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Airflow vs Rundeck: What are the differences?

Key Differences between Airflow and Rundeck


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.

Advice on Airflow and Rundeck
Needs advice
Apache SparkApache Spark

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.

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Gilroy Gordon
Solution Architect at IGonics Limited · | 2 upvotes · 267.6K views

For a non-streaming approach:

You could consider using more checkpoints throughout your spark jobs. Furthermore, you could consider separating your workload into multiple jobs with an intermittent data store (suggesting cassandra or you may choose based on your choice and availability) to store results , perform aggregations and store results of those.

Spark Job 1 - Fetch Data From 10 URLs and store data and metadata in a data store (cassandra) Spark Job 2..n - Check data store for unprocessed items and continue the aggregation

Alternatively for a streaming approach: Treating your data as stream might be useful also. Spark Streaming allows you to utilize a checkpoint interval -

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Pros of Airflow
Pros of Rundeck
  • 51
  • 14
    Task Dependency Management
  • 12
    Beautiful UI
  • 12
    Cluster of workers
  • 10
  • 6
    Open source
  • 5
    Complex workflows
  • 5
  • 3
    Good api
  • 3
    Apache project
  • 3
    Custom operators
  • 2
  • 3
    Role based access control
  • 3
    Easy to understand
  • 1
    Doesn't need containers

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Cons of Airflow
Cons of Rundeck
  • 2
    Observability is not great when the DAGs exceed 250
  • 2
    Running it on kubernetes cluster relatively complex
  • 2
    Open source - provides minimum or no support
  • 1
    Logical separation of DAGs is not straight forward
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    What is Airflow?

    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.

    What is Rundeck?

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

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    What companies use Airflow?
    What companies use Rundeck?
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