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Maybe we have drastically different use cases, but dynamic and weak typing are a disaster in the data space. Not sure why people build production systems in s

Maybe we have drastically different use cases, but dynamic and weak typing are a disaster in the data space. Not sure why people build production systems in such languages. If it matters anywhere it matters in the data space. We don't see the disconnect between decimal and int, but when you're expecting a character and you get varchar, (not sure about the apostrophe case, but I suspect your talking about quotes and embedded commas) and the number of fields or composition of fields changes (e.g. col1:sttring "jack, dorsey" col2:int 156 and the parser sees col1:string "jack" col2: "dorsey, 156" you want to know that is broken ASAP.

gregn610 10y ago
If you enjoy making sure your peas don't touch the carrots, then sure, strong typing is great in the data space. But when you get woken up because a spreadsheet comes through as 11.0 rather than 11 or you have to type Double.parseDouble(x) == Double.parseDouble(y) /* instead of pythons */ x == y 27 times to get a feed file parsed, then I'd say that the tools are lacking basic useability. And especially primitive in light of the handwriting reading, photo tagging, go playing,supermario winning possibilities of ML.
- pas 10y ago
  That means you don't have a clean "domain model" (or business logic layer) and a data filtering layer that creates the domain objects. You should apply business logic on the pure objects/entities, and you should make sure that the filtering handles the representational problems (parsing, data integrity, partial data, and so on). And ideally if something makes it through the data filtering layer into the logic layer and does not make sense there, then that should be handled. And that's where strong types help. It forces you to handle these cases, even if that means logging/alerting/ignoring, but at least you'll have to make a decision when you write the logic, instead of 3AM in the morning.

gregn610 10y ago

If you enjoy making sure your peas don't touch the carrots, then sure, strong typing is great in the data space. But when you get woken up because a spreadsheet comes through as 11.0 rather than 11 or you have to type Double.parseDouble(x) == Double.parseDouble(y) /* instead of pythons */ x == y 27 times to get a feed file parsed, then I'd say that the tools are lacking basic useability. And especially primitive in light of the handwriting reading, photo tagging, go playing,supermario winning possibilities of ML.

pas 10y ago
That means you don't have a clean "domain model" (or business logic layer) and a data filtering layer that creates the domain objects. You should apply business logic on the pure objects/entities, and you should make sure that the filtering handles the representational problems (parsing, data integrity, partial data, and so on). And ideally if something makes it through the data filtering layer into the logic layer and does not make sense there, then that should be handled. And that's where strong types help. It forces you to handle these cases, even if that means logging/alerting/ignoring, but at least you'll have to make a decision when you write the logic, instead of 3AM in the morning.