PySpark sql数据帧熊猫UDF-java.lang.IllegalArgumentException：要求失败：十进制精度8超过最大精度7

Question

我使用的是Database ricks 4.3（包括Apache Spark 2.3.1、Scala 2.11）、Python版本3.5。

我有一个Spark数据帧df_spark我在上面运行了分组为UDF的熊猫，以获得一个新的Spark数据帧df_spark2，它只有一列字符串类型。当我显示df_spark2的头部时，我得到了错误：

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

我在熊猫数据框架上测试了按UDF分组的熊猫，效果很好。代码为：

sample = df[df.acct_id==10030255388]
reformat.func(sample)

我也可以成功地显示df_spark2的模式

df_spark_2.schema

我得到：StructType（List（StructField（generation，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

Answer 1

我认为这个错误是从你触发df\u spark\u 2的地方开始的。count（）。collect（）。如果只想计算数据帧的计数，只需使用count（）-

df_spark_2.count()

下面对这两种功能进行简单解释。

count（）-返回此数据帧中的行数。

Collect（）-在驱动程序中将数据集的所有元素作为数组返回。这通常在返回足够小的数据子集的过滤器或其他操作之后很有用。