How Does YouTube Work In 2024? The Tech Behind Series

Julian Wallis
13 min read
Diving into the intricate workings of YouTube's tech stack reveals a fascinating blend of algorithms, AI, and a robust technological infrastructure that powers the platform to deliver an unparalleled and intuitive user experience.
How does youtube work? Magnifying glass highlighting Youtube homepage

If you were to go through your weekly statistics of “most-used apps” on your smartphone, there is a big likelihood that YouTube would be near the top of that list. 

But did you know that YouTube isn’t just an app or even just a website? It’s a full-fledged search engine, holding down the fort as the second-largest on the entire planet. Right after the obvious search giant, you know who I’m talking about.

Now, let’s take a little stroll behind the scenes, shall we? Ever wondered how YouTube works? How does it keep us hooked day after day? Or what technologies were used to build it and continually improve on its features and functionalities? Well, that’s what this article will look at. We’ll uncover the underlying technological foundation that is driving the second most visited website in the world every day. 

Introduction to YouTube ▶️

Picture YouTube as this colossal brain pulsating with energy. It’s not just a collection of viral videos, and epic fails; it’s a technological marvel, a complex web of algorithms and codes working together to give you that perfect video recommendation.

Fundamentally, YouTube is a video-sharing platform that has revolutionised how people consume and share video content online. Launched in 2005, YouTube quickly became a cultural phenomenon, offering a vast and diverse array of videos ranging from educational content and entertainment to user-generated uploads. 

Founded by Steve Chen, Chad Hurley, and Jawed Karim, the platform was later acquired by Google in 2006. Fundamentally, YouTube allows users to upload, view, and share videos, fostering a global community of content creators and viewers. 

The platform’s intuitive interface and powerful recommendation algorithms have contributed to its widespread adoption, making it a central hub for discovering and enjoying multimedia content. Users can find content on virtually any topic imaginable, and YouTube has become a space for educational tutorials, entertainment, music, vlogging, and much more. 

With billions of hours of content watched daily, YouTube has shaped digital media consumption and provided a platform for individuals and businesses to reach a global audience. As a social platform, YouTube facilitates interactions through likes, comments, and subscriptions, creating a dynamic ecosystem where creators can engage with their audience. 

The platform’s impact on entertainment, information dissemination, and cultural trends has solidified its position as a powerhouse in the digital landscape, and that position is unlikely to change anytime soon.

How Does YouTube Work? ⚙️

Curious about how YouTube predicts your next binge-worthy video? It’s not a crystal ball – although it sometimes seems like it. At the core of it all, the combination of machine learning and algorithms working in tandem is what powers the YouTube algorithm engine. 

YouTube operates as a video-sharing platform (the largest one in the world in fact), and its functionality involves several key components. YouTube is a platform that enables content creators to share videos with a global audience while users can discover, watch, and interact with a diverse range of content. 

The platform’s success is attributed to its user-friendly interface, robust recommendation system, and vast content library catering to various interests and preferences. So, how does that work exactly? Let’s look at all individual components of YouTube to get a holistic understanding of the app’s inner workings. Here’s an overview of how YouTube works.

Content Upload 📑

YouTube’s Content Creators are Individuals, organisations, and businesses that upload videos to YouTube. Creators can range from independent vloggers to major media companies. Without content creators, there wouldn’t be anything to see on YouTube. 

Video Storage and Processing 📹

YouTube’s content upload process is fairly straightforward; when a user uploads a video, it is stored on YouTube’s servers. The video undergoes processing, including various resolutions and formats to accommodate different devices and network conditions.

Content Delivery 📦

YouTube uses a Content Delivery Network (CDN) to distribute video content globally. This network consists of servers strategically located worldwide to reduce latency and ensure efficient video delivery. YouTube, before being acquired by Google, relied on Akamai as their CDN, but later built their own proprietary CDN to manage scalability and cost-savings.

Viewing Experience 👀

User Interface 🤓

Users access YouTube through its website or mobile apps. The user interface allows for searching, browsing, and interacting with videos.

Video Player 🎥

YouTube’s video player is embedded in the platform and supports various resolutions, playback speeds, and quality settings. It also facilitates features like subtitles, annotations, and user engagement options.

Understanding YouTube’s Recommendation Algorithm – All Factors Explained

YouTube employs a sophisticated recommendation system that suggests videos to users based on their watch history, preferences, and behaviour on the platform. 

This algorithm aims to enhance user engagement and satisfaction. The monetisation complex system is designed to deliver personalised content recommendations based on user preferences, viewing history, and engagement patterns. 

The algorithm employs a combination of machine learning techniques and data analysis to achieve this goal. It leverages user data, machine learning, and neural networks to recommend personalised content.

