Exploring Your Data

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Sample Dataset

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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": "[email protected]",
    "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.

Loading the Sample Dataset

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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/account/_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 account type).