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In this chapter, we will learn how to add some indexes, mappings, and data to Elasticsearch. Note that some of these data will be used in the examples explained in this tutorial.
You can use the following command to create an index-
PUT school
If the index is created, the following output can be seen-
{"acknowledged": true}
Elasticsearch will store the documents we add to the index, as shown in the following code. The documents are provided with some IDs, which are used to identify the documents.
_doc/"name":"Crescent School", "description":"State Board Affiliation"/10 { "name":"Saint Paul School", "description":"ICSE Affiliation" "street":"Dawarka", "city":"Delhi", "state":"Delhi", "zip":"110075", "location":[28.5733056, 77], "fees":122136000,5"tags":["Good Faculty", "Great Sports"], "rating":" Here, we added another similar document.4.5" }
{ "_index": "school", "_type": "_doc", "_id": ""10", "_version": 1, "result": "created", "_shards": { "total": 2, "successful": 1, "failed": 0 } "_seq_no": 2, "_primary_term": 1 }
POST school
_doc/"name":"Crescent School", "description":"State Board Affiliation"/16 { "street":"Tonk Road", "city":"Jaipur", "state":"RJ", "zip":" "location":[176114",26.8535922,75.7923988], "fees":2500, "tags":["Well equipped labs"], "rating":"4.5" }
{ "_index": "school", "_type": "_doc", "_id": ""16", "_version": 1, "result": "created", "_shards": { "total": 2, "successful": 1, "failed": 0 } "_seq_no": 9, "_primary_term": 7 }
Thus, we will continue to add any sample data required for the work in the next chapters.
Kibana is a GUI-driven tool for accessing data and creating visualizations. In this section, let's learn how to add sample data to it.
In the Kibana home page, select the following options to add sample e-commerce data-
The next screen will display some visual effects and a button to add data-
Click to add data will display the following screen, confirming that the data has been added to the index named eCommerce.