Drools VS Apache Hadoop

I've met a few young programmers who heard somewhere that object-oriented programming was bad and they want to get the enlightenment of functional programming that they've heard about. Frequently they travel from job to job like itinerant martial artists always looking for somewhere where they practice the true technique but they always seem disappointed as it is just as easy if not easier to screw up handling errors with monads than it is with exceptions and they find analogies like "a monad is like a burrito" just get them more confused.

As for something profound I'd point you to

https://github.com/cerner/clara-rules

which many people will struggle with because like many other production rules engines in LISP (and many other examples of simple compilers), there is hardly any code! Contrast that to the orders of magnitude larger rules engine Drools

https://github.com/kiegroup/drools

which is so crazy-complicated primarily because the Drools language is Java-based so you need all sorts of things that Clara or CLIPS don't need.

IMHO you're better off using something like https://www.drools.org/ for this. Non-devs writing code is a pipe-dream. It never works out.

https://www.drools.org/ an open source solution that allows you to use the UI to define rules. You can even import excel files.

Hi all, I am doing a project in rust right now (a web server with axum, postgres, redis), and am in need of a good rule engine like Drools in java (https://www.drools.org/). From what I have searched, I couldn't find any that are well maintained or provide similar levels of functionality.

Also ... https://www.drools.org/

Drools - Drools - Business Rules Management System (Java™, Open Source)

Drools (a Business Rule Engine),

Hopefully you already know that Drools is a business rules management system. You write rules in either "drl" syntax, in spreadsheets, or in glorified flowcharts, and then let your application throw data at it.

Did you check out tools like https://hadoop.apache.org/ ?

check out the various dockerfiles bundled with hadoop on GitHub. you can point to them from within docker-compose. they haven't been updated in a couple years tho.

There are different ways to implement parallel dataflows, such as using parallel data processing frameworks like Apache Hadoop, Apache Spark, and Apache Flink, or using cloud-based services like Amazon EMR and Google Cloud Dataflow. It is also possible to use parallel dataflow frameworks to handle big data and distributed computing, like Apache Nifi and Apache Kafka.

There are several frameworks available for batch processing, such as Hadoop, Apache Storm, and DataTorrent RTS.

A copy of Hadoop installed on each of these machines. You can download Hadoop from the Apache website, or you can use a distribution like Cloudera or Hortonworks.

The Apache™ Hadoop™ project develops open-source software for reliable, scalable, distributed computing.

So, clearly with your AP CS class and PLC logic knowledge, if you were dumped into a codebase like Hadoop, QT, or TensorFlow you'd be able to quickly and competently analyze what is going on with that code, understand all the libraries used, know the reasons why certain compromises were made, and be able to make suggestions on how to restructure the code in a different way? Because I've been programming for coming up on two decades and unless a system is within the domains that I have experience in, I would not be able to provide any useful information without a massive onboarding timeline, and definitely wouldn't be able to help redesign anything until actually coding within the system for a significant amount of time.

This requires the use of distributed computation tools such as Spark and Hadoop, Flink and Kafka are used. But for occasional experimentation, Pandas, Geopandas and Dask are some of the commonly used tools.