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  1. Stackups
  2. Utilities
  3. Task Scheduling
  4. Workflow Manager
  5. Airflow vs Argo

Airflow vs Argo

OverviewDecisionsComparisonAlternatives

Overview

Airflow
Airflow
Stacks1.7K
Followers2.8K
Votes128
Argo
Argo
Stacks763
Followers471
Votes6

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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Advice on Airflow, Argo

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

Airflow
Airflow
Argo
Argo

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.

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).

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.
DAG or Steps based declaration of workflows;Artifact support (S3, Artifactory, HTTP, Git, raw);Step level input & outputs (artifacts/parameters);Loops;Parameterization;Conditionals;Timeouts (step & workflow level);Retry (step & workflow level);Resubmit (memoized);Suspend & Resume;Cancellation;K8s resource orchestration;Exit Hooks (notifications, cleanup);Garbage collection of completed workflow;Scheduling (affinity/tolerations/node selectors);Volumes (ephemeral/existing);Parallelism limits;Daemoned steps;DinD (docker-in-docker);Script steps
Statistics
Stacks
1.7K
Stacks
763
Followers
2.8K
Followers
471
Votes
128
Votes
6
Pros & Cons
Pros
  • 53
    Features
  • 14
    Task Dependency Management
  • 12
    Beautiful UI
  • 12
    Cluster of workers
  • 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
Pros
  • 3
    Open Source
  • 2
    Autosinchronize the changes to deploy
  • 1
    Online service, no need to install anything
Integrations
No integrations available
Kubernetes
Kubernetes
Docker
Docker

What are some alternatives to Airflow, Argo?

Kubernetes

Kubernetes

Kubernetes is an open source orchestration system for Docker containers. It handles scheduling onto nodes in a compute cluster and actively manages workloads to ensure that their state matches the users declared intentions.

Rancher

Rancher

Rancher is an open source container management platform that includes full distributions of Kubernetes, Apache Mesos and Docker Swarm, and makes it simple to operate container clusters on any cloud or infrastructure platform.

Docker Compose

Docker Compose

With Compose, you define a multi-container application in a single file, then spin your application up in a single command which does everything that needs to be done to get it running.

Docker Swarm

Docker Swarm

Swarm serves the standard Docker API, so any tool which already communicates with a Docker daemon can use Swarm to transparently scale to multiple hosts: Dokku, Compose, Krane, Deis, DockerUI, Shipyard, Drone, Jenkins... and, of course, the Docker client itself.

Tutum

Tutum

Tutum lets developers easily manage and run lightweight, portable, self-sufficient containers from any application. AWS-like control, Heroku-like ease. The same container that a developer builds and tests on a laptop can run at scale in Tutum.

Portainer

Portainer

It is a universal container management tool. It works with Kubernetes, Docker, Docker Swarm and Azure ACI. It allows you to manage containers without needing to know platform-specific code.

Codefresh

Codefresh

Automate and parallelize testing. Codefresh allows teams to spin up on-demand compositions to run unit and integration tests as part of the continuous integration process. Jenkins integration allows more complex pipelines.

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.

CAST.AI

CAST.AI

It is an AI-driven cloud optimization platform for Kubernetes. Instantly cut your cloud bill, prevent downtime, and 10X the power of DevOps.

k3s

k3s

Certified Kubernetes distribution designed for production workloads in unattended, resource-constrained, remote locations or inside IoT appliances. Supports something as small as a Raspberry Pi or as large as an AWS a1.4xlarge 32GiB server.

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