Web Application Architecture Explained With a Real-World Example
This article takes a deep dive into web application architecture with designing a real-world use case. If you have zero knowledge on the domain, after going through this article, you’ll have a fundamental understanding of the components involved in the web application architecture and how…
Wide-column Database, Column Databases – A Deep Dive
Wide-column, column-oriented and column-family databases belong to the NoSQL family of databases built to store and query massive amounts of data, aka BigData. They are highly available and scalable, built to work in a distributed environment. Most of the wide-column databases do not support joins…
Design For Scale and High Availability – What Does 100 Million Users On A Google Service Mean?
Imagine a simple service with an API server and a database. Now when this service is owned by Google, the traffic is expected to be in the scale of 100 million users. That means: 10 billion requests/day 100k requests/second (average) 200k requests/second (peak) 2 million…
How Razorpay handled significant transaction bursts during events like IPL
Razorpay is India’s leading payment-gateway service that offers a suite of products to businesses to accept, process and disburse payments enabling them to establish an online presence. They dealt with an interesting problem: the problem of sudden transaction bursts during events such as the Indian…
Facebook’s photo storage architecture
Facebook built Haystack, an object storage system designed for storing photos on a large scale. The platform stores over 260 billion images which amounts to over 20 petabytes of data. One billion new photos are uploaded each week which is approx—60 terabytes of data. At…
Distributed Systems and Scalability Feed
Facebook photo storage architecture
Facebook built Haystack, an object storage system designed for storing photos on a large scale. The platform stores over 260 billion images which amounts to over 20 petabytes of data. One billion new photos are uploaded each week which is approx—60 terabytes of data. At peak, the platform serves over one million images per second.
In the original NAS-based photo storage architecture, Facebook faced throughput and latency issues as the photos and the associated metadata lookups in NAS caused excessive disk operations almost upto ten just for retrieving a single image.
Tail latency in distributed systems
Tail latency is that tiny percentage of responses from a system that are the slowest in comparison to most of the responses. They are often called as the 98th or 99th percentile response times. This may seem insignificant at first but for large applications like LinkedIn, this has noticeable effects. This could mean that for a page having a million views per day 10,000 of those page views would experience the delay. Read how LinkedIn deals with longtail network latencies.
There can be multiple causes of tail latency: increasing load on the system, complex and distributed systems, application bottlenecks, slow network, slow disk access and more. Read more on it.
RobinHood: Tail latency-aware caching
RobinHood is a research caching system for application servers in large distributed systems having diverse backends. The cache system dynamically partitions the cache space between different backend services and continuously optimizes the partition sizes.
Microsoft research has a talk on getting rid of long-tail latencies.
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