awesome-repositories.com
Blog
MCP
awesome-repositories.com

Discover the best open-source repositories with AI-powered search.

ExploreCurated searchesOpen-source alternativesSelf-hosted softwareBlogSitemap
ProjectMCP serverAboutHow we rankPress
LegalPrivacyTerms
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
mher avatar

mher/flower

0
View on GitHub↗
7,196 stars·1,150 forks·Python·19 viewsflower.readthedocs.io↗

Flower

Flower is a monitoring and administration tool for Celery task queues. It provides a real-time web dashboard and a REST API to monitor distributed task clusters, manage worker instances, and observe message broker health.

The project distinguishes itself by offering centralized control over the task lifecycle, allowing users to trigger, revoke, or terminate tasks and apply execution rate limits. It also includes a Prometheus metrics exporter to surface internal performance and status data for external monitoring and alerting systems.

The tool covers a broad range of observability and management capabilities, including queue volume tracking, worker pool size orchestration, and task execution history. Access to these administrative interfaces is secured through identity-based access control and third-party identity provider integrations.

Features

  • Web Dashboards - Ships a real-time web dashboard for monitoring and managing distributed task clusters and workers.
  • Distributed Worker Orchestration - Offers orchestration controls for worker status, pool sizes, and the ability to restart or shut down instances.
  • Task Queue Management - Provides a real-time web dashboard for the monitoring and management of distributed Celery task queues.
  • Worker Node Management - Provides centralized control over remote worker operations and their associated tasks through a web interface.
  • Worker Pool Management - Allows adjusting worker pool sizes and managing the lifecycle of distributed task processes.
  • RESTful API Interfaces - Provides a RESTful interface that translates HTTP requests into control commands for remote worker instances.
  • REST Administrative APIs - Exposes a programmatic REST API for triggering tasks and managing cluster settings.
  • Distributed Task Queues - Monitors message broker health and queue statistics to ensure efficient task delivery.
  • Control Interfaces - Enables the ability to revoke, terminate, or trigger tasks and apply execution rate limits.
  • Task Execution Monitoring - Monitors the lifecycle of distributed tasks, including their start, stop, execution history, and runtimes.
  • Cluster Administration - Provides centralized web and API-based administration for distributed Celery task clusters.
  • Cluster Health Monitoring - Provides a dashboard for querying the real-time health and operational status of distributed Celery worker clusters.
  • Cluster Management Tools - Provides a REST API for automating administrative tasks and managing the distributed cluster state.
  • Task Monitoring - Tracks real-time status, execution history, and performance of distributed tasks in a Celery cluster.
  • Message Broker Monitoring - Directly queries the message broker to provide real-time statistics on queue depth and message volume.
  • Administrative Panels - Ships a centralized administrative panel to control worker status, pool sizes and task lifecycles.
  • State Tracking - Updates internal task and worker status by listening to real-time event messages from the broker.
  • Performance Metrics Exporters - Exports internal execution data to external systems to track task runtimes and cluster health.
  • Prometheus Exporters - Exposes internal performance data in a scrapable format compatible with Prometheus monitoring systems.
  • Task Queues - Web-based monitoring for task clusters.
  • Admin Panels - Real-time monitoring and management for task queues.

Star history

Star history chart for mher/flowerStar history chart for mher/flower

How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.

AI search

Explore more awesome repositories

Describe what you need in plain English — the AI ranks thousands of curated open-source projects by relevance.

Start searching with AI

Open-source alternatives to Flower

Similar open-source projects, ranked by how many features they share with Flower.
  • roadrunner-server/roadrunnerroadrunner-server avatar

    roadrunner-server/roadrunner

    8,473View on GitHub↗

    RoadRunner is a high-performance application server and process manager designed to serve PHP applications using a persistent worker model. It eliminates bootload overhead and initialization time by keeping application processes alive between requests, acting as a protocol-agnostic proxy that routes traffic to a pool of supervised workers. The server is built with a plugin-based modular architecture, allowing it to be extended with custom Go plugins and compiled into tailored binaries. It distinguishes itself by providing a unified execution model for a wide array of communication protocols,

    Goapplication-servergolanghacktoberfest
    View on GitHub↗8,473
  • hazelcast/hazelcasthazelcast avatar

    hazelcast/hazelcast

    6,570View on GitHub↗

    Hazelcast is a distributed data platform that combines an in-memory data grid with a stream processing engine to support real-time analytics and event-driven applications. It functions as a partitioned, distributed key-value store that replicates data across cluster nodes to provide low-latency access and high availability. The platform also serves as a distributed SQL query engine, allowing users to execute standard SQL statements against both in-memory datasets and external data sources. What distinguishes Hazelcast is its use of a distributed consensus subsystem to maintain strongly consis

    Javabig-datacachingdata-in-motion
    View on GitHub↗6,570
  • temporalio/temporaltemporalio avatar

    temporalio/temporal

    18,411View on GitHub↗

    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

    Gocronjob-schedulerdistributed-crondistributed-systems
    View on GitHub↗18,411
  • rq/rqrq avatar

    rq/rq

    10,653View on GitHub↗

    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

    Pythonasyncbackground-jobsdelayed-jobs
    View on GitHub↗10,653
See all 30 alternatives to Flower→

Frequently asked questions

What does mher/flower do?

Flower is a monitoring and administration tool for Celery task queues. It provides a real-time web dashboard and a REST API to monitor distributed task clusters, manage worker instances, and observe message broker health.

What are the main features of mher/flower?

The main features of mher/flower are: Web Dashboards, Distributed Worker Orchestration, Task Queue Management, Worker Node Management, Worker Pool Management, RESTful API Interfaces, REST Administrative APIs, Distributed Task Queues.

What are some open-source alternatives to mher/flower?

Open-source alternatives to mher/flower include: roadrunner-server/roadrunner — RoadRunner is a high-performance application server and process manager designed to serve PHP applications using a… hazelcast/hazelcast — Hazelcast is a distributed data platform that combines an in-memory data grid with a stream processing engine to… temporalio/temporal — Temporal is a distributed workflow orchestration engine designed to manage fault-tolerant, stateful, and long-running… rq/rq — rq is a distributed task queue and background worker system for Python that uses a Redis backend to decouple task… zeebe-io/zeebe — Zeebe is a cloud-native workflow engine and distributed state machine designed for business process orchestration… nats-io/nats-server — NATS Server is a high-performance, lightweight messaging system designed for cloud-native applications, edge…