Platforms & Resources Worth Checking Out That Give You A Push Ahead In Your Software Career
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YouTube Architecture – How Does It Serve High-Quality Videos With Low Latency
Earlier I wrote an article on how YouTube stores petabyte-scale data every single day. How does it store so many videos without running out of storage space? This write-up is an insight into the video processing & delivery architecture of YouTube. How does it serve…
Full List Of Articles In The Decentralized Web Category
1 Why Bitcoin Cannot Be Used as a Real Currency in Our Everyday Lives – Simply Explained Article Link 2 The Ultimate Guide to Understanding Cloud Web Torrent Article Link 3 The Beginners Guide to Beaker Browser & P2P Peer to Peer Web Apps Article…
Full List Of Articles In The Software Engineering Category
1 What is cAdvisor? How Does it Work? Explained Article Link 2 What is Grafana? Why Use It? Everything You Should Know About It Article Link 3 What is Data Ingestion? How to Pick the Right Data Ingestion Tool? Article Link 4 What is Swagger?…
Full list of distributed systems articles
Real-world distributed architectures What Does 100 Million Users On A Google Service Mean? Article Link How Razorpay handled significant transaction bursts during events like IPL Article Link YouTube Architecture – How Does It Serve High-Quality Videos With Low Latency Article Link YouTube Database – How…
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.
> Spotify Engineering: From Live to Recording
> Ingesting LIVE video streams at a global scale at Twitch
> $64,944 spent on AWS, to support 25,000 customers, in August by ConvertKit.
> Read how Storytel engineering computes customer consumption of books transitioning from batch processing to streaming bookmarks data with Apache Beam and Google Cloud.
> How Pokemon Go scales to millions of requests per second?
> Insight into how Grab built a high-performance ad server.
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- Web Application Architecture Explained With Designing a Real-World Service
- Wide-column, Column-oriented and Column Family Databases – A Deep Dive with Bigtable and Cassandra
- Design For Scale and High Availability – What Does 100 Million Users On A Google Service Mean?
- How Razorpay handled significant transaction bursts during events like IPL
- Facebook’s Photo Storage Architecture