@mocking-bird/mongoose
v1.2.0
Published
Generates fixtures for `mongoose`. Simply provide the schema or model, and it will generate mock data based on the types and constraints of the schema.
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Mongoose Fixture
Generates fixtures for mongoose
. Simply provide the schema or model, and it will generate mock data
based on the types and constraints of the schema.
Table of contents
Installation
npm i -D @mocking-bird/mongoose
Usage
import { Schema } from 'mongoose';
import { MongooseFixture } from '@mocking-bird/mongoose';
const schema = new Schema({
name: String,
email: String,
age: { type: Number, min: 18, max: 100 },
workEmail: String,
address: {
street: String,
city: String,
country: String,
},
createdAt: Date,
updatedAt: Date,
});
const fixture = new MongooseFixture(schema);
const data = fixture.generate();
Example output:
{
"name": "Turner, Thompson and Mueller",
"email": "[email protected]",
"age": 55,
"workEmail": "[email protected]",
"address": {
"street": "Apt. 123 1234",
"city": "Lake Ethylburgh",
"country": "Gambia"
},
"createdAt": "2023-09-11T05:38:59.576Z",
"updatedAt": "2024-02-26T08:25:16.412Z",
"_id": "a84f58e2fcff9dfaf148d7bf"
}
Bulk generation
const data = fixture.bulkGenerate(1000);
Accurate data generation
Generated data are not only random-random but also contextually accurate based on field names and types. It leverages
the fuzzy search, or formally, approximate string search algorithm to search for the suitable faker
to generate
realistic data that relate to the field.
For example:
workEmail
->[email protected]
employeePhoneNumber
->550-459-6013
uploadedFileName
->file-1234.pdf
Of course, there are still some limitations when it comes to complex field names with multiple parts, in which case
the default fakers
are applied. The default fakers
are fallbacks in case the fuzzy search score is not high
enough. The default fakers
may return, depending on the field type, a random string, number, or date, and so on.
Options
FixtureOptions
| name | type | default | description |
| ------------------ | ------------- | ----------- | ----------------------------------------------------------- |
| rules
| Rule[]
| undefined
| Custom rules to apply for fixture generation |
| exclude
| FieldPath[]
| undefined
| Fields to exclude from fixture generation |
| requiredOnly
| boolean
| false
| Whether to generate only the required fields or not |
| isAccurate
| boolean
| true
| Should employ accurate data generation based on field names |
Rule
| name | type | isRequired | description |
| -------------- | ---------------------- | ---------- | ------------------------------------------------------------------------------------------ |
| path
| FieldPath
| true
| The path to the field, for which the rule applies |
| required
| boolean
| false
| Is the field required or not |
| size
| number
| false
| The size of the generated value, which may apply to arrays, strings or numbers |
| min
| number
| false
| The min value of the generated value. For arrays or strings the minimum size. |
| max
| number
| false
| The max value of the generated value. For arrays or string the maximum size. |
| enum
| string[]
, number[]
| false
| The enum to apply for the generated value |
| pattern
| RegExp
| false
| The pattern to apply for the generated value. The generated value will adhere to the regex |
FieldPath
FieldPath
is a string that represents the path of a field in the schema. It can be a nested path, such asaddress.street
. It can also be a wildcard path, such asaddress.*
, which means all fields underaddress
.
Example
fixture.generate(
{},
{
exclude: ['createdAt', 'updatedAt'],
isAccurate: false,
requiredOnly: true,
rules: [
{
path: 'address.city',
enum: ['Berlin', 'Frankfurt'],
},
{
path: 'age',
min: 18,
max: 60,
},
{
path: 'workEmail',
pattern: /@gmail.com$/,
},
],
},
);
Global Options
You can also set global options for all fixtures:
MongooseFixture.setGlobalOptions({
isAccurate: false,
requiredOnly: true,
});
Resolving paths
When working with nested data structures, you may want to resolve the paths to the fields. This is especially useful when you want to exclude or apply rules to fields that are nested.
fixture.generate({}, { exclude: ['address.city'] });
You can also use wildcard paths to exclude or apply rules to all fields under a certain path:
fixture.generate({}, { exclude: ['address.*'] });
fixture.generate({
'person.*.jobTitle': 'Software Engineer',
});
fixture.generate({
'person.**.is*': true,
}); // will override every field that starts with `is` to true, e.g., isDefault, isCool etc...
Overriding values
You can override the generated values by providing a map of values to override:
fixture.generate({
name: 'John Doe',
email: '[email protected]',
age: 25,
});
// or using wildcards
fixture.generate({
'address.**.buildingNo': '1234',
});
Schema rules
The generated values comply with the schema rules, for example:
const schema = new Schema({
name: { type: String, required: true },
age: { type: Number, min: 18, max: 100 },
city: { type: String, enum: ['Berlin', 'Frankfurt'] },
});
In this case, the age
will be a number between 18 and 100, and the city
will be either Berlin
or Frankfurt
.
| 🚧 IMPORTANT |
| :---------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| If you specify a custom rule, make sure it doesn't conflict with the schema rule. For example, in the example above, you cannot set the name
field to be not required |
Limitation
There is a limitation when it comes to custom schema validators. In the below example, the generated value cannot comply with the custom validator, as it's a function.
const schema = new Schema({
name: {
type: String,
validate: {
validator: (v) => v.length > 5,
message: 'Name must be longer than 5 characters',
},
},
});
Alternatively, you can define the same schema validator as a custom rule:
fixture.generate(schema, {
rules: [
{
path: 'name',
min: 6,
},
],
});