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ElasticSearch Overview
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ssinganamalla
July 03, 2017
Technology
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ElasticSearch Overview
ssinganamalla
July 03, 2017
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Transcript
ElasticSearch Srinivas Singanamalla
2 ElasticSearch Search Analytics Schema-less JSON Engine Java based Lucene
Opensource* Near Real Time Distributed Scalable
3 Google Trends
4
5 Jim Elasticsearchovsky Faceboook Inc. Alice Faceboookuserger Faceboook User No
intent of gender discrimination
6 Jim Elasticsearchovsky Faceboook Inc. Alice Faceboookuserger Faceboook User
7
8 Faceboook Post
9 Full Text Search • Query – message: “color pink”
• Results – “Why do girls have to like color pink ?” – “Pink and blue arrived as color for babies in the mid-19th century” – “I love to color blue” – “This blue color is amazing”
10 Phrase Search • Query – message: “blue color” •
Results – “This blue color is amazing”
11 Highlighting Search • Query – message: “elasticsearch” • Results
12 Social Likeability: The number of likes you get in
your social media content.
13 Numeric Search • Query – likes: 50 • Results
– None
14 Numeric Range Search • Query – likes > 50
• Results – “Let us get together and drain that swamp tomorrow” – “How could the polls be so wrong?”
15 Call me? 312 4?9 2868
16 Fuzzy Search • Query – message: “312492868” • Results
– “It is not good to share phone numbers, but here it is: 6312492868” – “Utility Bill number: 3124928685” – “Bel me 3124892868” 312 4?9 2868
17
18 Search Suggestions • Query – “tring out Elasticsearch” •
Results – “trying out Elasticsearch”
19 Other Searches • Search Boosting – author:douglas OR title:guide^5
• Proximity Search – “douglas guide” • Regular Expressions
20 Aggregations • Count • Sum • Average • Max/Min
• Median • Standard Deviation • Percentile
21 Search Query: REST API
22
23 Aggregation: Sum
24 Aggregation: Average
25 DEMO
26 Search and Aggregations Ingestion Inverted Index Doc Values Search
Aggregation
27 Inverted Index
28 Inverted Index Table Term Document Id Frequency i 1
1 love 1 1 to 1 1 color 1, 2 2 blue 1, 2 2 sky 2 1 amazing 2 1
29 Inverted Index: Numeric Search • Find documents where –
423 < clicks < 642
30 Aggregations: Sum Get me sum of all likes
31 Doc Values (Column Oriented) Doc Id Value 1 10
2 5 3 120 Likes Likes: [10, 5, 120] Getting all the likes is super fast Sum: 10 + 5 + 120 = 135
32 Filtered Sum Get me sum of all likes containing
“elasticsearch”
33 Filtered Sum • “Likes” field values: [10, 5, 120]
• query_bits: [0, 1, 1] • Apply_query: [0, 5, 120] • Calculate Sum 0 + 5 + 120 = 125
34 How can we use ES? API Server RDBMS ElasticSearch
35 How can we use ES?
36 Finally Ask not what ElasticSearch can do for you
-- ask what you can do with ElasticSearch.
37 • Icons are taken from http://www.freepik.com/