[ https://issues.apache.org/jira/browse/SPARK-30063?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16986438#comment-16986438 ]
Tim Kellogg commented on SPARK-30063: ------------------------------------- Additional notes on UDFs returning {{ArrayType(DoubleType())}}: * In pyarrow==0.8.0, the UDF return type must be a Python List. Returning pd.Series() will crash. * In pyarrow==0.14.1, you can return pd.Series() also, it'll recognize it as a Spark Array * In pyarrow==0.15.1 (latest), everything seems to break again, I believe it's due to a change in message format, introducing that leading -1 byte. So in short, pin to pyarrow==0.8.0 and coerce all arrays to native Python list before returning from the UDF. > Failure when returning a value from multiple Pandas UDFs > -------------------------------------------------------- > > Key: SPARK-30063 > URL: https://issues.apache.org/jira/browse/SPARK-30063 > Project: Spark > Issue Type: Bug > Components: PySpark > Affects Versions: 2.4.3, 2.4.4 > Environment: Happens on Mac & Ubuntu (Docker). Seems to happen on > both 2.4.3 and 2.4.4 > Reporter: Tim Kellogg > Priority: Major > Attachments: spark-debug.txt, variety-of-schemas.ipynb > > > I have 20 Pandas UDFs that I'm trying to evaluate all at the same time. > * PandasUDFType.GROUPED_AGG > * 3 columns in the input data frame being serialized over Arrow to Python > worker. See below for clarification. > * All functions take 2 parameters, some combination of the 3 received as > Arrow input. > * Varying return types, see details below. > _*I get an IllegalArgumentException on the Scala side of the worker when > deserializing from Python.*_ > h2. Exception & Stack Trace > {code:java} > 19/11/27 11:38:36 ERROR Executor: Exception in task 0.0 in stage 5.0 (TID 5) > java.lang.IllegalArgumentException > at java.nio.ByteBuffer.allocate(ByteBuffer.java:334) > at > org.apache.arrow.vector.ipc.message.MessageSerializer.readMessage(MessageSerializer.java:543) > at > org.apache.arrow.vector.ipc.message.MessageChannelReader.readNext(MessageChannelReader.java:58) > at > org.apache.arrow.vector.ipc.ArrowStreamReader.readSchema(ArrowStreamReader.java:132) > at > org.apache.arrow.vector.ipc.ArrowReader.initialize(ArrowReader.java:181) > at > org.apache.arrow.vector.ipc.ArrowReader.ensureInitialized(ArrowReader.java:172) > at > org.apache.arrow.vector.ipc.ArrowReader.getVectorSchemaRoot(ArrowReader.java:65) > at > org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:162) > at > org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:122) > at > org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:410) > at > org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37) > at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409) > at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409) > at > org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:255) > at > org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:247) > at > org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836) > at > org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836) > at > org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52) > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324) > at org.apache.spark.rdd.RDD.iterator(RDD.scala:288) > at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90) > at org.apache.spark.scheduler.Task.run(Task.scala:123) > at > org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408) > at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360) > at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414) > at > java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) > at > java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) > at java.lang.Thread.run(Thread.java:748) > 19/11/27 11:38:36 WARN TaskSetManager: Lost task 0.0 in stage 5.0 (TID 5, > localhost, executor driver): java.lang.IllegalArgumentException > at java.nio.ByteBuffer.allocate(ByteBuffer.java:334) > at > org.apache.arrow.vector.ipc.message.MessageSerializer.readMessage(MessageSerializer.java:543) > at > org.apache.arrow.vector.ipc.message.MessageChannelReader.readNext(MessageChannelReader.java:58) > at > org.apache.arrow.vector.ipc.ArrowStreamReader.readSchema(ArrowStreamReader.java:132) > at > org.apache.arrow.vector.ipc.ArrowReader.initialize(ArrowReader.java:181) > at > org.apache.arrow.vector.ipc.ArrowReader.ensureInitialized(ArrowReader.java:172) > at > org.apache.arrow.vector.ipc.ArrowReader.getVectorSchemaRoot(ArrowReader.java:65) > at > org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:162) > at > org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:122) > at > org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:410) > at > org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37) > at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409) > at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409) > at > org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:255) > at > org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:247) > at > org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836) > at > org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836) > at > org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52) > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324) > at org.apache.spark.rdd.RDD.iterator(RDD.scala:288) > at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90) > at org.apache.spark.scheduler.Task.run(Task.scala:123) > at > org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408) > at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360) > at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414) > at > java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) > at > java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) > at java.lang.Thread.run(Thread.java:748) > {code} > h2. Input Arrow Schema > I edited ArrowStreamPandasSerializer in pyspark/serializers.py to print out > schema & message. This is the input, in load_stream, the code is print(batch, > batch.schema, file=log_file) > {code:java} > <pyarrow.lib.RecordBatch object at 0x10640ecc8> > _0: double > _1: double > _2: double > metadata > -------- > OrderedDict() > {code} > h2. Output Arrow Schema > I edited ArrowStreamPandasSerializer in pyspark/serializers.py to print out > schema & message. This is the output, in dump_stream, the code is > print(batch, batch.schema, file=log_file) > {code:java} > <pyarrow.lib.RecordBatch object at 0x11ad5b638> _0: float > _1: float > _2: float > _3: int32 > _4: int32 > _5: int32 > _6: int32 > _7: int32 > _8: float > _9: float > _10: int32 > _11: list<item: float> > child 0, item: float > _12: list<item: float> > child 0, item: float > _13: float > _14: float > _15: int32 > _16: float > _17: list<item: float> > child 0, item: float > _18: list<item: float> > child 0, item: float > _19: float > {code} > h2. Arrow Message > I edited ArrowPythonReader.scala at line 163 to print out the Arrow message. > Debug code: > {code:java} > val fw = new java.io.FileWriter("spark-debug.txt", true) > try { > val buf = new Array[Byte](40000) > stream.read(buf) > fw.write(s"Spark reader\n") > for (b <- buf) { > fw.write(String.format("%02x", Byte.box(b))) > } > fw.write(s"\n") > } finally fw.close() > {code} > Debug output (some trailing 0's included for completeness). > {code:java} > 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 > {code} > > h2. Query Plan at Point of Failure > Right before the failure, I printed out the explain(True) output. > > {code:java} > == Parsed Logical Plan == > 'Project [structstojson(named_struct(), None) AS key#269, > unresolvedalias('accuracy, None), unresolvedalias('areaUnderPR, None), > unresolvedalias('areaUnderROC, None), unresolvedalias('confusionMatrix, > None), unresolvedalias('count, None), unresolvedalias('f1Score, None), > unresolvedalias('f1Score_0, None), unresolvedalias('positiveClassRate, None), > unresolvedalias('prCurve, None), unresolvedalias('precision, None), > unresolvedalias('precision_0, None), unresolvedalias('predictionRate, None), > unresolvedalias('recall, None), unresolvedalias('rocCurve, None), > unresolvedalias('specificity, None)] > +- Aggregate [udf(cast(label#146 as double), cast(prediction#15 as double)) > AS