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, this data was generated using 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 -H "Content-Type: application/json" -XPOST "localhost:9200/bank/_doc/_bulk?pretty&refresh" --data-binary "@accounts.json"
curl "localhost:9200/_cat/indices?v"

And the response:

health status index uuid                   pri rep docs.count docs.deleted store.size pri.store.size
yellow open   bank  l7sSYV2cQXmu6_4rJWVIww   5   1       1000            0    128.6kb        128.6kb

Which means that we just successfully bulk indexed 1000 documents into the bank index (under the _doc type).