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ElasticSearch Blueprints

You're reading from  ElasticSearch Blueprints

Product type Book
Published in Jul 2015
Publisher Packt
ISBN-13 9781783984923
Pages 192 pages
Edition 1st Edition
Languages

Table of Contents (15) Chapters

Elasticsearch Blueprints
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
1. Google-like Web Search 2. Building Your Own E-Commerce Solution 3. Relevancy and Scoring 4. Managing Relational Content 5. Analytics Using Elasticsearch 6. Improving the Search Experience 7. Spicing Up a Search Using Geo 8. Handling Time-based Data Index

The nearest hotel problem


Let's assume that we are at a particular point where the latitude is 1.234 and the longitude is 2.132. We need to find the nearest restaurants to this location.

For this purpose, the function_score query is the best option. We can use the decay (Gauss) functionality of the function score query to achieve this:

curl -XPOST 'http://localhost:9200/restaurants/_search' -d '{
  "query": {
    "function_score": {
      "functions": [
        {
          "gauss": {
            "location": {
              "scale": "1km",
              "origin": [
                1.231,
                1.012
              ]
            }
          }
        }
      ]
    }
  }
}'

Here, we tell Elasticsearch to give a higher score to the restaurants that are nearby the referral point we gave it. The closer it is, the higher is the importance.

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