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Airflow vs Argo: What are the differences?

  1. Concurrency Model: Airflow uses the Directed Acyclic Graph (DAG) model, where each task can depend on one or more tasks and can be executed in parallel. On the other hand, Argo adopts the Kubernetes way of orchestrating tasks using pods, allowing tasks to be executed in parallel within a single pod or on multiple pods.

  2. Native Containerization: Argo comes with native support for containerization, allowing users to package their code and dependencies into containers using Docker or other containerization technologies. In contrast, Airflow does not have native containerization support, requiring users to handle containerization separately if needed.

  3. Event-Driven Architecture: Airflow follows an event-driven architecture, where tasks are triggered based on events or schedules. It provides a centralized scheduler to manage task execution. Argo, on the other hand, follows a workflow-driven architecture, where tasks are executed based on the defined workflow constraints and dependencies. It uses a separate controller to manage workflow execution.

  4. User Interface: Airflow provides a web-based user interface (UI) that allows users to easily monitor and manage their workflows. The UI provides visualizations of task dependencies, task logs, and other useful information. Argo, on the other hand, does not have a built-in web UI. However, it provides a command-line interface (CLI) and a REST API for managing workflows.

  5. Native Kubernetes Integration: Argo is built specifically for Kubernetes and provides seamless integration with Kubernetes resources for managing and executing workflows. It leverages Kubernetes features such as pods, services, and persistent volumes to execute tasks. Airflow can also be deployed on Kubernetes, but it requires additional configuration and setup for integration with Kubernetes.

  6. Community and Ecosystem: Airflow has a larger and more mature community compared to Argo. It has been around for a longer time and has a wider range of plugins and integrations available. This larger community and ecosystem provide better support and resources for users. Argo, being relatively newer, has a smaller community and ecosystem but is growing rapidly.

In Summary, Airflow and Argo have key differences in their concurrency models, native containerization support, architecture, user interface, Kubernetes integration, and community/ecosystem size.

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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 · 265.1K views
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CassandraCassandra

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 - https://spark.apache.org/docs/latest/streaming-programming-guide.html#checkpointing

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Pros of Airflow
Pros of Argo
  • 51
    Features
  • 14
    Task Dependency Management
  • 12
    Beautiful UI
  • 12
    Cluster of workers
  • 10
    Extensibility
  • 6
    Open source
  • 5
    Complex workflows
  • 5
    Python
  • 3
    Good api
  • 3
    Apache project
  • 3
    Custom operators
  • 2
    Dashboard
  • 3
    Open Source
  • 2
    Autosinchronize the changes to deploy
  • 1
    Online service, no need to install anything

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Cons of Airflow
Cons of Argo
  • 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 Argo?

    Argo is an open source container-native workflow engine for getting work done on Kubernetes. Argo is implemented as a Kubernetes CRD (Custom Resource Definition).

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    What are some alternatives to Airflow and Argo?
    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.
    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.
    Jenkins
    In a nutshell Jenkins CI is the leading open-source continuous integration server. Built with Java, it provides over 300 plugins to support building and testing virtually any project.
    AWS Step Functions
    AWS Step Functions makes it easy to coordinate the components of distributed applications and microservices using visual workflows. Building applications from individual components that each perform a discrete function lets you scale and change applications quickly.
    Pachyderm
    Pachyderm is an open source MapReduce engine that uses Docker containers for distributed computations.
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