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Storage layers providing low-latency access to large datasets in distributed environments.
Distinguishing note: Focuses on POSIX-compliant high-performance distributed access.
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SeaweedFS is a distributed object store and high-performance file system designed to manage massive volumes of unstructured data. It utilizes a decoupled architecture that separates metadata management from raw data storage, allowing for independent scalability and the efficient handling of billions of files. By providing a POSIX-compliant interface, it enables applications to interact with a unified namespace while maintaining the performance characteristics of a distributed object store. The system distinguishes itself through a multi-region data fabric that supports active-active replicati
Provides low-latency access to large datasets across distributed clusters and cloud environments.
3FS is a distributed file system and RDMA storage cluster designed for high-performance AI training and inference workloads. It functions as a strongly consistent storage layer that utilizes a disaggregated architecture to pool SSDs and memory resources across multiple nodes. The system provides specialized storage implementations including an AI training checkpoint store for parallel state preservation and a distributed key-value cache store for decoder layer vectors to optimize inference processing. It ensures data integrity through chain replication and apportioned query distribution. The
Provides a high-performance distributed file system utilizing SSDs and RDMA for AI training and inference.
This project is a collection of structured study notes and conceptual breakdowns designed for the AWS Certified Cloud Practitioner exam. It serves as a technical reference and study guide, organizing cloud service details and architectural principles to assist in certification preparation. The knowledge base is built using markdown files and includes curated cheat sheets and interactive mind-map visualizations. These tools map complex certification topics into visual hierarchies to enable drill-down study paths and rapid revision. The materials cover a wide range of cloud capabilities, inclu
References high-performance file systems optimized for low-latency access to large datasets in compute-heavy workloads.