AnandInguva commented on code in PR #29938:
URL: https://github.com/apache/beam/pull/29938#discussion_r1450898762
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sdks/python/apache_beam/ml/transforms/base_test.py:
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@@ -588,5 +590,51 @@ def test_with_same_local_artifact_location(self):
artifact_location=artifact_location)
+class MLTransformDLQTest(unittest.TestCase):
+ def setUp(self) -> None:
+ self.artifact_location = tempfile.mkdtemp()
+
+ def tearDown(self):
+ shutil.rmtree(self.artifact_location)
+
+ def test_dlq_with_embeddings(self):
+ with beam.Pipeline() as p:
+ good, bad = (
+ p
+ | beam.Create([{
+ 'x': 1
+ },
+ {
+ 'x': 3,
+ },
+ {
+ 'x': 'Hello'
+ }
+ ],
+ )
+ | base.MLTransform(
+ write_artifact_location=self.artifact_location).with_transform(
+ FakeEmbeddingsManager(
+ columns=['x'])).with_exception_handling())
+
+ good_expected_elements = [{'x': 'olleH'}]
+
+ assert_that(
+ good,
+ equal_to(good_expected_elements),
+ label='good',
+ )
+
+ # batching happens in RunInference hence elements
+ # are in lists in the bad pcoll.
Review Comment:
>> I don't think this comment makes sense, the errors are because we're
throwing on non-strings, right?
IIUC, Yes, that is right but in `RunInference`, the scalars/strings are
converted to List using beam.BatchElements, and when that error occurs, the
list is sent to DLQ.
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