The hottest System Architecture Substack posts right now

And their main takeaways
Category
Top Technology Topics

Scaling Distributed Systems with the Scatter-Gather Pattern

Engineering At Scale • 60 implied HN points • 15 Feb 25
🕹 Technology Distributed Systems Microservices Cloud Computing Software Design System Architecture
  1. The Scatter-Gather pattern helps speed up data retrieval by splitting requests to multiple servers at once, rather than one after the other. This makes systems respond faster, especially when lots of data is needed.
  2. Using this pattern can improve system efficiency by preventing wasted time waiting for responses from each service. This means the system can handle more requests at once.
  3. However, implementing Scatter-Gather can be tricky. It requires careful handling of errors and managing different data sources to ensure the information is accurate and reliable.

Linux Context Switching Internals: Part 1 - Process State and Memory

Confessions of a Code Addict • 1106 implied HN points • 29 Dec 24
🕹 Technology Software Operating Systems Programming Computer Science System Architecture
  1. Context switching allows a computer to run multiple tasks by efficiently switching between different processes. It's important to understand it because it affects a system's performance.
  2. The Linux kernel uses specific structures, like 'task_struct' and 'mm_struct', to manage process states and memory. These structures help keep track of what each process is doing and how it uses memory.
  3. When a process runs out of CPU time or needs to wait, the kernel uses flags to decide when to switch to another process. This ensures that all processes get a chance to run, even if some are waiting for resources.

Forest & Desert

Software Design: Tidy First? • 1900 implied HN points • 11 Nov 24
🕹 Technology Software Design System Architecture Engineering Practices Project management
  1. In software development, there are two environments: a challenging 'desert' where mistakes are costly, and a supportive 'forest' where teams can work collaboratively and learn from their errors.
  2. Strategies that work in a desert, like detailed planning and strict reviews, won't help in a forest, which thrives on testing, incremental growth, and collaboration.
  3. To shift from a desert to a forest, teams need to understand why their current situation exists and take small steps toward creating a more supportive and effective work environment.

Senior Developers understand Composition Over Inheritance.

System Design Classroom • 599 implied HN points • 15 Jun 24
🕹 Technology Software Design Development Practices System Architecture Modularity
  1. Using composition is often better than inheritance because it allows for more flexible and maintainable code. You can mix and match simpler components to create complex systems.
  2. With composition, you can easily swap out parts of your code at runtime, making it better for projects that change frequently. It's all about keeping things modular and manageable.
  3. Though inheritance can be useful for stable hierarchies, focusing on composition is generally a safer bet, especially when you expect to make a lot of changes to your code.

Should You Build a Custom Data Orchestration System? Here’s What to Consider

SeattleDataGuy’s Newsletter • 317 implied HN points • 23 Oct 24
🕹 Technology Data Engineering Software Development Automation System Architecture
  1. Building your own data orchestration system can lead to many challenges, like handling dependencies and scheduling tasks correctly. It's important to think if it's really necessary or if existing tools will work better.
  2. A custom orchestrator needs to manage various functions like logging, alerting, and integrating with other tools. Without proper features, it can become complex and hard to maintain.
  3. Before you decide to create your own solution, consider what makes it different and better than what's already available. Make sure to also think about how you’ll get people to use your new system.
Get a weekly roundup of the best Substack posts, by hacker news affinity:

When Things Are Slow, Look for Queues

Push to Prod • 59 implied HN points • 13 Aug 24
🕹 Technology Software Development System Architecture Performance optimization Data Structures
  1. When a system gets slow, it’s often because of queues. Queues help manage requests but can create delays if not handled properly.
  2. Different types of queues can slow down your system, like thread pools, connection pools, and TCP queues. Keeping these optimized can improve performance.
  3. Using thread dumps can help identify problems in your system. They can show which threads are blocked and help you fix the slowdowns.

How VTEX improved the shopper experience with Amazon DynamoDB

VTEX’s Tech Blog • 119 implied HN points • 16 Apr 24
🕹 Technology E-commerce Cloud Computing Data Management System Architecture Performance optimization
  1. VTEX improved their shopping cart system by switching from Amazon S3 to Amazon DynamoDB. This change was made to enhance speed and make the shopping experience better for users.
  2. They faced challenges because some shopping cart items were too large for DynamoDB's limits. To fix this, they reduced the data size and created a process to store bigger items separately in S3.
  3. After gradually migrating to DynamoDB, VTEX achieved a 30% reduction in shopping cart API latency. This helped their overall efficiency and improved customer satisfaction.

I spent 7 hours reading another paper to understand more about Snowflake's internal. Here's what I found.

VuTrinh. • 79 implied HN points • 02 Mar 24
🕹 Technology Data Engineering Cloud Computing System Architecture Database Design
  1. Snowflake has a unique design with three main layers: storage, virtual warehouse, and cloud service. This structure helps manage data efficiently and ensures high availability.
  2. The system uses a special ephemeral storage for temporary data during queries, which allows for quick access and less strain on the overall system. This helps with performance and reduces network load.
  3. Snowflake is designed for flexibility, allowing it to adapt resources based on customer needs and workloads. This elasticity helps provide better performance and efficiency.

Tech Debt

The Weekly Gazette • 26 implied HN points • 27 Oct 24
🕹 Technology Cybersecurity Software Development Technical Debt System Architecture Internet Infrastructure
  1. Software systems, like the one behind HealthCare.gov, often fail due to poor planning and shortcuts taken during development. This can lead to major issues when many people try to use the system at once.
  2. Cybersecurity programs can unintentionally cause widespread problems. For example, a failed update from a security company led to major outages and millions of dollars in losses.
  3. Technical debt accumulates when programmers prioritize quick solutions over solid code. While it can't be completely avoided, it's important to understand and manage it to prevent future issues.

