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73 Repos

Awesome GitHub RepositoriesDistributed Task Queues

Frameworks for distributing background work across multiple nodes to ensure reliable and scalable task processing.

Distinguishing note: Specifically addresses distributed task distribution and message handling, distinct from local execution engines.

Explore 73 awesome GitHub repositories matching software engineering & architecture · Distributed Task Queues. Refine with filters or upvote what's useful.

Awesome Distributed Task Queues GitHub Repositories

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  • getredash/redashAvatar von getredash

    getredash/redash

    28,653Auf GitHub ansehen↗

    Redash is a self-hosted analytics platform and SQL data visualization tool. It provides a web-based SQL query editor for writing, executing, and scheduling database queries, and functions as a business intelligence dashboard for monitoring metrics via visual widgets. The platform distinguishes itself through its data source connectors, which integrate with various SQL, NoSQL, and API-based stores to retrieve information for analysis. It enables self-service analytics by allowing users to run queries with dynamic parameters and supports shared data reporting via public links or embedded dashbo

    Uses a Redis-backed distributed task queue to manage asynchronous query execution and scheduled jobs.

    Pythonanalyticsathenabi
    Auf GitHub ansehen↗28,653
  • celery/celeryAvatar von celery

    celery/celery

    28,596Auf GitHub ansehen↗

    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

    Scales application workloads by distributing high volumes of concurrent jobs across multiple worker nodes using a message broker architecture.

    Pythonamqppythonpython-library
    Auf GitHub ansehen↗28,596
  • redisson/redissonAvatar von redisson

    redisson/redisson

    24,355Auf GitHub ansehen↗

    Redisson is a Java library and Redis client that functions as a distributed Java object mapper, caching provider, and locking framework. It maps Java collections and concurrency primitives to distributed implementations backed by Redis and Valkey, providing synchronous, asynchronous, and reactive APIs for interacting with these data stores. The project distinguishes itself by providing a comprehensive suite of distributed coordination tools, including a locking framework for managing semaphores and countdown latches across multiple application nodes. It also serves as a distributed messaging

    Implements distributed blocking and delayed queues for asynchronous message processing and task distribution.

    Java
    Auf GitHub ansehen↗24,355
  • apache/rocketmqAvatar von apache

    apache/rocketmq

    22,461Auf GitHub ansehen↗

    RocketMQ is a cloud-native distributed messaging platform and streaming engine. It functions as a distributed transactional queue that ensures atomicity between local transactions and message delivery, and serves as an MQTT IoT message broker to bridge lightweight device traffic into high-performance data streams. The system is distinguished by a Kubernetes-native architecture that decouples compute from storage to allow independent scaling of traffic and data retention. It utilizes a tiered storage model to offload older data to remote storage and employs quorum-based replication and automat

    Partitions data into multiple queues across a cluster to enable parallel processing and horizontal scalability.

    Java
    Auf GitHub ansehen↗22,461
  • nats-io/nats-serverAvatar von nats-io

    nats-io/nats-server

    20,076Auf GitHub ansehen↗

    NATS Server is a high-performance, lightweight messaging system designed for cloud-native applications, edge computing, and distributed microservices. It functions as a distributed publish-subscribe broker that routes messages using hierarchical, dot-separated subject strings, enabling decoupled communication between services without requiring centralized broker lookups. The system supports core messaging patterns including asynchronous publish-subscribe, request-reply, and load-balanced queue processing. The platform distinguishes itself through a decentralized architecture that eliminates t

    Balances message processing across multiple service instances using named queue groups.

    Gocloudcloud-computingcloud-native
    Auf GitHub ansehen↗20,076
  • temporalio/temporalAvatar von temporalio

    temporalio/temporal

    18,411Auf GitHub ansehen↗

    Temporal is a distributed workflow orchestration engine designed to manage fault-tolerant, stateful, and long-running background processes. It functions as a platform for coordinating complex cross-service operations, ensuring consistency and reliability in distributed environments by decoupling workflow orchestration from task execution. The platform distinguishes itself through a deterministic, event-sourced execution model that reconstructs workflow state by re-executing code from an immutable event log. This approach isolates non-deterministic side effects into managed activities, allowin

    Hosts and executes workflow logic by polling for tasks from a task queue and managing the state of long-running processes.

    Gocronjob-schedulerdistributed-crondistributed-systems
    Auf GitHub ansehen↗18,411
  • allenai/olmocrAvatar von allenai

    allenai/olmocr

    17,396Auf GitHub ansehen↗

    Olmocr is a distributed document processing framework designed to convert PDF and image files into structured markdown. It functions as a vision-based document parser that utilizes multimodal neural networks to interpret complex visual layouts and translate them into standardized text representations. The system operates as a remote inference orchestrator, offloading heavy document analysis tasks to external servers or cloud APIs to minimize local computational requirements. By employing a stateless worker architecture, it decouples document ingestion from inference, allowing for the distribu

    Provides a framework for scaling document conversion tasks across multiple nodes using cloud storage as a task queue.

