Need For Speed
I open Netflix, ready to watch Interstellar for the hundredth time. I hit play.
Baam—the dreaded loading wheel appears.
Frustrating, right? Seconds feel like hours. But why does this even happen?
Every time you stream a movie, your device has to fetch a massive amount of data - loads of it. Think of it like ordering food at a restaurant. If the chef has everything prepped, your meal arrives in minutes. But if they’re starting from scratch, you’re in for a long wait.
That’s exactly how streaming works. If the data isn’t readily available, buffering kicks in, and suddenly, your movie night turns into a waiting game.
But what if your favorite movies started instantly— every single time? No buffering. No delays. Just play and enjoy. Sounds like magic?
Well, it’s not magic—it’s caching. And one of the most powerful tools for this? Redis.
In today’s world of streaming, where milliseconds can make or break an experience, even the slightest delay is a deal breaker. Users don’t just want their content fast—they want it now. No buffering. No waiting. Just hit play and go.
So how do platforms like Netflix and Hulu pull this off? What keeps them running smoothly even when millions of people are streaming at the same time? The answer lies in effective caching - and the secret behind it?
Redis. (There are others too, but for now, let’s roll with Redis.)
The Role of Caching in Streaming Services
Imagine this: our friend Jeremy wants some ice cream. He has two options—
1. Grab one from the nearby ice cream parlour.
2. Order it directly from the company’s storage.
If Jeremy picks the ice cream parlour, he gets his treat in just two minutes—quick and convenient! But if the parlour doesn’t have what he wants, he has no choice but to wait 10 minutes for the company’s storage to deliver it.
Naturally, the best way to get ice cream quickly is to check the parlour first. If it’s available, great! If not, he places an order, waits, and once it arrives, the parlour stocks that flavour for next time—ensuring others can grab it instantly later.
Now, swap out ice cream for video data, and you’ve got caching in streaming services.
If every request had to go all the way to a primary datastore, playback would be painfully slow. Instead, they use Redis, a high-speed, in-memory data store, to act as the “ice cream parlour” for frequently accessed data.
It helps in streaming services by:
1. Storing frequently accessed metadata (all your recommendations and angry ratings).
2. Caching video chunks and API responses (like storing frequently accessed calls, images, or data) helps reduce database load and improve performance.
3. Providing near-instant access to frequently used data.
This significantly reduces the need for repeated database queries, enhancing performance and efficiency. Making streaming feels instantaneous—(no more endless loading wheels).
But where did this idea come from? It all started with one frustrated developer…
Built out of pure frustration
Back in 2009, an Italian programmer named Salvatore Sanfilippo was working on a real-time web analytics system to track user activity on websites instantly but he ran into a wall.
The database he was using simply couldn't handle the load of tracking thousands of web pages in real-time! Every page view meant multiple database writes, and complex queries were bringing his servers to their knees.
The alternatives weren't great either.
Memcached could cache data but couldn't save it permanently.
MySQL was too slow for real-time operations he needed.
MongoDB was great for storing large amounts of data, but it was overkill for what he was trying to do.
Instead of giving up, Sanfilippo did what any passionate developer would—he built his own solution—something fast, lightweight, and capable of handling real-time data with ease. He wanted a system that could store and retrieve data quickly without the overhead of traditional databases.
With Redis, Sanfilippo didn’t just solve his problem—he changed the way the world handles data.
Redis’s Evolution
Originally developed by Salvatore Sanfilippo as a simple key-value store, Redis was designed to make data storage and retrieval more efficient. Over time, it evolved into a powerful in-memory data store, supporting advanced data structures like lists, sets, sorted sets, and hashes. Today, Redis is more than just a key-value store—it’s a high-performance, multi-purpose tool that powers everything from real-time analytics to AI-driven applications.
Unlike databases that store data on a hard drive, Redis keeps everything in the computer’s memory. Because of this, it can perform over 100,000 tasks every second, making it perfect for apps that need to respond immediately. Think of it as reaching into your pocket for a key, instead of running to the basement.
As Redis grew beyond a simple key-value store, its architecture evolved to handle even more demanding tasks.
The Architecture That Drives Speed
Let’s put ourselves in the shoes of Sanfilippo. Imagine you’re designing an alternative to the medley of problems you have. How would you go about it? You would probably consider the following factors:
Efficiency: You’d opt for efficiency first. This means processing tasks one by one rather than handling everything at once—you want a streamlined model, right? Redis achieves this with its Single Threaded Efficiency model. Instead of juggling multiple tasks like traditional databases, Redis processes requests one at a time, but at an incredibly fast pace.
Fast Data Retrieval: Next, you’d want to search for data quickly because you wouldn’t want your resources wasted just looking for the data you need. To do this, Redis uses Optimized Data Storage with smart, memory-efficient structures. These structures store data compactly, reducing memory usage and speeding up lookups.
Stability Through Separation: Finally, you’d want to keep different operations separate. For instance, writing new data shouldn’t slow down reading existing data. Redis handles this with Copy-on-Write for Stability. When saving data, it uses a method that ensures new writes don’t interfere with ongoing operations, keeping the system smooth and efficient.
