jorisvandenbossche commented on code in PR #41967:
URL: https://github.com/apache/arrow/pull/41967#discussion_r1634861855
##########
java/c/src/test/python/integration_tests.py:
##########
@@ -183,6 +190,55 @@ def round_trip_reader(self, schema, batches):
def test_string_array(self):
self.round_trip_array(lambda: pa.array([None, "a", "bb", "ccc"]))
+ def test_stringview_array(self):
+ # with nulls short strings
+ self.round_trip_array(lambda: pa.array([None, "a", "bb", "c"],
type=pa.string_view()))
+ # with nulls long and strings
+ self.round_trip_array(lambda: pa.array([None, "a", "bb"*10, "c"*13],
type=pa.string_view()))
+ # without nulls short strings
+ self.round_trip_array(lambda: pa.array(["a", "bb", "c"],
type=pa.string_view()))
+ # without nulls long and strings
+ self.round_trip_array(lambda: pa.array(["a", "bb"*10, "c"*13],
type=pa.string_view()))
+ # with multiple data buffers
+ data = []
+ for i in range(1, 501):
+ s = ''.join(str(j) for j in range(i))
+ data.append(s)
+ self.round_trip_array(lambda: pa.array(data, type=pa.string_view()))
Review Comment:
This will indeed create multiple data buffers, although that relies a bit on
the Builder's default size used for a data buffer. Another potentially slightly
easier way to create am array with multiple data buffers is by concatenating;
```
In [30]: arr1 = pa.array([None, "a", "bb", "c"], type=pa.string_view())
In [31]: arr2 = pa.array(["a", "bb"*10, "c"*13], type=pa.string_view())
In [32]: pa.concat_arrays([arr1, arr2]).buffers()
Out[32]:
[<pyarrow.Buffer address=0x7faeab209180 size=1 is_cpu=True is_mutable=True>,
<pyarrow.Buffer address=0x7faeab208100 size=112 is_cpu=True
is_mutable=True>,
<pyarrow.Buffer address=0x7faead33e5c0 size=0 is_cpu=True is_mutable=True>,
<pyarrow.Buffer address=0x7faeab2092c0 size=33 is_cpu=True is_mutable=True>]
```
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