 By understanding user preferences, calculating video embeddings, employing collaborative filtering, and prioritising engagement metrics, the algorithm continually refines its suggestions to keep users engaged and satisfied. Don’t worry; we’ll cover all of these in detail below.

The technical intricacies of the algorithm contribute to the dynamic and diverse content ecosystem that defines the YouTube platform. While the specifics of the algorithm are proprietary and constantly evolving, let’s explore some of its essential components and functions.

User Data and Profiling 🙋‍♂️

YouTube collects vast amounts of user data, including their watch history, search queries, demographics, and interaction with videos (likes, dislikes, comments, shares). This data forms the basis of user profiles, which the algorithm uses to understand individual preferences.

Machine Learning and Neural Networks 🧠

The core of the YouTube algorithm relies on machine learning, particularly neural networks.  These artificial intelligence models analyse patterns in the data to predict what videos a user might enjoy based on their historical behaviour. Neural networks can recognise complex relationships and patterns in data, allowing the algorithm to constantly adapt and improve its recommendations.

Content Embeddings and Similarity 📚

Each video on YouTube is represented by a vector known as an embedding. This vector captures various aspects of the video, such as its content, metadata, and user engagement. 

The algorithm calculates the similarity between video embeddings to recommend content similar to what a user has previously watched. For example, if a user frequently watches cooking tutorials, the algorithm may recommend videos with similar cooking themes.

Collaborative Filtering 🤝

Collaborative filtering is a technique where the algorithm makes recommendations based on users’ preferences with similar tastes. 

If User A and User B have similar watch histories, the algorithm might recommend videos that User B has found engaging to User A. This method helps the algorithm suggest content that aligns with broader user trends.

Watch Time and Session Dynamics ⏲️

One of the critical metrics YouTube prioritises is watch time. The algorithm aims to recommend videos that keep users engaged and lead to longer viewing sessions. Videos contributing to extended watch times are more likely to be promoted as they indicate content that captivates the audience.

Click-Through Rate (CTR) and Engagement Signals 🚨

The algorithm considers user interaction with recommendations, focusing on the click-through rate (CTR). Videos with higher CTRs are deemed more relevant and are likely to be suggested to a broader audience. Engagement signals such as likes, comments, and shares contribute to the algorithm’s understanding of a video’s popularity and quality.

Freshness and Trending Content 🔥

YouTube values fresh and trending content. The algorithm may prioritise recently uploaded videos or those related to trending topics to ensure that users encounter up-to-date and relevant content. This emphasis on freshness prevents the platform from stagnating and ensures a dynamic user experience.

YouTube Shorts ▶️

Unlike the traditional YouTube algorithm, their Shorts feature (YouTube’s answer to TikTok) works slightly differently. The Shorts algorithm discards conventional user metrics such as impressions and click-through rates; within the domain of Shorts, the primary metric of significance is ‘views’ instead. In contrast to the conventional methodology of utilising impressions for content evaluation, the YouTube Shorts algorithm adheres to a more straightforward approach.

Monetisation 💰

Creators can monetise their content through YouTube’s Partner Program, which allows ads to be displayed on their videos. Ad revenue is shared between YouTube and the content creator.

YouTube Premium has emerged as a key cog in monetising the platform. Users can subscribe to YouTube Premium, a subscription service that offers an ad-free experience, access to exclusive content, and other premium features.

User Interaction 🙎‍♂️

Comments, Likes, and Shares: Users can engage with videos by leaving comments, liking or disliking content, and sharing videos.

Subscriptions: Users can also subscribe to channels and receive upload notifications by clicking on the ‘bell icon’ when their favourite creators upload new content.

Content Policies and Moderation 🚓

YouTube enforces community guidelines to ensure that content on the platform aligns with its policies. This includes restrictions on hate speech, violence, and other forms of inappropriate content. YouTube also employs automated systems and human moderators to review and enforce its content policies.

Analytics and Insights 📊

YouTube provides creators with analytics and insights through its Creator Studio. Creators can track the performance of their videos, audience demographics, and engagement metrics.

Exploring YouTube’s Tech Stack

Frontend Technologies of YouTube 🖥️

YouTube relies on a lightweight JavaScript framework called Structured Page Fragments, often abbreviated as SPF, tailored for efficient navigation and seamless page updates within the platform. 

Through a combination of progressive enhancement and HTML5, SPF seamlessly integrates with websites, optimising user experiences by selectively updating specific page sections during navigation, thus avoiding reloading the entire page.

 This framework introduces a designated response format for transmitting document fragments, accompanied by a robust system for managing scripts and styles, an in-memory cache, and on-the-fly processing.

A big reason why YouTube utilises SPF is for its ability to offer the benefits of a static initial page load while capitalising on the enhanced performance and user experience associated with dynamic page loads. Users can expect accelerated initial page loading times, ensuring a responsive and persistent interface is maintained throughout the navigation process. 

