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Elasticsearch Essentials

You're reading from   Elasticsearch Essentials Harness the power of ElasticSearch to build and manage scalable search and analytics solutions with this fast-paced guide

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Product type Paperback
Published in Jan 2016
Publisher
ISBN-13 9781784391010
Length 240 pages
Edition 1st Edition
Languages
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Toc

Table of Contents (12) Chapters Close

Preface 1. Getting Started with Elasticsearch FREE CHAPTER 2. Understanding Document Analysis and Creating Mappings 3. Putting Elasticsearch into Action 4. Aggregations for Analytics 5. Data Looks Better on Maps: Master Geo-Spatiality 6. Document Relationships in NoSQL World 7. Different Methods of Search and Bulk Operations 8. Controlling Relevancy 9. Cluster Scaling in Production Deployments 10. Backups and Security Index

Search requests using Python


All the queries that we have discussed can be performed with the Elasticsearch Python client using the search function. To do this, first store the query inside a variable that is query in the following example:

query = {
     "query": {
        "match_all": {}
     },
   }

Call the search function with all the parameters including the index name, document type, and query. The size parameter used in the following search request can also be included inside the query itself:

response = es.search(index='twitter', doc_type='tweets', body=query, size=2, request_timeout=20)

Note

To search against more than one index, instead of using a string value, you need to use a list of index names. The same applies for document types too.

The response data comes in the following format:

{
 "hits": {
   "hits": [
     {
       "_score": 1,
       "_type": "tweets",
       "_id": "649956033515773953",
       "_source": {
         "contributors": null,
         "truncated": false,
   ...
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