PySpark 3 - 从列表列中删除项目的 UDF
PySpark 3 - UDF to remove items from list column
我正在数据框中创建一个列，它是一个包含 4 个结构的数组。它们中的任何一个都可以为 null，但由于我需要在此数组中包含固定数量的项，因此我需要在事后清除空项。我在尝试使用 UDF 删除空项时遇到错误。这是一个例子：
创建数据框，注意其中一个“a”值是None
spark = SparkSession.builder.getOrCreate()
df = spark.createDataFrame([{"a": "x", "b": "y", "c": "3"}, {"a": "1", "b": "9", "c": "G"}, {"a": None, "b": "Z", "c": "8"}])
用于删除空项的 UDF
@udf
def remove_null_items(s):
if s is not None:
return list(filter(None, s))
else:
return None
使用结构数组创建列
df = df.withColumn("name_list", func.array(
func.struct(func.col("a").alias("name")),
func.struct(func.col("b").alias("name")),
func.struct(func.col("c").alias("name")),
).alias("names"))
然后当我 运行 这个命令时：
df.select(remove_null_items('name_list')).show()
我收到这个错误：
Py4JJavaError: An error occurred while calling o241.showString.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 10 in stage 26.0 failed 1 times, most recent failure: Lost task 10.0 in stage 26.0 (TID 303, etl-01.iwave, executor driver): net.razorvine.pickle.PickleException: expected zero arguments for construction of ClassDict (for pyspark.sql.types._create_row)
at net.razorvine.pickle.objects.ClassDictConstructor.construct(ClassDictConstructor.java:23)
at net.razorvine.pickle.Unpickler.load_reduce(Unpickler.java:773)
at net.razorvine.pickle.Unpickler.dispatch(Unpickler.java:213)
at net.razorvine.pickle.Unpickler.load(Unpickler.java:123)
at net.razorvine.pickle.Unpickler.loads(Unpickler.java:136)
at org.apache.spark.sql.execution.python.BatchEvalPythonExec.$anonfun$evaluate(BatchEvalPythonExec.scala:83)
at scala.collection.Iterator$$anon.nextCur(Iterator.scala:484)
at scala.collection.Iterator$$anon.hasNext(Iterator.scala:490)
at scala.collection.Iterator$$anon.hasNext(Iterator.scala:458)
at scala.collection.Iterator$$anon.hasNext(Iterator.scala:458)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage2.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon.hasNext(WholeStageCodegenExec.scala:729)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd(SparkPlan.scala:340)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal(RDD.scala:872)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$adapted(RDD.scala:872)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:349)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:313)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:127)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run(Executor.scala:446)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:449)
at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128)
at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628)
at java.base/java.lang.Thread.run(Thread.java:829)
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2059)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage(DAGScheduler.scala:2008)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$adapted(DAGScheduler.scala:2007)
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:2007)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed(DAGScheduler.scala:973)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$adapted(DAGScheduler.scala:973)
at scala.Option.foreach(Option.scala:407)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:973)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2239)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2188)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2177)
at org.apache.spark.util.EventLoop$$anon.run(EventLoop.scala:49)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:775)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2099)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2120)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2139)
at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:467)
at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:420)
at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:47)
at org.apache.spark.sql.Dataset.collectFromPlan(Dataset.scala:3627)
at org.apache.spark.sql.Dataset.$anonfun$head(Dataset.scala:2697)
at org.apache.spark.sql.Dataset.$anonfun$withAction(Dataset.scala:3618)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId(SQLExecution.scala:100)
at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:160)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId(SQLExecution.scala:87)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:764)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)
at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3616)
at org.apache.spark.sql.Dataset.head(Dataset.scala:2697)
at org.apache.spark.sql.Dataset.take(Dataset.scala:2904)
at org.apache.spark.sql.Dataset.getRows(Dataset.scala:300)
at org.apache.spark.sql.Dataset.showString(Dataset.scala:337)
at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.base/java.lang.reflect.Method.invoke(Method.java:566)
