Slides from my intro to search and analytics applications with Elasticsearch

1. Managing Your Content With
Elasticsearch
Samantha Quiñones / @ieatkillerbees

2. About Me
• Software Engineer & Data Nerd since 1997
• Doing “media stuff” since 2012
• Principal @ AOL since 2014
• @ieatkillerbees
• http://samanthaquinones.com

3. What We’ll Cover
• Intro to Elasticsearch
• CRUD
• Creating Mappings
• Analyzers
• Basic Querying & Searching
• Scoring & Relevance
• Aggregations Basics

4. But First…
• Download - https://www.elastic.co/downloads/elasticsearch
• Clone - https://github.com/squinones/elasticsearch-tutorial.git

5. What is Elasticsearch?
• Near real-time (documents are available for search quickly after
being indexed) search engine powered by Lucene
• Clustered for H/A and performance via federation with shards and
replicas

6. What’s it Used For?
• Logging (we use Elasticsearch to centralize traffic logs, exception
logs, and audit logs)
• Content management and search
• Statistical analysis

7. Installing Elasticsearch
$ curl -L -O https://download.elastic.co/elasticsearch/release/org/elasticsearch/
distribution/tar/elasticsearch/2.1.1/elasticsearch-2.1.1.tar.gz
$ tar -zxvf elasticsearch*
$ cd elasticsearch-2.1.1/bin
$ ./elasticsearch

8. Connecting to Elasticsearch
• Via Java, there are two native clients which connect to an ES
cluster on port 9300
• Most commonly, we access Elasticsearch via HTTP API

9. HTTP API
curl -X GET "http://localhost:9200/?pretty"

10. Data Format
• Elasticsearch is a document-oriented database
• All operations are performed against documents (object graphs
expressed as JSON)

11. Analogues
Elasticsearch MySQL MongoDB
Index Database Database
Type Table Collection
Document Row Document
Field Column Field

12. Index Madness
• Index is an overloaded term.
• As a verb, to index a document is store a document in an index.
This is analogous to an SQL INSERT operation.
• As a noun, an index is a collection of documents.
• Fields within a document have inverted indexes, similar to how a
column in an SQL table may have an index.

13. Indexing Our First Document
curl -X PUT "http://localhost:9200/test_document/test/1" -d '{ "name": "test_name" }’

14. Retrieving Our First Document
curl -X GET "http://localhost:9200/test_document/test/1"

15. Let’s Look at Some Stackoverflow Posts!
$ vi queries/bulk_insert_so_data.json

16. Bulk Insert
curl -X PUT "http://localhost:9200/_bulk" --data-binary "@queries/
bulk_insert_so_data.json"

17. First Search
curl -X GET "http://localhost:9200/stack_overflow/_search"

18. Query String Searches
curl -X GET "http://localhost:9200/stack_overflow/_search?q=title:php"

19. Query DSL
curl -X POST "http://localhost:9200/stack_overflow/_search" -d
'{
"query" : {
"match" : {
"title" : "php"
}
}
}'

20. Compound Queries
curl -X POST "http://localhost:9200/stack_overflow/_search" -d '{
"query" : {
"filtered": {
"query" : {
"match" : {
"title" : "(php OR python) AND (flask OR laravel)"
}
},
"filter": {
"range": {
"score": {
"gt": 3
}
}
}
}
}
}'

21. Full-Text Searching
curl -X POST "http://localhost:9200/stack_overflow/_search" -d
'{
"query" : {
"match" : {
"title" : "php loop"
}
}
}'

22. Relevancy
• When searching (in query context), results are scored by a
relevancy algorithm
• Results are presented in order from highest to lowest score

23. Phrase Searching
curl -X POST "http://localhost:9200/stack_overflow/_search" -d
'{
"query" : {
"match" : {
"title": {
"query": "for loop",
"type": "phrase"
}
}
}
}'

24. Highlighting Searches
curl -X POST "http://localhost:9200/stack_overflow/_search" -d
'{
"query" : {
"match" : {
"title": {
"query": "for loop",
"type": "phrase"
}
}
},
"highlight": {
"fields" : {
"title" : {}
}
}
}'

