MINOR: [C++][Compute] Remove resolved TODO comments in case_when null validation

[HyukjinKwon]

Rationale for this change

arrow/cpp/src/arrow/compute/kernels/scalar_if_else.cc

Line 1771 in 8ccdbe7

/// TODO(wesm): should this be a DCHECK? Or checked elsewhere

arrow/cpp/src/arrow/compute/kernels/scalar_if_else.cc

Line 1819 in 8ccdbe7

/// TODO(wesm): should this be a DCHECK? Or checked elsewhere

arrow/cpp/src/arrow/compute/kernels/scalar_if_else.cc

Line 1861 in 8ccdbe7

/// TODO(wesm): should this be a DCHECK? Or checked elsewhere

arrow/cpp/src/arrow/compute/kernels/scalar_if_else.cc

Line 1903 in 8ccdbe7

/// TODO(wesm): should this be a DCHECK? Or checked elsewhere

arrow/cpp/src/arrow/compute/kernels/scalar_if_else.cc

Line 1921 in 8ccdbe7

/// TODO(wesm): should this be a DCHECK? Or checked elsewhere

arrow/cpp/src/arrow/compute/kernels/scalar_if_else.cc

Line 1939 in 8ccdbe7

/// TODO(wesm): should this be a DCHECK? Or checked elsewhere

arrow/cpp/src/arrow/compute/kernels/scalar_if_else.cc

Line 1967 in 8ccdbe7

/// TODO(wesm): should this be a DCHECK? Or checked elsewhere

arrow/cpp/src/arrow/compute/kernels/scalar_if_else.cc

Line 1981 in 8ccdbe7

/// TODO(wesm): should this be a DCHECK? Or checked elsewhere

are all user-facing checks so cannot be DCHECK.

(Generated by ChatGPT)

import pyarrow as pa
import pyarrow.compute as pc

print("=" * 60)
print("PyArrow case_when Validation Examples")
print("=" * 60)

# Helper function to test case_when
def test_case_when(name, cond_array, value1, value2, line_number):
    try:
        result = pc.case_when(cond_array, value1, value2)
        print(f"✅ {name}: Success")
        return True
    except pa.ArrowInvalid as e:
        print(f"❌ {name} (line {line_number}): {e}")
        return False

# Create condition structs
valid_cond = pa.array([
    {'c1': True}, {'c1': False}
], type=pa.struct([pa.field('c1', pa.bool_())]))

invalid_cond = pa.array([
    {'c1': True}, None  # ← OUTER NULL triggers validation!
], type=pa.struct([pa.field('c1', pa.bool_())]))

print("\n1. BINARY TYPE (line 1771 - enable_if_base_binary)")
print("-" * 60)
binary1 = pa.array([b'v1', b'v1'])
binary2 = pa.array([b'v2', b'v2'])
test_case_when("Valid binary", valid_cond, binary1, binary2, 1771)
test_case_when("Invalid binary", invalid_cond, binary1, binary2, 1771)

print("\n2. LIST TYPE (line 1818 - enable_if_var_size_list)")
print("-" * 60)
list1 = pa.array([[1, 2], [3, 4]])
list2 = pa.array([[5, 6], [7, 8]])
test_case_when("Valid list", valid_cond, list1, list2, 1818)
test_case_when("Invalid list", invalid_cond, list1, list2, 1818)

print("\n3. LIST_VIEW TYPE (line 1859 - enable_if_list_view)")
print("-" * 60)
listview1 = pa.array([[1, 2], [3, 4]], type=pa.list_view(pa.int64()))
listview2 = pa.array([[5, 6], [7, 8]], type=pa.list_view(pa.int64()))
test_case_when("Valid list_view", valid_cond, listview1, listview2, 1859)
test_case_when("Invalid list_view", invalid_cond, listview1, listview2, 1859)

print("\n4. MAP TYPE (line 1900)")
print("-" * 60)
map1 = pa.array([[('k1', 100)], [('k2', 200)]], type=pa.map_(pa.string(), pa.int64()))
map2 = pa.array([[('k3', 300)], [('k4', 400)]], type=pa.map_(pa.string(), pa.int64()))
test_case_when("Valid map", valid_cond, map1, map2, 1900)
test_case_when("Invalid map", invalid_cond, map1, map2, 1900)

print("\n5. STRUCT TYPE (line 1917)")
print("-" * 60)
struct1 = pa.array([{'x': 1}, {'x': 2}], type=pa.struct([pa.field('x', pa.int64())]))
struct2 = pa.array([{'x': 3}, {'x': 4}], type=pa.struct([pa.field('x', pa.int64())]))
test_case_when("Valid struct", valid_cond, struct1, struct2, 1917)
test_case_when("Invalid struct", invalid_cond, struct1, struct2, 1917)

print("\n6. FIXED_SIZE_LIST TYPE (line 1934)")
print("-" * 60)
fsl1 = pa.array([[1, 2], [3, 4]], type=pa.list_(pa.int64(), 2))
fsl2 = pa.array([[5, 6], [7, 8]], type=pa.list_(pa.int64(), 2))
test_case_when("Valid fixed_size_list", valid_cond, fsl1, fsl2, 1934)
test_case_when("Invalid fixed_size_list", invalid_cond, fsl1, fsl2, 1934)

print("\n7. DICTIONARY TYPE (line 1974)")
print("-" * 60)
dict1 = pa.array(['a', 'b']).dictionary_encode()
dict2 = pa.array(['c', 'd']).dictionary_encode()
test_case_when("Valid dictionary", valid_cond, dict1, dict2, 1974)
test_case_when("Invalid dictionary", invalid_cond, dict1, dict2, 1974)

print("\n" + "=" * 60)
print("KEY POINT: Outer nulls (None in struct) trigger validation")
print("           Inner nulls ({'c1': None}) are allowed")
print("=" * 60)

What changes are included in this PR?

This PR removes obsolete TODOs. They cannot be DCHECK.

Are these changes tested?

Yes, as described above.

Are there any user-facing changes?

No, dev-only.

[github-actions]

Thanks for opening a pull request!

If this is not a minor PR. Could you open an issue for this pull request on GitHub? https://github.com/apache/arrow/issues/new/choose

Opening GitHub issues ahead of time contributes to the Openness of the Apache Arrow project.

Then could you also rename the pull request title in the following format?

GH-${GITHUB_ISSUE_ID}: [${COMPONENT}] ${SUMMARY}

or

MINOR: [${COMPONENT}] ${SUMMARY}

See also:

• Other pull requests
• Contribution Guidelines - Contributing Overview

[kou]

+1

[conbench-apache-arrow]

After merging your PR, Conbench analyzed the 3 benchmarking runs that have been run so far on merge-commit 5b1af01.

There weren't enough matching historic benchmark results to make a call on whether there were regressions.

The full Conbench report has more details.