accuracy#232, _auc_pr(cast(label#146 as double), cast(probability#16 as > double)) AS areaUnderPR#233, udf(cast(label#146 as double), > cast(probability#16 as double)) AS areaUnderROC#225, > array(array(udf(cast(label#146 as double), cast(prediction#15 as double)), > udf(cast(label#146 as double), cast(prediction#15 as double))), > array(udf(cast(label#146 as double), cast(prediction#15 as double)), > udf(cast(label#146 as double), cast(prediction#15 as double)))) AS > confusionMatrix#238, _count(cast(label#146 as double)) AS count#234, > udf(cast(label#146 as double), cast(prediction#15 as double)) AS f1Score#231, > udf(cast(label#146 as double), cast(prediction#15 as double)) AS > f1Score_0#228, _rate(cast(label#146 as double)) AS positiveClassRate#227, > named_struct(x, udf(cast(label#146 as double), cast(probability#16 as > double)), y, udf(cast(label#146 as double), cast(probability#16 as double))) > AS prCurve#230, udf(cast(label#146 as double), cast(prediction#15 as double)) > AS precision#236, udf(cast(label#146 as double), cast(prediction#15 as > double)) AS precision_0#235, _rate(cast(prediction#15 as double)) AS > predictionRate#237, udf(cast(label#146 as double), cast(prediction#15 as > double)) AS recall#229, named_struct(x, udf(cast(label#146 as double), > cast(probability#16 as double)), y, udf(cast(label#146 as double), > cast(probability#16 as double))) AS rocCurve#239, udf(cast(label#146 as > double), cast(prediction#15 as double)) AS specificity#226] > +- Project [encounterID#13, dim1#11, dim2#12, prediction#15, > probability#16, model_id#23, label#146, test AS customer#156, foo AS > solution#157, bar AS insight#158, model AS model_name#159, 1.0 AS > version#160, model1 AS model_id#161, current_timestamp() AS timestamp#162] > +- Project [encounterID#13, dim1#11, dim2#12, prediction#15, > probability#16, model_id#23, label#146] > +- Join Inner, (encounterID#13 = encounterID#145) > :- Project [dim1#11, dim2#12, encounterID#13, prediction#15, > probability#16, model_id#23] > : +- Filter ((false || NOT test#40) = false) > : +- Project [dim1#11, dim2#12, encounterID#13, > prediction#15, probability#16, model_id#23, (true && (dim1#11 = foo)) AS > test#40] > : +- Project [dim1#11, dim2#12, encounterID#13, > prediction#15, probability#16, model_id#23] > : +- Join Cross > : :- LogicalRDD [dim1#11, dim2#12, encounterID#13, > label#14, prediction#15, probability#16], false > : +- LogicalRDD [model_id#23], false > +- Project [encounterID#145, label#146] > +- Join Cross > :- LogicalRDD [dim1#143, dim2#144, encounterID#145, > label#146, prediction#147, probability#148], false > +- LogicalRDD [model_id#23], false== Analyzed Logical Plan > == > key: string, accuracy: float, areaUnderPR: float, areaUnderROC: float, > confusionMatrix: array<array<int>>, count: int, f1Score: float, f1Score_0: > float, positiveClassRate: int, prCurve: > struct<x:array<float>,y:array<float>>, precision: float, precision_0: float, > predictionRate: int, recall: float, rocCurve: > struct<x:array<float>,y:array<float>>, specificity: float > Project [structstojson(named_struct(), Some(America/Los_Angeles)) AS key#269, > accuracy#232, areaUnderPR#233, areaUnderROC#225, confusionMatrix#238, > count#234, f1Score#231, f1Score_0#228, positiveClassRate#227, prCurve#230, > precision#236, precision_0#235, predictionRate#237, recall#229, rocCurve#239, > specificity#226] > +- Aggregate [udf(cast(label#146 as double), cast(prediction#15 as double)) > AS accuracy#232, _auc_pr(cast(label#146 as double), cast(probability#16 as > double)) AS areaUnderPR#233, udf(cast(label#146 as double), > cast(probability#16 as double)) AS areaUnderROC#225, > array(array(udf(cast(label#146 as double), cast(prediction#15 as double)), > udf(cast(label#146 as double), cast(prediction#15 as double))), > array(udf(cast(label#146 as double), cast(prediction#15 as double)), > udf(cast(label#146 as double), cast(prediction#15 as double)))) AS > confusionMatrix#238, _count(cast(label#146 as double)) AS count#234, > udf(cast(label#146 as double), cast(prediction#15 as double)) AS f1Score#231, > udf(cast(label#146 as double), cast(prediction#15 