Dask is a parallel computing framework and distributed task scheduler designed to scale Python data science workflows from single machines to large clusters. It functions as a cluster resource manager that orchestrates computational logic by representing tasks and their dependencies as directed acyclic graphs. This architecture allows the system to automate the distribution of workloads across available hardware while managing complex execution requirements. The project distinguishes itself through a lazy evaluation engine that defers data operations until they are explicitly requested, enabl
braft is an embeddable C++ library that implements the Raft consensus algorithm, providing a distributed consensus engine for building fault-tolerant, replicated state machines. At its core, it manages leader election, log replication, cluster membership changes, and state machine synchronization across a cluster of nodes, ensuring strong consistency and data durability even in the face of node failures. The library distinguishes itself through a comprehensive set of mechanisms for reliable distributed coordination. It uses a randomized timeout-based leader election process with term manageme
Storm is a distributed stream processing framework designed to execute unbounded computations across a cluster to process real-time data streams. It functions as a data pipeline orchestrator that allows users to define and deploy declarative data flow graphs connecting streaming sources to processing components. The system operates as a multi-tenant distributed compute engine that isolates workloads and limits resource usage across shared clusters using dedicated pools and access control. It is also a secure distributed processing engine that employs encrypted node communication and SSL-secur
This is a Raft consensus library and distributed consensus engine implemented in Go. It provides the primitives necessary to build fault-tolerant distributed services by implementing a replicated state machine that ensures a group of servers agree on a shared system state through leader election and log replication. The project distinguishes itself through a pluggable architecture for storage backends and snapshot storage, decoupling the consensus logic from physical persistence. It includes specialized mechanisms for leadership transfer, protocol version management to support rolling upgrade
Apache Mesos es un kernel de sistemas distribuidos y gestor de recursos de clúster que abstrae CPU, memoria y almacenamiento a través de un grupo de nodos. Funciona como un orquestador de infraestructura distribuida, proporcionando una capa para ejecutar múltiples frameworks de orquestación en un conjunto compartido de máquinas físicas o virtuales.
Las características principales de apache/mesos son: Distributed Computing Frameworks, Two-Level Scheduling, Offer-Based Allocation, Multi-Framework Orchestration, Distributed Cluster Management, Distributed Infrastructure Orchestration, Workload Isolation, Resource Isolation.
Las alternativas de código abierto para apache/mesos incluyen: dask/dask — Dask is a parallel computing framework and distributed task scheduler designed to scale Python data science workflows… apache/storm — Storm is a distributed stream processing framework designed to execute unbounded computations across a cluster to… baidu/braft — braft is an embeddable C++ library that implements the Raft consensus algorithm, providing a distributed consensus… hashicorp/raft — This is a Raft consensus library and distributed consensus engine implemented in Go. It provides the primitives… ltsopensource/light-task-scheduler — Light Task Scheduler is a distributed job scheduling and workflow orchestration platform designed for managing… aurae-runtime/aurae — Aurae is a memory-safe distributed systems runtime daemon written in Rust that acts as a container and process…