3 ms·

Great reply, cheers. EDIT: I would like to hear more about COBOL and Z/OS. Can you explain your efficiency statement?

bws111 9y ago
One of the common things that mainframes and COBOL are used for is financial systems, and that means a lot of fixed-point decimal data. So suppose you have a file containing an account number and a balance, like '9876543210 000000123.45', and you want to add to that balance. In Python, once you have that record read in, you have to split it apart by some means. That takes CPU work. Python will then store the pieces as new objects, and that takes CPU work. Then you have two strings, and one of them (the balance) must be converted to a number that can be operated on, more CPU work. Then you can do the math, using the Decimal module, which is more work. The result then has to be converted back to a string and written back to the file. COBOL, on the other hand, understands record formats, understands decimal date, and knows that the hardware supports decimal data. So the same task (add to decimal number in record) that on Python may take many thousands of CPU instructions can be done in three: PACK (convert printable (zoned) decimal number to packed decimal, ADDPACKED (perform the addition), and UNPACK (back to printable number). And those three operations are performed in dedicated decimal hardware in the CPU. The important thing to remember is that in the mainframe world, not only is the hardware priced according to the performance of the machine, the software is also. This is why there are so many performance levels (over 100) to choose from. Users naturally want to buy the smallest machine they can get that will handle their workload, and they aren't interested in wasting any processor power doing things not absolutely required by their business needs. For this reason, you won't find any mainframe shops running traditional mainframe workload in a language like Python.

One of the common things that mainframes and COBOL are used for is financial systems, and that means a lot of fixed-point decimal data. So suppose you have a file containing an account number and a balance, like '9876543210 000000123.45', and you want to add to that balance. In Python, once you have that record read in, you have to split it apart by some means. That takes CPU work. Python will then store the pieces as new objects, and that takes CPU work. Then you have two strings, and one of them (the balance) must be converted to a number that can be operated on, more CPU work. Then you can do the math, using the Decimal module, which is more work. The result then has to be converted back to a string and written back to the file. COBOL, on the other hand, understands record formats, understands decimal date, and knows that the hardware supports decimal data. So the same task (add to decimal number in record) that on Python may take many thousands of CPU instructions can be done in three: PACK (convert printable (zoned) decimal number to packed decimal, ADDPACKED (perform the addition), and UNPACK (back to printable number). And those three operations are performed in dedicated decimal hardware in the CPU. The important thing to remember is that in the mainframe world, not only is the hardware priced according to the performance of the machine, the software is also. This is why there are so many performance levels (over 100) to choose from. Users naturally want to buy the smallest machine they can get that will handle their workload, and they aren't interested in wasting any processor power doing things not absolutely required by their business needs. For this reason, you won't find any mainframe shops running traditional mainframe workload in a language like Python.