl -XGET http://node1:9200/twitter/tweet/_search -d ‘{
"query": {
"filtered" : {
"query" : {
"bool" : {
!
"must" : {
"match" : {
"author.first_name" : {
"query" : "loic",
"fuzziness" : 0.1
}
}
},
!
"must" : {
"multi_match" : {
"query" : "elasticsearch",
"fields" : ["title^10","body"]
}
}
}
},
!
"filter": {
"and" : [
{"terms" : { "tags" : ["search","scale","store"] } },
{"range" : { "created_at" : {"from": "2013" } } } ,
{"term": { "featured" : true } }
]
}
}
}
}’
Query DSL
"must" : {
"multi_match" : {
"query" : "elasticsearch",
"fields" : ["title^10","body"]
}
}

22. $ curl -XGET http://node1:9200/twitter/tweet/_search -d ‘{
"query": {
"filtered" : {
"query" : {
"bool" : {
!
"must" : {
"match" : {
"author.first_name" : {
"query" : "loic",
"fuzziness" : 0.1
}
}
},
!
"must" : {
"multi_match" : {
"query" : "elasticsearch",
"fields" : ["title^10","body"]
}
}
}
},
!
"filter": {
"and" : [
{"terms" : { "tags" : ["search","scale","store"] } },
{"range" : { "created_at" : {"from": "2013" } } } ,
{"term": { "featured" : true } }
]
}
}
}
}’
Query DSL
"filter": {
"and" : [
{"terms" : { "tags" : ["search","scale","store"] } },
{"range" : { "created_at" : {"from": "2013" } } } ,
{"term": { "featured" : true } }
]
}

23. Facets

24. Ranges
Term
Term
Ranges
Facets

25. $ curl -XPOST http://node1:9200/articles/_search -d '{
"aggregations" : {
"tag_cloud" : { "terms" : {"field" : "tags"} }
}
}'
Tag Cloud
"aggregations" : {
"tag_cloud" :[
{"terms": "Quebec", "count" : 5},
{"terms": "Montréal", "count" : 3},
...
]
}

26. $ curl -XPOST http://node1:9200/students/_search?search_type=count -d '{
"facets": {
"scores-per-subject" : {
"terms_stats" : {
"key_field" : "subject",
"value_field" : "score"
}
}
}
}'
Stats
"facets" : {
"scores-per-subject" : {
"_type" : "terms_stats",
"missing" : 0,
"terms" : [ {
"term" : "math",
"count" : 4,
"total_count" : 4,
"min" : 25.0,
"max" : 92.0,
"total" : 267.0,
"mean" : 66.75
}, […]
}
}

27. Advanced facets : Aggregations
{
"rank": "21",
"city": "Boston",
"state": "MA",
"population2012": "636479",
"population2010": "617594",
"land_area": "48.277",
"density": "12793",
"ansi": "619463",
"location": {
"lat": "42.332",
"lon": "71.0202"
}
}

28. curl -XGET "node1:9200/cities/_search?pretty" -d '{
"aggs" : {
"mean_density_by_state" : {
"terms" : {
"field" : "state"
},
"aggs": {
"mean_density": {
"avg" : {
"field" : "density"
}
}
}
}
}
}'
Advanced facets : Aggregations

29. "aggregations" : {
"mean_density_by_state" : {
"terms" : [ {
"term" : "CA",
"doc_count" : 69,
"mean_density" : {
"value" : 5558.623188405797
}
}, {
"term" : "TX",
"doc_count" : 32,
"mean_density" : {
"value" : 2496.625
}
}, {
"term" : "FL",
"doc_count" : 20,
"mean_density" : {
"value" : 4006.6
}
}, {
"term" : "CO",
"doc_count" : 11,
Advanced facets : Aggregations

30. Ranges
Term
Facets

31. Facets
Terms
Terms Stats
Statistical
Range
Histogram
Date Histogram
Filter
Query
Geo Distance

32. Noeud 1
Cluster
État du cluster : Vert
Node 1
Cluster
Shard 0
Shard 1
cluster state : Yellow
Architecture
$ curl -XPUT localhost:9200/twitter -d '{
"index" : {
"number_of_shards" : 2,
"number_of_replicas" : 1
}
}'

