How to search...better!
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This document provides an introduction to searching with Elasticsearch. It demonstrates how to perform basic searches on an indexed Twitter dataset using curl commands. It also summarizes Elasticsearch concepts like inverted indexes, analyzers, tokenization, normalization, and filters. Elasticsearch.NET and NEST clients for .NET Core are briefly compared.
![DEMO
[mapping]](https://image.slidesharecdn.com/howtosearch-190307113602/85/How-to-search-better-21-320.jpg)
![Inverted index: separate words / terms
[The,quick,brown,fox,jumped,over,the,lazy,dog]
[Quick,brown,foxes,leap,over,lazy,dogs,in,summer]](https://image.slidesharecdn.com/howtosearch-190307113602/85/How-to-search-better-25-320.jpg)
![Inverted index: sort unique terms
[The,brown,dog,fox,jumped,lazy,over,quick,the]
[Quick,brown,dogs,foxes,in,lazy,leap,over,summer]](https://image.slidesharecdn.com/howtosearch-190307113602/85/How-to-search-better-26-320.jpg)
![DEMO
[analyzer]](https://image.slidesharecdn.com/howtosearch-190307113602/85/How-to-search-better-40-320.jpg)
How to search...better!
- 12. verb > curl XGET http://localhost:9200?pretty
- 13. node > curl XGET http://localhost:9200?pretty
- 14. port > curl XGET http://localhost:9200?pretty
- 15. query string > curl XGET http://localhost:9200?pretty
- 16. DEMO
- 17. > GET /twitter/_search?q=2014 11 results
- 18. > GET /twitter/_search?q=2014 11 results > GET /twitter/_search?q=2014-09-13 11 results
- 19. > GET /twitter/_search?q=2014 11 results > GET /twitter/_search?q=2014-09-13 11 results > GET /twitter/_search?q=date:2014-09-13 1 result
- 20. > GET /twitter/_search?q=2014 11 results > GET /twitter/_search?q=2014-09-13 11 results > GET /twitter/_search?q=date:2014-09-13 1 result > GET /twitter/_search?q=date:2014 no result
- 21. DEMO [mapping]
- 22. date = type:date _all = type:string
- 23. date = exact value _all = full text
- 24. Inverted index “The quick brown fox jumped over the lazy dog” “Quick brown foxes leap over lazy dogs in summer”
- 25. Inverted index: separate words / terms [The,quick,brown,fox,jumped,over,the,lazy,dog] [Quick,brown,foxes,leap,over,lazy,dogs,in,summer]
- 26. Inverted index: sort unique terms [The,brown,dog,fox,jumped,lazy,over,quick,the] [Quick,brown,dogs,foxes,in,lazy,leap,over,summer]
- 27. Inverted index: list docs containing terms term doc-01 doc-02 Quick X The X brown X X dog X dogs X fox X ... ... ...
- 29. analyzer = tokenizer + token filters
- 30. Standard analyzer “The Quick Brown Fox jumped over the Lazy Dog!”
- 34. English analyzer “The Quick Brown Fox jumped over the Lazy Dog!”
- 40. DEMO [analyzer]
- 42. Elasticsearch.NET vs NEST ● Low level client that provides a one-to-one mapping with the Elasticsearch REST API ● No deps ● Almost completely generated from the official REST API spec which makes it easy keep up to date ● High level client that internally uses Elasticsearch.NET client ● Map request/response to strongly typed objects