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Using Debug Mode in Uvicorn Python Files in PyCharm

Pandiyan MuruganLeave a comment

Introduction

Debugging is an essential part of the development process. It allows developers to identify and fix issues in their code, leading to more efficient and robust applications. When working with Uvicorn, a lightning-fast ASGI server for Python web applications, it’s crucial to know how to leverage the debug mode to streamline the debugging process. In this blog post, we’ll explore how to use debug mode in Uvicorn Python files in PyCharm, a popular integrated development environment (IDE).

Prerequisites

Before we dive into the details, ensure that you have the following prerequisites in place:

  1. Python and PyCharm: Make sure you have Python installed on your machine, along with the PyCharm IDE. You can download the latest versions from the official Python and JetBrains websites.
  2. Uvicorn: Install the Uvicorn server if it’s not already installed. You can do this using the pip package manager by running the command pip install uvicorn.

Step-by-Step Guide

Now, let’s go through the step-by-step process of using debug mode in Uvicorn Python files in PyCharm:

Step 1: Set Up the Project

Open your project in PyCharm, or create a new one if needed. Ensure that you have a Python file containing the Uvicorn server code you want to debug. If you don’t have such a file, create a new Python file and import the necessary dependencies.

Step 2: Install Dependencies

If you haven’t already installed Uvicorn, open the terminal within PyCharm and run the command pip install uvicorn to install it.

Step 3: Modify the Uvicorn Server Code

Locate the section of the code where the Uvicorn server is instantiated. This is typically the section that looks similar to the following:

if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=8000)

To enable debug mode, modify the code as follows:

import uvicorn

if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=8000, debug=True)

By adding debug=True as an argument to the uvicorn.run() function, we enable debug mode in Uvicorn.

Step 4: Set Breakpoints

Next, set breakpoints in your code where you want the debugger to stop. Breakpoints allow you to pause the execution of the program at specific locations and inspect the values of variables and the program’s state. To set a breakpoint, click on the left gutter of the code editor next to the line numbers. You can set multiple breakpoints if necessary.

Step 5: Start the Debugger

Once you’ve set the breakpoints, you can start the debugger. Click on the “Debug” button in the toolbar or go to the “Run” menu and choose the “Debug” option. This action will initiate the Uvicorn server in debug mode.

Step 6: Debugging Process

With the debugger running, the Uvicorn server will start in debug mode. Execution will pause at the breakpoints you set, allowing you to analyze the program’s behavior and inspect variables. You can use various debugging features provided by PyCharm, such as stepping through the code, evaluating expressions, and watching variables.

Step 7: Analyze and Fix Issues As the program execution pauses at breakpoints, you can examine the values of variables, step through the code line by line, and analyze the program’s behavior to identify any issues or bugs. Use the debugging features provided by PyCharm, such as the Variables pane, Console, and Watches, to gain insights into the state of your application and track any anomalies.

While in debug mode, you can take advantage of PyCharm’s powerful debugging tools, such as:

  1. Stepping: Step through the code line by line using the Step Over (F8), Step Into (F7), and Step Out (Shift+F8) buttons. This allows you to follow the flow of execution and understand how the program progresses.
  2. Variable Inspection: Inspect the values of variables at different points in the code. The Variables pane in PyCharm displays all the variables in the current scope, allowing you to examine their values and make informed decisions about the program’s behavior.
  3. Conditional Breakpoints: Set breakpoints with conditions to pause execution only when specific conditions are met. This can be helpful when you want to focus on a particular scenario or when you want to investigate a specific branch of code.
  4. Expression Evaluation: Use the Console in PyCharm to evaluate expressions and test hypotheses about the program’s behavior. You can execute Python statements and inspect variables interactively to gain a deeper understanding of your code.
  5. Watch Variables: Add variables to the Watches pane to monitor their values continuously during the debugging process. This helps you keep track of important variables and detect any unexpected changes.

Step 8: Fixing Issues As you analyze the behavior of your Uvicorn server in debug mode, you may encounter bugs or unexpected behavior. The insights gained from the debugger can help you identify the root cause of the problem more efficiently.

When you encounter an issue, use the debugger to examine the state of variables, step through the code to understand the flow, and evaluate expressions to pinpoint problematic areas. With this information, you can make the necessary changes to your code to fix the issue.

Conclusion

Debugging is a crucial aspect of software development, and knowing how to use debug mode in Uvicorn Python files in PyCharm can greatly enhance your debugging experience. By leveraging breakpoints, variable inspection, stepping, and other debugging features, you can gain valuable insights into your code’s behavior and quickly identify and resolve issues.