    Python
    Auf GitHub ansehen↗17,396
  • optimalbits/bullAvatar von OptimalBits

    OptimalBits/bull

    16,243Auf GitHub ansehen↗

    Bull is a Node.js library for managing distributed jobs and message queues using Redis as the primary data store. It functions as a distributed task worker, job scheduler, and priority queue manager designed to handle asynchronous workloads across multiple processes. The project distinguishes itself by providing a persistent communication channel that decouples servers through the exchange of serializable data objects. It ensures distributed system reliability by detecting stalled tasks and recovering from process crashes to ensure every queued job is completed. The system covers a broad ran

    Implements a distributed task queue to process background work across multiple nodes reliably and at scale.

    JavaScriptjobjob-queuemessage
    Auf GitHub ansehen↗16,243
  • aleju/imgaugAvatar von aleju

    aleju/imgaug

    14,742Auf GitHub ansehen↗

    imgaug is a Python library for machine learning data augmentation and computer vision dataset expansion. It provides tools to increase the volume and variety of training sets by applying random geometric, color, and noise transformations to images. The library ensures spatial consistency by synchronizing transformations across images and their associated annotations, such as bounding boxes, keypoints, and segmentation maps. It uses a compositional pipeline pattern to chain multiple augmentations into sequences and employs deterministic seed management to reproduce specific data samples. The

    Distributes image processing workloads across multiple CPU cores using background processes.

    Python
    Auf GitHub ansehen↗14,742
  • triggerdotdev/trigger.devAvatar von triggerdotdev

    triggerdotdev/trigger.dev

    13,696Auf GitHub ansehen↗

    Trigger.dev is a platform for building durable, event-driven background workflows. It functions as a workflow engine that allows developers to define complex, long-running processes using standard code rather than proprietary configuration languages. By utilizing a durable execution model, the system checkpoints progress, ensuring that tasks can automatically resume from the exact point of failure after a crash or interruption. The platform distinguishes itself through its focus on stateful, multi-step automation and real-time feedback. It supports the orchestration of AI agents and external

    Manages execution throughput and concurrency by routing background jobs through prioritized distributed queues.

    TypeScriptaiai-agent-frameworkai-agents
    Auf GitHub ansehen↗13,696
  • pydanny/cookiecutter-djangoAvatar von pydanny

    pydanny/cookiecutter-django

    13,539Auf GitHub ansehen↗

    cookiecutter-django is a template-based project generator and production-ready scaffold for Django web applications. It functions as a boilerplate that injects user-defined variables into predefined file templates to automate the creation of a standardized directory structure and initial project configuration. The project provides a production blueprint that integrates a customizable user authentication system, environment-variable configuration, and a containerized development environment. It bundles Django with databases and task queues to ensure consistency across local and production work

    Integrates distributed task queues to offload long-running operations to background worker processes.

    Python
    Auf GitHub ansehen↗13,539
  • mperham/sidekiqAvatar von mperham

    mperham/sidekiq

    13,536Auf GitHub ansehen↗

    Sidekiq is a Ruby background processing framework and asynchronous task runner. It functions as a Redis-backed background job processor that offloads heavy or time-consuming work from web requests to separate worker processes to ensure the main application remains responsive. The system operates as a Redis task queue, storing pending jobs in Redis to be processed concurrently by multiple threads. It provides a framework for distributed task queueing and asynchronous job scheduling to coordinate work across multiple server instances. The project covers Ruby application scaling by executing ba

    Provides a distributed task queue that uses Redis as the primary persistent storage and message broker.

    Ruby
    Auf GitHub ansehen↗13,536
  • php-cs-fixer/php-cs-fixerAvatar von PHP-CS-Fixer

    PHP-CS-Fixer/PHP-CS-Fixer

    13,463Auf GitHub ansehen↗

    This tool is a command-line utility designed to automatically detect and correct coding standard violations in PHP source files. It functions as a static analysis and refactoring engine that ensures consistent project-wide formatting by applying predefined community conventions or custom organizational rules. The project distinguishes itself through a modular rule-based engine that supports both automated style correction and codebase modernization. It allows developers to update legacy syntax to align with newer language versions and testing framework requirements, facilitating the adoption

    Distributes file analysis and transformation workloads across multiple CPU cores to improve performance on large codebases.

    PHPcode-standardscode-stylephp
    Auf GitHub ansehen↗13,463
  • hibiken/asynqAvatar von hibiken

    hibiken/asynq

    12,914Auf GitHub ansehen↗

    Asynq is a distributed background job processing framework for Go applications. It manages asynchronous task queues by offloading heavy operations to persistent storage, allowing the main application to remain responsive while background workers handle workloads. The system utilizes Redis to manage task state, concurrency, and message distribution across multiple worker instances. It employs atomic Lua scripting and sorted sets to ensure reliable job acquisition, precise scheduling of delayed tasks, and fault-tolerant processing through a two-stage acknowledgement flow. The framework support

    Uses a persistent key-value store to manage job state and message distribution across distributed worker processes.

