English as the new programming language for Apache spark

.. by sending all your queries to OpenAI... (not that anyone cares anymore)

So in few years all programming languages will be similar to assembly, I remember years ago we studied assembly in classes and wrote programs using it, but now maybe it isn’t the case.

imagine when they bring business people to write workflows cause "it's just plain english" and the look on their faces when things don't "quite" work the way they expect it to and have no clue how to debug. Then they have to create an IT support ticket to figure out how to write a SQL statement.

One thing I find somewhat amusing about this is that all of the generated code is against the PySpark API. And the PySpark API is itself an interop layer to the native Scala APIs for Spark.

So you have LLM-based English prompts as an interop layer to Python + PySpark, which is itself an interop layer onto the Spark core. Also, the generated Spark SQL strings inside the DataFrame API have their own little compiler into Spark operations.

When Databricks wrote PySpark, it was because many programmers knew Python but weren't willing to learn Scala just to use Spark. Now, they are offering a way for programmers to not bother learning the PySpark APIs, and leverage the interop layers all the way down, starting from English prompts.

This makes perfect sense when you zoom out and think about what their goal is -- to get your data workflows running on their cluster runtime. But it does make a programmer like me -- who developed a lot of systems while Spark was growing up -- wonder just how many layers future programmers will be forced to debug through when things go wrong. Debugging PySpark code is hard enough, even when you know Python, the PySpark APIs, and the underlying Spark core architecture well. But if all the PySpark code I had ever written had started from English prompts, it might make debugging those inevitable job crashes even more bewildering.

I haven't, in this description, mentioned the "usual" programming layers we have to contend with, like Python's interpreter, the JVM, underlying operating system, cloud APIs, and so on.

If I were to take a guess, programmers of the future are going to need more help debugging across programming language abstractions, system abstraction layers, and various code-data boundaries than they currently make do with.

The annoying (?) part of Scala Spark is the lack of notebook ecosystem. Also spark-submit requires a compiled jar for Scala yet only the main python script for Python. I would've loved Scala Spark if the eco system was in place.