| Name | db-mockdata JSON |
| Version |
0.4.4
JSON |
| download |
| home_page | None |
| Summary | Simple package for creating mockdata for a live database, according to specified relational schema. |
| upload_time | 2024-11-21 11:33:10 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | MIT License |
| keywords |
mock
database
faker
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
## Mock data generation
This is small project for generating artificial / mock data, conforming to the specified DB schema.
It can be useful to either generate pseudo-realistic data in the database, or prepare large amounts of mock data for stress testing.
The package allows for generating mock data for specified database schema.
## Simple configuration file schema:
```
{
"connection": "postgresql+psycopg2://admin:test@172.17.0.1:5432/ChmielDB",
"tables": {
"Projects":{
"id": "PK serial",
"project_name": "first_name",
"project_owner": "FK Users.id"
},
"Users": {
"id": "PK serial",
"first_name": "first_name",
"last_name": "last_name",
"email": "email UNIQUE",
"password": "password",
"role": "OPTION IN (USER, ADMIN)",
"address": "address",
"birth_date": "timestamp",
"phone_number": "phone"
},
"IntermediaryTable: Projects_Users": {
"project_id": "FK Projects.id",
"user_id": "FK Users.id"
},
},
"objects_count": {
"Users": 25,
"Projects": 10,
"Projects_Users": 250,
}
```
## Advanced configuration file schema:
```
{
"connection": "postgresql+psycopg2://admin:test@172.22.0.1:5432/JobMarketDB",
"tables": {
"app_users": {
"user_id": "PK UUID",
"company": "FK_UUID company.company_id",
"email": "email UNIQUE",
"first_name": "first_name",
"last_name": "last_name",
"phone": "first_name",
"role": "OPTION IN (USER,ADMIN)",
"is_blocked": "bool",
"email_verified": "bool",
"employee_verified": "bool",
"created_at": "timestamp RANGE(2023-01-01 00:00:00,2024-12-24 22:12:00)",
"password_hash": "first_name"
},
"skills": {
"skill_id": "PK UUID",
"profile_id": "FK_UUID user_profiles.profile_id",
"skill_name": "first_name",
"proficiency_level": "first_name",
"updated_at": "timestamp RANGE(2023-01-01 00:00:00,2024-12-24 22:12:00)"
},
"chat_messages": {
"message_id": "PK UUID",
"chat_id": "FK_UUID chats.chat_id",
"content": "long_text RANGE(6, 20)",
"created_by": "FK_UUID app_users.user_id",
"created_by_display": "first_name",
"read_by": "first_name",
"deleted_by": "first_name",
"created_at": "timestamp RANGE(2023-01-01 00:00:00,2024-12-24 22:12:00)",
"updated_at": "timestamp RANGE(2023-01-01 00:00:00,2024-12-24 22:12:00)"
},
"job": {
"job_id": "PK UUID",
"company_id": "FK_UUID company.company_id",
"job_title": "first_name",
"job_description": "long_text RANGE(6, 20)",
"required_skills": "jsonb:json1",
"required_experience": "long_text RANGE(6, 20)",
"location": "first_name",
"salary": "float RANGE(100,12000) DISTRIBUTION(normal,mean=1,std=1)",
"created_at": "timestamp RANGE(2023-01-01 00:00:00,2024-12-24 22:12:00) RANGE()"
},
"experiences": {
"experience_id": "PK UUID",
"profile_id": "FK_UUID user_profiles.profile_id",
"company_name": "FK_UUID company.company_id",
"role": "first_name",
"start_date": "timestamp RANGE(2023-01-01 00:00:00,2024-12-24 22:12:00)",
"end_date": "timestamp RANGE(2023-01-01 00:00:00,2024-12-24 22:12:00)",
"updated_at": "timestamp RANGE(2023-01-01 00:00:00,2024-12-24 22:12:00)"
},
"educations": {
"education_id": "PK UUID",
"profile_id": "first_name",
"institution_name": "first_name",
"degree": "first_name",
