我正在使用Databricks 4.3（包括Apache Spark 2.3.1，Scala 2.11），Python 3.5版。
我有一个Spark数据框df_spark，并且在其上对UDF进行了分组熊猫操作，以获取一个新的Spark数据框df_spark2，该数据框只有一列字符串类型。 当我显示df_spark2的头部时，出现错误：
org.apache.spark.SparkException: Job aborted due to stage failure: Task 18 in stage 12.0 failed 4 times, most recent failure: Lost task 18.3 in stage 12.0 (TID 1973, 10.96.133.5, executor 0): java.lang.IllegalArgumentException: requirement failed: Decimal" precision 8 exceeds max precision 7
我在熊猫数据框中测试了由UDF分组的熊猫，它运行良好。代码是：
sample = df[df.acct_id==10030255388]
reformat.func(sample)
我也可以成功显示df_spark2的架构
df_spark_2.schema
我得到了：StructType（List（StructField（Donation，StringType，true）））
df_spark = spark.createDataFrame(df)
from pyspark.sql.functions import pandas_udf, PandasUDFType
@pandas_udf('''Donation string''', PandasUDFType.GROUPED_MAP) # first parameter is the schema of the output dataframe
def reformat(df):
Donation = df[['amount','charges','organization','rowColor']].to_json(orient='records')
temp_dict = {}
temp_dict[df.acct_id.unique().item()] = Donation
temp_df = pd.DataFrame.from_dict(data = temp_dict, orient='index', columns=['Donation'])
return(temp_df)
df_spark_2 = df_spark.groupby("acct_id").apply(reformat)
#I got the messages: df_spark_2:pyspark.sql.dataframe.DataFrame = [Donation: string]
df_spark_2.head()
#here is where I got error messages.
---------------------------------------------------------------------------
Py4JJavaError Traceback (most recent call last)
<command-2141407> in <module>()
----> 1 df_spark_2.head()
/databricks/spark/python/pyspark/sql/dataframe.py in head(self, n)
1193 """
1194 if n is None:
-> 1195 rs = self.head(1)
1196 return rs[0] if rs else None
1197 return self.take(n)
/databricks/spark/python/pyspark/sql/dataframe.py in head(self, n)
1195 rs = self.head(1)
1196 return rs[0] if rs else None
-> 1197 return self.take(n)
1198
1199 @ignore_unicode_prefix
/databricks/spark/python/pyspark/sql/dataframe.py in take(self, num)
520 [Row(age=2, name=u'Alice'), Row(age=5, name=u'Bob')]
521 """
--> 522 return self.limit(num).collect()
523
524 @since(1.3)
/databricks/spark/python/pyspark/sql/dataframe.py in collect(self)
479 # Default path used in OSS Spark / for non-DF-ACL clusters:
480 with SCCallSiteSync(self._sc) as css:
--> 481 sock_info = self._jdf.collectToPython()
482 return list(_load_from_socket(sock_info, BatchedSerializer(PickleSerializer())))
483
/databricks/spark/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:
/databricks/spark/python/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()
/databricks/spark/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 o332.collectToPython.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 44 in stage 3.0 failed 4 times, most recent failure: Lost task 44.3 in stage 3.0 (TID 329, 10.96.134.14, executor 1): java.lang.IllegalArgumentException: requirement failed: Decimal precision 8 exceeds max precision 7
at scala.Predef$.require(Predef.scala:224)
at org.apache.spark.sql.types.Decimal.set(Decimal.scala:114)
at org.apache.spark.sql.types.Decimal$.apply(Decimal.scala:453)
at org.apache.spark.sql.types.Decimal.apply(Decimal.scala)
at org.apache.spark.sql.vectorized.ArrowColumnVector$DecimalAccessor.getDecimal(ArrowColumnVector.java:360)
at org.apache.spark.sql.vectorized.ArrowColumnVector.getDecimal(ArrowColumnVector.java:105)
at org.apache.spark.sql.execution.vectorized.MutableColumnarRow.getDecimal(MutableColumnarRow.java:130)
/databricks/spark/python/lib/py4j-0.10.7-src.zip/py4j/java_gateway.py in __call__(self, *args)
1255 answer = self.gateway_client.send_command(command)
1256 return_value = get_return_value(
-> 1257 answer, self.gateway_client, self.target_id, self.name)
1258
1259 for temp_arg in temp_args:
/databricks/spark/python/pyspark/sql/utils.py in deco(*a, **kw)
61 def deco(*a, **kw):
62 try:
---> 63 return f(*a, **kw)
64 except py4j.protocol.Py4JJavaError as e:
65 s = e.java_exception.toString()
/databricks/spark/python/lib/py4j-0.10.7-src.zip/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
326 raise Py4JJavaError(
327 "An error occurred while calling {0}{1}{2}.\n".
