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  1. Stackups
  2. Stackups
  3. Airflow vs Luigi

Airflow vs Luigi

OverviewDecisionsComparisonAlternatives

Overview

Airflow
Airflow
Stacks1.7K
Followers2.8K
Votes128
Luigi
Luigi
Stacks79
Followers211
Votes9
GitHub Stars18.5K
Forks2.4K

Airflow vs Luigi: What are the differences?

  1. Programming Language: Airflow is written in Python whereas Luigi is written in Python.
  2. Framework: Airflow uses a directed acyclic graph (DAG) to define and orchestrate workflows, allowing users to create complex workflows by defining tasks and their dependencies. Luigi, on the other hand, also uses a DAG-like structure to define workflows but focuses more on simplicity and ease of use.
  3. Ecosystem: Airflow has a larger ecosystem and community support compared to Luigi, which means users can benefit from a wider range of plugins and integrations available for use within Airflow. Luigi has a smaller ecosystem but is known for its tight integration with other Python libraries and tools.
  4. Scalability: Airflow is designed to scale horizontally, allowing users to easily handle large volumes of data and perform distributed processing. Luigi is also scalable but may require additional configuration and setup to handle larger workloads.
  5. Monitoring and Alerting: Airflow provides robust monitoring and alerting capabilities, allowing users to track the progress of their workflows, set up notifications, and handle failures. Luigi, on the other hand, provides basic monitoring features but may require additional tools or customizations for advanced monitoring and alerting.
  6. Workflow Visualization: Airflow provides a graphical interface to view and visualize workflows, making it easy to understand the structure and dependencies of tasks. Luigi also provides a visualization feature but it is more limited compared to Airflow's interface.

In Summary, Airflow and Luigi are both powerful workflow orchestration tools, but Airflow has a larger ecosystem, advanced monitoring capabilities, and a more extensive workflow visualization interface, while Luigi focuses on simplicity, tight Python integration, and ease of use.

Advice on Airflow, Luigi

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
Luigi
Luigi

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 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.

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.
dependency resolution; workflow management; visualization
Statistics
GitHub Stars
-
GitHub Stars
18.5K
GitHub Forks
-
GitHub Forks
2.4K
Stacks
1.7K
Stacks
79
Followers
2.8K
Followers
211
Votes
128
Votes
9
Pros & Cons
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
Pros
  • 5
    Hadoop Support
  • 3
    Python
  • 1
    Open soure
Integrations
No integrations available
Hadoop
Hadoop
Python
Python

What are some alternatives to Airflow, Luigi?

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.

Zenaton

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.

Unito

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

Shipyard

na

Camunda

Camunda

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

Workflowy

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

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

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.

K2

K2

Drive process excellence across your organization by connecting people, systems, and data to orchestrate how and when work gets done.

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