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.base/java.lang.Thread.run(Thread.java:829)
Caused by: net.razorvine.pickle.PickleException: expected zero arguments for construction of ClassDict (for pyspark.sql.types._create_row)
at net.razorvine.pickle.objects.ClassDictConstructor.construct(ClassDictConstructor.java:23)
at net.razorvine.pickle.Unpickler.load_reduce(Unpickler.java:773)
at net.razorvine.pickle.Unpickler.dispatch(Unpickler.java:213)
at net.razorvine.pickle.Unpickler.load(Unpickler.java:123)
at net.razorvine.pickle.Unpickler.loads(Unpickler.java:136)
at org.apache.spark.sql.execution.python.BatchEvalPythonExec.$anonfun$evaluate(BatchEvalPythonExec.scala:83)
at scala.collection.Iterator$$anon.nextCur(Iterator.scala:484)
at scala.collection.Iterator$$anon.hasNext(Iterator.scala:490)
at scala.collection.Iterator$$anon.hasNext(Iterator.scala:458)
at scala.collection.Iterator$$anon.hasNext(Iterator.scala:458)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage2.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon.hasNext(WholeStageCodegenExec.scala:729)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd(SparkPlan.scala:340)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal(RDD.scala:872)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$adapted(RDD.scala:872)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:349)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:313)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:127)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run(Executor.scala:446)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:449)
at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128)
at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628)
... 1 more
有什么我应该做的不同的事情吗？
不需要UDF。您可以使用 Spark SQL filter
df.select(F.expr('filter(name_list, x -> x.name is not null) as newcol')).show()
+---------------+
| newcol|
+---------------+
|[[x], [y], [3]]|
|[[1], [9], [G]]|
| [[Z], [8]]|
+---------------+
如果你确实需要一个UDF，你可以使用：
@func.udf('array<struct<name:string>>')
def remove_null_items(s):
if s is not None:
return list(filter(lambda s: s['name'] is not None, s))
else:
return None
df.select(remove_null_items('name_list')).show()
+----------------------------+
|remove_null_items(name_list)|
+----------------------------+
| [[x], [y], [3]]|
| [[1], [9], [G]]|
| [[Z], [8]]|
+----------------------------+
我正在数据框中创建一个列，它是一个包含 4 个结构的数组。它们中的任何一个都可以为 null，但由于我需要在此数组中包含固定数量的项，因此我需要在事后清除空项。我在尝试使用 UDF 删除空项时遇到错误。这是一个例子：
创建数据框，注意其中一个“a”值是None
spark = SparkSession.builder.getOrCreate()
df = spark.createDataFrame([{"a": "x", "b": "y", "c": "3"}, {"a": "1", "b": "9", "c": "G"}, {"a": None, "b": "Z", "c": "8"}])
用于删除空项的 UDF
@udf
def remove_null_items(s):
if s is not None:
return list(filter(None, s))
else:
return None
使用结构数组创建列
df = df.withColumn("name_list", func.array(
func.struct(func.col("a").alias("name")),
func.struct(func.col("b").alias("name")),
func.struct(func.col("c").alias("name")),
).alias("names"))
然后当我 运行 这个命令时：
df.select(remove_null_items('name_list')).show()
我收到这个错误：
Py4JJavaError: An error occurred while calling o241.showString.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 10 in stage 26.0 failed 1 times, most recent failure: Lost task 10.0 in stage 26.0 (TID 303, etl-01.iwave, executor driver): net.razorvine.pickle.PickleException: expected zero arguments for construction of ClassDict (for pyspark.sql.types._create_row)
at net.razorvine.pickle.objects.ClassDictConstructor.construct(ClassDictConstructor.java:23)
at net.razorvine.pickle.Unpickler.load_reduce(Unpickler.java:773)
at net.razorvine.pickle.Unpickler.dispatch(Unpickler.java:213)
at net.razorvine.pickle.Unpickler.load(Unpickler.java:123)
at net.razorvine.pickle.Unpickler.loads(Unpickler.java:136)
at org.apache.spark.sql.execution.python.BatchEvalPythonExec.$anonfun$evaluate(BatchEvalPythonExec.scala:83)
at scala.collection.Iterator$$anon.nextCur(Iterator.scala:484)
at scala.collection.Iterator$$anon.hasNext(Iterator.scala:490)
at scala.collection.Iterator$$anon.hasNext(Iterator.scala:458)
at scala.collection.Iterator$$anon.hasNext(Iterator.scala:458)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage2.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon.hasNext(WholeStageCodegenExec.scala:729)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd(SparkPlan.scala:340)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal(RDD.scala:872)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$adapted(RDD.scala:872)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:349)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:313)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:127)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run(Executor.scala:446)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:449)