33. Noeud 1
Cluster
État du cluster : Vert
Noeud 1
Cluster
Shard 0
Shard 1
État du cluster : Jaune
Node 1
Cluster
Shard 0
Shard 1
cluster state : Green
Node 2
Shard 0
Shard 1
adding a second node
Architecture

34. Node 1
Cluster
Shard 0
Shard 1
Node 2
Shard 1
Shard 0
Architecture

35. Node 1
Cluster
Shard 0
Node 3
Shard 1
Node 2
Shard 1
Shard 0
Architecture

36. Node 1
Cluster
Shard 0
Node 3
Shard 1
Node 2
Shard 1
Shard 0
Architecture

37. Node 1
Cluster
Shard 0
Node 3 Node 4
Shard 1
Node 2
Shard 1
Shard 0
Architecture

38. Node 1
Cluster
Shard 0
Node 3 Node 4
Shard 1
Node 2
Shard 1
Shard 0
Architecture

39. Node 1
Cluster
Shard 0
Node 3 Node 4
Shard 1
Node 2
Shard 1
Shard 0
Architecture

40. Node 1
Cluster
Shard 0
Node 3 Node 4
Shard 1
Node 2
Shard 1
Shard 0
$ curl -X PUT http://node1:9200/twitter/tweet/1 -d '{
"user": "loicbertron",
"post_date": "2014-03-12T18:30:00",
"message": "Introducing #ElasticSearch"
}'
Architecture

41. Node 1
Cluster
Shard 0
Node 3 Node 4
Shard 1
Node 2
Shard 1
Shard 0
Doc 1
$ curl -X PUT http://node1:9200/twitter/tweet/1 -d '{
"user": "loicbertron",
"post_date": "2014-03-12T18:30:00",
"message": "Introducing #ElasticSearch"
}'
Architecture

42. Node 1
Cluster
Shard 0
Node 3 Node 4
Shard 1
Node 2
Shard 1
Shard 0
Doc 1
$ curl -X PUT http://node1:9200/twitter/tweet/1 -d '{
"user": "loicbertron",
"post_date": "2014-03-12T18:30:00",
"message": "Introducing #ElasticSearch"
}'
Architecture

43. Cluster
Shard 0
Shard 1Shard 1
Shard 0
Doc 1 Doc 1
$ curl -X PUT http://node1:9200/twitter/tweet/1 -d '{
"user": "loicbertron",
"post_date": "2014-03-12T18:30:00",
"message": "Introducing #ElasticSearch"
}'
Architecture
Node 1 Node 2 Node 3 Node 4

44. Cluster
Shard 0
Shard 1Shard 1
Shard 0
Doc 1 Doc 1
{
"ok":true,
"_index":"twitter",
"_type":"tweet",
"_id":"1"
"_version":"1"
}
Architecture
Node 1 Node 2 Node 3 Node 4

45. Cluster
Shard 0
Shard 1Shard 1
Shard 0
Doc 1 Doc 1
Architecture
Node 1 Node 2 Node 3 Node 4
$ curl -X PUT http://node1:9200/twitter/tweet/2 -d '{
"user": "loicbertron",
"post_date": "2014-03-12T18:45:00",
"message": "The crowd is on fire #ElasticSearch"
}'

46. Cluster
Shard 0
Shard 1Shard 1
Shard 0
Doc 1 Doc 1
Doc 2
Architecture
Node 1 Node 2 Node 3 Node 4
$ curl -X PUT http://node1:9200/twitter/tweet/2 -d '{
"user": "loicbertron",
"post_date": "2014-03-12T18:45:00",
"message": "The crowd is on fire #ElasticSearch"
}'

47. Cluster
Shard 0
Shard 1Shard 1
Shard 0
Doc 1 Doc 1
Doc 2
Architecture
Node 1 Node 2 Node 3 Node 4
$ curl -X PUT http://node1:9200/twitter/tweet/2 -d '{
"user": "loicbertron",
"post_date": "2014-03-12T18:45:00",
"message": "The crowd is on fire #ElasticSearch"
}'

48. Cluster
Shard 0
Shard 1Shard 1
Shard 0
Doc 1 Doc 1
Doc 2 Doc 2
Architecture
Node 1 Node 2 Node 3 Node 4
$ curl -X PUT http://node1:9200/twitter/tweet/2 -d '{
"user": "loicbertron",
"post_date": "2014-03-12T18:45:00",
"message": "The crowd is on fire #ElasticSearch"
}'

