9 repository-uri
Systems designed to execute high-volume background operations across multiple nodes.
Distinguishing note: Focuses on the execution of distributed tasks as a primary capability.
Explore 9 awesome GitHub repositories matching software engineering & architecture · Distributed Task Processors. Refine with filters or upvote what's useful.
FFmpeg is a cross-platform multimedia framework designed for the recording, conversion, and streaming of audio and video content. It functions as a comprehensive toolkit that provides both a command-line utility for direct media manipulation and a collection of low-level libraries for integration into custom applications. At its core, the project utilizes a packet-based stream engine and a format-agnostic abstraction layer to handle diverse media standards, containers, and network protocols. The framework distinguishes itself through a modular, graph-based filter execution model that allows f
Executes media processing workflows concurrently across multiple processor cores.
Celery is an asynchronous job processor and distributed task queue designed to offload time-consuming operations to background worker nodes. By utilizing a message-passing architecture, it decouples task producers from consumers, allowing applications to maintain responsiveness while scaling workloads across multiple isolated environments. The system functions as a distributed workload orchestrator that manages the lifecycle of deferred operations through persistent queues. It distinguishes itself by providing a pluggable transport abstraction, which allows the core task logic to remain indep
Processes high volumes of messages in real-time by scheduling background operations and maintaining system health.
FossFLOW is an open source metadata search engine and data platform designed to aggregate and normalize repository information from multiple code hosting services. It functions as a developer productivity utility, enabling users to discover software projects and analyze contributor networks through a unified, searchable index. The platform distinguishes itself by utilizing vector-based semantic search, which converts project descriptions and code metadata into numerical embeddings to facilitate discovery based on conceptual relevance. To maintain a consistent view of disparate data, the syste
Executes high-volume background indexing and normalization tasks across a distributed worker architecture.
Gensim is a natural language processing toolkit designed for large-scale text analysis and the training of semantic vector embeddings. It provides a framework for identifying latent thematic structures within document collections and calculating semantic similarity between text segments using unsupervised statistical algorithms. The project is distinguished by its ability to handle datasets that exceed available system memory through incremental corpus streaming, which processes documents one at a time from disk. It utilizes sparse vector representations and dictionary-based token mapping to
Distributes heavy computational tasks across multiple processor cores to accelerate training and inference.
Py12306 is a distributed system designed for the automation of railway ticket booking and seat availability monitoring. It enables users to manage multiple accounts and execute reservation workflows automatically, including the resolution of security challenges encountered during the booking process. The platform distinguishes itself through a distributed architecture that coordinates multiple worker nodes via a central data store, allowing for scalable task execution and automatic failover. It utilizes parallel, multi-threaded query processing to maximize the frequency of availability checks
Coordinates multiple worker nodes to execute high-volume background booking and monitoring operations.
SurfSense is a self-hosted platform designed for building retrieval-augmented generation pipelines and managing private knowledge bases. It functions as a containerized research stack that allows users to index diverse data sources and query them using language models, ensuring that all information retrieval is grounded in specific source citations. The platform distinguishes itself through its modular architecture, which supports the integration of custom tools and diverse language models via a unified abstraction layer. It facilitates secure, collaborative research environments by implement
Handles resource-intensive media conversion tasks asynchronously to support multi-modal information consumption.
VERT is a media conversion platform designed to transform images, audio, video, and documents into various formats. It functions as a batch file processor that allows users to apply consistent conversion settings and custom naming patterns to multiple assets simultaneously, bundling the final outputs into compressed archives for streamlined organization. The system distinguishes itself through a distributed architecture that routes heavy media transcoding tasks across local hardware or remote server infrastructure. This approach optimizes performance by balancing computational workloads, allo
Routes heavy media conversion tasks across local and remote infrastructure to optimize performance and computational efficiency.
Resque is a Ruby library for enqueueing and processing asynchronous tasks using Redis as a data store. It functions as a distributed task processor and queue manager, allowing long-running work to be moved out of the main request cycle. The system executes background jobs in isolated child processes to prevent memory leaks and provides a web-based dashboard for monitoring queue depths, worker activity, and failed job statistics. Capability areas include distributed worker coordination via signals, error handling with job retry mechanisms, and priority-ordered queue management. It also suppor
Functions as a distributed task processor that executes background operations across multiple worker nodes.
Tdarr este un instrument distribuit de procesare video și automatizare a bibliotecilor media. Funcționează ca o arhitectură server-nod care gestionează scanarea, analiza și normalizarea fișierelor audio și video pe baza unor reguli personalizate. Sistemul distribuie sarcinile de calcul intense, cum ar fi transcodarea și verificările de sănătate, către mai multe noduri la distanță pentru a optimiza utilizarea hardware-ului. Utilizează un pipeline bazat pe plugin-uri pentru a executa secvențe de filtre și transformări, automatizând conversia media prin FFmpeg și HandBrake pentru a standardiza formatele de fișiere și containerele. Proiectul acoperă auditarea sănătății bibliotecii media pentru a verifica integritatea fișierelor și erorile de redare, precum și indexarea bazată pe metadate pentru a extrage proprietățile tehnice din fișierele video. Include monitorizarea sistemului de fișiere pentru a declanșa automat joburile de procesare atunci când sunt detectate fișiere media noi.
Distributes heavy media transcoding workloads across multiple hardware nodes using FFmpeg and HandBrake.