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Luigi vs Metaflow: What are the differences?
Introduction
In this comparison, we will highlight the key differences between Luigi and Metaflow, two popular workflow management tools.
Programming Paradigm: Luigi primarily relies on Python for defining workflows in a script-like manner, whereas Metaflow is designed around the concept of "flow" where a flow is a python class with methods that define various steps of the workflow.
Ease of Use: Luigi provides a simple interface and is easy to set up for basic tasks, while Metaflow is more suitable for complex workflows due to its strong integration with AWS and support for data science needs.
Scalability: Luigi is better suited for smaller workflows or projects with limited scalability requirements, while Metaflow is designed to handle large-scale workflows efficiently, making it ideal for enterprise-level projects.
Monitoring and Visualization: Metaflow offers built-in tools for easy monitoring and visualization of workflow steps, metrics, and dependencies, providing a comprehensive view of the workflow's progress and performance compared to Luigi.
Support for Data Science: Metaflow is specifically tailored for data science projects, with features like easy experiment tracking, versioning, and integration with popular data science libraries, making it the preferred choice for data-focused workflows over Luigi.
Integration with Data Stores: Metaflow seamlessly integrates with popular data storage technologies like AWS S3, while Luigi provides flexibility to work with different storage systems but may require additional configuration and setup for seamless integration.
In Summary, Luigi and Metaflow offer distinct advantages in workflow management, with Luigi being more straightforward for simpler tasks and Metaflow excelling in scalability and support for data science projects.
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.
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
Pros of Luigi
- Hadoop Support5
- Python3
- Open soure1
