Introduction Elasticsearch is a NoSQL database for storing data in the form of JSON requests to that can store, access, and alter large quantities of data that can easily be queried. Instead of using “records” in a “table”, like with SQL-based databases such as MySQL, it stores data as JSON “documents” in an “index”. This … Continued
Introduction There are multiple ways to list all of the indexes contained in an Elasticsearch cluster. One common way of doing this is to use cURL and Kibana to issue HTTP requests to communicate with the Elasticsearch cluster to have it return all of the index names and their respective UUIDs. The Elasticsearch list indexes … Continued
Introduction Docker containers offer a simple way to create and package your apps in their own environment. If you’re interested in starting a Docker container running Elasticsearch, the process is actually quite simple. In this article, we’ll show you how to create an image and spin up an Elasticsearch Docker container using Dockerfile. Prerequisites Before … Continued
Introduction Scrolling in Elasticsearch allows you retrieve a large number of documents, in steps or iterations, similar to pagination or a “cursor” in relational databases. In this article we’ll explore the Elasticsearch concept of scrolling, and how we can implement it in an application using the Python low-level client’s “helpers” library. Let’s go over how … Continued
Introduction to Elasticsearch web applications in Python This article is part two of an article series that will show you how to build a Python web application designed to return Elasticsearch information. The first part of the series demonstrated how to create the web server using the Bottle framework. It also provided some example code … Continued
Introduction to Elasticsearch web applications in Python Web applications are a great way to bring your Elasticsearch cluster’s search functionality to the front end for a better user experience. This article will demonstrate how to utilize Elasticsearch’s efficient search and analytics engine in a simple web app that can execute complex queries of its document … Continued
Introduction to the _mapping schema for Elasticsearch schema The _mapping of an index in Elasticsearch is the essentially the schema for the documents. It’s the layout of the index’s fields that sets up a blueprint for storing and organizing fields, and their respective data, when documents are indexed to the Elasticsearch index. Getting the mapping … Continued
Introduction to Elasticsearch and Python The Python low-level REST client for Elasticsearch maps closely to the Java API for the Elasticsearch client. This article demonstrates how you can use the client to create a _mapping schema for a document with an encoded bytes string, and then index an encoded dictionary. The article will demonstrate how … Continued
Introduction Part one of this series demonstrated how you can use Python to crawl for files in a directory, and then use the os library’s open() method to open the files and return their content as string data. This article will show you how to use the helpers module, in the low-level Python client, to … Continued
Introduction One useful feature of Python is its built-in os and glob libraries that allow you to get files in a directory and read their content. This article will show you how to use Python to index files and their content, found using glob, in order to index the data in an Elasticsearch index. Let’s … Continued
