Apache Spark：无法将分组数据保存为CSV

时间：2018-05-15 12:41:59

标签： python apache-spark pyspark pyspark-sql

我想做一件简单的事情。我想将所有事件计数分为2分钟的时间戳。

工作正常。

df = df.groupBy(window(df["time_value"], "2 minutes")).count()

df.show()

+--------------------+-----+
| window|count|
+--------------------+-----+
|[2018-04-10 15:00...| 770|
|[2018-04-10 00:42...| 100|
|[2018-04-10 04:14...| 54|
|[2018-04-06 15:54...| 36|
|[2018-04-10 04:46...| 304|
|[2018-04-10 20:36...| 347|
|[2018-04-10 03:26...| 41|
|[2018-04-10 21:10...| 85|
|[2018-04-10 11:44...| 426|
|[2018-04-10 12:32...| 754|
|[2018-04-10 00:28...| 61|
|[2018-04-10 05:36...| 478|
|[2018-04-06 07:04...| 18|
|[2018-04-10 22:14...| 195|
|[2018-04-10 23:40...| 175|
|[2018-04-10 00:20...| 229|
|[2018-04-10 03:10...| 209|
|[2018-04-10 01:28...| 67|
|[2018-04-09 18:52...| 9|
|[2018-04-10 19:06...| 3548|
+--------------------+-----+
only showing top 20 rows

但现在我写了，

df.write.csv("good_loc.csv")

An error occurred while calling o161.csv.
: java.lang.UnsupportedOperationException: CSV data source does not support struct<start:timestamp,end:timestamp> data type.
at org.apache.spark.sql.execution.datasources.csv.CSVUtils$.org$apache$spark$sql$execution$datasources$csv$CSVUtils$$verifyType$1(CSVUtils.scala:127)
at org.apache.spark.sql.execution.datasources.csv.CSVUtils$$anonfun$verifySchema$1.apply(CSVUtils.scala:131)
at org.apache.spark.sql.execution.datasources.csv.CSVUtils$$anonfun$verifySchema$1.apply(CSVUtils.scala:131)
at scala.collection.Iterator$class.foreach(Iterator.scala:893)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
at scala.collection.IterableLike$class.foreach(IterableLike.scala:72)
at org.apache.spark.sql.types.StructType.foreach(StructType.scala:98)
at org.apache.spark.sql.execution.datasources.csv.CSVUtils$.verifySchema(CSVUtils.scala:131)
at org.apache.spark.sql.execution.datasources.csv.CSVFileFormat.prepareWrite(CSVFileFormat.scala:65)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:135)
at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand.run(InsertIntoHadoopFsRelationCommand.scala:145)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$lzycompute(commands.scala:58)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult(commands.scala:56)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.doExecute(commands.scala:74)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:117)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:117)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:138)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:135)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:116)
at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:92)
at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:92)
at org.apache.spark.sql.execution.datasources.DataSource.writeInFileFormat(DataSource.scala:437)
at org.apache.spark.sql.execution.datasources.DataSource.write(DataSource.scala:473)
at org.apache.spark.sql.execution.datasources.SaveIntoDataSourceCommand.run(SaveIntoDataSourceCommand.scala:50)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$lzycompute(commands.scala:58)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult(commands.scala:56)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.doExecute(commands.scala:74)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:117)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:117)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:138)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:135)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:116)
at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:92)
at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:92)
at org.apache.spark.sql.DataFrameWriter.runCommand(DataFrameWriter.scala:609)
at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:233)
at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:217)
at org.apache.spark.sql.DataFrameWriter.csv(DataFrameWriter.scala:597)
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:280)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:214)
at java.lang.Thread.run(Thread.java:748)

