Adventures in Machine Learning

Building High-Performance Flask Apps with Redis Task Queues and Angular Polling

Setting Up Redis and

Running Web and Worker Processes on A Single Dyno

Are you planning to build a web application? If so, you will likely need to handle a great amount of traffic, background jobs, and caching.

Handling all of these requirements on a single server can be challenging. However, you can do it using Redis.

In this article, we will discuss how to set up Redis and run web and worker processes on a single dyno.

Installing Redis and Updating the Procfile

Redis is an open-source, in-memory data structure store used for caching and real-time data processing. To use Redis with your Heroku application, you need to install Redis.

First, we will install Redis on our local machine. Then, we will update the Procfile, which is a file that Heroku uses to know how to start our application.

To install Redis on your local machine, you need to follow these steps:

1. Download Redis tar file from their website.

The current stable version at the time of writing this article is 6.2.4.

2. Extract the files from the tar file using the command tar xvzf redis-{version}.tar.gz,

where {version} is the version number of the downloaded Redis tar file.

3. Change into the extracted Redis directory using the command cd redis-{version}.

4. To compile Redis, run the command make.

Once you have installed Redis, the next step is to update the Procfile. You can do this using the following steps:

1.

Open your applications Procfile. 2.

Add the following line at the end of the file:

web: python app.py –log-to-stdout

3. Save the file.

With these steps, you have now installed Redis and updated your Procfile.

Running Web and Worker Processes on A Single Dyno

Now that we have Redis installed and our Procfile updated, we can run web and worker processes on a single dyno. A dyno is a lightweight container that runs your application on Heroku.

To run web and worker processes on a single dyno, we will use a bash script that starts both processes. To create the bash script, follow these steps:

1.

Open your editor and create a new file called start.sh. 2.

Add the following lines to the file:

# Start web

gunicorn -w 4 -b “0.0.0.0:$PORT” app:app &

# Start worker

python worker.py

The first line of this script starts the web process, which is used to handle incoming HTTP requests. We are using gunicorn, which is a Python web server that is used to run Python applications.

The -w option sets the number of worker processes, and the -b option sets the address and port to listen on. The app:app specifies that our Flask application is called app.

The second line of the script starts the worker process, which is used to handle background tasks like sending emails and processing payments. Since we are running both the web and worker processes on a single dyno, we have to run them in the background using the & symbol.

Now that we have our bash script, we need to tell Heroku to use it when starting our application. We can do this using the following command:

1.

Add a file called Procfile to your applications root directory. 2.

Add the following line to the file:

web: sh start.sh

With these steps, you now have your web and worker processes running on a single dyno! Whenever a web request comes in, gunicorn will handle it. Whenever a background task needs to run, the worker process will handle it.

Updates

As technology advances, updates are necessary to keep your application running smoothly. In recent years, Python 3 has become the default version of Python.

If your application is still running on Python 2, you need to update your application to Python 3.

To update your application to Python 3, follow these steps:

1.

Update any third-party libraries that you are using to Python 3-compatible versions. 2.

Test your application to ensure that it is still functioning properly. 3.

Update your local Python environment to Python 3. 4.

Update your Procfile to specify the new Python version:

web: python3 app.py –log-to-stdout

5. Deploy your updated application to Heroku.

In addition to Python version updates, there are other updates that are necessary to ensure the security and scalability of your application. These updates may include library updates, server updates, and security patches.

Conclusion

In this article, we discussed how to set up Redis and run web and worker processes on a single dyno. We also discussed the importance of updates, including Python 3 support.

By following these steps to set up Redis and keep your application up-to-date, you can ensure that your application is secure, scalable, and reliable.

Overview of The Flask App

Flask is a popular Python web framework, known for its simplicity and flexibility. Many developers choose Flask because it has a small codebase and is easy to read and understand.

In this section, we will take a closer look at a Flask app and its functionality. Our Flask app is a word-counting application that takes text from a URL and returns the total number of words in the text.

The app has a simple user interface that allows users to enter a URL and submit it to the app. The app then scrapes the HTML from the URL and extracts the text.

The app then counts the number of words in the text and returns the result to the user. One of the key features of our Flask app is its ability to handle multiple requests concurrently.

