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https://issues.apache.org/jira/browse/SPARK-30063?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16987417#comment-16987417
]
Hyukjin Kwon commented on SPARK-30063:
--------------------------------------
{quote}
Set the PYTHONHASHSEED environment variable such that the whole cluster has the
same value. I suggest that the original value is still random, for the same
reason why it's random in the first place. This would alleviate a lot of crazy
PySpark bugs that appear like concurrency bugs.
{quote}
{{PYTHONHASHSEED}} I think we already do it.
{quote}
Make Spark friendlier to different Arrow versions, or force override the
PyArrow version so that it's harder to get it wrong.
{quote}
Yeah, we're trying to put a lot of efforts.
https://github.com/apache/spark/blob/master/docs/sql-pyspark-pandas-with-arrow.md#compatibiliy-setting-for-pyarrow--0150-and-spark-23x-24x
is one of the efforts we put. This incompatiblity was from Arrow for
clarification.
{quote}
Consider upgrading Arrow. Newer versions are even friendlier to use
{quote}
We already did SPARK-29376.
> 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.
>
>
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