testting.md

Unit test and validation and integration

We can create specific unit and integration tests to ensure that each stage of your data pipeline works correctly. Below are examples of tests that could be written for each of the critical components in the project:

1. Unit Tests for web_scraping scripts

Test: test_adzuna_scraper.py

This test ensures that the adzuna.py scraper correctly fetches and processes data.

import unittest
from scripts.web_scraping.adzuna import fetch_adzuna_data

class TestAdzunaScraper(unittest.TestCase):
    def test_fetch_adzuna_data(self):
        # Assuming fetch_adzuna_data returns a list of dictionaries
        data = fetch_adzuna_data()
        self.assertIsInstance(data, list)
        self.assertGreater(len(data), 0)
        for job in data:
            self.assertIn('title', job)
            self.assertIn('company', job)
            self.assertIn('location', job)
            self.assertIn('salary', job)

if __name__ == '__main__':
    unittest.main()

Test: test_muse_scraper.py

Similar to the Adzuna test, but for the Muse data.

import unittest
from scripts.web_scraping.muse import fetch_muse_data

class TestMuseScraper(unittest.TestCase):
    def test_fetch_muse_data(self):
        data = fetch_muse_data()
        self.assertIsInstance(data, list)
        self.assertGreater(len(data), 0)
        for job in data:
            self.assertIn('title', job)
            self.assertIn('company', job)
            self.assertIn('location', job)
            self.assertIn('salary', job)

if __name__ == '__main__':
    unittest.main()

2. Unit Tests for etl script

Test: test_etl_process.py

This test ensures that the ETL process correctly processes the scraped data.

import unittest
from scripts.etl.etlscript import process_data

class TestETLProcess(unittest.TestCase):
    def test_process_data(self):
        # Mocked raw data
        raw_data = [{'title': 'Data Scientist', 'company': 'Company A', 'location': 'Berlin', 'salary': '60,000'}]
        processed_data = process_data(raw_data)
        
        self.assertIsInstance(processed_data, list)
        self.assertGreater(len(processed_data), 0)
        for job in processed_data:
            self.assertIn('title', job)
            self.assertIn('company', job)
            self.assertIn('location', job)
            self.assertIn('salary', job)
            self.assertIsInstance(job['salary'], int)  # Assuming ETL converts salary to an integer

if __name__ == '__main__':
    unittest.main()

3. Unit Tests for database connection

Test: test_db_connection.py

This test checks that the database connection is established correctly.

import unittest
from scripts.database.db_connection import create_connection

class TestDBConnection(unittest.TestCase):
    def test_create_connection(self):
        conn = create_connection()
        self.assertIsNotNone(conn)
        self.assertTrue(conn.is_connected())

if __name__ == '__main__':
    unittest.main()

4. Integration Tests

Test: test_full_pipeline.py

This test ensures that data flows correctly through the entire pipeline, from scraping to processing to loading.

import unittest
from scripts.web_scraping.adzuna import fetch_adzuna_data
from scripts.etl.etlscript import process_data
from scripts.database.db_connection import create_connection, load_data_to_db

class TestFullPipeline(unittest.TestCase):
    def test_full_pipeline(self):
        raw_data = fetch_adzuna_data()
        self.assertGreater(len(raw_data), 0)
        
        processed_data = process_data(raw_data)
        self.assertGreater(len(processed_data), 0)
        
        conn = create_connection()
        success = load_data_to_db(conn, processed_data)
        self.assertTrue(success)

if __name__ == '__main__':
    unittest.main()

5. Performance Testing

Test: test_pipeline_performance.py

This test checks that the data pipeline performs efficiently.

import timeit
from scripts.web_scraping.adzuna import fetch_adzuna_data
from scripts.etl.etlscript import process_data
from scripts.database.db_connection import create_connection, load_data_to_db

def test_pipeline_performance():
    setup_code = """
from scripts.web_scraping.adzuna import fetch_adzuna_data
from scripts.etl.etlscript import process_data
from scripts.database.db_connection import create_connection, load_data_to_db
    """
    test_code = """
raw_data = fetch_adzuna_data()
processed_data = process_data(raw_data)
conn = create_connection()
load_data_to_db(conn, processed_data)
    """
    execution_time = timeit.timeit(stmt=test_code, setup=setup_code, number=1)
    assert execution_time < 5  # Arbitrary threshold

if __name__ == '__main__':
    test_pipeline_performance()

6. CI/CD Integration

In your ci.yml file, you should ensure these tests run automatically with each commit. Below is a sample configuration for running these tests using GitHub Actions:

name: CI Pipeline

on: [push, pull_request]

jobs:
  test:
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v2
    - name: Set up Python
      uses: actions/setup-python@v2
      with:
        python-version: '3.8'
    - name: Install dependencies
      run: |
        python -m pip install --upgrade pip
        pip install -r requirements.txt
    - name: Run tests
      run: |
        pytest

Summary

These tests cover key aspects of your data pipeline, ensuring each stage from web scraping to ETL to database storage works correctly. The integration of these tests into a CI/CD pipeline will ensure that any changes to your project are thoroughly vetted before deployment, minimizing the risk of errors in production. This approach can be applied to other components of your project as well, ensuring robust and reliable operation.