49. Cluster
Shard 0
Shard 1Shard 1
Shard 0
Doc 1 Doc 1
Doc 2 Doc 2
{
"ok":true,
"_index":"twitter",
"_type":"tweet",
"_id":"2"
"_version":"1"
}
Architecture
Node 1 Node 2 Node 3 Node 4

50. Cluster
Shard 0
Shard 1Shard 1
Shard 0
Doc 1 Doc 1
Doc 2 Doc 2
$ curl -XGET http://node1:9200/twitter/tweet/_search -d '{
"query": {
"term": { "message": "ElasticSearch" }
}
}'
Architecture
Node 1 Node 2 Node 3 Node 4

51. Cluster
Shard 0
Shard 1Shard 1
Shard 0
Doc 1 Doc 1
Doc 2 Doc 2
$ curl -XGET http://node1:9200/twitter/tweet/_search -d '{
"query": {
"term": { "message": "ElasticSearch" }
}
}'
Architecture
Node 1 Node 2 Node 3 Node 4

52. Cluster
Shard 0
Shard 1Shard 1
Shard 0
Doc 1 Doc 1
Doc 2 Doc 2
$ curl -XGET http://node1:9200/twitter/tweet/_search -d '{
"query": {
"term": { "message": "ElasticSearch" }
}
}'
Architecture
Node 1 Node 2 Node 3 Node 4

53. Cluster
Shard 0
Shard 1Shard 1
Shard 0
Doc 1
Doc 1
Doc 2
Doc 2
$ curl -XGET http://node1:9200/twitter/tweet/_search -d '{
"query": {
"term": { "message": "ElasticSearch" }
}
}'
Architecture
Node 1 Node 2 Node 3 Node 4

54. Cluster
Shard 0
Shard 1Shard 1
Shard 0
Doc 1
Doc 1
Doc 2
Doc 2
$ curl -XGET http://node1:9200/twitter/tweet/_search -d '{
"query": {
"term": { "message": "ElasticSearch" }
}
}'
Architecture
Node 1 Node 2 Node 3 Node 4

55. Cluster
Shard 0
Shard 1Shard 1
Shard 0
Doc 1 Doc 1
Doc 2 Doc 2
Architecture
Node 1 Node 2 Node 3 Node 4

56. Cluster
Shard 1Shard 1
Shard 0
Doc 1
Doc 2 Doc 2
Architecture
Node 2 Node 3 Node 4

57. Cluster
Shard 1
Node 2
Shard 1
Doc 2 Doc 2
Shard 0
Doc 1
Architecture
Node 3 Node 4
Shard 0
Doc 1

58. Cluster
Shard 1
Node 2
Shard 1
Doc 2 Doc 2
Shard 0
Doc 1
Architecture
Node 3 Node 4
Shard 0
Doc 1

59. Cluster
Shard 1Shard 1
Doc 2 Doc 2
Shard 0
Doc 1
Architecture
Node 2 Node 3 Node 4
$ curl -X PUT http://node1:9200/twitter/tweet/3 -d '{
"user": "loicbertron",
"post_date": "2014-03-12T19:00:00",
"message": "A third message about #ElasticSearch"
}'
Shard 0
Doc 1

60. Cluster
Shard 1Shard 1
Doc 2 Doc 2
Shard 0
Doc 1
Doc 3
Architecture
Node 2 Node 3 Node 4
$ curl -X PUT http://node1:9200/twitter/tweet/3 -d '{
"user": "loicbertron",
"post_date": "2014-03-12T19:00:00",
"message": "A third message about #ElasticSearch"
}'
Shard 0
Doc 1

61. Cluster
Shard 1Shard 1
Doc 2 Doc 2
Shard 0
Doc 1
Doc 3
Architecture
Node 2 Node 3 Node 4
$ curl -X PUT http://node1:9200/twitter/tweet/3 -d '{
"user": "loicbertron",
"post_date": "2014-03-12T19:00:00",
"message": "A third message about #ElasticSearch"
}'
Shard 0
Doc 1
Doc 3

