Takuya Ueshin created SPARK-33277: ------------------------------------- Summary: Python/Pandas UDF right after off-heap vectorized reader could cause executor crash. Key: SPARK-33277 URL: https://issues.apache.org/jira/browse/SPARK-33277 Project: Spark Issue Type: Bug Components: PySpark, SQL Affects Versions: 3.0.1 Reporter: Takuya Ueshin
Python/Pandas UDF right after off-heap vectorized reader could cause executor crash. E.g.,: {code:java} spark.range(0, 100000, 1, 1).write.parquet(path) spark.conf.set("spark.sql.columnVector.offheap.enabled", True) def f(x): return 0 fUdf = udf(f, LongType()) spark.read.parquet(path).select(fUdf('id')).head() {code} This is because, the Python evaluation consumes the parent iterator in a separate thread and it consumes more data from the parent even after the task ends and the parent is closed. If an off-heap column vector exists in the parent iterator, it could cause segmentation fault which crashes the executor. -- This message was sent by Atlassian Jira (v8.3.4#803005) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org