Scalability At Youtube Case Study

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Pages: 7

Scalability at YouTube

Problems Youtube was facing
First of I will start talking about the problems they faced at YouTube was facing. YouTube has faced a lot of problems in its life time so far and the main issues they faced were with scalability. The main reason YouTube faced so many issues was due to its large and rapid growth, YouTube went from a small website to a technology giant and large player in the IT industry practically overnight. The solution to dealing with this large and sudden growth was to keep things simple, by using simplicity this allowed YouTube to cope with the large growth. The simplicity approach was used to conquer a lot of problems. Make it simple, look for the simplest solution to a problem, sure the problem
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Another technique used would be expert tweaking or in other words not all data needs to be consistent for example we ask the question is comments being eventually consistent good enough? The answer is yes, comments is an important part of YouTube however it is a good enough consistency model to have comments be consistent eventually. However if data was a financial transaction it would not be good enough so different consistency models are needed for different kinds of data.
One of the main techniques used and one that is always a hot topic at YouTube is jitter. If your system does not jitter then u thundering herds, Thundering herds problem occurs when a large number of processes waiting for an event are awoken when that event occurs, but only one process is able to proceed at a time. To solve this problem jittering is used. A good example of how this problem occurs is caching, let’s say that the most view or popular videos on YouTube are cached for 24 hours then when the cache expires all machine caches will expire at the same time this will create a thundering herd. By jittering this prevents this from happen the solution is that if you set cache expiration to randomly expire between 18-30 hours then machines cache will expire