Microservices vs. Monoliths: Why Startups Are Getting "Nano-Services" All Wrong

Tech Thoughts • 2 HN points • 08 Sep 24
🕹 Technology Software Development Startup Strategies System Architecture Cloud Computing
  1. Startups should avoid jumping into microservices too early. It's better to keep things simple with a basic structure while you're still figuring out your product.
  2. Creating too many tiny services, or 'nano-services', adds unnecessary complexity. This can slow you down and make it harder to manage your product.
  3. Focus on finding your product's market fit first. Once you have traction and need to scale, then it's time to consider adopting more complex systems like microservices.

That time I broke login at Box

Basta’s Notes • 204 implied HN points • 19 May 23
🕹 Technology Coding Software Development System Architecture Security
  1. Reusing code can be beneficial, but be cautious of reusing business logic as it can lead to unexpected dependencies.
  2. When rewriting code, consider extending existing functionality rather than reusing entire systems to prevent potential issues.
  3. Make sure to thoroughly understand the business logic behind the code to avoid unintended consequences, especially when dealing with complex systems.

The API Middle-End

The API Changelog • 1 implied HN point • 05 Dec 24
🕹 Technology Web Development APIs Software Engineering Data processing System Architecture
  1. The API middle-end is an important layer that handles logic between the frontend and backend. It helps process requests and responses more efficiently.
  2. Using a middle-end can improve API performance by adapting and translating data without heavy delays in service, like caching and asynchronous operations.
  3. This concept can benefit both API producers and consumers by creating a more tailored and efficient interaction with the API, similar to how GraphQL APIs manage multiple data sources.

Magic of Abstraction in Kotlin

Better Engineers • 0 implied HN points • 06 Jul 22
🕹 Technology Software Development Programming Languages Software Engineering System Architecture
  1. Abstraction helps hide complex code, making it easier to manage and change later. This way, users don’t need to see all the details, which simplifies their experience.
  2. Using constants instead of magic numbers improves clarity and makes future changes easier. By giving a meaningful name to a constant, we can change its value without affecting the logic in our functions.
  3. Creating interfaces allows for flexibility in our code. We can build different implementations for the same interface, making it easier to adapt the software for different platforms or needs.

What do we mean by ‘High Availability’ in the Cloud?

realkinetic • 0 implied HN points • 25 May 23
🕹 Technology Cloud Computing Infrastructure Data Management System Architecture Incident Response
  1. Availability is expressed as a percentage of uptime; higher percentages require substantial investment and multi-team efforts
  2. Achieving high availability in the cloud involves significant costs and considerations like multi-master databases, multi-zonal deployments, and failover testing
  3. Five nines (99.999%) availability is considered the gold standard, but it requires extensive resources, multi-region support, and rigorous infrastructure and data replication

Building a Fly.io-like Scheduler Part 2: Resource Requirements and Multi-Region

aspiring.dev • 0 implied HN points • 26 Feb 23
🕹 Technology Software Development Cloud Computing System Architecture Project management Resource Allocation
  1. We can make scheduler systems smarter by adding task requirements like region and resource slots. This means a worker can only take on a task if it has the right resources available.
  2. Workers compare the incoming requests against their available resources. If they can't meet the requirements, they simply ignore the task instead of taking it.
  3. The system can be expanded to include more detailed requirements in the future, such as specific CPU types or GPU support, making it adaptable to different tasks and workloads.

Thoughts on Oddly Specific Versioning in Observability

A Small, Good Thing • 0 implied HN points • 30 Dec 24
🕹 Technology Software Development Data Analysis System Architecture Cloud Computing Information Technology
  1. Many people just want basic monitoring tools that are easy to use and affordable. They care more about practical solutions than getting into complex observability concepts.
  2. There's a balance between reliability, shipping speed, and team well-being that needs to be carefully managed. It's important not to sacrifice too much reliability just to be fast.
  3. The focus should be on delivering a cost-effective way to monitor systems, rather than just aiming for the latest version of observability. It's essential to figure out who will handle the work involved.

How to Design Software — Image Uploaders

Joseph Gefroh • 0 implied HN points • 19 Oct 19
🕹 Technology Software Design Image Processing Scalability Security System Architecture
  1. When designing a system for image uploading, it's important to consider technical concerns such as displaying, authorizing, validating, processing, storing, and associating the images.
  2. Tradeoffs to think about include scaling to handle large uploads efficiently, ensuring security to prevent vulnerabilities, managing authorization based on business logic, and maintaining consistency in the image uploading workflow.
  3. A well-designed image uploading system should support creating and using various image variants, offloading processing to separate services, ensuring consistent growth across subsystems, and establishing clear architectural boundaries for scalability.

[Mock]Design a URL Shortening service like goo.gl or bit.ly

The ZenMode • 0 implied HN points • 04 Mar 24
🕹 Technology Design System Architecture Security Scaling Database
  1. A URL shortening service converts long URLs into short, unique ones for easy sharing and remembering.
  2. The steps in shortening a URL involve inputting the long URL, generating a unique short URL, storing the mapping, and redirecting users to the original URL.
  3. Designing a URL shortening service includes high scalability, availability, key generation methods, security measures like rate limiting, and handling data growth using tools like distributed databases.