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  1. Stackups
  2. Utilities
  3. Background Jobs
  4. Background Processing
  5. Airflow vs Resque

Airflow vs Resque

OverviewDecisionsComparisonAlternatives

Overview

Resque
Resque
Stacks118
Followers126
Votes9
GitHub Stars9.5K
Forks1.7K
Airflow
Airflow
Stacks1.7K
Followers2.8K
Votes128

Airflow vs Resque: What are the differences?

Introduction

In this Markdown code, we will highlight the key differences between Airflow and Resque.

  1. Workflow Orchestration: Airflow is a workflow orchestration tool that allows users to schedule and monitor workflows, while Resque is a background job queue system for Ruby apps. Airflow supports complex workflows with dependencies and conditional logic, while Resque focuses on executing individual jobs in a distributed environment.

  2. Language Support: Airflow supports workflows written in Python, SQL, and other scripting languages, whereas Resque is specifically designed for Ruby applications. This means that Airflow can be used for a wider range of use cases and integrate with different systems, while Resque is more limited in terms of language support.

  3. Scalability: Airflow is designed to scale horizontally to handle large amounts of workflow executions and can be set up in a distributed environment, while Resque is more suitable for smaller-scale applications and may require additional configuration for scaling across multiple nodes.

  4. Fault Tolerance: Airflow provides built-in fault tolerance mechanisms such as task retries, task-level retries, and dead-letter queues, ensuring that workflows can recover from failures and continue processing. On the other hand, Resque relies on external tools or plugins for implementing fault tolerance measures.

  5. Monitoring and Logging: Airflow comes with a comprehensive user interface for monitoring workflows, viewing logs, and managing tasks, making it easier for users to track the progress of their workflows. Resque lacks a built-in dashboard for monitoring job queues and may require additional tools or integrations for logging and monitoring.

In Summary, the key differences between Airflow and Resque lie in their workflow orchestration capabilities, language support, scalability, fault tolerance mechanisms, and monitoring features.

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Advice on Resque, Airflow

Anonymous
Anonymous

Jan 19, 2020

Needs advice

I am so confused. I need a tool that will allow me to go to about 10 different URLs to get a list of objects. Those object lists will be hundreds or thousands in length. I then need to get detailed data lists about each object. Those detailed data lists can have hundreds of elements that could be map/reduced somehow. My batch process dies sometimes halfway through which means hours of processing gone, i.e. time wasted. I need something like a directed graph that will keep results of successful data collection and allow me either pragmatically or manually to retry the failed ones some way (0 - forever) times. I want it to then process all the ones that have succeeded or been effectively ignored and load the data store with the aggregation of some couple thousand data-points. I know hitting this many endpoints is not a good practice but I can't put collectors on all the endpoints or anything like that. It is pretty much the only way to get the data.

294k views294k
Comments

Detailed Comparison

Resque
Resque
Airflow
Airflow

Background jobs can be any Ruby class or module that responds to perform. Your existing classes can easily be converted to background jobs or you can create new classes specifically to do work. Or, you can do both.

Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed.

-
Dynamic: Airflow pipelines are configuration as code (Python), allowing for dynamic pipeline generation. This allows for writting code that instantiate pipelines dynamically.;Extensible: Easily define your own operators, executors and extend the library so that it fits the level of abstraction that suits your environment.;Elegant: Airflow pipelines are lean and explicit. Parameterizing your scripts is built in the core of Airflow using powerful Jinja templating engine.;Scalable: Airflow has a modular architecture and uses a message queue to talk to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity.
Statistics
GitHub Stars
9.5K
GitHub Stars
-
GitHub Forks
1.7K
GitHub Forks
-
Stacks
118
Stacks
1.7K
Followers
126
Followers
2.8K
Votes
9
Votes
128
Pros & Cons
Pros
  • 5
    Free
  • 3
    Scalable
  • 1
    Easy to use on heroku
Pros
  • 53
    Features
  • 14
    Task Dependency Management
  • 12
    Cluster of workers
  • 12
    Beautiful UI
  • 10
    Extensibility
Cons
  • 2
    Observability is not great when the DAGs exceed 250
  • 2
    Open source - provides minimum or no support
  • 2
    Running it on kubernetes cluster relatively complex
  • 1
    Logical separation of DAGs is not straight forward

What are some alternatives to Resque, Airflow?

Sidekiq

Sidekiq

Sidekiq uses threads to handle many jobs at the same time in the same process. It does not require Rails but will integrate tightly with Rails 3/4 to make background processing dead simple.

Beanstalkd

Beanstalkd

Beanstalks's interface is generic, but was originally designed for reducing the latency of page views in high-volume web applications by running time-consuming tasks asynchronously.

GitHub Actions

GitHub Actions

It makes it easy to automate all your software workflows, now with world-class CI/CD. Build, test, and deploy your code right from GitHub. Make code reviews, branch management, and issue triaging work the way you want.

Hangfire

Hangfire

It is an open-source framework that helps you to create, process and manage your background jobs, i.e. operations you don't want to put in your request processing pipeline. It supports all kind of background tasks – short-running and long-running, CPU intensive and I/O intensive, one shot and recurrent.

Apache Beam

Apache Beam

It implements batch and streaming data processing jobs that run on any execution engine. It executes pipelines on multiple execution environments.

Zenaton

Zenaton

Developer framework to orchestrate multiple services and APIs into your software application using logic triggered by events and time. Build ETL processes, A/B testing, real-time alerts and personalized user experiences with custom logic.

Luigi

Luigi

It is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in.

Unito

Unito

Build and map powerful workflows across tools to save your team time. No coding required. Create rules to define what information flows between each of your tools, in minutes.

delayed_job

delayed_job

Delayed_job (or DJ) encapsulates the common pattern of asynchronously executing longer tasks in the background. It is a direct extraction from Shopify where the job table is responsible for a multitude of core tasks.

Shipyard

Shipyard

na

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