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I pulled this chapter together from dozens of sources that were at times somewhat contradictory. Facts on the ground change over time and depend who is telling the story and what audience they're addressing. I tried to create as coherent a narrative as I could. If there are any errors I'd be more than happy to fix them. Keep in mind this article is not a technical deep dive. It's a big picture type article. For example, I don't mention the word microservice even once :-)
Given our discussion in the What is Cloud Computing? chapter, you might expect Netflix to serve video using AWS. Press play in a Netflix application and video stored in S3 would be streamed from S3, over the internet, directly to your device.
Another relevant factoid is Netflix is subscription based. Members pay Netflix monthly and can cancel at any time. When you press play to chill on Netflix, it had better work. Unhappy members unsubscribe.
The client is the user interface on any device used to browse and play Netflix videos. It could be an app on your iPhone, a website on your desktop computer, or even an app on your Smart TV. Netflix controls each and every client for each and every device.
Everything that happens before you hit play happens in the backend, which runs in AWS. That includes things like preparing all new incoming video and handling requests from all apps, websites, TVs, and other devices.
In 2007 Netflix introduced their streaming video-on-demand service that allowed subscribers to stream television series and films via the Netflix website on personal computers, or the Netflix software on a variety of supported platforms, including smartphones and tablets, digital media players, video game consoles, and smart TVs.
Netflix succeeded. Netflix certainly executed well, but they were late to the game, and that helped them. By 2007 the internet was fast enough and cheap enough to support streaming video services. That was never the case before. The addition of fast, low-cost mobile bandwidth and the introduction of powerful mobile devices like smart phones and tablets, has made it easier and cheaper for anyone to stream video at any time from anywhere. Timing is everything.
Building out a datacenter is a lot of work. Ordering equipment takes a long time. Installing and getting all the equipment working takes a long time. And as soon they got everything working they would run out of capacity, and the whole process had to start over again.
The long lead times for equipment forced Netflix to adopt what is known as a vertical scaling strategy. Netflix made big programs that ran on big computers. This approach is called building a monolith. One program did everything.
What Netflix was good at was delivering video to their members. Netflix would rather concentrate on getting better at delivering video rather than getting better at building datacenters. Building datacenters was not a competitive advantage for Netflix, delivering video is.
It took more than eight years for Netflix to complete the process of moving from their own datacenters to AWS. During that period Netflix grew its number of streaming customers eightfold. Netflix now runs on several hundred thousand EC2 instances.
The advantage of having three regions is that any one region can fail, and the other regions will step in handle all the members in the failed region. When a region fails, Netflix calls this evacuating a region.
The header image is meant to intrigue you, to draw you into selecting a video. The idea is the more compelling the header image, the more likely you are to watch a video. And the more videos you watch, the less likely you are to unsubscribe from Netflix.
The first thing Netflix does is spend a lot of time validating the video. It looks for digital artifacts, color changes, or missing frames that may have been caused by previous transcoding attempts or data transmission problems.
A pipeline is simply a series of steps data is put through to make it ready for use, much like an assembly line in a factory. More than 70 different pieces of software have a hand in creating every video.
The idea behind a CDN is simple: put video as close as possible to users by spreading computers throughout the world. When a user wants to watch a video, find the nearest computer with the video on it and stream to the device from there.
In 2007, when Netflix debuted its new streaming service, it had 36 million members in 50 countries, watching more than a billion hours of video each month, streaming multiple terabits of content per second.
At the same time, Netflix was also devoting a lot of effort into all the AWS services we talked about earlier. Netflix calls the services in AWS its control plane. Control plane is a telecommunications term identifying the part of the system that controls everything else. In your body, your brain is the control plane; it controls everything else.
In 2011, Netflix realized at its scale it needed a dedicated CDN solution to maximize network efficiency. Video distribution is a core competency for Netflix and could be a huge competitive advantage.
The number of OCAs on a site depends on how reliable Netflix wants the site to be, the amount of Netflix traffic (bandwidth) that is delivered from that site, and the percentage of traffic a site allows to be streamed.
Within a location, a popular video like House of Cards is copied to many different OCAs. The more popular a video, the more servers it will be copied to. Why? If there was only one copy of a very popular video, streaming the video to members would overwhelm the server. As they say, many hands make light work.
Right now, up to 100% of Netflix content is being served from within ISP networks. This reduces costs by relieving internet congestion for ISPs. At the same time, Netflix members experience a high-quality viewing experience. And network performance improves for everyone.
What may not be immediately obvious is that the OCAs are independent of each other. OCAs act as self-sufficient video-serving archipelagos. Members streaming from one OCA are not affected when other OCAs fail.
The data in a cache is generally stored in fast access hardware such as RAM (Random-access memory) and may also be used in correlation with a software component. A cache's primary purpose is to increase data retrieval performance by reducing the need to access the underlying slower storage layer.
RAM and In-Memory Engines: Due to the high request rates or IOPS (Input/Output operations per second) supported by RAM and In-Memory engines, caching results in improved data retrieval performance and reduces cost at scale. To support the same scale with traditional databases and disk-based hardware, additional resources would be required. These additional resources drive up cost and still fail to achieve the low latency performance provided by an In-Memory cache.
Applications: Caches can be applied and leveraged throughout various layers of technology including Operating Systems, Networking layers including Content Delivery Networks (CDN) and DNS, web applications, and Databases. You can use caching to significantly reduce latency and improve IOPS for many read-heavy application workloads, such as Q&A portals, gaming, media sharing, and social networking. Cached information can include the results of database queries, computationally intensive calculations, API requests/responses and web artifacts such as HTML, JavaScript, and image files. Compute-intensive workloads that manipulate data sets, such as recommendation engines and high-performance computing simulations also benefit from an In-Memory data layer acting as a cache. In these applications, very large data sets must be accessed in real-time across clusters of machines that can span hundreds of nodes. Due to the speed of the underlying hardware, manipulating this data in a disk-based store is a significant bottleneck for these applications.
Design Patterns: In a distributed computing environment, a dedicated caching layer enables systems and applications to run independently from the cache with their own lifecycles without the risk of affecting the cache. The cache serves as a central layer that can be accessed from disparate systems with its own lifecycle and architectural topology. This is especially relevant in a system where application nodes can be dynamically scaled in and out. If the cache is resident on the same node as the application or systems utilizing it, scaling may affect the integrity of the cache. In addition, when local caches are used, they only benefit the local application consuming the data. In a distributed caching environment, the data can span multiple cache servers and be stored in a central location for the benefit of all the consumers of that data.
Amazon ElastiCache is a web service that makes it easy to deploy, operate, and scale an in-memory data store or cache in the cloud. The service improves the performance of web applications by allowing you to retrieve information from fast, managed, in-memory data stores, instead of relying entirely on slower disk-based databases. Learn how you can implement an effective caching strategy with this technical whitepaper on in-memory caching.
Because memory is orders of magnitude faster than disk (magnetic or SSD), reading data from in-memory cache is extremely fast (sub-millisecond). This significantly faster data access improves the overall performance of the application.
A single cache instance can provide hundreds of thousands of IOPS (Input/output operations per second), potentially replacing a number of database instances, thus driving the total cost down. This is especially significant if the primary database charges per throughput. In those cases the price savings could be dozens of percentage points.
By redirecting significant parts of the read load from the backend database to the in-memory layer, caching can reduce the load on your database, and protect it from slower performance under load, or even from crashing at times of spikes.