Exploring Your Data

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.

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).