F. H. created SPARK-35717:
-----------------------------
Summary: pandas_udf crashes in conjunction with .filter()
Key: SPARK-35717
URL: https://issues.apache.org/jira/browse/SPARK-35717
Project: Spark
Issue Type: Bug
Components: PySpark
Affects Versions: 3.1.2, 3.1.1, 3.0.0
Environment: Centos 8 with PySpark from conda
Reporter: F. H.
I wrote the following UDF that always returns some "byte"-type array:
{code:python}
from typing import Iterator
@f.pandas_udf(returnType=t.ByteType())
def spark_gt_mapping_fn(batch_iter: Iterator[pd.Series]) -> Iterator[pd.Series]:
mapping = dict()
mapping[(-1, -1)] = -1
mapping[(0, 0)] = 0
mapping[(0, 1)] = 1
mapping[(1, 0)] = 1
mapping[(1, 1)] = 2
def gt_mapping_fn(v):
if len(v) != 2:
return -3
else:
a, b = v
return mapping.get((a, b), -2)
for x in batch_iter:
yield x.apply(gt_mapping_fn).astype("int8")
{code}
However, every time I'd like to filter on the resulting column, I get the
following error:
{code:python}
# works:
(
df
.select(spark_gt_mapping_fn(f.col("genotype.calls")).alias("GT"))
.limit(10).toPandas()
)
# fails:
(
df
.select(spark_gt_mapping_fn(f.col("genotype.calls")).alias("GT"))
.filter("GT > 0")
.limit(10).toPandas()
)
{code}
{code:java}
Py4JJavaError: An error occurred while calling o672.collectToPython. :
org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in
stage 9.0 failed 4 times, most recent failure: Lost task 0.3 in stage 9.0 (TID
125) (ouga05.cmm.in.tum.de executor driver):
org.apache.spark.util.TaskCompletionListenerException: Memory was leaked by
query. Memory leaked: (16384) Allocator(stdin reader for python3)
0/16384/34816/9223372036854775807 (res/actual/peak/limit) at
org.apache.spark.TaskContextImpl.invokeListeners(TaskContextImpl.scala:145) at
org.apache.spark.TaskContextImpl.markTaskCompleted(TaskContextImpl.scala:124)
at org.apache.spark.scheduler.Task.run(Task.scala:147) at
org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:497)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1439) at
org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:500) at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748) Driver stacktrace: at
org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2258)
at
org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:2207)
at
org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:2206)
at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62) at
scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55) at
scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49) at
org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2206) at
org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:1079)
at
org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:1079)
at scala.Option.foreach(Option.scala:407) at
org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:1079)
at
org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2445)
at
org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2387)
at
org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2376)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49) at
org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:868) at
org.apache.spark.SparkContext.runJob(SparkContext.scala:2196) at
org.apache.spark.SparkContext.runJob(SparkContext.scala:2217) at
org.apache.spark.SparkContext.runJob(SparkContext.scala:2236) at
org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:472) at
org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:425) at
org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:47)
at org.apache.spark.sql.Dataset.$anonfun$collectToPython$1(Dataset.scala:3519)
at org.apache.spark.sql.Dataset.$anonfun$withAction$1(Dataset.scala:3687) at
org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:103)
at
org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:163)
at
org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:90)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:775) at
org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)
at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3685) at
org.apache.spark.sql.Dataset.collectToPython(Dataset.scala:3516) at
sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at
sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at
sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498) at
py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244) at
py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) at
py4j.Gateway.invoke(Gateway.java:282) at
py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132) at
py4j.commands.CallCommand.execute(CallCommand.java:79) at
py4j.GatewayConnection.run(GatewayConnection.java:238) at
java.lang.Thread.run(Thread.java:748) Caused by:
org.apache.spark.util.TaskCompletionListenerException: Memory was leaked by
query. Memory leaked: (16384) Allocator(stdin reader for python3)
0/16384/34816/9223372036854775807 (res/actual/peak/limit) at
org.apache.spark.TaskContextImpl.invokeListeners(TaskContextImpl.scala:145) at
org.apache.spark.TaskContextImpl.markTaskCompleted(TaskContextImpl.scala:124)
at org.apache.spark.scheduler.Task.run(Task.scala:147) at
org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:497)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1439) at
org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:500) at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
... 1 more
{code}
I tried this with different versions of PySpark and PyArrow, always with the
same result.
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