    Goasynchronous-tasksbackground-jobsgo
    Auf GitHub ansehen↗12,914
  • phodal/githubAvatar von phodal

    phodal/github

    11,028Auf GitHub ansehen↗

    This project serves as a comprehensive knowledge base and technical reference for navigating the GitHub development platform. It provides a structured collection of documentation and operational practices designed to assist contributors in understanding the ecosystem and managing software development lifecycles. The repository functions as a guide for implementing workflow automation, detailing how to define and manage sequences that trigger based on repository events. By utilizing declarative configuration and version-controlled logic, it enables the orchestration of development tasks direct

    Orchestrates automated tasks across isolated compute nodes using a centralized job queue for scalable execution.

    Rich Text Formatbookbooksgithub
    Auf GitHub ansehen↗11,028
  • rq/rqAvatar von rq

    rq/rq

    10,653Auf GitHub ansehen↗

    rq is a distributed task queue and background worker system for Python that uses a Redis backend to decouple task submission from execution. It functions as a reliable message queue and task scheduler, allowing Python functions or asyncio coroutines to be processed asynchronously across multiple worker processes. The project distinguishes itself through reliable queuing mechanisms that prevent job loss during worker crashes using atomic operations. It provides specialized orchestration capabilities, including the prevention of duplicate jobs, job execution prioritization, and the ability to m

    Distributes units of work across multiple worker processes using a Redis backend for scalable task processing.

    Pythonasyncbackground-jobsdelayed-jobs
    Auf GitHub ansehen↗10,653
  • hangfireio/hangfireAvatar von HangfireIO

    HangfireIO/Hangfire

    10,015Auf GitHub ansehen↗

    Hangfire is a background job scheduler and distributed task queue for .NET applications. It serves as a job orchestration framework that offloads heavy processing to background workers using a SQL-backed processor to manage job state across multiple servers. The framework distinguishes itself through reliable task scheduling, where job metadata and arguments are persisted in an external database to ensure tasks survive application restarts. It supports advanced orchestration patterns, including the ability to chain dependent tasks so that a child job triggers automatically upon the successful

    Provides a distributed task queue that offloads heavy processing to background workers across multiple servers.

    C#background-jobsbackground-threadbackground-worker
    Auf GitHub ansehen↗10,015
  • tporadowski/redisAvatar von tporadowski

    tporadowski/redis

    9,987Auf GitHub ansehen↗

    Redis is a high-performance in-memory key-value store that functions as a distributed cache, message broker, and NoSQL database. It provides sub-millisecond read and write access to data stored in RAM and can operate as a vector database for indexing high-dimensional embeddings. The system supports a wide range of data storage and synchronization primitives, including the management of strings, hashes, lists, sets, and JSON documents. It enables real-time data operations through atomic transactions, hybrid persistence using snapshots and append-only logs, and high-availability configurations

    Provides distributed task queues using lists to implement producer-consumer patterns for asynchronous communication.

    Credisredis-for-windowsredis-msi-installer
    Auf GitHub ansehen↗9,987
  • aphyr/distsys-classAvatar von aphyr

    aphyr/distsys-class

    9,717Auf GitHub ansehen↗

    This project provides educational materials and courseware focused on the theoretical and practical foundations of distributed systems design. It serves as a comprehensive curriculum covering the disciplines of consensus, data consistency, reliability engineering, and scalability. The instructional content focuses on achieving cluster agreement through consensus algorithms and managing system-wide state via coordination frameworks. It includes a dedicated guide to data theory, exploring replication strategies, consistency models, and data convergence. The courseware covers a broad capability

    Provides instructional content on using distributed task queues to decouple services and smooth load spikes.

    Auf GitHub ansehen↗9,717
  • resque/resqueAvatar von resque

    resque/resque

    9,480Auf GitHub ansehen↗

    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

    Utilizes Redis as the primary persistent storage and message broker for distributed task queues.

    Rubyasynchronous-tasksasynctaskbackground-jobs
    Auf GitHub ansehen↗9,480
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Unter-Tags erkunden

  • Distributed Barrier SynchronizationCoordination mechanisms that block execution until a set number of distributed tasks reach a common checkpoint. **Distinct from Distributed Task Queues:** Focuses on synchronization checkpoints across nodes, unlike general task distribution.
  • Local Multiprocessing1 Sub-TagDistribution of compute-intensive tasks across multiple CPU cores on a single machine to bypass the global interpreter lock. **Distinct from Distributed Task Queues:** Focuses on single-machine multi-core distribution via the multiprocessing module rather than multi-node distributed queues
  • Redis-Backed Queues5 Sub-TagsDistributed task queues that utilize Redis as the primary persistent storage and message broker. **Distinct from Distributed Task Queues:** Distinct from Distributed Task Queues: focuses specifically on Redis-backed implementations rather than generic distributed queuing architectures.
  • Rendering Workload DistributionMechanisms for splitting rendering tasks across multiple machines or cloud environments to increase throughput. **Distinct from Distributed Task Queues:** Specific to the distribution of rendering compute, not general background task queues.
  • Task Routing StrategiesMechanisms for assigning deferred operations to specific named queues and priority levels. **Distinct from Distributed Task Queues:** Focuses on the routing logic and priority assignment within a queue system, whereas Distributed Task Queues is the broader infrastructure.