Update Array Elements in a Document with MQL Positional Operators

MongoDB offers a rich query language that’s great for create, read, update, and delete operations as well as complex multi-stage aggregation pipelines. There are many ways to model your data within MongoDB and regardless of how it looks, the MongoDB Query Language (MQL) has you covered.

One of the lesser recognized but extremely valuable features of MQL is in the positional operators that you’d find in an update operation.

Let’s say that you have a document and inside that document, you have an array of objects. You need to update one or more of those objects in the array, but you don’t want to replace the array or append to it. This is where a positional operator might be valuable.

In this tutorial, we’re going to look at a few examples that would benefit from a positional operator within MongoDB.

Zap, Tweet, and Repeat! How to Use Zapier with MongoDB

I’m a huge fan of automation when the scenario allows for it. Maybe you need to keep track of guest information when they RSVP to your event, or maybe you need to monitor and react to feeds of data. These are two of many possible scenarios where you probably wouldn’t want to do things manually.

There are quite a few tools that are designed to automate your life. Some of the popular tools include IFTTT, Zapier, and Automate. The idea behind these services is that given a trigger, you can do a series of events.

In this tutorial, we’re going to see how to collect Twitter data with Zapier, store it in MongoDB using a Realm webhook function, and then run aggregations on it using the MongoDB query language (MQL).

Capturing Hacker News Mentions with Node.js and MongoDB

If you’re in the technology space, you’ve probably stumbled upon Hacker News at some point or another. Maybe you’re interested in knowing what’s popular this week for technology or maybe you have something to share. It’s a platform for information.

The problem is that you’re going to find too much information on Hacker News without a particularly easy way to filter through it to find the topics that you’re interested in. Let’s say, for example, you want to know information about Bitcoin as soon as it is shared. How would you do that on the Hacker News website?

In this tutorial, we’re going to learn how to parse through Hacker News data as it is created, filtering for only the topics that we’re interested in. We’re going to do a sentiment analysis on the potential matches to rank them, and then we’re going to store this information in MongoDB so we can run reports from it. We’re going to do it all with Node.js and some simple pipelines.

Client-Side Field Level Encryption (CSFLE) in MongoDB with Golang

One of the many great things about MongoDB is how secure you can make your data in it. In addition to network and user-based rules, you have encryption of your data at rest, encryption over the wire, and now recently, client-side encryption known as client-side field level encryption (CSFLE).

So, what exactly is client-side field level encryption (CSFLE) and how do you use it?

With field level encryption, you can choose to encrypt certain fields within a document, client-side, while leaving other fields as plain text. This is particularly useful because when viewing a CSFLE document with the CLI, Compass, or directly within Altas, the encrypted fields will not be human readable. When they are not human readable, if the documents should get into the wrong hands, those fields will be useless to the malicious user. However, when using the MongoDB language drivers while using the same encryption keys, those fields can be decrypted and are queryable within the application.

In this quick start themed tutorial, we’re going to see how to use MongoDB field level encryption with the Go programming language (Golang). In particular, we’re going to be exploring automatic encryption rather than manual encryption.

Visually Showing Atlas Search Highlights with JavaScript and HTML

When it comes to finding specific words or phrases within text, you’re probably going to want to use a natural language search option like full-text search (FTS). Sure, you could probably create a complicated and difficult-to-maintain set of regular expressions to search within text, but that is an option that most developers don’t want. Not to mention it won’t cover the full scope of what a natural language processor typically accomplishes.

In a previous tutorial titled Building an Autocomplete Form Element with Atlas Search and JavaScript, I wrote about searching for recipes, as they are being typed, in MongoDB Atlas using the autocomplete operator. While this tutorial accomplished the job quite well, it didn’t elaborate on what exactly was being matched for any given term.

In this tutorial, we’re going to see how to use Atlas Search and work with the highlight data to visually show any matches on the terms in a user facing application. Highlighting is a powerful tool with Search to allow your users to find the exact text that they want in its proper context.

Building an Autocomplete Form Element with Atlas Search and JavaScript

When you’re developing a web application, a quality user experience can make or break your application. A common application feature is to allow users to enter text into a search bar to find a specific piece of information. Rather than having the user enter information and hope it’s valid, you can help your users find what they are looking for by offering autocomplete suggestions as they type.

So what could go wrong?

If your users are like me, they’ll make multiple spelling mistakes for every one word of text. If you’re creating an autocomplete field using regular expressions on your data, programming to account for misspellings and fat fingers is tough!

In this tutorial, we’re going to see how to create a simple web application that surfaces autocomplete suggestions to the user. These suggestions can be easily created using the full-text search features available in Atlas Search.

Create a Stream Deck Plugin to Interact with Webhooks

I recently jumped on the hype train when it comes to streaming and picked up an Elgato Stream Deck. If you’re unfamiliar, these devices are essentially hotkey peripherals with LCD adjustable keys that allow you to quickly perform certain tasks. Could a keyboard shortcut get the job done? For a lot of tasks, definitely, but the Stream Deck software is where the magic comes in.

The Stream Deck software allows you to connect certain services or multi-stage shortcuts to a specific key, something a standard keyboard shortcut probably won’t do well. In addition, you’re able to design your own actions using simple JavaScript and HTML.

In this tutorial, we’re going to see how to create a Stream Deck action, one that sends HTTP requests to remote webhook services, using JavaScript.