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  1. Stackups
  2. Utilities
  3. Task Scheduling
  4. Cloud Task Management
  5. Amazon SWF vs Celery

Amazon SWF vs Celery

OverviewComparisonAlternatives

Overview

Amazon SWF
Amazon SWF
Stacks35
Followers79
Votes0
Celery
Celery
Stacks1.7K
Followers1.6K
Votes280
GitHub Stars27.5K
Forks4.9K

Amazon SWF vs Celery: What are the differences?

# Introduction

1. **Workflow Orchestration**: Amazon SWF is a fully managed orchestration service for coordinating tasks and managing workflows, while Celery is a distributed task queue that can be used to process tasks in real-time.
2. **Scaling**: Amazon SWF allows for scaling of task execution without the need to manage infrastructure, while Celery requires manual scaling and management of worker nodes.
3. **Integration**: Amazon SWF is tightly integrated with other Amazon Web Services, providing seamless communication and interaction with various AWS services, whereas Celery can integrate with a wider range of systems and services through custom plugins and extensions.
4. **Visibility and Monitoring**: Amazon SWF offers detailed visibility into workflow execution history and progress, with built-in monitoring and logging capabilities, while Celery may require additional tooling and setup for monitoring and visualizing task execution.
5. **Long-Running Workflows**: Amazon SWF excels at handling long-running workflows and complex coordination scenarios, providing features such as coordinated retries and error handling, which may require additional configuration and implementation in Celery.
6. **Cost Structure**: Amazon SWF has a pay-as-you-go pricing model based on workflow executions and workflow task executions, while Celery is open-source and can be deployed on cost-effective infrastructure, but may require more upfront resource allocation for setup and maintenance.

In Summary, Amazon SWF and Celery differ in workflow orchestration, scaling capabilities, integration options, visibility and monitoring features, support for long-running workflows, and cost structures.

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Detailed Comparison

Amazon SWF
Amazon SWF
Celery
Celery

Amazon Simple Workflow allows you to structure the various processing steps in an application that runs across one or more machines as a set of “tasks.” Amazon SWF manages dependencies between the tasks, schedules the tasks for execution, and runs any logic that needs to be executed in parallel. The service also stores the tasks, reliably dispatches them to application components, tracks their progress, and keeps their latest state.

Celery is an asynchronous task queue/job queue based on distributed message passing. It is focused on real-time operation, but supports scheduling as well.

Maintaining application state;Tracking workflow executions and logging their progress;Holding and dispatching tasks;Controlling which tasks each of your application hosts will be assigned to execute
-
Statistics
GitHub Stars
-
GitHub Stars
27.5K
GitHub Forks
-
GitHub Forks
4.9K
Stacks
35
Stacks
1.7K
Followers
79
Followers
1.6K
Votes
0
Votes
280
Pros & Cons
No community feedback yet
Pros
  • 99
    Task queue
  • 63
    Python integration
  • 40
    Django integration
  • 30
    Scheduled Task
  • 19
    Publish/subsribe
Cons
  • 4
    Sometimes loses tasks
  • 1
    Depends on broker

What are some alternatives to Amazon SWF, Celery?

Kafka

Kafka

Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.

RabbitMQ

RabbitMQ

RabbitMQ gives your applications a common platform to send and receive messages, and your messages a safe place to live until received.

Amazon SQS

Amazon SQS

Transmit any volume of data, at any level of throughput, without losing messages or requiring other services to be always available. With SQS, you can offload the administrative burden of operating and scaling a highly available messaging cluster, while paying a low price for only what you use.

NSQ

NSQ

NSQ is a realtime distributed messaging platform designed to operate at scale, handling billions of messages per day. It promotes distributed and decentralized topologies without single points of failure, enabling fault tolerance and high availability coupled with a reliable message delivery guarantee. See features & guarantees.

ActiveMQ

ActiveMQ

Apache ActiveMQ is fast, supports many Cross Language Clients and Protocols, comes with easy to use Enterprise Integration Patterns and many advanced features while fully supporting JMS 1.1 and J2EE 1.4. Apache ActiveMQ is released under the Apache 2.0 License.

ZeroMQ

ZeroMQ

The 0MQ lightweight messaging kernel is a library which extends the standard socket interfaces with features traditionally provided by specialised messaging middleware products. 0MQ sockets provide an abstraction of asynchronous message queues, multiple messaging patterns, message filtering (subscriptions), seamless access to multiple transport protocols and more.

Apache NiFi

Apache NiFi

An easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic.

Gearman

Gearman

Gearman allows you to do work in parallel, to load balance processing, and to call functions between languages. It can be used in a variety of applications, from high-availability web sites to the transport of database replication events.

Memphis

Memphis

Highly scalable and effortless data streaming platform. Made to enable developers and data teams to collaborate and build real-time and streaming apps fast.

IronMQ

IronMQ

An easy-to-use highly available message queuing service. Built for distributed cloud applications with critical messaging needs. Provides on-demand message queuing with advanced features and cloud-optimized performance.

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