at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128)
at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628)
at java.base/java.lang.Thread.run(Thread.java:829)
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2059)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage(DAGScheduler.scala:2008)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$adapted(DAGScheduler.scala:2007)
at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2007)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed(DAGScheduler.scala:973)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$adapted(DAGScheduler.scala:973)
at scala.Option.foreach(Option.scala:407)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:973)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2239)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2188)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2177)
at org.apache.spark.util.EventLoop$$anon.run(EventLoop.scala:49)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:775)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2099)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2120)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2139)
at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:467)
at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:420)
at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:47)
at org.apache.spark.sql.Dataset.collectFromPlan(Dataset.scala:3627)
at org.apache.spark.sql.Dataset.$anonfun$head(Dataset.scala:2697)
at org.apache.spark.sql.Dataset.$anonfun$withAction(Dataset.scala:3618)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId(SQLExecution.scala:100)
at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:160)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId(SQLExecution.scala:87)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:764)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)
at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3616)
at org.apache.spark.sql.Dataset.head(Dataset.scala:2697)
at org.apache.spark.sql.Dataset.take(Dataset.scala:2904)
at org.apache.spark.sql.Dataset.getRows(Dataset.scala:300)
at org.apache.spark.sql.Dataset.showString(Dataset.scala:337)
at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.base/java.lang.reflect.Method.invoke(Method.java:566)
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.base/java.lang.Thread.run(Thread.java:829)
Caused by: net.razorvine.pickle.PickleException: expected zero arguments for construction of ClassDict (for pyspark.sql.types._create_row)
at net.razorvine.pickle.objects.ClassDictConstructor.construct(ClassDictConstructor.java:23)
at net.razorvine.pickle.Unpickler.load_reduce(Unpickler.java:773)
at net.razorvine.pickle.Unpickler.dispatch(Unpickler.java:213)
at net.razorvine.pickle.Unpickler.load(Unpickler.java:123)
at net.razorvine.pickle.Unpickler.loads(Unpickler.java:136)
at org.apache.spark.sql.execution.python.BatchEvalPythonExec.$anonfun$evaluate(BatchEvalPythonExec.scala:83)
at scala.collection.Iterator$$anon.nextCur(Iterator.scala:484)
at scala.collection.Iterator$$anon.hasNext(Iterator.scala:490)
at scala.collection.Iterator$$anon.hasNext(Iterator.scala:458)
at scala.collection.Iterator$$anon.hasNext(Iterator.scala:458)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage2.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon.hasNext(WholeStageCodegenExec.scala:729)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd(SparkPlan.scala:340)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal(RDD.scala:872)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$adapted(RDD.scala:872)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:349)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:313)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:127)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run(Executor.scala:446)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:449)
at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128)
at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628)
... 1 more
有什么我应该做的不同的事情吗？
不需要UDF。您可以使用 Spark SQL filter
df.select(F.expr('filter(name_list, x -> x.name is not null) as newcol')).show()
+---------------+
| newcol|
+---------------+
|[[x], [y], [3]]|
|[[1], [9], [G]]|
| [[Z], [8]]|
+---------------+
如果你确实需要一个UDF，你可以使用：
@func.udf('array<struct<name:string>>')
def remove_null_items(s):
if s is not None:
return list(filter(lambda s: s['name'] is not None, s))
else:
return None
df.select(remove_null_items('name_list')).show()
+----------------------------+
|remove_null_items(name_list)|
+----------------------------+
| [[x], [y], [3]]|
| [[1], [9], [G]]|
| [[Z], [8]]|
+----------------------------+