--> 328 format(target_id, ".", name), value)
329 else:
330 raise Py4JError(
Py4JJavaError: An error occurred while calling o332.collectToPython.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 44 in stage 3.0 failed 4 times, most recent failure: Lost task 44.3 in stage 3.0 (TID 329, 10.96.134.14, executor 1): java.lang.IllegalArgumentException: requirement failed: Decimal precision 8 exceeds max precision 7
at scala.Predef$.require(Predef.scala:224)
at org.apache.spark.sql.types.Decimal.set(Decimal.scala:114)
at org.apache.spark.sql.types.Decimal$.apply(Decimal.scala:453)
at org.apache.spark.sql.types.Decimal.apply(Decimal.scala)
at org.apache.spark.sql.vectorized.ArrowColumnVector$DecimalAccessor.getDecimal(ArrowColumnVector.java:360)
at org.apache.spark.sql.vectorized.ArrowColumnVector.getDecimal(ArrowColumnVector.java:105)
at org.apache.spark.sql.execution.vectorized.MutableColumnarRow.getDecimal(MutableColumnarRow.java:130)
at org.apache.spark.sql.execution.collect.Collector$.collect(Collector.scala:64)
at org.apache.spark.sql.execution.collect.Collector$.collect(Collector.scala:70)
at org.apache.spark.sql.execution.ResultCacheManager.getOrComputeResult(ResultCacheManager.scala:497)
at org.apache.spark.sql.execution.CollectLimitExec.executeCollectResult(limit.scala:48)
at org.apache.spark.sql.Dataset$$anonfun$collectToPython$1.apply(Dataset.scala:3236)
at org.apache.spark.sql.Dataset$$anonfun$collectToPython$1.apply(Dataset.scala:3234)
at org.apache.spark.sql.Dataset$$anonfun$53.apply(Dataset.scala:3334)
at org.apache.spark.sql.execution.SQLExecution$$anonfun$withCustomExecutionEnv$1.apply(SQLExecution.scala:89)
at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:175)
at org.apache.spark.sql.execution.SQLExecution$.withCustomExecutionEnv(SQLExecution.scala:84)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:126)
at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3333)
at org.apache.spark.sql.Dataset.collectToPython(Dataset.scala:3234)
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:380)
at py4j.Gateway.invoke(Gateway.java:295)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:251)
at java.lang.Thread.run(Thread.java:748)
Caused by: java.lang.IllegalArgumentException: requirement failed: Decimal precision 8 exceeds max precision 7
at scala.Predef$.require(Predef.scala:224)
at org.apache.spark.sql.types.Decimal.set(Decimal.scala:114)
at org.apache.spark.sql.types.Decimal$.apply(Decimal.scala:453)
at org.apache.spark.sql.types.Decimal.apply(Decimal.scala)
at org.apache.spark.sql.vectorized.ArrowColumnVector$DecimalAccessor.getDecimal(ArrowColumnVector.java:360)
at org.apache.spark.sql.vectorized.ArrowColumnVector.getDecimal(ArrowColumnVector.java:105)
at org.apache.spark.sql.execution.vectorized.MutableColumnarRow.getDecimal(MutableColumnarRow.java:130)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$10$$anon$1.hasNext(WholeStageCodegenExec.scala:620)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
at org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:148)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:96)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53)
at org.apache.spark.scheduler.Task.run(Task.scala:112)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:384)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
... 1 more
答案 0 :(得分：0)
我认为错误是从触发df_spark_2.count（）。collect（）的地方开始的。 如果只想获取数据帧的计数，则只需使用count（）-
df_spark_2.count()
下面是这两种功能的简单说明。
count（）-返回此DataFrame中的行数。
Collect（）-将数据集的所有元素作为数组返回到 驱动程序。通常在过滤器或其他过滤器之后很有用 返回足够小的数据子集的操作。