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  1. Stackups
  2. DevOps
  3. Continuous Deployment
  4. Server Configuration And Automation
  5. Airflow vs Rundeck

Airflow vs Rundeck

OverviewDecisionsComparisonAlternatives

Overview

Rundeck
Rundeck
Stacks204
Followers343
Votes7
Airflow
Airflow
Stacks1.7K
Followers2.8K
Votes128

Airflow vs Rundeck: What are the differences?

Key Differences between Airflow and Rundeck

Introduction:

Airflow and Rundeck are both popular open-source workflow management and job scheduling platforms. While they serve similar purposes, there are certain key differences that set them apart and define their respective use cases.

1. Architecture:

Airflow follows a Directed Acyclic Graph (DAG) architecture, where workflows are defined using Python code. On the other hand, Rundeck follows a more traditional task-based architecture, allowing users to create and schedule individual tasks without the need for coding.

2. Ease of Use:

Airflow requires proficiency in Python and coding skills to define and customize workflows. Although it offers more flexibility and extensive libraries, it has a steeper learning curve for non-programmers. Rundeck, on the other hand, has a user-friendly web interface that allows users to create and manage tasks using a graphical UI.

3. Community and Integration:

Airflow has a larger community and extensive integration capabilities. It offers numerous plugins, connections, and hooks, making it easier to connect with various external systems and frameworks. Rundeck, while still having a good community, might have limited integration options compared to Airflow.

4. Scale and Performance:

Airflow is designed to handle large-scale workflows and can process tasks concurrently. It offers robust scalability and high performance, making it suitable for handling complex and resource-intensive workflows. Rundeck, although capable of handling large-scale job orchestration, may not provide the same level of scalability and performance as Airflow.

5. Monitoring and Visualization:

Airflow provides comprehensive monitoring and visualization capabilities, allowing users to track the progress and status of tasks and workflows easily. It offers a built-in web-based user interface for monitoring and a rich set of logging features. Rundeck also provides monitoring features, but the visualization capabilities might not be as extensive as Airflow's.

6. Workflow Scheduling and Dependencies:

Airflow provides advanced workflow scheduling features, including data dependencies and complex scheduling options. It allows users to define dependencies between tasks and handle retries and failure scenarios efficiently. Rundeck, while offering basic task dependencies, may not offer the same level of flexibility and control in managing complex workflows.

In summary, Airflow offers more flexibility, scalability, and integration capabilities through its DAG-based architecture, Python code customization, and extensive plugin ecosystem. Rundeck, on the other hand, provides a simpler user interface, ease of use for non-programmers, and basic task-based scheduling capabilities.

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Advice on Rundeck, 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

Rundeck
Rundeck
Airflow
Airflow

A self-service operations platform used for support tasks, enterprise job scheduling, deployment, and more.

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
Stacks
204
Stacks
1.7K
Followers
343
Followers
2.8K
Votes
7
Votes
128
Pros & Cons
Pros
  • 3
    Easy to understand
  • 3
    Role based access control
  • 1
    Doesn't need containers
Pros
  • 53
    Features
  • 14
    Task Dependency Management
  • 12
    Cluster of workers
  • 12
    Beautiful UI
  • 10
    Extensibility
Cons
  • 2
    Running it on kubernetes cluster relatively complex
  • 2
    Observability is not great when the DAGs exceed 250
  • 2
    Open source - provides minimum or no support
  • 1
    Logical separation of DAGs is not straight forward
Integrations
Ansible
Ansible
Jenkins
Jenkins
No integrations available

What are some alternatives to Rundeck, Airflow?

Ansible

Ansible

Ansible is an IT automation tool. It can configure systems, deploy software, and orchestrate more advanced IT tasks such as continuous deployments or zero downtime rolling updates. Ansible’s goals are foremost those of simplicity and maximum ease of use.

Chef

Chef

Chef enables you to manage and scale cloud infrastructure with no downtime or interruptions. Freely move applications and configurations from one cloud to another. Chef is integrated with all major cloud providers including Amazon EC2, VMWare, IBM Smartcloud, Rackspace, OpenStack, Windows Azure, HP Cloud, Google Compute Engine, Joyent Cloud and others.

Terraform

Terraform

With Terraform, you describe your complete infrastructure as code, even as it spans multiple service providers. Your servers may come from AWS, your DNS may come from CloudFlare, and your database may come from Heroku. Terraform will build all these resources across all these providers in parallel.

Capistrano

Capistrano

Capistrano is a remote server automation tool. It supports the scripting and execution of arbitrary tasks, and includes a set of sane-default deployment workflows.

Puppet Labs

Puppet Labs

Puppet is an automated administrative engine for your Linux, Unix, and Windows systems and performs administrative tasks (such as adding users, installing packages, and updating server configurations) based on a centralized specification.

Salt

Salt

Salt is a new approach to infrastructure management. Easy enough to get running in minutes, scalable enough to manage tens of thousands of servers, and fast enough to communicate with them in seconds. Salt delivers a dynamic communication bus for infrastructures that can be used for orchestration, remote execution, configuration management and much more.

Fabric

Fabric

Fabric is a Python (2.5-2.7) library and command-line tool for streamlining the use of SSH for application deployment or systems administration tasks. It provides a basic suite of operations for executing local or remote shell commands (normally or via sudo) and uploading/downloading files, as well as auxiliary functionality such as prompting the running user for input, or aborting execution.

AWS OpsWorks

AWS OpsWorks

Start from templates for common technologies like Ruby, Node.JS, PHP, and Java, or build your own using Chef recipes to install software packages and perform any task that you can script. AWS OpsWorks can scale your application using automatic load-based or time-based scaling and maintain the health of your application by detecting failed instances and replacing them. You have full control of deployments and automation of each component

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.

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.

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