Other Tips and Tricks

This book has covered almost all the areas that need to be known for the creation of a web application using Flask. Much has been covered, and a lot more needs to be explored. In this final chapter, we will go through some additional recipes that can be used to add value to the application if needed.

In this chapter, we will learn how to implement full-text search using Whoosh and Elasticsearch. Full-text search becomes important for a web application that offers a lot of content and options, such as an e-commerce site. Next, we will catch up on signals that help decouple applications by sending notifications (signals) when an action is performed somewhere in the application. This action is caught by a subscriber/receiver, which can perform an action accordingly. This is followed by implementing caching for our Flask application.

We will also see how email...

Implementing full-text search with Whoosh

Whoosh is a fast, featureful, full-text indexing and searching library that's implemented in Python. It has a pure Pythonic API and allows developers to add search functionality to their applications easily and efficiently. In this recipe, we will use a package called Flask-WhooshAlchemy, which integrates the text-search functionality of Whoosh with SQLAlchemy for use in Flask applications.

Getting ready

The Flask-WhooshAlchemy package can be installed via pip using the following command:

 $ pip3 install git+git://github.com/gyllstromk/Flask-WhooshAlchemy.git

This will install the required packages and dependencies. This library is being cloned and installed from GitHub because...

Implementing full-text search with Elasticsearch

Elasticsearch is a search server based on Lucene, which is an open source information-retrieval library. Elasticsearch provides a distributed full-text search engine with a RESTful web interface and schema-free JSON documents. In this recipe, we will implement full-text search using Elasticsearch for our Flask application.

Getting ready

We will use a Python library called elasticsearch, which makes dealing with Elasticsearch a lot easier:

$ pip3 install elasticsearch 

We also need to install the Elasticsearch server itself. This can be downloaded from https://www.elastic.co/downloads/elasticsearch. Unpack the package and run the following command:

$ bin/elasticsearch 

This will...

Working with signals

Signals can be thought of as events that happen in our application. These events can be subscribed by certain receivers who then invoke a function whenever the event occurs. The occurrence of events is broadcasted by senders who can specify the arguments that can be used by the function, which will be triggered by the receiver.

You should refrain from modifying any application data in the signals because signals aren't executed in a specified order and can easily lead to data corruption.

Getting ready

We will use a Python library called blinker that provides the signals feature. Flask has built-in support for blinker and uses signaling itself to a good extent. There are certain core signals provided...

Using caching with your application

Caching becomes an important and integral part of any web application when scaling or increasing the response time of your application becomes a question. Caching is the first thing that is implemented in these cases. Flask, by itself, does not provide any caching support by default, but Werkzeug does. Werkzeug has some basic support to cache with multiple backends, such as Memcached and Redis.

Getting ready

We will install a Flask extension called flask-caching, which simplifies the process of caching a lot:

$ pip3 install flask-caching

We will use our catalog application for this purpose and implement caching for some methods.

...

Implementing email support for Flask applications

The ability to send emails is usually one of the most basic functions of any web application. It is usually easy to implement with any application. With Python-based applications, it is quite simple to implement with the help of smtplib. In the case of Flask, this is further simplified by an extension called Flask-Mail.

Getting ready

Flask-Mail can be easily installed via pip:

$ pip3 install Flask-Mail 

Let's look at a simple case where an email will be sent to a catalog manager in the application whenever a new category is added.

How to do it...

...

Understanding asynchronous operations

Some of the operations in a web application can be time-consuming and make the overall application feel slow for the user, even though it's not actually slow. This hampers the user experience significantly. To deal with this, the simplest way to implement the asynchronous execution of operations is with the help of threads. In this recipe, we will implement this using the threading libraries of Python. In Python 3, the thread package has been deprecated. Although it is still available as _thread, it is highly recommended to use threading.

Getting ready

We will use the application from the Implementing email support for Flask applications recipe. Many of us will have noticed that,...

Working with Celery

Celery is a task queue for Python. There used to be an extension to integrate Flask and Celery, but with Celery 3.0, it became obsolete. Now, Celery can be directly used with Flask by just using a bit of configuration. In the Understanding asynchronous operations recipe, we implemented asynchronous processing to send an email. In this recipe, we will implement the same using Celery.

Getting ready

Celery can be installed simply from PyPI:

$ pip install celery 

To make Celery work with Flask, we will need to modify our Flask app config file a bit. Here, we will use Redis as the broker (thanks to its simplicity).

Make sure that you run the Redis server for the connection to happen. To install and run a Redis...