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Elasticsearch Essentials

Elasticsearch Essentials

By : Bharvi Dixit
4.3 (6)
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Elasticsearch Essentials

Elasticsearch Essentials

4.3 (6)
By: Bharvi Dixit

Overview of this book

With constantly evolving and growing datasets, organizations have the need to find actionable insights for their business. ElasticSearch, which is the world's most advanced search and analytics engine, brings the ability to make massive amounts of data usable in a matter of milliseconds. It not only gives you the power to build blazing fast search solutions over a massive amount of data, but can also serve as a NoSQL data store. This guide will take you on a tour to become a competent developer quickly with a solid knowledge level and understanding of the ElasticSearch core concepts. Starting from the beginning, this book will cover these core concepts, setting up ElasticSearch and various plugins, working with analyzers, and creating mappings. This book provides complete coverage of working with ElasticSearch using Python and performing CRUD operations and aggregation-based analytics, handling document relationships in the NoSQL world, working with geospatial data, and taking data backups. Finally, we’ll show you how to set up and scale ElasticSearch clusters in production environments as well as providing some best practices.
Table of Contents (12 chapters)
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11
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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