[C++] Remove Scalar legacy cast implementation

[danepitkin]

Describe the enhancement requested

Let's delete the legacy implementation Scalar.CastTo() from Arrow.

Arrow has a compute kernel for cast operations that is now included in the base library[1]. PyArrow has migrated Scalar cast usage to this computer kernel[2].

[1] #34295
[2] #35395

Sub issues

• [C++] Add Compute Kernel for Casting from String to Dictionary #39049
• [C++] Add Compute Kernel for Casting between Different Timestamp Types #39050
• [C++] Add Compute Kernel for Casting from list_view to list #39051

Component(s)

C++, Python

[llama90]

Hello,

I am interested in this issue and looking into it.

First, I found functions that are using the legacy Scalar.CastTo() in the code.

I have reorganized these functions to call the Cast compute kernel where tests are possible.

file name	code snippet	note
file_parquet.cc	auto maybe_min = min->CastTo(field.type());	-
file_parquet.cc	auto maybe_max = max->CastTo(field.type());	-
partition_test.cc	ASSERT_OK_AND_ASSIGN(auto dict_hello, MakeScalar("hello")->CastTo(DictStr("")->type()));	1
partition_test.cc	ASSERT_OK_AND_ASSIGN(auto dict_hello, MakeScalar("hello")->CastTo(dict_type));	1
partition_test.cc	ASSERT_OK_AND_ASSIGN(auto day, StringScalar("2020-06-08").CastTo(temporal));	✅
scalar.cc	auto maybe_repr = CastTo(utf8());	-
scalar.cc	ARROW_ASSIGN_OR_RAISE(auto cast_value, from_.CastTo(dict_type.value_type()));	-
scalar.cc	return Int32Scalar(0).CastTo(dict_type.index_type()).Value(&out.index);	-
scalar.h	Result<std::shared_ptr<Scalar>> CastTo(std::shared_ptr<DataType> to) const;	-
scalar_test.cc	TimestampScalar(value, timestamp(in)).CastTo(timestamp(out)).ValueOrDie();	2
scalar_test.cc	TimestampScalar(1024, timestamp(TimeUnit::MILLI)).CastTo(utf8()));	2
scalar_test.cc	TimestampScalar(1024, timestamp(TimeUnit::MILLI)).CastTo(int64()));	2
scalar_test.cc	.CastTo(date64()));	2
scalar_test.cc	ASSERT_OK_AND_ASSIGN(auto cast_to_other, scalar->CastTo(other_type))	✅
scalar_test.cc	ASSERT_OK_AND_ASSIGN(auto cast_from_other, other_scalar->CastTo(type))	✅
scalar_test.cc	StringScalar(std::string(repr)).CastTo(type));	✅
scalar_test.cc	ASSERT_OK_AND_ASSIGN(auto cast_to_string, scalar->CastTo(utf8()));	✅
scalar_test.cc	EXPECT_OK_AND_ASSIGN(auto cast_scalar, scalar.CastTo(to_type));	3
scalar_test.cc	scalar.CastTo(to_type));	3
scalar_test.cc	ASSERT_OK_AND_ASSIGN(auto cast_alpha, alpha->CastTo(ty));	1

• 1: Unsupported cast from string to dictionary using function cast_dictionary
• 2: ValueOrDie called on an error: Invalid: Casting from timestamp[ns] to timestamp[us] would lose data: 1234
• 3: Unsupported cast from list_view<item: int16> to list using function cast_list
• In some functions, the same test cases may yield different errors depending on the additional cast kernel implementation.
  • This means that in some cases, the errors are indicated based on the error messages triggered by the functions executed first.

Looking at the table, the calling functions can be organized as follows:

1. Cases where the cast operation is implemented and calling the Cast function works without additional implementation.
2. Cases where additional implementation of the cast operation is needed.

For now, I have submitted a PR for the cases that are working happy case, and I am curious if this aligns with the intentions mentioned in the issue.

If PR aligns with the intent, I would like to proceed by separating the happy cases from those that require additional cast implementation, and I would appreciate your feedback.

[kou]

Let's create sub issues and use separated PRs!

[llama90]

Hello @kou

Thank you for your response.

I've created the issue excluding the happy cases and will first complete the PR for the happy cases.

I'm a bit concerned as I have never dealt with compute kernels and have not yet fully finalized the sorting for the dictionary for ChunkedArray and the other issues.

However, I've recently had more time to look into Arrow, so I will proceed step by step.

[kou]

Thanks!
I've added sub-issues to this issue description.