State of Backend #2 – Disney+ Hotstar Replaced Redis and Elasticsearch with ScyllaDB. Here’s Why.
To get the content delivered to your inbox, you can subscribe to the newsletter at the end of this post. Here is the previous issue. Disney+ Hotstar Replaced Redis and Elasticsearch with ScyllaDB to Implement the ‘Continue Watching’ Feature. Here’s Why: The continue watching feature…
State of Backend #1- Distributed Task Scheduling with Akka, Kafka and Cassandra
This is the first issue of the newsletter I’ve kickstarted for insights into the backend engineering space encircling topics like distributed systems, databases, data engineering, system design, architecture, scalability and the like, also the latest tools and technologies in the space. To get the content…
Live Video Streaming Infrastructure at Twitch
Twitch engineering developed a scalable, HA live streaming solution to enable broadcasters on their platform live stream their gameplay with minimum latency. The live streaming solution (developed by Twitch) is also made available as a Service to the world through AWS IVS (Interactive Video Streaming)…
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…
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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