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Mastering Elasticsearch 5.x

You're reading from   Mastering Elasticsearch 5.x Master the intricacies of Elasticsearch 5 and use it to create flexible and scalable search solutions

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Product type Paperback
Published in Feb 2017
Publisher Packt
ISBN-13 9781786460189
Length 428 pages
Edition 3rd Edition
Languages
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Author (1):
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Bharvi Dixit Bharvi Dixit
Author Profile Icon Bharvi Dixit
Bharvi Dixit
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Toc

Table of Contents (13) Chapters Close

Preface 1. Revisiting Elasticsearch and the Changes FREE CHAPTER 2. The Improved Query DSL 3. Beyond Full Text Search 4. Data Modeling and Analytics 5. Improving the User Search Experience 6. The Index Distribution Architecture 7. Low-Level Index Control 8. Elasticsearch Administration 9. Data Transformation and Federated Search 10. Improving Performance 11. Developing Elasticsearch Plugins 12. Introducing Elastic Stack 5.0

Built-in functions under the function score query


The following are the built-in functions available to be used with the function_score query:

  • The weight function

  • The field_value_factor function

  • The script_score function

  • Decay functions - linear, exp, gauss

Let's look at them one by one.

The weight function

The weight function allows you to apply a simple boost to each document without the boost being normalized: a weight of 2 results in 2 * _score. For example:

curl -XGET "http://localhost:9200/library/_search" -d' 
{ 
  "query": { 
    "function_score": { 
      "query": { 
        "match": { 
          "tags": "novel" 
        } 
      }, 
      "functions": [ 
        { 
          "filter": { 
            "term": { 
              "tags": "classics" 
            } 
          }, 
          "weight": 2 
        } 
      ], 
      "boost_mode": "replace" 
    } 
  } 
}'

The preceding query will match all the books which belong to the novel category, but will give a higher score to books that also...

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