Dummy data generator for BigQuery

Generate dummy/mock data for BigQuery using json schema file.

Generated dummy data file can be loaded to BigQuery.

It requires Python >=2.6, !=3.0.*, !=3.1.*

How to use

Clone this repository.

git clone git@github.com:tokencard/mock-data-generator-bigquery.git

Install the dependencies

pip install six

Run generator.

python dummy_bq.py <table_schema.json>

Then, dummy data file named "output.json" is created.

cat output.json

Arguments

option	description	default
-o	output file name	output.json
-l	number of lines(rows) of dummy file	1000

Json Schema file

Json schema file for BigQuery defines field name, data type and mode.

attribute	description	remark
name	field name
type	data type	INTEGER, STRING, FLOAT, TIMESTAMP, RECORD is supported
mode	write mode	REQUIRED, NULLABLE, REPEATED is supported

Loading data into Bigquery (example)

    ## This loads data to:
    ### Project: development-65e9a64cd95055da
    ### Dataset: kraken_test_ledger_history
    ### Table: contis_scheme_transactions

    bq load --location europe-west2 --noreplace --source_format=NEWLINE_DELIMITED_JSON --project_id development-65e9a64cd95055da kraken_test_ledger_history.contis_scheme_transactions output.json

Examples

BigQuery schema file (examples/flat.json)

[
    {
        "name": "hogehoge",
        "type": "INTEGER",
        "mode": "REQUIRED"
    },
    {
        "name": "pokopoko",
        "type": "STRING",
        "mode": "NULLABLE"
    },
    {
        "name": "created_at",
        "type": "TIMESTAMP",
        "mode": "REQUIRED"
    }
]

Generate 10 lines dummy data to output.json

python dummy_bq.py examples/flat.json -l 10

{"hogehoge": 9997, "created_at": "2016-12-10 18:13:19"}
{"hogehoge": 2531, "created_at": "2016-12-10 23:53:18"}
{"hogehoge": 4029, "created_at": "2016-12-11 13:53:50"}
{"hogehoge": 1084, "created_at": "2016-12-11 14:41:07", "pokopoko": "5G4QCT4HZJ48"}
{"hogehoge": 3386, "created_at": "2016-12-11 09:16:28", "pokopoko": "9W37EJ1QC0BY"}
{"hogehoge": 8680, "created_at": "2016-12-11 12:52:20", "pokopoko": "KLP2XNJ0T8ZU"}
{"hogehoge": 4594, "created_at": "2016-12-10 20:13:28"}
{"hogehoge": 5584, "created_at": "2016-12-11 06:08:55"}
{"hogehoge": 6726, "created_at": "2016-12-11 09:05:07"}
{"hogehoge": 754, "created_at": "2016-12-10 22:48:56"}

Above data can be uploaded to BigQuery using console or cli.