Imagine reading a long book like "A Song of Fire and Ice," "The Lord of the Rings," or "Harry Potter." Now imagine that there was a specific detail in one of those books that you needed to revisit. You wouldn't want to search every page in those long books to find what you were looking for. Instead, you'd want to use some sort of book index to help you quickly locate what you were looking for. This same concept of indexing content within a book can be carried to MongoDB Atlas Search with search indexes.
Atlas Search makes it easy to build fast, relevant, full-text search on top of your data in the cloud. It's fully integrated, fully managed, and available with every MongoDB Atlas cluster running MongoDB version 4.2 or higher.
Correctly defining your indexes is important because they are responsible for making sure that you're receiving relevant results when using Atlas Search. There is no one-size-fits-all solution and different indexes will bring you different benefits.
In this tutorial, we're going to get a gentle introduction to creating indexes that will be valuable for various full-text search use cases.
Read MoreWhen thinking about full-text search, text and other string data is probably the first thing to come to mind. In fact, if you've been keeping up with my tutorials, you might remember Building an Autocomplete Form Element with Atlas Search and JavaScript or Visually Showing Atlas Search Highlights with JavaScript and HTML, both of which were on text search examples in MongoDB Atlas Search.
Being able to use natural language search on text data is probably one of the most popular use-cases, but there are scenarios where you might need to narrow the results even further.
Let's say you're building a restaurant review application like Yelp or a bed and breakfast booking system like Airbnb. Sure, you'll enter some kind of text criteria for what you're looking for, but there's also a location aspect to it. For example, if you want to find a place to get a cheeseburger within walking distance of your current location, you probably don't want your search results to contain entries from another country. This is an example of a geo search, where you would want to return results based on location coordinates.
In this tutorial, we're going to see how to use Atlas Search and the compound operator to search based on text entered and within a certain geographical area. For the text entered, we'll use the autocomplete operator, and for the geospatial component, we'll use the geoWithin operator.
Read MoreMongoDB 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.
Read MoreI'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).
Read MoreI've been a gamer since the 1990s, so 2D side-scrolling platformer games like Super Mario Bros. hold a certain place in my heart. Today, 2D games are still being created, but with the benefit of having connectivity to the internet, whether that be to store your player state information, to access new levels, or something else.
Every year, MongoDB holds an internal company-wide hackathon known as Skunkworks. During Skunkworks, teams are created and using our skills and imagination, we create something to make MongoDB better or something that uses MongoDB in a neat way. For Skunkworks 2020, I (Nic Raboy) teamed up with Barry O'Neill to create a side-scrolling platformer game with Unity that queries and sends data between MongoDB and the game. Internally, this project was known as The Untitled Leafy Game.
In this tutorial, we're going to see what went into creating a game like The Untitled Leafy Game using Unity as the game development framework and MongoDB Realm for data storage and back end.
Read MoreOne 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.
Read MoreWhen 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.
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