25. Aggregations
• Run statistical operations over your data
• Also near real-time!
• Complex aggregations are abstracted away behind simple
interfaces— you don’t need to be a statistician

26. Analyzing Tags
curl -X POST "http://localhost:9200/stack_overflow/_search" -d
'{
"size": 0,
"aggs": {
"all_tags": {
"terms": {
"field": "tags",
"size": 0
}
}
}
}'

27. Nesting Aggregations
curl -X POST “http://localhost:9200/stack_overflow/_search" -d
'{
"size": 0,
"aggs": {
"all_tags": {
"terms": {
"field": "tags",
"size": 0
},
"aggs": {
"avg_score": {
"avg": { "field": "score"}
}
}
}
}
}'

28. Break Time!

29. Under the Hood
• Elasticsearch is designed from the ground-up to run in a distributed
fashion.
• Indices (collections of documents) are partitioned in to shards.
• Shards can be stored on a single or multiple nodes.
• Shards are balanced across the cluster to improve performance
• Shards are replicated for redundancy and high availability

30. What is a Cluster?
• One or more nodes (servers) that work together to…
• serve a dataset that exceeds the capacity of a single server…
• provide federated indexing (writes) and searching (reads)…
• provide H/A through sharing and replication of data

31. What are Nodes?
• Individual servers within a cluster
• Can providing indexing and searching capabilities

32. What is an Index?
• An index is logically a collection of documents, roughly analogous
to a database in MySQL
• An index is in reality a namespace that points to one or more
physical shards which contain data
• When indexing a document, if the specified index does not exist, it
will be created automatically

33. What are Shards?
• Low-level units that hold a slice of available data
• A shard represents a single instance of lucene and is fully-
functional, self-contained search engine
• Shards are either primary or replicas and are assigned to nodes

34. What is Replication?
• Shards can have replicas
• Replicas primarily provide redundancy for when shards/nodes fail
• Replicas should not be allocated on the same node as the shard it
replicates

35. Default Topology
• 5 primary shards per index
• 1 replica per shard

36. NODE
Clustering & Replication
NODE
R1 P2 P3 R2 R3P4 R5 P1 R4 P5

37. Cluster Health
curl -X GET “http://localhost:9200/_cluster/health"
curl -X GET "http://localhost:9200/_cat/health?v"

38. _cat API
• Display human-readable information about parts of the ES system
• Provides some limited documentation of functions

39. aliases
> $ http GET ':9200/_cat/aliases?v'
alias index filter routing.index routing.search
posts posts_561729df8ce4e * - -
posts.public posts_561729df8ce4e * - -
posts.write posts_561729df8ce4e - - -
Display all configured aliases

40. allocation
> $ http GET ':9200/_cat/allocation?v'
shards disk.used disk.avail disk.total disk.percent host
33 2.6gb 21.8gb 24.4gb 10 host1
33 3gb 21.4gb 24.4gb 12 host2
34 2.6gb 21.8gb 24.4gb 10 host3
Show how many shards are allocated per node, with disk utilization info

41. count
> $ http GET ':9200/_cat/count?v'
epoch timestamp count
1453790185 06:36:25 182763
> $ http GET ‘:9200/_cat/count/posts?v’
epoch timestamp count
1453790467 06:41:07 164169
> $ http GET ‘:9200/_cat/count/posts.public?v’
epoch timestamp count
1453790472 06:41:12 164169=
Display a count of documents in the cluster, or a specific index

42. fielddata
> $ http -b GET ':9200/_cat/fielddata?v'
id host ip node
total site_id published
7tjeJNY3TMajqRkmYsJyrA host1 10.97.183.146 node1 1.1mb 170.1kb 996.5kb
__xrpsKAQW6yyCY8luLQdQ host2 10.97.180.138 node2 1.6mb 329.3kb 1.3mb
bdoNNXHXRryj22YqjnqECw host3 10.97.181.190 node3 1.1mb 154.7kb 991.7kb
Shows how much memory is allocated to fielddata (metadata used for sorts)