The app uses Flask’s built-in concurrency support, known as “Greenlets,” to handle multiple requests at the same time. This allows the app to handle a large number of requests without becoming overwhelmed.

Local Development Environment and Heroku Deployment

Setting up a local development environment is essential when building a Flask app. With a local development environment, you can test your app and make changes without affecting the live version.

In this section, we will discuss how to set up a local development environment and deploy the app to Heroku.

Setting Up a Local Development Environment

To set up a local development environment for your Flask app, follow these steps:

1. Install Python on your machine.

2. Install pip, which is a package manager for Python.

3. Create a virtual environment using the command python3 -m venv env, where env is the name of your virtual environment.

4. Activate your virtual environment using the command source env/bin/activate.

5. Install Flask and any other dependencies using pip.

6. Write the code for your Flask app using your favorite text editor.

These steps will create a local development environment for your Flask app. You can start testing your app by running the Flask development server using the command flask run.

The development server will start on your local machine and allow you to test your app in your web browser.

Deploying to Staging and Production Environments

Once your Flask app is complete, the next step is to deploy it to a staging environment. A staging environment is a production-like environment that allows you to test your app in a more realistic setting before deploying it to production.

Here are the steps to deploy your Flask app to a staging environment:

1. Create a new Heroku app for your staging environment.

2. Push your code to the staging Heroku app using Git.

3. Configure any necessary environment variables for your app.

4. Scale your app to handle the expected traffic.

5. Test your app in the staging environment to ensure that it is working correctly.

If your app is working correctly in the staging environment, the next step is to deploy it to the production environment. Here are the steps to deploy your Flask app to the production environment:

1.

Create a new Heroku app for your production environment. 2.

Push your code to the production Heroku app using Git. 3.

Configure any necessary environment variables for your app. 4.

Scale your app to handle the expected traffic. 5.

Test your app in the production environment to ensure that it is working correctly. It is important to only release your app to production after thorough testing in your staging environment.

Deploying an untested app to production can cause significant issues for your users.

Conclusion

In this article, we discussed the functionality of a Flask app and how it can handle concurrent requests. We also discussed the importance of setting up a local development environment and how to deploy your app to both staging and production environments.

By following these steps, you can ensure that your Flask app is running smoothly and serving your users as intended. Setting Up PostgreSQL, SQLAlchemy, and Alembic for Database Handling

When building web applications, data is a crucial aspect.

Every application needs a database to persist user data, transactional data, and other information critical to the proper functioning of the app. In this section, we will discuss how to set up and handle a PostgreSQL database in a Flask application using SQLAlchemy and Alembic.

Installing Required Libraries for Database Handling

Before we can begin setting up our database, we need to install the libraries required for database handling in our Flask application. These libraries include PostgreSQL, SQLAlchemy, and Alembic.

To install these libraries, follow these steps:

1. Install PostgreSQL on your machine.

2. Install SQLAlchemy using pip.

3. Install Alembic using pip.

With these libraries installed, we can now begin setting up our database.

Handling Database Migrations

When building a web application, it is essential to keep your database schema in sync with your application code. This can be done through database migrations.

A migration is a change to the database schema that is recorded and can be applied or rolled back as needed. We will use Alembic to handle database migrations in our Flask application.

Alembic is a lightweight database migration tool that works seamlessly with SQLAlchemy. Here are the steps to handle database migrations using Alembic:

1.

Create a new directory called migrations in your application root directory. 2.

Initialize Alembic using the command alembic init migrations. 3.

Configure your database using SQLAlchemy in your application. 4.

Create your first migration using the command alembic revision -m “Initial Migration”. With these steps, you can now begin writing migration code that will update your database schema as needed.

Each time you make changes to your database schema, you will create a new migration using Alembic.

Back-End Logic for Scraping and Processing Webpage Word Counts

When building our word-counting application, we need to have back-end logic for scraping and processing webpage word counts. In this section, we will discuss how to use requests, BeautifulSoup, and NLTK for word count processing and how to scrape web pages and generate word-frequency pairs.

Using Requests, BeautifulSoup, and NLTK for Word Count Processing

Requests and BeautifulSoup are two libraries used for web scraping in Python. Requests is a library used for making HTTP requests, and BeautifulSoup is a library used for parsing HTML.

