How Actor model/Actors run in clusters facilitating asynchronous communication in distributed systems
This blog post is a continuation of my previous post on understanding the actor model to build non-blocking, high-throughput distributed systems. If you haven’t read it, I suggest giving it a read here. Various Actor model frameworks, like Akka, ProtoActor, Actix, etc., have cluster implementations…
Understanding the Actor model to build non-blocking, high-throughput distributed systems
Imagine a scenario where on an e-commerce site, ‘n’ users are concurrently competing to buy a product limited in number. Multiple requests (in the form of multiple threads) would compete to purchase a certain product. Here is a product purchase code snippet that’ll help us…
Technical Consultant: How can I become one? Explained with an example
This write-up offers insight into technical consulting. You’ll understand what a technical consultant is, the skills required to be one and how you can become one. So, without further ado. Let’s get started. What is a technical consultant? A technical consultant, an IT consultant or…
IT consultant: How do I become one? Explained with a real-world use case
This article takes a deep dive into the world of IT consulting. I’ll discuss what an IT (Information Technology) consultant is, their responsibilities, the skills required, how you can become one and more. So, without further ado. Let’s get started. What does an IT consultant…
Software architecture course – From zero to mastering the fundamentals
If you are looking for a course on software architecture that’ll help you get a grip on the domain. Let me tell you about the Zero to Software Architect learning track that I’ve authored, consisting of three courses that helps you learn the domain starting…
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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