62. Cluster
Shard 1Shard 1
Doc 2 Doc 2
Shard 0
Doc 1
Doc 3
{
"ok":true,
"_index":"twitter",
"_type":"tweet",
"_id":"3"
"_version":"1"
}
Architecture
Node 2 Node 3 Node 4
Shard 0
Doc 1
Doc 3

63. Cluster
Shard 1Shard 1
Doc 2 Doc 2
Shard 0
Doc 1
Doc 3
$ curl -XGET http://node1:9200/twitter/tweet/_search -d '{
"query": {
"term": { "message": "ElasticSearch" }
}
}'
Architecture
Node 2 Node 3 Node 4
Shard 0
Doc 1
Doc 3

64. Cluster
Shard 1Shard 1
Doc 2
Doc 2
Shard 0
Doc 1Doc 3
$ curl -XGET http://node1:9200/twitter/tweet/_search -d '{
"query": {
"term": { "message": "ElasticSearch" }
}
}'
Architecture
Node 2 Node 3 Node 4
Shard 0
Doc 1
Doc 3

65. Cluster
Shard 1Shard 1
Doc 2 Doc 2
Architecture
Node 2 Node 4

66. How users see search ?
ResultUser Query List of results

67. How search engine works?
1. Fetch document field
2. Pick configured anlyser
3. Parse text inot tokens
4. Apply token filters
5. Store into index

68. Analyzer
curl -XGET "http://localhost:9200/docs/_analyze?
analyzer=standard&pretty=1" -d "Édith Piaf vedette du feu d'artifice"

69. Analyzer
{
"tokens" : [ {
"token" : "édith",
"start_offset" : 0,
"end_offset" : 5,
"type" : "<ALPHANUM>",
"position" : 1
}, {
"token" : "piaf",
"start_offset" : 6,
"end_offset" : 10,
"type" : "<ALPHANUM>",
"position" : 2
}, {
"token" : "vedette",
"start_offset" : 11,
"end_offset" : 18,
"type" : "<ALPHANUM>",
"position" : 3
}, {
"token" : "du",
"start_offset" : 19,
"end_offset" : 21,
"type" : "<ALPHANUM>",
"position" : 4
}, {
"token" : "feu",
"start_offset" : 22,
"end_offset" : 25,
"type" : "<ALPHANUM>",
"position" : 5
}, {
"token" : "d'artifice",
"start_offset" : 26,
"end_offset" : 36,
"type" : "<ALPHANUM>",
"position" : 6
} ]
}

70. composed of a single tokenizer and zero or more filters
Analyzer

71. Cutting out a string of words & transforming :
!
Whitespace tokenizer :
«Édith piaf» -> «Édith», «Piaf»
!
Standard tokenizer :
«Édith piaf!» -> «édith», «piaf»
Tokenizer

72. Modify, delete or add tokens
!
Asciifolding filter :
«Édith Piaf» -> «Edith Piaf»
!
Stemmer filter (english) :
«stemming» -> «stem»
«fishing», «fished», «fisher» -> «fish»
«cats,catlike» -> «cat»
!
Phonetic :
«quick» -> «Q200»
«quik» -> «Q200»
!
Edge nGram :
«Montreal» -> [«Mon», «Mont», «Montr»]
Filters

73. Analyzer
{
"tokens" : [ {
"token" : "edith",
"start_offset" : 0,
"end_offset" : 5,
"type" : "<ALPHANUM>",
"position" : 1
}, {
"token" : "piaf",
"start_offset" : 6,
"end_offset" : 10,
"type" : "<ALPHANUM>",
"position" : 2
}, {
"token" : "vedet",
"start_offset" : 11,
"end_offset" : 18,
"type" : "<ALPHANUM>",
"position" : 3
}, {
"token" : "feu",
"start_offset" : 22,
"end_offset" : 25,
"type" : "<ALPHANUM>",
"position" : 5
},
!
!
{
"token" : "artific",
"start_offset" : 26,
"end_offset" : 36,
"type" : "<ALPHANUM>",
"position" : 6
} ]
}

74. 1.Documents get indexed
2.I come back often on the search page to run my request
3.I hope that my document will be well ranked to be on top of the results page
4.if not, i won’t never see my document
Regular search engine usage

75. 1. Register my query
2. When document get indexed, the percolator look for a match again registered queries
Percolator

