使用Jupyter笔记本电脑和PySpark。我首先创建一个数据框，然后执行.printSchematic()和.show()很好：
代码：
import pandas as pd
from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
# Example dataset
df_pd = pd.DataFrame(
data={'integers': [1, 2, 3],
'floats': [-1.0, 0.5, 2.7],
'integer_arrays': [[1, 2], [3, 4, 5], [6, 7, 8, 9]]}
)
df = spark.createDataFrame(df_pd)
df.printSchema()
df.show()
结果：
root
|-- floats: double (nullable = true)
|-- integer_arrays: array (nullable = true)
| |-- element: long (containsNull = true)
|-- integers: long (nullable = true)
+------+--------------+--------+
|floats|integer_arrays|integers|
+------+--------------+--------+
| -1.0| [1, 2]| 1|
| 0.5| [3, 4, 5]| 2|
| 2.7| [6, 7, 8, 9]| 3|
+------+--------------+--------+
然后我将其通过UDF，并且.printSchema()很好，但是.show()显示错误
代码：
# Python function
def square(x):
return x**2
# UDF
from pyspark.sql.functions import udf
from pyspark.sql.types import IntegerType
square_udf_int = udf(lambda z: square(z), IntegerType())
new_df = df.select('integers',
'floats',
square_udf_int('integers').alias('int_squared'),
square_udf_int('floats').alias('float_squared'))
new_df.printSchema()
new_df.show()
结果：
root
|-- integers: long (nullable = true)
|-- floats: double (nullable = true)
|-- int_squared: integer (nullable = true)
|-- float_squared: integer (nullable = true)
错误消息：
---------------------------------------------------------------------------
Py4JJavaError Traceback (most recent call last)
<ipython-input-246-f51d3e043cb1> in <module>()
----> 1 new_df.show()
D:\spark-2.3.1-bin-hadoop2.7\python\pyspark\sql\dataframe.pyc in show(self, n, truncate, vertical)
348 """
349 if isinstance(truncate, bool) and truncate:
--> 350 print(self._jdf.showString(n, 20, vertical))
351 else:
352 print(self._jdf.showString(n, int(truncate), vertical))
D:\spark-2.3.1-bin-hadoop2.7\python\lib\py4j-0.10.7-src.zip\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:
D:\spark-2.3.1-bin-hadoop2.7\python\pyspark\sql\utils.pyc 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()
D:\spark-2.3.1-bin-hadoop2.7\python\lib\py4j-0.10.7-src.zip\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 o5498.showString.
: java.lang.IllegalArgumentException
at org.apache.xbean.asm5.ClassReader.<init>(Unknown Source)
at org.apache.xbean.asm5.ClassReader.<init>(Unknown Source)
at org.apache.xbean.asm5.ClassReader.<init>(Unknown Source)
at org.apache.spark.util.ClosureCleaner$.getClassReader(ClosureCleaner.scala:46)
at org.apache.spark.util.FieldAccessFinder$$anon$3$$anonfun$visitMethodInsn$2.apply(ClosureCleaner.scala:449)
at org.apache.spark.util.FieldAccessFinder$$anon$3$$anonfun$visitMethodInsn$2.apply(ClosureCleaner.scala:432)
at scala.collection.TraversableLike$WithFilter$$anonfun$foreach$1.apply(TraversableLike.scala:733)
at scala.collection.mutable.HashMap$$anon$1$$anonfun$foreach$2.apply(HashMap.scala:103)
at scala.collection.mutable.HashMap$$anon$1$$anonfun$foreach$2.apply(HashMap.scala:103)
at scala.collection.mutable.HashTable$class.foreachEntry(HashTable.scala:230)
at scala.collection.mutable.HashMap.foreachEntry(HashMap.scala:40)
at scala.collection.mutable.HashMap$$anon$1.foreach(HashMap.scala:103)
at scala.collection.TraversableLike$WithFilter.foreach(TraversableLike.scala:732)
at org.apache.spark.util.FieldAccessFinder$$anon$3.visitMethodInsn(ClosureCleaner.scala:432)
at org.apache.xbean.asm5.ClassReader.a(Unknown Source)
at org.apache.xbean.asm5.ClassReader.b(Unknown Source)
at org.apache.xbean.asm5.ClassReader.accept(Unknown Source)
at org.apache.xbean.asm5.ClassReader.accept(Unknown Source)
at org.apache.spark.util.ClosureCleaner$$anonfun$org$apache$spark$util$ClosureCleaner$$clean$14.apply(ClosureCleaner.scala:262)
at org.apache.spark.util.ClosureCleaner$$anonfun$org$apache$spark$util$ClosureCleaner$$clean$14.apply(ClosureCleaner.scala:261)
at scala.collection.immutable.List.foreach(List.scala:381)
at org.apache.spark.util.ClosureCleaner$.org$apache$spark$util$ClosureCleaner$$clean(ClosureCleaner.scala:261)
at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:159)
at org.apache.spark.SparkContext.clean(SparkContext.scala:2299)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1.apply(RDD.scala:797)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1.apply(RDD.scala:796)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:363)
at org.apache.spark.rdd.RDD.mapPartitions(RDD.scala:796)
at org.apache.spark.sql.execution.python.EvalPythonExec.doExecute(EvalPythonExec.scala:93)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127)
at org.apache.spark.sql.execution.InputAdapter.inputRDDs(WholeStageCodegenExec.scala:371)
at org.apache.spark.sql.execution.ProjectExec.inputRDDs(basicPhysicalOperators.scala:41)
at org.apache.spark.sql.execution.BaseLimitExec$class.inputRDDs(limit.scala:62)
at org.apache.spark.sql.execution.LocalLimitExec.inputRDDs(limit.scala:97)
at org.apache.spark.sql.execution.WholeStageCodegenExec.doExecute(WholeStageCodegenExec.scala:605)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127)
at org.apache.spark.sql.execution.SparkPlan.getByteArrayRdd(SparkPlan.scala:247)
at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:337)
at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:38)
at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collectFromPlan(Dataset.scala:3273)
at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2484)
at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2484)
at org.apache.spark.sql.Dataset$$anonfun$52.apply(Dataset.scala:3254)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:77)
at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3253)
at org.apache.spark.sql.Dataset.head(Dataset.scala:2484)
at org.apache.spark.sql.Dataset.take(Dataset.scala:2698)
at org.apache.spark.sql.Dataset.showString(Dataset.scala:254)
at jdk.internal.reflect.GeneratedMethodAccessor50.invoke(Unknown Source)
at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.base/java.lang.reflect.Method.invoke(Method.java:564)
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:844)