csv-model-generator
v0.3.1
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a pg-promise generator of fake data using faker
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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:
- takes your model structure, given that
-m path/to/model.fake
; generates dummy data to stdout - 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.