How Redis Powers Real-Time Applications
Well, now that you've seen just how engrossed Salvatore was in cracking these challenges, here is a little list I have made to present Redis’s use case:
Caching:
In everyday terms, when you open an app—especially a chat app—you expect everything to load instantly. Redis helps achieve this by caching data that is accessed over and over, like user profiles. Instead of reaching out to a slower database every time you need to see someone’s profile, Redis keeps a copy in fast-access memory. This means that as soon as you open the app or click on a conversation, the profile information is already there, making the experience smooth and responsive.
Session Management:
What do we mean by it? Session management is like keeping a note of what a user is doing while they're using an app. It remembers details such as when you're logged in or what's in your shopping cart so you don't have to log in or fill your cart again on every page.
Instead of saving these notes in a slower database, Redis keeps them in memory. This means when you use a social media app, for example, your login stays active and your information is quickly remembered—even if you close your browser. Redis makes the whole experience faster and more secure by handling these "notes" in real-time.
Geolocation:
Heck, Redis can even be used to store and track real-time location data, making it possible to build applications that require location-based features, such as ride-hailing services or social media check-ins. For example, a ride-hailing service could use Redis to track the location of drivers and riders in real-time.
These diverse applications highlight Redis’s versatility, but what really makes it indispensable are its performance advantages:
Why is it the Go-To Choice for Real-Time Systems
A little list here too about the same
Ultra-Low Latency – Responds almost instantly—usually in less than a millisecond—so users don’t experience any delay.
High Throughput – Can handle millions of requests per second, keeping things running smoothly even during heavy traffic.
Pub/Sub Messaging – Lets different parts of your system talk to each other in real time, much like a live chat or news feed.
Scalability – Easily grows by adding more servers, ensuring that performance stays high as your system expands.
Time-to-Live (TTL) – Automatically removes old or unused data to keep the memory clean and efficient.
Multi-Model Storage – Supports various data types—from simple key-value pairs to more complex structures like lists and maps—making it versatile for different needs.
To see these benefits in action, let’s explore how industry giants like Netflix and Hulu harness Redis for scalable streaming
How Netflix and Hulu Use Redis to Power Scalable Streaming
In large-scale streaming services, delivering content swiftly and reliably is paramount (You don’t want trp ratings dropping because you delivered the content late). Both Netflix and Hulu have harnessed the power of Redis to meet these demands, a lil sneak peek
Netflix: Scaling with Dynomite and Redis
Netflix developed Dynomite, a distributed datastore that builds on Redis's features to support data availability across multiple regions.
This integration offers several advantages:
Elastic Scalability: Netflix spreads its work across many servers. This means no single server is overwhelmed, and if one server has a problem, the others can pick up the slack. How does it do it? By deploying Redis clusters across multiple nodes, it effectively distributes workloads, minimizing single points of failure
Caching API Responses: Utilizing Redis to cache frequently accessed metadata reduces latency and alleviates the load on primary databases, this helps the system deliver content quickly without constantly going back to the main, slower database.
High Availability: Dynomite lets Netflix operate in multiple regions (or parts of the world) at the same time. This means that even if one region experiences issues, users in other regions can still enjoy uninterrupted streaming.
For an in-depth exploration of Dynomite's performance benchmarks on AWS, refer to Netflix's technical blog post. [3]
Hulu: Managing Billions of Video Requests with Redis [2]
Facing the challenge of serving over 4 billion videos, Hulu integrated Redis to bolster its infrastructure:
Session Storage: Redis keeps track of user sessions across many servers. This means if one server fails, another can quickly take over, ensuring a smooth and continuous experience for the user.
Content Delivery Optimization: Hulu caches video details and thumbnails in Redis. This allows videos and images to load faster and reduces the load on the main servers, making the service more responsive.
Rate Limiting and Traffic Management: Redis efficiently manages a high number of requests at once. This helps prevent system overload during busy times, ensuring the service remains stable even under heavy traffic.
Challenges and Problems Faced
Despite Redis’s advantages, even the best have their weak spots—a chink in the armor, if you will. Large-scale implementations come with their own set of challenges:
Memory Constraints: Being an in-memory store, Redis requires careful memory management to prevent excessive costs.
Data Persistence Issues: Ensuring data consistency in case of crashes requires additional configurations.
Replication Overhead: Scaling Redis clusters demands efficient replication strategies to balance performance and reliability.
Sharding Complexity: Splitting data into manageable pieces (or shards) requires careful planning to distribute workloads effectively.
Conclusion
Redis has become an integral part of large-scale streaming services, eliminating lag and keeping content delivery smooth. But the road isn’t without challenges. Scaling efficiently, managing traffic spikes, and ensuring high availability across multiple regions require constant innovation. Fortunately, Redis continues to evolve, adapting to the ever-growing demands of real-time content delivery. As streaming services push the boundaries of speed and quality, Redis remains the silent hero, ensuring that every movie night is seamless.
Take a simple movie night, I had recently, for instance.
I settled into my couch, ready to watch Interstellar for the hundredth time. I hit play.
Baam—no buffering. No waiting. Just pure speed.
References:
https://architecturenotes.co/p/redis
https://venturenox.com/blog/the-power-of-redis-in-transforming-real-time-applications/
https://blogs.vmware.com/tanzu/case-study-how-hulu-scaled-serving-4-billion-videos-using-redis/
https://netflixtechblog.com/dynomite-with-redis-on-aws-benchmarks-5c942fc7ca38