In this blog post, we walked through the step-by-step process of enabling debug mode in Uvicorn Python files, setting breakpoints, starting the debugger, and utilizing PyCharm’s debugging tools. By following these steps, you can effectively debug your Uvicorn server code, leading to more robust and reliable web applications.

Debugging is a skill that improves with practice, so don’t hesitate to experiment with different scenarios and explore PyCharm’s debugging capabilities. Happy debugging!

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Mocking in Next.js with Jest: How to create mocks for API responses and dependencies

Pandiyan MuruganLeave a comment

Mocking is an essential part of unit testing in Next.js with Jest. It allows us to create a fake version of a dependency or API response and test our code in isolation. In this blog post, we will explore how to create mocks for API responses and dependencies in Next.js with Jest.

What is mocking?

Mocking is the process of creating a fake version of a dependency or API response that our code depends on. By creating a mock, we can test our code in isolation without relying on external dependencies. This allows us to control the behavior of the mocked dependency or API response and test various scenarios.

Why use mocking?

There are several benefits to using mocking in our tests:

  • Isolation: By mocking dependencies and API responses, we can test our code in isolation without relying on external factors.
  • Control: We can control the behavior of the mocked dependency or API response and test various scenarios.
  • Speed: Mocking can make our tests run faster by reducing the need for external calls.

Creating mocks for API responses

When testing Next.js applications that rely on external APIs, we can create mocks for API responses using Jest’s jest.mock() function. This function allows us to replace the original module with a mock module that returns the data we want.

Here’s an example of how to create a mock for an API response in a Next.js application:

// api.js
import axios from 'axios';

export async function getUsers() {
  const response = await axios.get('/api/users');
  return response.data;
}


// __mocks__/axios.js
const mockAxios = jest.genMockFromModule('axios');

mockAxios.get = jest.fn(() => Promise.resolve({ data: [{ id: 1, name: 'John' }] }));

export default mockAxios;

In this example, we have created a mock for the **axios**module that returns a fake response with a single user. The mock is defined in the **__mocks__**directory, which is automatically recognized by Jest.

To use this mock in our test, we can simply call **jest.mock('axios')**at the beginning of our test file:

// api.test.js
import { getUsers } from './api';
import axios from 'axios';

jest.mock('axios');

describe('getUsers', () => {
  it('returns a list of users', async () => {
    axios.get.mockResolvedValue({ data: [{ id: 1, name: 'John' }, { id: 2, name: 'Jane' }] });

    const result = await getUsers();

    expect(result).toEqual([{ id: 1, name: 'John' }, { id: 2, name: 'Jane' }]);
  });
});

In this test, we have mocked the axios.get() method to return a list of two users. We then call the getUsers() function and assert that it returns the correct data.

Creating mocks for dependencies

In addition to mocking API responses, we can also create mocks for dependencies that our code depends on. This can be useful when testing functions that rely on complex or external dependencies.

Here’s an example of how to create a mock for a dependency in a Next.js application:

// utils.js
import moment from 'moment';

export function formatDate(date) {
  return moment(date).format('MMMM Do YYYY, h:mm:ss a');
}


// __mocks__/moment.js
const moment = jest.fn((timestamp) => ({
  format: () => `Mocked date: ${timestamp}`,
}));

export default moment;

In this example, we have created a mock for the moment module that returns a formatted string with the timestamp value. The mock is defined in the __mocks__ directory, which is automatically recognized by Jest.

To use this mock in our test, we can simply call jest.mock('moment') at the beginning of our test file:

// utils.test.js
import { formatDate } from './utils';
import moment from 'moment';

jest.mock('moment');

describe('formatDate', () => {
  it('returns a formatted date string', () => {
    const timestamp = 1617018563137;
    const expected = 'Mocked date: 1617018563137';

    const result = formatDate(timestamp);

    expect(moment).toHaveBeenCalledWith(timestamp);
    expect(result).toEqual(expected);
  });
});

In this test, we have mocked the moment() function to return a formatted string with the timestamp value. We then call the formatDate() function and assert that it returns the correct string.

Conclusion

Mocking is an essential part of unit testing in Next.js with Jest. It allows us to create a fake version of a dependency or API response and test our code in isolation. In this blog post, we explored how to create mocks for API responses and dependencies in Next.js with Jest. We saw how to use jest.mock() to create mocks for external APIs and how to create mocks for dependencies. By using mocking in our tests, we can test our code in isolation, control the behavior of dependencies and API responses, and make our tests run faster.