beam VS hoogle

The "streaming systems" book answers your question and more: https://www.oreilly.com/library/view/streaming-systems/97814.... It gives you a history of how batch processing started with MapReduce, and how attempts at scaling by moving towards streaming systems gave us all the subsequent frameworks (Spark, Beam, etc.).

As for the framework called MapReduce, it isn't used much, but its descendant https://beam.apache.org very much is. Nowadays people often use "map reduce" as a shorthand for whatever batch processing system they're building on top of.

Apache Beam is one of many tools that you can use

Flexible runtime ☁️: Temporian programs can run seamlessly in-process in Python, on large datasets using Apache Beam.

To perform the tests I'm using a Kafka cluster on Kubernetes from the Beam repo (here).

Apache Beam: Streaming framework which can be run on several runner such as Apache Flink and GCP Dataflow

Apache Beam: Batch/streaming data processing 🔗Link

What you are looking for is Dataflow. It can be a bit tricky to wrap your head around at first, but I highly suggest leaning into this technology for most of your data engineering needs. It's based on the open source Apache Beam framework that originated at Google. We use an internal version of this system at Google for virtually all of our pipeline tasks, from a few GB, to Exabyte scale systems -- it can do it all.

That being said, there is a learning curve in understanding how Apache Beam works. Take a look at the beam website for more information.

Dimensions behave somewhat like a "type system" for math. These dimensional-analysis tricks act like the trick you see in Haskell sometimes, where you can easily guess an implementation of an expression once you know it's type (or e.g. search by type signature https://hoogle.haskell.org/ )

Haskell Spotlight makes vscode a client for hoogle. It isn't too different than jumping into your browser and type https://hoogle.haskell.org/. The main advantage is that you have everything in one place

https://hackage.haskell.org/package/base and https://hoogle.haskell.org are automatically up to date and better searchable than almost any other reference of any other programming language. maintaining a redundant reference that needs to be kept up to date manually is simply stupid.

Ideally like this: https://zio.dev/reference/#concurrency

Or this: https://hoogle.haskell.org/?hoogle=fork

And yeah I get it, it's hard to Google for punctuation operators in languages because it doesn't give useful search results (but not impossible, for example, Haskell has a search engine for documentation that handles symbols/punctuation).

{ "customLocalFormatters.formatters": [ { "command": "make format", "languages": ["haskell"] } ], "emeraldwalk.runonsave": { "commands": [ { "match": "*.hs", "isAsync": true, "cmd": "make retag retag_file=${file}" } ] }, "ghcid.command": "make ghcid", "goto-documentation.customDocs": { "hs": "https://hoogle.haskell.org/?hoogle=${query}" } }

You had a look at Hoogle?

https://hoogle.haskell.org/

For some type signatures there is (are) only one (or only a few) meaningful implementation(s).