WARNING: Version 1.3 of Elasticsearch has passed its EOL date.
This documentation is no longer being maintained and may be removed. If you are running this version, we strongly advise you to upgrade. For the latest information, see the current release documentation.
Exploring Your Dataedit
Sample Datasetedit
Now that we’ve gotten a glimpse of the basics, let’s try to work on a more realistic dataset. I’ve prepared a sample of fictitious JSON documents of customer bank account information. Each document has the following schema:
{ "account_number": 0, "balance": 16623, "firstname": "Bradshaw", "lastname": "Mckenzie", "age": 29, "gender": "F", "address": "244 Columbus Place", "employer": "Euron", "email": "bradshawmckenzie@euron.com", "city": "Hobucken", "state": "CO" }
For the curious, I generated this data from www.json-generator.com/
so please ignore the actual values and semantics of the data as these are all randomly generated.
Loading the Sample Datasetedit
You can download the sample dataset (accounts.json) from here. Extract it to our current directory and let’s load it into our cluster as follows:
curl -XPOST 'localhost:9200/bank/account/_bulk?pretty' --data-binary @accounts.json curl 'localhost:9200/_cat/indices?v'
And the response:
curl 'localhost:9200/_cat/indices?v' health index pri rep docs.count docs.deleted store.size pri.store.size yellow bank 5 1 1000 0 424.4kb 424.4kb
Which means that we just successfully bulk indexed 1000 documents into the bank index (under the account type).