43. health
> $ http -b GET ':9200/_cat/health?v'
epoch timestamp cluster status node.total node.data shards pri relo init unassign pending_tasks
1453829723 17:35:23 ampehes_prod_cluster green 3 3 100 50 0 0 0 0

44. indices
> $ http -b GET 'eventhandler-prod.elasticsearch.amppublish.aws.aol.com:9200/_cat/indices?v'
health status index pri rep docs.count docs.deleted store.size pri.store.size
green open posts_561729df8ce4e 5 1 468629 20905 4gb 2gb
green open slideshows 5 1 3893 6 86mb 43mb

45. master
> $ http -b GET ':9200/_cat/master?v'
id host ip node
7tjeJNY3TMajqRkmYsJyrA host1 10.97.183.146 node1

46. nodes
> $ http -b GET ':9200/_cat/nodes?v'
host ip heap.percent ram.percent load node.role master name
127.0.0.1 127.0.0.1 50 100 2.47 d * Mentus

47. pending tasks
% curl 'localhost:9200/_cat/pending_tasks?v'
insertOrder timeInQueue priority source
1685 855ms HIGH update-mapping [foo][t]
1686 843ms HIGH update-mapping [foo][t]
1693 753ms HIGH refresh-mapping [foo][[t]]
1688 816ms HIGH update-mapping [foo][t]
1689 802ms HIGH update-mapping [foo][t]
1690 787ms HIGH update-mapping [foo][t]
1691 773ms HIGH update-mapping [foo][t]

48. shards
> $ http -b GET ':9200/_cat/shards?v'
index shard prirep state docs store ip node
posts_561729df8ce4e 2 r STARTED 94019 410.5mb 10.97.180.138 host1
posts_561729df8ce4e 2 p STARTED 94019 412.7mb 10.97.181.190 host2
posts_561729df8ce4e 0 p STARTED 93307 413.6mb 10.97.183.146 host3
posts_561729df8ce4e 0 r STARTED 93307 415mb 10.97.180.138 host1
posts_561729df8ce4e 3 p STARTED 94182 407.1mb 10.97.183.146 host2
posts_561729df8ce4e 3 r STARTED 94182 403.4mb 10.97.180.138 host1
posts_561729df8ce4e 1 r STARTED 94130 447.1mb 10.97.180.138 host1
posts_561729df8ce4e 1 p STARTED 94130 447mb 10.97.181.190 host2
posts_561729df8ce4e 4 r STARTED 93299 421.5mb 10.97.183.146 host3
posts_561729df8ce4e 4 p STARTED 93299 398.8mb 10.97.181.190 host2

49. segments
> $ http -b GET ':9200/_cat/segments?v'
index shard prirep ip segment generation docs.count docs.deleted size size.memory committed searchable version
compound
posts_561726fecd9c6 0 p 10.97.183.146 _a 10 24 0 227.7kb 69554 true true 4.10.4 true
posts_561726fecd9c6 0 p 10.97.183.146 _b 11 108 0 659.1kb 103242 true true 4.10.4 false
posts_561726fecd9c6 0 p 10.97.183.146 _c 12 7 0 90.7kb 54706 true true 4.10.4 true
posts_561726fecd9c6 0 p 10.97.183.146 _d 13 6 0 82.2kb 49706 true true 4.10.4 true
posts_561726fecd9c6 0 p 10.97.183.146 _e 14 8 0 119kb 67162 true true 4.10.4 true
posts_561726fecd9c6 0 p 10.97.183.146 _f 15 1 0 35.9kb 32122 true true 4.10.4 true
posts_561726fecd9c6 0 r 10.97.180.138 _a 10 24 0 227.7kb 69554 true true 4.10.4 true
posts_561726fecd9c6 0 r 10.97.180.138 _b 11 108 0 659.1kb 103242 true true 4.10.4 false

50. CRUD Operations

51. Document Model
• Documents represent objects
• By default, all fields in all documents are analyzed, and indexed

52. Metadata
• _index - The index in which a document resides
• _type - The class of object that a document represents
• _id - The document’s unique identifier. Auto-generated when not
provided