Traceback (most recent call last):
File "/usr/hdp/current/spark2-client/python/pyspark/sql/readwriter.py", line 766, in csv
self._jwrite.csv(path)
File "/usr/hdp/current/spark2-client/python/lib/py4j-0.10.4-src.zip/py4j/java_gateway.py", line 1133, in __call__
answer, self.gateway_client, self.target_id, self.name)
File "/usr/hdp/current/spark2-client/python/pyspark/sql/utils.py", line 63, in deco
return f(*a, **kw)
File "/usr/hdp/current/spark2-client/python/lib/py4j-0.10.4-src.zip/py4j/protocol.py", line 319, in get_return_value
format(target_id, ".", name), value)
Py4JJavaError: An error occurred while calling o161.csv.
: java.lang.UnsupportedOperationException: CSV data source does not support struct<start:timestamp,end:timestamp> data type.
at org.apache.spark.sql.execution.datasources.csv.CSVUtils$.org$apache$spark$sql$execution$datasources$csv$CSVUtils$$verifyType$1(CSVUtils.scala:127)
at org.apache.spark.sql.execution.datasources.csv.CSVUtils$$anonfun$verifySchema$1.apply(CSVUtils.scala:131)
at org.apache.spark.sql.execution.datasources.csv.CSVUtils$$anonfun$verifySchema$1.apply(CSVUtils.scala:131)
at scala.collection.Iterator$class.foreach(Iterator.scala:893)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
at scala.collection.IterableLike$class.foreach(IterableLike.scala:72)
at org.apache.spark.sql.types.StructType.foreach(StructType.scala:98)
at org.apache.spark.sql.execution.datasources.csv.CSVUtils$.verifySchema(CSVUtils.scala:131)
at org.apache.spark.sql.execution.datasources.csv.CSVFileFormat.prepareWrite(CSVFileFormat.scala:65)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:135)
at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand.run(InsertIntoHadoopFsRelationCommand.scala:145)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$lzycompute(commands.scala:58)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult(commands.scala:56)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.doExecute(commands.scala:74)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:117)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:117)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:138)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:135)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:116)
at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:92)
at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:92)
at org.apache.spark.sql.execution.datasources.DataSource.writeInFileFormat(DataSource.scala:437)
at org.apache.spark.sql.execution.datasources.DataSource.write(DataSource.scala:473)
at org.apache.spark.sql.execution.datasources.SaveIntoDataSourceCommand.run(SaveIntoDataSourceCommand.scala:50)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$lzycompute(commands.scala:58)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult(commands.scala:56)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.doExecute(commands.scala:74)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:117)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:117)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:138)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:135)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:116)
at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:92)
at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:92)
at org.apache.spark.sql.DataFrameWriter.runCommand(DataFrameWriter.scala:609)
at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:233)
at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:217)
at org.apache.spark.sql.DataFrameWriter.csv(DataFrameWriter.scala:597)
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:280)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:214)
at java.lang.Thread.run(Thread.java:748)

我无法理解错误的原因。我已经创建了数据框，我只是展示了它并且显示正常。现在，我只是想将它保存在csv中，不要了解正在发生的事情。

2 个答案:

答案 0 :(得分：3)

您的列window是一个StructType。因此，您不能将其直接写为CSV。你需要将它弄平一点。在2列start和end中将其转换为字符串，或将其结构展开。 CSV只能有“简单”类型，如字符串，整数或时间戳。

试试这个：

df.select(
    'window.start',
    'window.end',
    'count',
).write.csv("good_loc.csv")

答案 1 :(得分：2)

您无法将数组类型写入CSV。在保存之前，将字段窗口（数组）转换/更改为窗口（字符串）。 

from pyspark.sql.functions import udf
from pyspark.sql.types import StringType

def array_to_string(my_list):
    return '[' + ','.join([str(elem) for elem in my_list]) + ']'

array_to_string_udf = udf(array_to_string,StringType())

df = df.withColumn('window-stringified',array_to_string_udf(df["window"]))

然后您可以在保存之前删除旧列（数组类型）。

df.drop("window").write.csv(...)
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