3 مستودعات
Tools for identifying the specific node and shard containing data for a given distribution key.
Distinct from Distributed Data Processing: Distinct from general distributed data processing: focuses on locating data shards for troubleshooting.
Explore 3 awesome GitHub repositories matching data & databases · Data Location Trackers. Refine with filters or upvote what's useful.
IPFS is a peer-to-peer hypermedia protocol and content-addressed storage system that identifies data by cryptographic hashes rather than network locations. It enables the creation of a decentralized web by organizing files and directories as directed acyclic graphs of linked content identifiers. The project differentiates itself through the use of a distributed hash table for locating peers and a system of signed records to map human-readable names to changing content. It also provides HTTP gateways that translate standard web requests into peer-to-peer queries, allowing decentralized data to
Queries distributed hash tables to identify which peers are hosting specific content identifiers.
Citus is a PostgreSQL extension that transforms a standard database into a distributed system. It functions as a sharding framework and distributed SQL engine, enabling horizontal scaling by partitioning tables across a cluster of nodes. By utilizing a coordinator-worker topology, the system manages metadata and routes queries to the appropriate nodes, allowing for parallel execution of complex operations across distributed data shards. The platform distinguishes itself through its specialized support for multi-tenant architectures and real-time analytical processing. It enables tenant-based
Identifies the specific worker node and shard containing data for a given tenant or distribution key.
SparkInternals is a technical reference and architecture guide detailing the internal design and implementation of the Apache Spark distributed computing engine. It serves as a study of big data engine analysis, focusing on how the system manages cluster execution and the interaction between driver nodes, executors, and workers. The project provides a detailed breakdown of how logical plans are converted into physical execution stages. It specifically analyzes the mechanics of data shuffle operations, memory management, and the coordination of distributed job scheduling. The documentation co
Retrieves distributed data segments from multiple worker nodes using a tracker to locate and fetch blocks.