53. Retrieving Documents
curl -X GET "http://localhost:9200/test_document/test/1"
curl -X HEAD “http://localhost:9200/test_document/test/1"
curl -X HEAD "http://localhost:9200/test_document/test/2"

54. Updating Documents
curl -X PUT "http://localhost:9200/test_document/test/1" -d '{
"name": "test_name",
"conference": "php benelux"
}'
curl -X GET "http://localhost:9200/test_document/test/1"

55. Explicit Creates
curl -X PUT "http://localhost:9200/test_document/test/1/_create" -d '{
"name": "test_name",
"conference": "php benelux"
}'

56. Auto-Generated IDs
curl -X POST "http://localhost:9200/test_document/test" -d '{
"name": "test_name",
"conference": "php benelux"
}'

57. Deleting Documents
curl -X DELETE "http://localhost:9200/test_document/test/1"

58. Bulk API
• Perform many operations in a single request
• Efficient batching of actions
• Bulk queries take the form of a stream of single-line JSON objects
that define actions and document bodies

59. Bulk Actions
• create - Index a document IFF it doesn’t exist already
• index - Index a document, replacing it if it exists
• update - Apply a partial update to a document
• delete - Delete a document

60. Bulk API Format
{ action: { metadata }}n
{ request body }n
{ action: { metadata }}n
{ request body }

61. Sizing Bulk Requests
• Balance quantity of documents with size of documents
• Docs list the sweet-spot between 5-15 MB per request
• AOL Analytics Cluster indexes 5000 documents per batch (approx
7MB)

62. Searching Documents
• Structured queries - queries against concrete fields like “title” or
“score” which return specific documents.
• Full-text queries - queries that find documents which match a search
query and return them sorted by relevance

63. Search Elements
• Mappings - Defines how data in fields are interpreted
• Analysis - How text is parsed and processed to make it searchable
• Query DSL - Elasticsearch’s query language

64. About Queries
• Leaf Queries - Searches for a value in a given field. These queries
are standalone. Examples: match, range, term
• Compound Queries - Combinations of leaf queries and other
compound queries which combine operations together either
logically (e.g. bool queries) or alter their behavior (e.g. score
queries)

65. Empty Search
curl -X GET "http://localhost:9200/stack_overflow/_search"
curl -X POST "http://localhost:9200/stack_overflow/_search" -d
'{
"query": { "match_all": {} }
}'

66. Timing Out Searches
curl -X GET "http://localhost:9200/stack_overflow/_search?timeout=1s"
curl -X POST "http://localhost:9200/stack_overflow/_search" -d '{
"timeout": "1s",
"query": { "match_all": {} }
}'

67. Multi-Index/Type Searches
curl -X GET "http://localhost:9200/test_document,stack_overflow/_search"

68. Multi-Index Use Cases
• Dated indices for logging
• Roll-off indices for content-aging
• Analytic roll-ups

69. Pagination
curl -X GET "http://localhost:9200/stack_overflow/_search?size=5&from=5"
curl -X POST "http://localhost:9200/stack_overflow/_search" -d '{
"size": 5,
"from": 5,
"query": { "match_all": {} }
}'

70. Pagination Concerns
• Since searches are distributed across multiple shards, paged
queries must be sorted at each shard, combined, and resorted
• The cost of paging in distributed data sets can increase
exponentially
• It is a wise practice to set limits to how many pages of results can
be returned

71. Full Text Queries
• match - Basic term matching query
• multi_match - Match which spans multiple fields
• common_terms - Match query which preferences uncommon words
• query_string - Match documents using a search “mini-dsl”
• simple_query_string - A simpler version of query_string that never
throws exceptions, suitable for exposing to users

72. Term Queries
• term - Search for an exact value
• terms - Search for an exact value in multiple fields
• range - Find documents where a value is in a certain range
• exists - Find documents that have any non-null value in a field
• missing - Inversion of `exists`
• prefix - Match terms that begin with a string
• wildcard - Match terms with a wildcard
• regexp - Match terms against a regular expression
• fuzzy - Match terms with configurable fuzziness

