The hottest Systems Architecture Substack posts right now

Apache Kafka is a system for handling large amounts of data messages, making it easier for companies like LinkedIn to manage and analyze user activity and other important metrics.
In Kafka, messages are organized into topics and divided into partitions, allowing for better performance and scalability. This way, different servers can handle parts of the data at once.
Kafka uses a pull model for consumers, meaning they can request data as they need it. This helps prevent overwhelming the consumers with too much data at once.

Always have your business logic on both the frontend and the server. If you only trust the client side, you risk getting incorrect data.
Your server needs to handle requests from various sources, including non-standard browsers and bots. These can bypass your frontend checks if they're not replicated on the server.
Any important checks for security and data integrity should happen on the server to prevent unexpected issues. This means you'll often have to duplicate checks that you already do on the frontend.

Kafka uses the operating system's filesystem to store data, which helps it run faster by leveraging the page cache. This avoids the need to keep too much data in memory, making it simpler to manage.
The way Kafka reads and writes data is done in a sequential order, which is more efficient than random access. This design improves performance, as accessing data in a sequence reduces delays.
Kafka groups messages together before sending them, which helps reduce the number of requests made to the system. This batching process improves performance by allowing larger, more efficient data transfers.

Faster computers let us use simpler solutions instead of complicated ones. This means we can solve problems more easily, without all the stress of complex systems.
In the past, computers were so slow that we had to be very clever to get things done. Now, with stronger machines, we can just get the job done without excessive tweaking.
Sometimes, when faced with a problem, it's worth it to think about simpler approaches. These 'dumb' solutions can often work just as well for many situations.

Simplicity and maintainability are more important than performance and complexity in software design. Focus on creating code that's easy for others to understand and work with.
Avoid over-complicated platforms like microservices, especially if your application doesn't need them. Start with basic tools and scale only when necessary.
Your goal as a software engineer should be to deliver a product that customers can use easily. Keeping things simple helps with maintenance and helps new team members get up to speed faster.

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Meta has introduced developer tools that help manage large-scale projects efficiently. These tools assist engineers in solving problems and improving systems.
Big companies like Discord and Uber are using massive data points to create valuable insights. This helps them to effectively manage their data and understand trends better.
Data engineering continues to evolve, with tools like BigQuery and dbt Mesh enhancing data practices. Staying updated with these tools can improve data analysis and management.

S3 is a big storage system used for data, and understanding how it's built can help improve data handling. It's cool to know how tech like this works.
Running Kafka at scale is interesting, especially for companies like Pinterest. It shows how important reliable data flow is in tech.
There's a trend of making things simpler and more efficient in engineering. Sometimes, going back to basics can solve complex problems.

Fly.io uses synchronous scheduling, meaning you either get a compute resource when you ask for it or you don't. This makes it simpler to handle workloads like serverless functions.
The scheduler's design allows workers to manage their own availability, removing the need for a separate database. This lets workers freely join or leave the system without issues.
In this system, a coordinator requests and schedules tasks on available worker nodes. The first worker to respond gets the task, making it efficient for various jobs like running Docker containers or AI inference.

Agentic RAG uses a system of smaller agents to answer questions across multiple documents. Each smaller agent focuses on its own document, which helps organize the information better.
This setup allows for comparing different documents and summarizing specific ones easily. It's a flexible way to dig into complex topics.
The architecture is designed to scale by adding more agents as needed. This means it can grow and adapt to handle more information over time.