From a performance standpoint, SPF leverages established methods for static rendering, loading smaller responses and minimising resource consumption with each navigation. Development with SPF is versatile, supporting any server-side language and template system and fostering productivity by allowing the use of the same code for static and dynamic rendering.

Backend Technologies of YouTube 🏋️‍♂️

YouTube is primarily written in Java, Python, and C++. These programming languages are used to handle different aspects of YouTube’s operations, such as video processing, data storage, and content delivery. Python is used by YouTube mainly for scripting tasks, backend development and deployment, and other purposes where its simplicity and readability are advantageous.

YouTube is hosted on Google’s infrastructure, leveraging services like Google Cloud Storage, Bigtable, and BigQuery. YouTube uses databases like MySQL for structured data, while NoSQL databases like Bigtable are used for scalability and flexibility.

Developed by Google, YouTube’s backend tech stack also includes Protocol Buffers that are used for serialising structured data, providing a language-agnostic way to encode data.

Video Processing Technologies Used by YouTube 📹

YouTube uses FFmpeg, an open-source software for handling multimedia data, including video processing and transcoding. YouTube also employs Google’s infrastructure to process and serve video content efficiently.

Content Delivery Technologies 🚛

YouTube uses its proprietary CDNs to distribute content globally, ensuring faster loading times for users worldwide. Google has a network of caching servers worldwide to reduce latency and improve the delivery of YouTube videos.

Storage 📀

For storage needs, YouTube uses Google’s cloud services to store and retrieve large amounts of data, including videos and user-generated content. YouTube likely uses distributed file systems to store and retrieve large files efficiently.

Security 🔏

YouTube enforces secure communication over the web using the HTTPS protocol. YouTube also focuses on Content Security Policies (CSP), an additional layer of security to mitigate risks associated with cross-site scripting (XSS) attacks.

How Is YouTube Using Machine Learning & AI? 🤖

Much like other platforms, one of YouTube’s most significant value propositions, extending beyond the content itself, lies in its intricate system of recommendations and curated suggested videos. Upon entering YouTube, users are presented with a familiar interface featuring new videos labelled as “recommended” based on sophisticated AI algorithms

After watching a video, users are guided to another page containing additional recommendations. For frequent users, including myself, active searches for videos become increasingly rare as YouTube’s algorithms adeptly shape up the viewing experience.

The efficacy of YouTube’s exceptional recommendations is achieved through a two-tiered AI algorithm consisting of candidate generation and the ranking network. 

In candidate generation, the system comprehensively assesses the user’s history, comparing it with key information from other users, such as the quantity and types of videos watched and demographic data. The ranking network, leveraging a “rich set of features describing the video and user,” enables the system to manage vast video libraries while tailoring recommendations to individual users, offering them meaningful content.

The significance of the ranking system is underscored by the daily influx of thousands of new video uploads to YouTube. To ensure high-quality recommendations, YouTube gauges the time users spend watching videos as a proxy for video quality. 

The AI algorithms utilise this data to predict, based on a user’s history, the likelihood of them watching a given video. This is accomplished through a weighted logistic regression approach, where positive videos (those clicked on by the user) are given more significance, allowing the algorithm to focus on videos with user interaction. Both the candidate generation and ranking network are pivotal to YouTube’s value creation.

YouTube’s integration of AI extends beyond recommendations, as seen in the implementation of the ‘chapters’ feature. This innovative addition lets users preview video content by hovering over a video, triggering the ‘chapters’ to play. 

With this feature, YouTube wants to make it easier for people to navigate videos with video chapters, and they are using AI to add video chapters automatically. They use machine learning to recognise text to auto-generate video chapters. This machine-learning-driven advancement streamlines the video upload process for creators and provides viewers with a sneak peek into the content and an easy way to access the parts of the video that interest them the most.

Conclusion – YouTube’s Impressive Tech Stack ⏯️

At the end of the day, YouTube’s technological ecosystem is impressive, to say the least. We’re talking about a digital infrastructure that can handle data for an application that gets over 117 Billion monthly visits. But it’s not just about the tech jargon; it’s about the relentless pursuit of improvement. YouTube isn’t just resting on its laurels; it’s a dynamic force, a constantly evolving entity that’s here to blow your mind with new features and functionalities.

YouTube’s success story serves as an exemplary model for businesses aiming to leverage technology to scale and flourish. The lessons learned from their innovative tech solutions, coupled with their dedication to enhancing user experience, provide valuable insights for companies navigating the digital landscape.
 As we champion the spirit of technological evolution, we encourage you to contact us. Share the vision of your project, and let’s initiate a conversation on how we can use our years of experience and expertise to guide your business to new heights.

Published On

January 15, 2024