Overview

Airflow

Stacks1.7K

Followers2.8K

Votes128

Digdag

Stacks17

Followers22

Votes0

GitHub Stars1.3K

Forks230

Airflow vs Digdag: What are the differences?

Key Differences between Airflow and Digdag

Airflow and Digdag are both workflow management platforms but they have key differences that set them apart from each other.

Task Dependency: Airflow uses a directed acyclic graph (DAG) to represent dependencies between tasks, which allows for complex workflows with multiple task dependencies. Digdag, on the other hand, uses a simpler dependency model with one-to-one dependencies between tasks. This makes Airflow more suited for complex workflows with intricate dependencies.
Execution Model: Airflow follows a pull-based execution model, where tasks are executed by workers that periodically poll the Airflow scheduler for new tasks. Digdag, on the other hand, uses a push-based execution model where tasks are executed immediately after their dependencies are satisfied. This makes Digdag more suited for real-time processing and low-latency workflows.
Scripting Language: Airflow uses Python as its scripting language, allowing for a wide range of customizations and integrations with existing Python libraries and frameworks. Digdag, on the other hand, uses a proprietary YAML-based language for defining workflows, which limits the customizability and integrations compared to Airflow.
Workflow Visualization: Airflow provides a web-based user interface that allows users to visualize the workflow dependencies and monitor the execution status of tasks. Digdag, on the other hand, lacks a dedicated web-based UI for workflow visualization and monitoring, making it less user-friendly for visualizing complex workflows.
Community Ecosystem: Airflow has a larger and more active community compared to Digdag, with a vast number of plugins, integrations, and community-contributed resources available. Digdag's community ecosystem is relatively smaller, limiting the availability of plugins and integrations compared to Airflow.
Maturity and Scalability: Airflow is a more mature and widely adopted workflow management system with a proven track record of large-scale deployments. Digdag, on the other hand, is a relatively newer and less mature platform with limited scalability features compared to Airflow.

In summary, Airflow and Digdag have key differences in their task dependency models, execution models, scripting languages, workflow visualization capabilities, community ecosystems, and maturity and scalability levels.

Advice on Airflow, Digdag

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	Digdag
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.	It is a simple tool that helps you to build, run, schedule, and monitor complex pipelines of tasks. It handles dependency resolution so that tasks run in series or in parallel.
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.	Multi-Cloud;Multi-lingual;Error handling; Modular; Extensible;Admin UI
Statistics
GitHub Stars -	GitHub Stars 1.3K
GitHub Forks -	GitHub Forks 230
Stacks 1.7K	Stacks 17
Followers 2.8K	Followers 22
Votes 128	Votes 0
Pros & Cons
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	No community feedback yet
Integrations
No integrations available	Dropbox Google Drive Hadoop WordPress Jira Python

What are some alternatives to Airflow, Digdag?

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

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

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

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

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.

Shipyard

na

Camunda

Camunda enables organizations to operationalize and automate AI, integrating human tasks, existing and future systems without compromising security, governance, or innovation.

Workflowy

It is an organizational tool that makes life easier. It's a surprisingly powerful way to take notes, make lists, collaborate, brainstorm, plan and generally organize your brain.

Temporal

It is a microservice orchestration platform which enables developers to build scalable applications without sacrificing productivity or reliability. Temporal server executes units of application logic, workflows, in a resilient manner that automatically handles intermittent failures, and retries failed operations.

Apache Oozie

It is a server-based workflow scheduling system to manage Hadoop jobs. Workflows in it are defined as a collection of control flow and action nodes in a directed acyclic graph. Control flow nodes define the beginning and the end of a workflow as well as a mechanism to control the workflow execution path.