Using Mockerena

To generate fake data you can either:

1. Save schemas and generate data using an id or

2. You can generate data on-the-fly.

To save a schema, POST to /api/schema:

{
    "schema": "mock_example",
    "num_rows": 10,
    "file_format": "csv",
    "file_name": "mock_{}_example",
    "include_header": true,
    "delimiter": ",",
    "quote_character": "'",
    "columns": [
        {
            "name": "foo",
            "truncate": false,
            "type": "word",
            "description": "First column",
            "percent_empty": 0.2
        },
        {
            "name": "bar",
            "type": "random_element",
            "description": "Second column",
            "truncate": false,
            "args": {
                "elements": ["that"]
            },
            "function": "this + this"
        }
    ]
}

To breakdown what is happening at the schema-level above, here are what each item means:

schema - The name of the schema

num_rows - The default number of records to return

file_format - The default format of the file

file_name - The name of the file that is generated. {} is used to insert a datetime

include_header - Include the header for the CSV

exclude_null - Squash nulls for JSON formats

is_nested - Generate nested JSON

delimiter - CSV column separator

quote_character - Quoting character for CSV or TSV

On a column-level:

name - Column header name

type - Faker data type. See /api/types for a list of all types

description - A description of what what the column is for

truncate - Drop column after generation

percent_empty - Likeliness that the column will be empty. 0 to 1, 1 being 100%

args - Arguments passed into type

function - Post-processing function (see below)

format - Date format (use standard python date format strings)

To generate data, GET to /api/schema/{schema_id}/generate. You should receive something like this:

foo,bar
lose,thatthat
now,thatthat
and,thatthat
,thatthat
such,thatthat
government,thatthat
around,thatthat
room,thatthat
behind,thatthat
television,thatthat

You can optionally POST to /api/schema/generate directly to generate data without having to permanently save the schema.

Functions

Mockerena mostly uses Faker providers to generate random data. Click here for the full list of providers from Faker. With Mockerena, we’ve supplied a few additional providers that are available here.

You can also use the types endpoint /api/types to retrieve a complete list of all provider types.

Templates

Mockerena gives users the flexibility to define return types that aren’t listed as options for file_format. Any file format that isn’t immediately recognized, requires a template. So for example:

{
    "schema": "mock_example",
    "num_rows": 10,
    "file_format": "xml",
    "file_name": "mock_{}_example",
    "template": "<root>{% for r in records %}<record><foo>{{r['foo']}}</foo><bar>{{r['bar']}}</bar></record>{% endfor %}</root>"
    "columns": [
        {
            "name": "foo",
            "truncate": false,
            "type": "word",
            "description": "First column",
            "percent_empty": 0.2
        },
        {
            "name": "bar",
            "type": "random_element",
            "description": "Second column",
            "truncate": false,
            "args": {
                "elements": ["that"]
            },
            "function": "this + this"
        }
    ]
}

Would return and XML response like:

<root>
    <record>
        <foo>lose</foo>
        <bar>thatthat</bar>
    </record>
    <record>
        <foo>now</foo>
        <bar>thatthat</bar>
    </record>
    <record>
        <foo>and</foo>
        <bar>thatthat</bar>
    </record>
    <record>
        <foo></foo>
        <bar>thatthat</bar>
    </record>
    ....
</root>

And since Mockerena uses Jinja2 as the templating engine, you can leverage their robust set of filters and tests to further control how data populates the template.

Responses

Added v1.1.0

Responses allow for custom responses to be randomly returned For example:

{
    "schema": "mock_example",
    "file_format": "csv",
    "file_name": "mock_{}_example",
    "columns": [
        {
            "name": "foo",
            "type": "word"
        }
    ],
    "responses": [
        {
            "status_code": 201,
            "weight": 2
        },
        {
            "status_code": 502,
            "data": "",
            "content_type": "text/plain",
            "headers": {
                "Last-Modified": "Thur, 19 Sep 2019 19:25:10 GMT"
            },
            "weight": 1
        }
    ]
}

To breakdown what is happening:

status_code - Override response code returned. Default is 200

data - Override data returned. Default is usual dataset

content_type - Override content type. Default is based off file_format, or “text/plain”

headers - Override response headers

weight - Probability response is returned. For example, a response with a weight of 2 is twice as likely to return than a response with a weight of 1

So in the example approximately 2 out of 3 attempts will return the normal response with a status code of 201, but 1 out of 3 attempts will return a response with a status code of 502, empty content and a last modified header with the timestamp “Thur, 19 Sep 2019 19:25:10 GMT”.