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I am trying Pandas UDF and facing the IllegalArgumentException. I also tried replicating examples from PySpark Documentation GroupedData to check but still getting the error.
Following is the environment configuration
from pyspark.sql.functions import pandas_udf, PandasUDFType
@pandas_udf('int', PandasUDFType.GROUPED_AGG)
def min_udf(v):
return v.min()
sorted(gdf.agg(min_udf(df.age)).collect())
Output
Py4JJavaError Traceback (most recent call last)
<ipython-input-66-94a0a39bfe30> in <module>
----> 1 sorted(gdf.agg(min_udf(sample_data.sqft)).collect())
~/Desktop/test/venv/lib/python3.7/site-packages/pyspark/sql/dataframe.py in collect(self)
532 """
533 with SCCallSiteSync(self._sc) as css:
--> 534 sock_info = self._jdf.collectToPython()
535 return list(_load_from_socket(sock_info, BatchedSerializer(PickleSerializer())))
536
~/Desktop/test/venv/lib/python3.7/site-packages/py4j/java_gateway.py in __call__(self, *args)
1255 answer = self.gateway_client.send_command(command)
1256 return_value = get_return_value(
-> 1257 answer, self.gateway_client, self.target_id, self.name)
1258
1259 for temp_arg in temp_args:
~/Desktop/test/venv/lib/python3.7/site-packages/pyspark/sql/utils.py in deco(*a, **kw)
61 def deco(*a, **kw):
62 try:
---> 63 return f(*a, **kw)
64 except py4j.protocol.Py4JJavaError as e:
65 s = e.java_exception.toString()
~/Desktop/test/venv/lib/python3.7/site-packages/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
326 raise Py4JJavaError(
327 "An error occurred while calling {0}{1}{2}.\n".
--> 328 format(target_id, ".", name), value)
329 else:
330 raise Py4JError(
Py4JJavaError: An error occurred while calling o665.collectToPython.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 2 in stage 25.0 failed 1 times, most recent failure: Lost task 2.0 in stage 25.0 (TID 232, 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:858)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:858)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:310)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:123)
at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1891)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1879)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1878)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1878)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:927)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:927)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:927)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2112)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2061)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2050)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:738)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2061)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2082)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2101)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2126)
at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:990)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:385)
at org.apache.spark.rdd.RDD.collect(RDD.scala:989)
at org.apache.spark.sql.execution.SparkPlan.executeCollect(SparkPlan.scala:299)
at org.apache.spark.sql.Dataset$$anonfun$collectToPython$1.apply(Dataset.scala:3263)
at org.apache.spark.sql.Dataset$$anonfun$collectToPython$1.apply(Dataset.scala:3260)
at org.apache.spark.sql.Dataset$$anonfun$52.apply(Dataset.scala:3370)
at org.apache.spark.sql.execution.SQLExecution$$anonfun$withNewExecutionId$1.apply(SQLExecution.scala:80)
at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:127)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:75)
at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3369)
at org.apache.spark.sql.Dataset.collectToPython(Dataset.scala:3260)
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: 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:858)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:858)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:310)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:123)
at org.apache.spark.executor.Executor$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)
... 1 more
414
Pandas UDFs are user defined functions that are executed by Spark using Arrow to transfer data and Pandas to work with the data, which allows vectorized operations. A Pandas UDF is defined using the pandas_udf as a decorator or to wrap the function, and no additional configuration is required.
If you install PySpark using pip, then PyArrow can be brought in as an extra dependency of the SQL module with the command pip install pyspark[sql] . Otherwise, you must ensure that PyArrow is installed and available on all cluster nodes. You can install using pip or conda from the conda-forge channel.
Description. User-Defined Aggregate Functions (UDAFs) are user-programmable routines that act on multiple rows at once and return a single aggregated value as a result. This documentation lists the classes that are required for creating and registering UDAFs.
Since Spark 3.2, a new library called Pandas on Spark API was integrated into PySpark. We will see why and when it should be used. Pandas is a very powerful library that all data scientists know, but Pandas code can only run on a single machine, so it does not scale out.
Using “When Otherwise” on DataFrame. PySpark SQL “Case When” on DataFrame. PySpark When Otherwise – when () is a SQL function that returns a Column type and otherwise () is a function of Column, if otherwise () is not used, it returns a None/NULL value.
PySpark SQL “Case When” on DataFrame. PySpark When Otherwise – when () is a SQL function that returns a Column type and otherwise () is a function of Column, if otherwise () is not used, it returns a None/NULL value. PySpark SQL Case When – This is similar to SQL expression, Usage: CASE WHEN cond1 THEN result WHEN cond2 THEN result...
PySpark When Otherwise – when () is a SQL function that returns a Column type and otherwise () is a function of Column, if otherwise () is not used, it returns a None/NULL value. PySpark SQL Case When – This is similar to SQL expression, Usage: CASE WHEN cond1 THEN result WHEN cond2 THEN result...
Clause WHEN takes a condition, if condition true it returns a value from THEN If the condition is false it goes to the next condition and so on. If none of the condition matches, it returns a value from the ELSE clause. Below example uses PySpark SQL expr () Function to express SQL like expressions.
This is caused by incompatibility between Spark and Arrow libraries. In general each Spark release supports only a narrow range of Arrow versions (within minor release). Additionally there are some format incompatibilities between Arrow versions.
You can check the official documentation for details
Compatibility Setting for PyArrow >= 0.15.0 and Spark 2.3.x, 2.4.x
Since Arrow 0.15.0, a change in the binary IPC format requires an environment variable to be compatible with previous versions of Arrow <= 0.14.1. This is only necessary to do for PySpark users with versions 2.3.x and 2.4.x that have manually upgraded PyArrow to 0.15.0. The following can be added to conf/spark-env.sh to use the legacy Arrow IPC format:
ARROW_PRE_0_15_IPC_FORMAT=1This will instruct PyArrow >= 0.15.0 to use the legacy IPC format with the older Arrow Java that is in Spark 2.3.x and 2.4.x. Not setting this environment variable will lead to a similar error as described in SPARK-29367 when running pandas_udfs or toPandas() with Arrow enabled. More information about the Arrow IPC change can be read on the Arrow 0.15.0 release blog.
In practice I'd recommend using exactly the same Arrow version as used for build:
etc.
192
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