In this tutorial, we’ll use Lassie, a Python library for retrieving content from websites, to fetch information regarding a Qbox YouTube video as JSON. We’ll then store that data in our Qbox Elasticsearch cluster using elasticsearch-py, Elasticsearch’s official low-level Python client. We’ll also use elasticsearch-py to query and return the record we indexed.

Although this example is minimal and the choice of a YouTube video to index is somewhat arbitrary, the concept it demonstrates has larger practical applications. For example, a company could build a vertical search engine collecting all information about it found online. The user-friendliness of Lassie and Python would enable a task like this to be done in relatively fewer lines of code and with syntax easily understood, even by those new to programming.

The penetration testing world is fast moving and persistently demands new ideas, tools and methods for solving problems and breaking things. In recent years many people have gotten used to the idea of using Elasticsearch in the penetration testing workflow, most notably for hacking web applications.

More and more companies and websites are opening bug bounty programs. If you have new tools in your arsenal that other people don’t use or understand yet, then you could be making a great deal more money from Bug Bounty hunting. This tutorial teaches you how to use new tools with Elasticsearch to give you that competitive edge.

This guide is about using the Elasticsearch Python client to do useful things with Elasticsearch. The Python client makes use of the Elasticsearch REST interface.

Let’s start by installing some dependencies:

In the previous posts in this series we created a basic Django app and populated a database with automatically generated data. We also added data to the elasticsearch index in bulk, wrote a basic command, and added a mapping to the elasticsearch index. In this final article we will add functional frontend items, write queries, allow the index to update, and discuss a bonus tip.

In the previous posts, we created a basic Django app, and populated a database with automatically generated data. In this post, we will add data to the elasticsearch index in bulk, write a basic command, and add a mapping to the elasticsearch index.

In this series, we are creating a Django app with Elasticsearch-based search integrated. The previous article focused on the creation of a basic Django app. As we cannot show the great applications of Elasticsearch features without data to use, we shall populate a database with automatically generated data in this post.