"location": "country+city(\"en_US\",\"en_GB\",\"fr_FR\",\"de_DE\",\"it_IT\",\"es_ES\",\"pl_PL\",\"nl_NL\",\"pt_PT\",\"sv_SE\",\"da_DK\",\"fi_FI\",\"no_NO\",\"cs_CZ\",\"hu_HU\",\"en_CA\",\"sk_SK\")",
"start_date": "timestamp RANGE(2023-01-01 00:00:00,2024-12-24 22:12:00)",
"end_date": "timestamp RANGE(2023-01-01 00:00:00,2024-12-24 22:12:00)",
"updated_at": "timestamp RANGE(2023-01-01 00:00:00,2024-12-24 22:12:00)"
},
"company": {
"company_id": "PK UUID",
"company_name": "first_name",
"location": "first_name",
"industry": "first_name",
"description": "first_name",
"verified": "bool",
"created_at": "timestamp RANGE(2023-01-01 00:00:00,2024-12-24 22:12:00)",
"updated_at": "timestamp RANGE(2023-01-01 00:00:00,2024-12-24 22:12:00)"
},
"chats": {
"chat_id": "PK UUID",
"name": "first_name",
"created_by": "first_name",
"deleted_by": "first_name",
"last_message": "first_name",
"tags": "first_name",
"created_at": "timestamp RANGE(2023-01-01 00:00:00,2024-12-24 22:12:00)",
"updated_at": "timestamp RANGE(2023-01-01 00:00:00,2024-12-24 22:12:00)"
},
"IntermediaryTable: user_chats":{
"chat_id": "FK_UUID chats.chat_id",
"user_id": "FK_UUID app_users.user_id"
},
"user_profiles": {
"profile_id": "PK UUID",
"user_id": "FK_UUID app_users.user_id",
"resume_path": "first_name",
"profile_picture_path": "first_name",
"updated_at": "timestamp RANGE(2023-01-01 00:00:00,2024-12-24 22:12:00)"
},
"user_settings": {
"settings_id": "PK UUID",
"user_id": "FK_UUID app_users.user_id",
"offers_notification": "bool",
"newsletter_notification": "bool",
"recruiter_messages": "bool",
"push_notification": "bool",
"updated_at": "timestamp RANGE(2023-01-01 00:00:00,2024-12-24 22:12:00)"
}
},
"json_schemas": [
{
"json1": {
"fields": [
{
"skills": {
"type": "array",
"item_count": "RANGE(1, 5)",
"content": {
"type": "object",
"fields": {
"name": {
"type": "string",
"options": ["Python", "JavaScript", "Java", "C++", "Go", "Ruby"]
},
"level": {
"type": "integer",
"range": [1, 5]
}
}
}
}
}
]
}
}
],
"objects_count": {
"app_users": 200,
"skills": 500,
"chat_messages": 8000,
"job": 250,
"experiences": 100,
"educations": 200,
"company": 20,
"chats": 50,
"user_profiles": 500,
"user_settings": 500,
"user_chats": 10000
}
}
```
### Allowed column keywords:
TODO
#### Disclaimer:
The program only checks for uniqueness and integrity withing itself, there can still be error if there's already existing data in the database.
Raw data
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"description": "## Mock data generation\n\nThis is small project for generating artificial / mock data, conforming to the specified DB schema.\nIt can be useful to either generate pseudo-realistic data in the database, or prepare large amounts of mock data for stress testing.\n\n\nThe package allows for generating mock data for specified database schema.\n\n## Simple configuration file schema:\n\n``` \n{\n \"connection\": \"postgresql+psycopg2://admin:test@172.17.0.1:5432/ChmielDB\",\n \"tables\": {\n \"Projects\":{\n \"id\": \"PK serial\",\n \"project_name\": \"first_name\",\n \"project_owner\": \"FK Users.id\"\n },\n \"Users\": {\n \"id\": \"PK serial\",\n \"first_name\": \"first_name\",\n \"last_name\": \"last_name\",\n \"email\": \"email UNIQUE\",\n \"password\": \"password\",\n \"role\": \"OPTION IN (USER, ADMIN)\",\n \"address\": \"address\",\n \"birth_date\": \"timestamp\",\n \"phone_number\": \"phone\"\n },\n \"IntermediaryTable: Projects_Users\": {\n \"project_id\": \"FK 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