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Exploring Your Data
editExploring Your Data
editSample Dataset
editNow 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 Dataset
editYou 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).