NLTK, on the other hand, is a library used for natural language processing in Python. To process word counts in our Flask application, we will use these libraries in the following steps:

1.

Use the requests library to retrieve the HTML content of the given URL. 2.

Use the BeautifulSoup library to parse the HTML content and retrieve only the textual content. 3.

Preprocess the textual content by removing non-essential elements such as style tags and scripts. 4.

Use the NLTK library to tokenize the text and generate a list of words. 5.

Count the occurrence of each word using Python’s built-in collections module. With these steps, we can generate a list of word-frequency pairs, which can be returned to the user as the total word count for the page.

Scraping Web Pages and Generating Word-Frequency Pairs

To scrape web pages and generate word-frequency pairs, we will need to use the back-end logic discussed above. Here is an overview of the process:

1.

Take the URL input from the user through the Flask front-end. 2.

Use the back-end logic to retrieve the HTML content of the given URL, parse the content, preprocess it, tokenize it, and count the occurrence of each word. 3.

Generate a list of word-frequency pairs sorted by frequency in descending order. 4.

Return the list of word-frequency pairs to the user through the Flask front-end. By following the above process, we can provide the user with an accurate count of words on a web page.

Conclusion

In this article, we discussed how to set up and handle a PostgreSQL database in a Flask application using SQLAlchemy and Alembic. We also learned how to scrape web pages and generate word-frequency pairs using back-end logic consisting of requests, BeautifulSoup, and NLTK.

By implementing these techniques, we can create a powerful and efficient Flask application capable of handling multiple requests and returning accurate word counts for web pages.

Implementing Redis for Task Queue Handling

When building complex web applications, background tasks become essential. Background tasks can include sending emails, processing payments, and other time-consuming tasks that cannot be handled synchronously.

In this section, we will discuss how to set up Redis for task queue handling in a Flask application.

Setting Up Redis Task Queue for Text Processing

Redis is an in-memory data structure store that can be used as a task queue. In our Flask application, we will use Redis to handle background tasks related to text processing.

Here is an overview of the process for setting up Redis task queue for text processing:

1. Install Redis on your machine.

2. Install the Redis Python library using pip.

3. Configure your Flask application to use Redis as the task queue backend.

4. Create a Celery task to handle text processing using Redis as the task queue.

5. Configure your Redis task queue with the appropriate settings.

6. Start the task queue with the command ‘celery -A app.celery worker -l info’

With these steps, we have set up our Redis task queue for text processing.

Now, we can start processing tasks related to text processing in the background.

Front-End Angular Setup for Continuous Polling of the Back-End

In many web applications, it is essential to continuously poll the back-end for updates. This can be done through front-end frameworks like Angular.

In this section, we will discuss how to set up Angular for continuous polling and how to test the application locally before pushing it to Heroku.

Setting Up Angular for Continuous Polling

To set up Angular for continuous polling, we will use the Http module provided by Angular. Here are the steps to set up continuous polling using Angular:

1.

Open your Angular application’s component. 2.

Import the Http module using the following commands:

– import { Http } from ‘@angular/http’;

– import ‘rxjs/add/operator/map’;

3. Create a new function that sends an HTTP GET request to the back-end, retrieves the data, and returns the retrieved data.

4. Use the setInterval() method to call the above function at fixed intervals.

5. Display the retrieved data in the front-end using Angular.

With these steps, we can set up Angular for continuous polling of the back-end.

Testing the Application Locally before Pushing to Heroku

Testing your application locally is essential before pushing it to production. Here are the steps to test your application locally:

1.

Start your Flask application on your local machine. 2.

Start your Angular application by running ‘ng serve’ on your local machine. 3.

Navigate to your Angular application in your web browser. 4.

Test the functionality of your application to ensure that it is working as intended. By following these steps, you can identify and fix any issues before pushing your application to Heroku.

Conclusion

In this article, we discussed how to set up Redis for task queue handling in a Flask application. We also discussed how to set up Angular for continuous polling and how to test your application locally before pushing it to Heroku.

By following these steps, you can build powerful web applications capable of handling background tasks, continuous data updating, and multiple requests.

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