The hottest Distributed Computing Substack posts right now

And their main takeaways
Category
Top Technology Topics

All you need to know about the Google File System

VuTrinh. • 119 implied HN points • 11 May 24
🕹 Technology Data Systems Distributed Computing Systems Design Fault Tolerance
  1. Google File System (GFS) is designed to handle huge files and many users at once. Instead of overwriting data, it mainly focuses on adding new information to files.
  2. The system uses a single master server to manage file information, making it easier to keep track of where everything is stored. Clients communicate directly with chunk servers for faster data access.
  3. GFS prioritizes reliability by storing multiple copies of data on different chunk servers. It constantly checks for errors and can quickly restore lost or corrupted data from healthy replicas.

A Guide to Optimising your Spark Application Performance (Part 1).

SwirlAI Newsletter • 432 implied HN points • 02 Jul 23
🕹 Technology Data processing Optimization Performance Distributed Computing
  1. Understanding Spark architecture is crucial for optimizing performance and identifying bottlenecks.
  2. Differentiate between narrow and wide transformations in Spark, and be cautious of expensive shuffle operations.
  3. Utilize strategies like partitioning, bucketing, and caching to maximize parallelism and performance in Spark applications.

So... what are RPCs [System Design Sundays]

Technology Made Simple • 219 implied HN points • 25 Sep 23
🕹 Technology Systems Design Communication Distributed Computing APIs Networking
  1. Remote Procedure Calls (RPCs) allow for program procedures to execute in a different address space without the programmer having to explicitly write details for the remote interaction.
  2. RPCs are prevalent in modern systems design due to their efficiency, scalability, and flexibility in enabling communication between various services.
  3. RPCs are a powerful tool for building distributed computing systems, offering advantages such as efficiency, scalability, and flexibility in communication between services.

Scale, Scale, Scale

Gradient Flow • 179 implied HN points • 05 May 22
🕹 Technology AI Podcasts Data science Distributed Computing Summit
  1. The importance of scale in AI startups highlighted by the proficiency in distributed systems over ML and AI.
  2. Exploring the impact of distributed computing on machine learning and AI through metrics.
  3. Insights from the Data Exchange podcast on topics like scaling language models, applying ML to optimization, and blending data science with domain expertise.
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Hyperscaling, Computational Humanness, and Paid Sick Leave

Gradient Flow • 19 implied HN points • 13 Mar 20
🕹 Technology AI Machine Learning Data Analysis Distributed Computing Open Source
  1. Access to paid sick leave is crucial, as it has been shown to reduce flu cases by about 10% or more.
  2. Distributed computing is becoming increasingly important, especially in the context of machine learning models that require extensive training.
  3. There are new tools and databases available for data enrichment and time series management in the tech industry.

The Reliability Revolution: Idempotent APIs and the Future of Distributed Computing

Engineering At Scale • 0 implied HN points • 04 Jul 23
🕹 Technology APIs Distributed Computing Fault Tolerance System reliability
  1. Building reliable systems in an unreliable world is crucial for the success of products and services.
  2. Failures in distributed systems can lead to challenges like duplicate transactions, but idempotent APIs can help ensure consistency.
  3. Idempotent APIs are key in guaranteeing data integrity, simplifying error handling, and enhancing fault tolerance in distributed systems.