76. Real Time Updates !
Percolator

77. Percolator
curl -XPUT 'http://node1:9200/twitter/.percolator/elasticsearch' -d '{
"query" : {
"match" : {
"message" : "elasticsearch"
}
}
}'

78. Percolator
$ curl -X GET http://node1:9200/twitter/tweet/_percolate -d '{
"doc" : {
"user": "loicbertron",
"post_date": "2014-03-12T19:00:00",
"message": "A third message about #ElasticSearch"
}
}'

79. Percolator
{
"took" : 19,
"_shards" : {
"total" : 5,
"successful" : 5,
"failed" : 0
},
"total" : 1,
"matches" : [
{
"_index" : "twitter",
"_id" : "elasticsearch"
}
]
}

80. {
"name": "Jules Verne",
"biography": "One of the greatest author",
!
"books": [
{
"title": "Vingt mille lieues sous les mers",
"genre": "Novel",
"publisher": "Hetzel"
}
{
"title": "Les Châteaux en Californie",
"genre": "Drama",
"publisher": "Marc Soriano"
}
]
}
Inner objects

81. curl -XPUT node1:9200/authors/bare_author/1 -d'{
"name": "Jules Verne",
"biography": « One of the greets author"
}'
curl -XPOST node1:9200/authors/book/1?parent=1 -d '{
"title": "Les Châteaux en Californie",
"genre": "Drama",
"publisher": "Marc Soriano"
}'
!
curl -XPOST node1:9200/authors/book/2?parent=1 -d '{
"title": "Vingt mille lieues sous les mers",
"genre": "Novel",
"publisher": "Hetzel"
!
}'
Parents / Childs

82. Others features
• Suggest API : Did you mean ?, Autocomplete, …
• Results Highlight
• More like this
• Backup Data : Snapshot / Restore
• File System
• Amazon S3
• HDFS
• Google Compute Engine
• Microsoft Azure
• Hadoop connector

83. Clients
• Perl
• Python
• Ruby
• Php
• Javascript
• Java
• .Net
• Scala
• Clojure
• Erlang
• Eventmachine
• Bash
• Ocaml
• Smalltalk
• Cold Fusion

84. Who’s using it ?

85. Questions

86. Thank you
Thank you David Pilato for his presentation : https://speakerdeck.com/dadoonet/tours-jug-elasticsearch
Thank you Kevin Kluge for his presentation : https://speakerdeck.com/elasticsearch/elasticsearch-in-20-minutes

87. Bonus :)

88. Suggest
curl -s -XPOST 'localhost:9200/_search?search_type=count' -d '{
"suggest" : {
"my-title-suggestions-1" : {
"text" : "devloping",
"term" : {
"size" : 3,
"field" : "title"
}
}
}
}'

89. Suggest
"suggest": {
"my-title-suggestions-1": [
{
"text": "devloping",
"offset": 0,
"length": 9,
"options": [
{
"text": "developing",
"freq": 77,
"score": 0.8888889
},
{
"text": "deloping",
"freq": 1,
"score": 0.875
},
{
"text": "deploying",
"freq": 2,
"score": 0.7777778
}
]
}

90. More Like This
curl -XGET 'http://node1:9200/twitter/tweet/1/_mlt?mlt_fields=tag,content&min_doc_freq=1'
{
"more_like_this" : {
"fields" : ["name.first", "name.last"],
"like_text" : "text like this one",
"min_term_freq" : 1,
"max_query_terms" : 12,
"percent_terms_to_match" : 0.95
}
}

91. Highlight

92. {
"query" : {...},
"highlight" : {
"number_of_fragments" : 3,
"fragment_size" : 150,
"tag_schema" : "styled",
"fields" : {
"_all" : { "pre_tags" : ["<em>"], "post_tags" : ["</em>"] },
"bio.title" : { "number_of_fragments" : 0 },
"bio.author" : { "number_of_fragments" : 0 },
"bio.content" : { "number_of_fragments" : 5, "order" : "score" }
}
}
}
Highlight

93. Hadoop

94. Hadoop
• Java library for integrating Elasticsearch and Hadoop
• Pig, Hive, Cascading, MapReduce
• Search and Real Time Analytics with Elasticsearch, Hadoop as Data Lake
• Scales with Hadoop