I see now what you meant. I apologize for my misunderstanding in that to be a general statement. Knowing that, I agree with your statements much more now. From what I can tell, the multi-process architecture in Python would require everything you mentioned. Not removing the GIL is indeed unfortunate.
Clojure is niche in a way. It is a Lisp dialect. However, it is based on the JVM and has built-in conveniences for using Java functions, classes, and libraries. Therefore, comparing libraries for enterprise applications is more accurately Clojure+Java vs. Python. My expertise is not in either language ecosystem, but my impression is that Python has better libraries for data analysis and as a system scripting language, and Clojure+Java is better for general applications and everything else.
One benefit of Clojure that makes parallelization easy is the immutable data structures. New data structures are cheaply made by seamlessly referencing originating data structures. Example: making a copy of array A and appending an item as array B, means that array B is mostly a reference to array A. Immutable data structures also offer expectations of behavior that mutable state naturally offends, such as avoiding race conditions or locks. In the case of my earlier example, I simply added some "future" wrapper functions and "dereference" annotations [1]. The entire program would execute to reach the final output, then work in reverse order to trigger and plan the future functions. Order of execution was based on dependencies, not strictly on the order of the code. It was fascinating was to watch the logs for when functions would start or complete. If you are familiar with operations planning and critical path analysis, the overall run-time reflected the critical path.
Clojure is niche in a way. It is a Lisp dialect. However, it is based on the JVM and has built-in conveniences for using Java functions, classes, and libraries. Therefore, comparing libraries for enterprise applications is more accurately Clojure+Java vs. Python. My expertise is not in either language ecosystem, but my impression is that Python has better libraries for data analysis and as a system scripting language, and Clojure+Java is better for general applications and everything else.
One benefit of Clojure that makes parallelization easy is the immutable data structures. New data structures are cheaply made by seamlessly referencing originating data structures. Example: making a copy of array A and appending an item as array B, means that array B is mostly a reference to array A. Immutable data structures also offer expectations of behavior that mutable state naturally offends, such as avoiding race conditions or locks. In the case of my earlier example, I simply added some "future" wrapper functions and "dereference" annotations [1]. The entire program would execute to reach the final output, then work in reverse order to trigger and plan the future functions. Order of execution was based on dependencies, not strictly on the order of the code. It was fascinating was to watch the logs for when functions would start or complete. If you are familiar with operations planning and critical path analysis, the overall run-time reflected the critical path.
[1]: https://www.conj.io/store/v1/org.clojure/clojure/1.8.0/clj/c...