73. Compound Queries
• constant_score - Wraps a query in filter context, giving all results a constant score
• bool - Combines multiple leaf queries with `must`, `should`, `must_not` and `filter` clauses
• dis_max - Similar to bool, but creates a union of subquery results scoring each document with the
maximum score of the query that produced it
• function_score - Modifies the scores of documents returned by a query . Useful for altering the
distribution of results based on recency, popularity, etc.
• boosting - Takes a `positive` and `negative` query, returning the results of `positive` while
reducing the scores of documents that also match `negative`
• filtered - Combines a query clause in query context with one in filter context
• limit - Perform the query over a limited number of documents in each shard

74. What are Mappings?
• Similar to schemas, they define the types of data found in fields
• Determines how individual fields are analyzed & stored
• Sets the format of date fields
• Sets rules for mapping dynamic fields

75. Mapping Types
• Indices have one or more mapping types which group documents
logically.
• Types contain meta fields, which can be used to customize
metadata like _index, _id, _type, and _source
• Types can also list fields that have consistent structure across types.

76. Data Types
• Scalar Values - string, long, double, boolean
• Special Scalars - date, ip
• Structural Types - object, nested
• Special Types - geo_shape, geo_point, completion
• Compound Types - string arrays, nested objects

77. Dynamic vs Explicit Mapping
• Dynamic fields are not defined prior to indexing
• Elasticsearch selects the most likely type for dynamic fields, based
on configurable rules
• Explicit fields are defined exactly prior to indexing
• Types cannot accept data that is the wrong type for an explicit
mapping

78. Shared Fields
• Fields that are defined in multiple mapping types must be identical
if:
• They have the same name
• Live in the same index
• Map to the same field internally

79. Examining Mappings
curl -X GET "http://localhost:9200/stack_overflow/post/_mapping"

80. Dynamic Mappings
• Mappings are generated when a type is created, if no mapping
was previously specified.
• Elasticsearch is good at identifying fields much of the time, but it’s
far from perfect!
• Fields can contain basic data-types, but importantly, mappings
optimize a field for either structured (exact) or full-text searching

81. Structured Data vs Full Text
• Exact values contain exact strings which are not subject to natural
language interpretation.
• Full-text values must be interpreted in the context of natural
language

82. Exact Value
• “samantha@tembies.com” is an email address in all contexts

83. Natural Language
• “us” can be interpreted differently in natural language
• Abbreviation for “United States”
• The English dative personal pronoun
• An alternative symbol for µs
• The French word us

84. Analyzing Text
• Elasticsearch is optimized for full text search
• Text is analyzed in a two-step process
• First, text is tokenized in to individual terms
• Second, terms are normalized through a filter

85. Analyzers
• Analyzers perform the analysis process
• Character filters clean up text, removing or modifying the text
• Tokenizers break the text down in to terms
• Token filters modify, remove, or add terms

86. Standard Analyzer
• General purpose analyzer that works for most natural language.
• Splits text on word boundaries, removes punctuation, and
lowercases all tokens.

87. Standard Analyzer
curl -X GET "http://localhost:9200/_analyze?analyzer=standard&text="Reverse+text+with
+strrev($text)!""

88. Whitespace Analyzer
• Analyzer that splits on whitespace and lowercases all tokens

89. Whitespace Analyzer
curl -X GET "http://localhost:9200/_analyze?analyzer=whitespace&text="Reverse+text+with
+strrev($text)!""

90. Keyword Analyzer
• Tokenizes the entire text as a single string.
• Used for things that should be kept whole, like ID numbers, postal
codes, etc

91. Keyword Analyzer
curl -X GET "http://localhost:9200/_analyze?analyzer=keyword&text="Reverse+text+with
+strrev($text)!""

92. Language Analyzers
• Analyzers optimized for specific natural languages.
• Reduce tokens to stems (jumper, jumped → jump)

93. Language Analyzers
curl -X GET "http://localhost:9200/_analyze?analyzer=english&text="Reverse+text+with
+strrev($text)!""

