csv-model-generator

a simple text generator that takes your fake schema and builds out dummy data.

Description

This package quickly builds out dummy csv data that conforms exactly to your schema which should be readily comsumable by your model.

THEORY

Most of the magic is done with streaming data piped in from a bash script or redirected out into a file

Installation

Either through cloning with git or by using yarn (the recommended way):

yarn add -D csv-model-generator

This will install a binary called model-csv to your node_modules. It will not be available in your system path. Instead, the local installation can be run by calling it from within an npm script (such as yarn data:generate).

Usage

This library does two things:

1. takes your model structure, given that -m path/to/model.fake; generates dummy data to stdout
2. iterates over the above data; uses your -m path/to/model.insertBatch to perform batch insert

[note]: these method names are also dynamic and can be tailored to whatever your method names are: -m another/path/to/some/model.functionName

A note on usage

If you call model-csv directly from yarn (yarn model-csv), BEWARE, it must be done in silent mode, since yarn outputs all sorts of stuff you don't want in your data

yarn model-csv 🚫
yarn -s model-csv ✅

Generating Fake data

(required) your method MUST return an object with keys that match the schema columns, and data (values) must be executable functions (function references to be executed later).

model-csv -m model/User.fake -c 20 --out > data/demo_users.csv

Explanation

we're calling the model-csv binary, and passing 2 args:

• (-m, --model=) the location of the model that contains the method, in the form of
  • path/to/some/model.functionName,
• (-c, --count=) the quantity of records to create
  and we're piping the results to some file to be created

Read in fake data, write to the database

(required) you must have a model with a method named insertBatch that receives an array of objects that can be put directly into a query.

cat data/demo_users.csv | model-csv -m model/User.insertBatch

We're piping the contents of data/demo_users.csv we generated in the step above into our seed binary, and giving it the location of our model.