luigi
Apache Camel
luigi | Apache Camel | |
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14 | 21 | |
17,327 | 5,318 | |
0.5% | 0.7% | |
6.3 | 10.0 | |
10 days ago | 3 days ago | |
Python | Java | |
Apache License 2.0 | Apache License 2.0 |
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luigi
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Ask HN: What is the correct way to deal with pipelines?
I agree there are many options in this space. Two others to consider:
- https://airflow.apache.org/
- https://github.com/spotify/luigi
There are also many Kubernetes based options out there. For the specific use case you specified, you might even consider a plain old Makefile and incrond if you expect these all to run on a single host and be triggered by a new file showing up in a directory…
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In the context of Python what is a Bob Job?
Maybe if your use case is “smallish” and doesn’t require the whole studio suite you could check out apscheduler for doing python “tasks” on a schedule and luigi to build pipelines.
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Lessons Learned from Running Apache Airflow at Scale
What are you trying to do? Distributed scheduler with a single instance? No database? Are you sure you don't just mean "a scheduler" ala Luigi? https://github.com/spotify/luigi
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Apache Airflow. How to make the complex workflow as an easy job
It's good to know what Airflow is not the only one on the market. There are Dagster and Spotify Luigi and others. But they have different pros and cons, be sure that you did a good investigation on the market to choose the best suitable tool for your tasks.
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DevOps Fundamentals for Deep Learning Engineers
MLOps is a HUGE area to explore, and not surprisingly, there are many startups showing up in this space. If you want to get it on the latest trends, then I would look at workflow orchestration frameworks such as Metaflow (started off at Netflix, is now spinning off into its own enterprise business, https://metaflow.org/), Kubeflow (used at Google, https://www.kubeflow.org/), Airflow (used at Airbnb, https://airflow.apache.org/), and Luigi (used at Spotify, https://github.com/spotify/luigi). Then you have the model serving itself, so there is Seldon (https://www.seldon.io/), Torchserve (https://pytorch.org/serve/), and TensorFlow Serving (https://www.tensorflow.org/tfx/guide/serving). You also have the actual export and transfer of DL models, and ONNX is the most popular here (https://onnx.ai/). Spark (https://spark.apache.org/) still holds up nicely after all these years, especially if you are doing batch predictions on massive amount of data. There is also the GitFlow way of doing things and Data Version Control (DVC, https://dvc.org/) is taken a pole position there.
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Data pipelines with Luigi
At Wonderflow we're doing a lot of ML / NLP using Python and recently we are enjoying writing data pipelines using Spotify's Luigi.
- Noobie who is trying to use K8s needs confirmation to know if this is the way or he is overestimating Kubernetes.
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Open Source ETL Project For Startups
💡【About Luigi】 https://github.com/spotify/luigi Luigi was built at Spotify since 2012, it's open source and mainly used for getting data insights by showing recommendations, toplists, A/B test analysis, external reports, internal dashboards, etc.
- Resources/tutorials to help me learn about ETL?
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Using Terraform to make my many side-projects 'pick up and play'
So to sum that up, I went from having nothing for my side-project set up in AWS to having a Kubernetes cluster with the basic metrics and dashboard, a proper IAM-linked ServiceAccount support for a smooth IAM experience in K8s, and Luigi deployed so that I could then run a Luigi workflow using an ad-hoc run of a CronJob. That's quite remarkable to me. All that took hours to figure out and define when I first did it, over six months ago.
Apache Camel
- Show HN: Winglang – a new Cloud-Oriented programming language
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Ask HN: What is the correct way to deal with pipelines?
"correct" is a value judgement that depends on lots of different things. Only you can decide which tool is correct. Here are some ideas:
- https://camel.apache.org/
- https://www.windmill.dev/
- https://github.com/huginn/huginn
Your idea about a queue (in redis, or postgres, or sqlite, etc) is also totally valid. These off-the-shelf tools I listed probably wouldn't give you a huge advantage IMO.
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Is there something like airflow but written in Scala/Java?
Apache Camel Apache Nifi Spring Cloud
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Why messaging is much better than REST for inter-microservice communications
This reminds me more of Apache Camel[0] than other things it's being compared to.
> The process initiator puts a message on a queue, and another processor picks that up (probably on a different service, on a different host, and in different code base) - does some processing, and puts its (intermediate) result on another queue
This is almost exactly the definition of message routing (ie: Camel).
I'm a bit doubtful about the pitch because the solution is presented as enabling you to maintain synchronous style programming while achieving benefits of async processing. This just isn't true, these are fundamental tradeoffs. If you need a synchronous answer back then no amount of queuing, routing, prioritisation, etc etc will save you when the fundamental resource providing that is unavailable, and the ultimate outcome that your synchronous client now hangs indefinitely waiting for a reply message instead of erroring hard and fast is not desirable at all. If you go into this ad hoc, and build in a leaky abstraction that asynchronous things are are actually synchronous and vice versa, before you know it you are going to have unstable behaviour or even worse, deadlocks all over your system and the worst part - the true state of the system is now hidden in which messages are pending in transient message queues everywhere.
What really matters here is to fundamentally design things from the start with patterns that allow you to be very explicit about what needs to be synchronous vs async (building on principles of idempotency, immutability, coherence, to maximise the cases where async is the answer).
The notion of Apache Camel is to make all these decisions a first class elements of your framework and then to extract out the routing layer as a dedicated construct. The fact it generalises beyond message queues (treating literally anything that can provide a piece of data as a message provider) is a bonus.
[0] https://camel.apache.org/
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Can I continuously write to a CSV file with a python script while a Java application is continuously reading from it?
Since you're writing a Java app to consume this, I highly recommend Apache Camel to do the consuming of messages for it. You can trivially aim it at file systems, message queues, databases, web services and all manner of other sources to grab your data for you, and you can change your mind about what that source is, without having to rewrite most of your client code.
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S3 to S3 transform
For a simple sequential Pipeline, my goto would be Apache Camel. As soon as you want complexity its either Apache Nifi or a micro service architecture.
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🗞️ We have just released our JBang! catalog 🛍️
🐪 Apache Camel : Camel JBang, A JBang-based Camel app for easily running Camel routes.
- 7GUIs of Java/Object Oriented Design?
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System Design: Enterprise Service Bus (ESB)
Apache Camel
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Advanced: Java, JVM and general knowledge
So, my advice is this. Expand your knowledge. Pursue higher education on topics you are familiar with, but also explore topics you are not. Read documentation, but question it. I just found out about something called Apache Camel today that I am excited to read up on. Why is it better than Spring? Is it really? What's happening here? This is always what excites me as a developer and engineer. There is so much to learn.
What are some alternatives?
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
Kedro - Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.
Apache Kafka - Mirror of Apache Kafka
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
Apache Pulsar - Apache Pulsar - distributed pub-sub messaging system
mrjob - Run MapReduce jobs on Hadoop or Amazon Web Services
Apache ActiveMQ Artemis - Mirror of Apache ActiveMQ Artemis
Dask - Parallel computing with task scheduling
Spring Boot - Spring Boot
Pinball
Aeron - Efficient reliable UDP unicast, UDP multicast, and IPC message transport