94. Analyzers
• Analyzers are applied when documents are indexed
• Analyzers are applied when a full-text search is performed against
a field, in order to produce the correct set of terms to search for

95. Character Filters
• html_strip - Removes HTML from text
• mapping - Filter based on a map of original → new ( { “ph”: “f” })
• pattern_replace - Similar to mapping, using regular expressions

96. Index Templates
• Template mappings that are applied to newly created indices
• Templates also contain index configuration information
• Powerful when combined with dated indices

97. Scoring
• Scoring is based on a boolean model and scoring function
• Boolean model applies AND/OR logic to an inverse index to
produce a list of matching documents

98. Term Frequency
• Terms that appear frequently in a document increase the
document’s relevancy score.
• term_frequency(term in document) = √number_of_appearances

99. Inverse Document Frequency
• Terms that appear in many documents reduce a document’s
relevancy score
• inverse_doc_frequency(term) = 1 + log(number_of_docs /
(frequency + 1))

100. Field Length Normalization
• Terms that appear in shorter fields increase the relevancy of a
document.
• norm(document) = 1 / √number_of_terms

101. Example from the Docs
• Given the text “quick brown fox” the term “fox” scores…
• Term Frequency: 1.0
• Inverse Doc Frequency: 0.30685282
• Field Norm: 0.5
• Score: 0.15342641

102. Basic Relevancy
{
"size": 100,
"query": {
"filtered": {
"query": {
"match": {
"contents": "miley cyrus"
}
},
"filter": {
"and": [ { "terms": { "site_id": [ 698 ] } } ]
}
}
}
}

103. Non-Preferenced Result Recency

104. Recency-Adjusted Query
{
"query": {
"function_score": {
"functions": [
{
"gauss": {
"published": {
"origin": "now",
"scale": "10d",
"offset": "1d",
"decay": 0.3
}
}
}
],
"query": {
"filtered": {
"query": { "match": { "contents": "miley cyrus" } },
"filter": { "and": [ { "terms": { "site_id": [ 698 ] } } ] }
}
}
}
}
}

105. Preferenced Result Recency

106. Aggregations & Analytics

107. Importing Energy Data
curl -X PUT "http://localhost:9200/energy_use" --data-binary "@queries/
mapping_energy.json"
curl -X PUT "http://localhost:9200/_bulk" --data-binary "@queries/
bulk_insert_energy_data.json"
curl -X GET "http://localhost:9200/energy_use/_search"

108. Average Energy Use
curl -X POST "http://localhost:9200/energy_use/_search" -d '{
"size": 0,
"aggs": {
"average_laundry_use": {
"avg": {
"field": "laundry"
}
},
"average_kitchen_use": {
"avg": {
"field": "kitchen"
}
},
"average_heater_use": {
"avg": {
"field": "heater"
}
},
"average_other_use": {
"avg": {
"field": "other"
}
}
}
}'

109. Multiple Aggregations
curl -X POST “http://localhost:9200/energy_use/_search" -d '{
"size": 0,
"aggs": {
"average_laundry_use": { "avg": { "field": "laundry" } },
"min_laundry_use": { "min": { "field": "laundry"} },
"max_laundry_use": { "max": { "field": "laundry"} }
}
}'

110. Nesting Aggregations
curl -X POST “http://localhost:9200/energy_use/_search" -d '{
"size": 0,
"aggs": {
"by_date": {
"terms": { "field": "date" },
"aggs": {
"average_laundry_use": { "avg": { "field": "laundry" } },
"min_laundry_use": { "min": { "field": "laundry"} },
"max_laundry_use": { "max": { "field": "laundry"} }
}
}
}
}'

111. Stats/Extended Stats
curl -X POST "http://localhost:9200/energy_use/_search" -d '{
"size": 0,
"aggs": {
"by_date": {
"terms": { "field": "date" },
"aggs": {
"laundry_stats": { "extended_stats": { "field": "laundry" } }
}
}
}
}'

112. Bucket Aggregations
• Date Histogram
• Term/Terms
• Geo*
• Significant Terms

113. Questions?
Use Cases?
Exploration Ideas?
https://joind.in/talk/e2e4b