System Design #3: Leveraging the Backends for frontends pattern to avert API gateway from becoming a system bottleneck
In this system design series, I discuss the intricacies of designing distributed scalable systems and the related concepts. This will help you immensely with your software architecture and system design interview rounds, in addition to helping you become a better software engineer. Here is the first post…
System Design #2: Understanding API gateway and the need for it
In this system design series, I discuss the intricacies of designing distributed scalable systems and the related concepts. This will help you immensely with your software architecture and system design interview rounds, in addition to helping you become a better software engineer. Here is the first…
System Design #1: CDN and Load balancers (Understanding the request flow)
In this system design series, I’ve started on this blog, I discuss the intricacies of designing distributed scalable systems and the related concepts. This will help you immensely with your software architecture and system design interview rounds, in addition to helping you become a better…
System Design #5: How Actor model/Actors run in clusters facilitating asynchronous communication in distributed systems
This blog post is a continuation of my previous post, in the system design series, 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. Various Actor model frameworks, like Akka, ProtoActor, Actix,…
System Design#4: Understanding the Actor model to build non-blocking, high-throughput distributed systems
This is the fourth post in the system design series where I discuss the intricacies of designing distributed scalable systems and the related concepts. This will help you immensely with your software architecture and system design interview rounds, in addition to helping you become a better…
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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