The hottest Fault Tolerance Substack posts right now

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.
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.
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.

Kafka ensures system consistency in the microservices world by allowing events to be recorded and processed consistently even during service downtime.
Kafka enables a decoupled, event-driven approach to microservices communication, providing fault tolerance and scalability as the number of services grows.
The benefits of Kafka in microservices include event-driven architecture, fault tolerance, and scalability, all contributing to a reliable and consistent system.

Learn about using Async communication and messaging patterns
Understand how AWS services like SQS, SNS, and EventBridge aid in system recovery and design
Discover AWS WAF's role in error prevention and coding patterns for fault tolerance

Avoid having gatekeepers in your release cycle to reduce costs and improve organizational efficiency.
Challenge bad processes and strive for daily value delivery to engineers and users.
Embrace DevOps principles like automation, collaboration, and continuous testing for faster, high-quality software delivery.

Use Dead Letter Queue (DLQ) to recover from failures and get notified in fault tolerant systems.
Set up DLQ with two queues - Original for processing messages and DLQ to capture failed messages for retries.
Implement monitoring for DLQ growth to alert on-call engineers and prevent issues from affecting customers.

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Consider using FIFO queues in AWS for order processing to ensure messages are processed in the right order and avoid duplicates.
Re-design solutions to leverage asynchronous communication over synchronous for a more resilient system.
Implement retry mechanisms and explore Dead Letter Queues to handle failures and define thresholds for message processing.

A distributed system is a collection of components on multiple computers that appear as a single, unified system to users. They are commonly used in database and file systems.
Key characteristics of distributed systems include concurrency, scalability, fault tolerance, and decentralization, enabling efficient operation across multiple machines.
In distributed systems, concepts like fault tolerance, recovery & durability, the CAP theorem, and quorums & consensus are crucial for maintaining reliability, consistency, and coordination among nodes.

Building reliable systems in an unreliable world is crucial for the success of products and services.
Failures in distributed systems can lead to challenges like duplicate transactions, but idempotent APIs can help ensure consistency.
Idempotent APIs are key in guaranteeing data integrity, simplifying error handling, and enhancing fault tolerance in distributed systems.