as double)) AS > f1Score_0#228, _rate(cast(label#146 as double)) AS positiveClassRate#227, > named_struct(x, udf(cast(label#146 as double), cast(probability#16 as > double)), y, udf(cast(label#146 as double), cast(probability#16 as double))) > AS prCurve#230, udf(cast(label#146 as double), cast(prediction#15 as double)) > AS precision#236, udf(cast(label#146 as double), cast(prediction#15 as > double)) AS precision_0#235, _rate(cast(prediction#15 as double)) AS > predictionRate#237, udf(cast(label#146 as double), cast(prediction#15 as > double)) AS recall#229, named_struct(x, udf(cast(label#146 as double), > cast(probability#16 as double)), y, udf(cast(label#146 as double), > cast(probability#16 as double))) AS rocCurve#239, udf(cast(label#146 as > double), cast(prediction#15 as double)) AS specificity#226] > +- Project [encounterID#13, dim1#11, dim2#12, prediction#15, > probability#16, model_id#23, label#146, test AS customer#156, foo AS > solution#157, bar AS insight#158, model AS model_name#159, 1.0 AS > version#160, model1 AS model_id#161, current_timestamp() AS timestamp#162] > +- Project [encounterID#13, dim1#11, dim2#12, prediction#15, > probability#16, model_id#23, label#146] > +- Join Inner, (encounterID#13 = encounterID#145) > :- Project [dim1#11, dim2#12, encounterID#13, prediction#15, > probability#16, model_id#23] > : +- Filter ((false || NOT test#40) = false) > : +- Project [dim1#11, dim2#12, encounterID#13, > prediction#15, probability#16, model_id#23, (true && (dim1#11 = foo)) AS > test#40] > : +- Project [dim1#11, dim2#12, encounterID#13, > prediction#15, probability#16, model_id#23] > : +- Join Cross > : :- LogicalRDD [dim1#11, dim2#12, encounterID#13, > label#14, prediction#15, probability#16], false > : +- LogicalRDD [model_id#23], false > +- Project [encounterID#145, label#146] > +- Join Cross > :- LogicalRDD [dim1#143, dim2#144, encounterID#145, > label#146, prediction#147, probability#148], false > +- LogicalRDD [model_id#23], false== Optimized Logical Plan > == > Aggregate [{} AS key#269, udf(label#146, prediction#15) AS accuracy#232, > _auc_pr(label#146, probability#16) AS areaUnderPR#233, udf(label#146, > probability#16) AS areaUnderROC#225, array(array(udf(label#146, > prediction#15), udf(label#146, prediction#15)), array(udf(label#146, > prediction#15), udf(label#146, prediction#15))) AS confusionMatrix#238, > _count(label#146) AS count#234, udf(label#146, prediction#15) AS f1Score#231, > udf(label#146, prediction#15) AS f1Score_0#228, _rate(label#146) AS > positiveClassRate#227, named_struct(x, udf(label#146, probability#16), y, > udf(label#146, probability#16)) AS prCurve#230, udf(label#146, prediction#15) > AS precision#236, udf(label#146, prediction#15) AS precision_0#235, > _rate(prediction#15) AS predictionRate#237, udf(label#146, prediction#15) AS > recall#229, named_struct(x, udf(label#146, probability#16), y, udf(label#146, > probability#16)) AS rocCurve#239, udf(label#146, prediction#15) AS > specificity#226] > +- Project [prediction#15, probability#16, label#146] > +- Join Inner, (encounterID#13 = encounterID#145) > :- Project [encounterID#13, prediction#15, probability#16] > : +- Filter ((isnotnull(test#40) && (NOT test#40 = false)) && > isnotnull(encounterID#13)) > : +- InMemoryRelation [dim1#11, dim2#12, encounterID#13, > prediction#15, probability#16, model_id#23, test#40], StorageLevel(disk, > memory, deserialized, 1 replicas) > : +- *(2) Project [dim1#11, dim2#12, encounterID#13, > prediction#15, probability#16, model_id#23, (dim1#11 = foo) AS test#40] > : +- CartesianProduct > : :- *(1) Project [dim1#11, dim2#12, encounterID#13, > prediction#15, probability#16] > : : +- Scan > ExistingRDD[dim1#11,dim2#12,encounterID#13,label#14,prediction#15,probability#16] > : +- Scan ExistingRDD[model_id#23] > +- Join Cross > :- Project [encounterID#145, label#146] > : +- Filter isnotnull(encounterID#145) > : +- LogicalRDD [dim1#143, dim2#144, encounterID#145, label#146, > prediction#147, probability#148], false > +- Project > +- LogicalRDD [model_id#23], false== Physical Plan == > !AggregateInPandas [udf(label#146, prediction#15), _auc_pr(label#146, > probability#16), udf(label#146, probability#16), udf(label#146, > prediction#15), udf(label#146, prediction#15), udf(label#146, prediction#15), > udf(label#146, prediction#15), _count(label#146), udf(label#146, > prediction#15), udf(label#146, prediction#15), _rate(label#146), > udf(label#146, probability#16), udf(label#146, probability#16), > udf(label#146, prediction#15), udf(label#146, prediction#15), > _rate(prediction#15), udf(label#146, prediction#15), udf(label#146, > probability#16), udf(label#146, probability#16), udf(label#146, > prediction#15)], [{} AS key#269, udf(label, prediction)#201 AS accuracy#232, > _auc_pr(label, probability)#209 AS areaUnderPR#233, udf(label, > probability)#208 AS areaUnderROC#225, array(array(udf(label, prediction)#213, > udf(label, prediction)#214), array(udf(label, prediction)#215, udf(label, > prediction)#216)) AS confusionMatrix#238, _count(label)#210 AS count#234, > udf(label, prediction)#206 AS f1Score#231, udf(label, prediction)#207 AS > f1Score_0#228, _rate(label)#212 AS positiveClassRate#227, named_struct(x, > udf(label, probability)#219, y, udf(label, probability)#220) AS prCurve#230, > udf(label, prediction)#202 AS precision#236, udf(label, prediction)#203 AS > precision_0#235, _rate(prediction)#211 AS predictionRate#237, udf(label, > prediction)#204 AS recall#229, named_struct(x, udf(label, probability)#217, > y, udf(label, probability)#218) AS rocCurve#239, udf(label, prediction)#205 > AS specificity#226] > +- Exchange SinglePartition > +- *(4) Project [prediction#15, probability#16, label#146] > +- *(4) BroadcastHashJoin [encounterID#13], [encounterID#145], Inner, > BuildLeft > :- BroadcastExchange HashedRelationBroadcastMode(List(input[0, > string, true])) > : +- *(1) Project [encounterID#13, prediction#15, probability#16] > : +- *(1) Filter ((isnotnull(test#40) && (NOT test#40 = false)) > && isnotnull(encounterID#13)) > : +- InMemoryTableScan [encounterID#13, prediction#15, > probability#16, test#40], [isnotnull(test#40), (NOT test#40 = false), > isnotnull(encounterID#13)] > : +- InMemoryRelation [dim1#11, dim2#12, > encounterID#13, prediction#15, probability#16, model_id#23, test#40], > StorageLevel(disk, memory, deserialized, 1 replicas) > : +- *(2) Project [dim1#11, dim2#12, > encounterID#13, prediction#15, probability#16, model_id#23, (dim1#11 = foo) > AS test#40] > : +- CartesianProduct > : :- *(1) Project [dim1#11, dim2#12, > encounterID#13, prediction#15, probability#16] > : : +- Scan > ExistingRDD[dim1#11,dim2#12,encounterID#13,label#14,prediction#15,probability#16] > : +- Scan ExistingRDD[model_id#23] > +- CartesianProduct > :- *(2) Project [encounterID#145, label#146] > : +- *(2) Filter isnotnull(encounterID#145) > : +- Scan > ExistingRDD[dim1#143,dim2#144,encounterID#145,label#146,prediction#147,probability#148] > +- *(3) Project > +- Scan ExistingRDD[model_id#23] > {code} > h2. Related Bugs > I have a related bug that I've gotten where the schema in the input Arrow > message was transmiitted incorrectly. In that case, the input schema should > have been <long, float, long> but was transmitted as <long, long, float>. As > a result, the float column was interpreted as a long (equivalent C code to > illustrate behavior: ) > {code:java} > long reinterpret(double floating_point_number) { > return *(long*)(&floating_point_number) > } > {code} > I got around this bug by making all 3 columns float and converting them to > long within the UDF via Pandas Series.apply(np.int). Strangely, a > Column.astype('float') didn't seem to have an effect, I had to make them > float at the source. > Along the way, I had trouble with [Python's dict keys being > non-deterministic|[https://stackoverflow.com/questions/14956313/why-is-dictionary-ordering-non-deterministic].] > This led columns being passed to GroupedData.agg() in different orders for > each worker and driver process. I've mitigated this by explicitly ordering > the columns before sending them to agg. I don't think this is an issue > anymore, but I'm calling it out just in case. > > -- 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