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Posted to commits@spark.apache.org by ru...@apache.org on 2025/08/07 01:42:01 UTC
(spark) branch master updated: [SPARK-52980][FOLLOWUP] Fix Python Arrow UDTF test cases
This is an automated email from the ASF dual-hosted git repository.
ruifengz pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/spark.git
The following commit(s) were added to refs/heads/master by this push:
new acfe6d461400 [SPARK-52980][FOLLOWUP] Fix Python Arrow UDTF test cases
acfe6d461400 is described below
commit acfe6d4614007baf65c344b63de5df903887f372
Author: Allison Wang <al...@databricks.com>
AuthorDate: Thu Aug 7 09:41:48 2025 +0800
[SPARK-52980][FOLLOWUP] Fix Python Arrow UDTF test cases
### What changes were proposed in this pull request?
This change fixes https://github.com/apache/spark/actions/runs/16772683954/job/47491522789 to be compatible with pyarrow=15.0, pandas=2.2.0 as minimum versions.
### Why are the changes needed?
to fix failed tests
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
CI
### Was this patch authored or co-authored using generative AI tooling?
no
Closes #51880 from allisonwang-db/fix-failed-tests.
Authored-by: Allison Wang <al...@databricks.com>
Signed-off-by: Ruifeng Zheng <ru...@apache.org>
---
python/pyspark/sql/tests/arrow/test_arrow_udtf.py | 13 ++++++++-----
1 file changed, 8 insertions(+), 5 deletions(-)
diff --git a/python/pyspark/sql/tests/arrow/test_arrow_udtf.py b/python/pyspark/sql/tests/arrow/test_arrow_udtf.py
index a3efa9c2bc5a..da15d48fceda 100644
--- a/python/pyspark/sql/tests/arrow/test_arrow_udtf.py
+++ b/python/pyspark/sql/tests/arrow/test_arrow_udtf.py
@@ -98,12 +98,15 @@ class ArrowUDTFTests(ReusedSQLTestCase):
size = batch_size[0].as_py()
for batch_id in range(3):
+ # Create arrays for each column
+ batch_id_array = pa.array([batch_id] * size, type=pa.int32())
+ name_array = pa.array([f"batch_{batch_id}"] * size, type=pa.string())
+ count_array = pa.array(list(range(size)), type=pa.int32())
+
+ # Create record batch from arrays and names
batch = pa.record_batch(
- {
- "batch_id": pa.array([batch_id] * size, type=pa.int32()),
- "name": pa.array([f"batch_{batch_id}"] * size, type=pa.string()),
- "count": pa.array(list(range(size)), type=pa.int32()),
- }
+ [batch_id_array, name_array, count_array],
+ names=["batch_id", "name", "count"],
)
yield batch
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