我需要从我的 Databricks 笔记本将 pyspark Dataframe 发送到 Eventhub。问题发生在这部分代码:
ehWriteConf = {
'eventhubs.connectionString' : EVENT_HUB_CONNECTION_STRING
}
def send_to_eventhub(df:DataFrame):
ds = df.select(struct(*[c for c in df.columns]).alias("body"))\
.select("body")\
.write.format("eventhubs")\
.options(**ehWriteConf)\
.save()
我在对数据帧进行一些处理后调用此方法:
# write feature_df into our EventHub
send_to_eventhub(feature_df)
一些类似的问题表明这是一个库版本问题，因此我已经尝试了我找到的几个答案，例如安装以下库的兼容版本:
com.microsoft.azure:azure-eventhubs-spark_2.12:2.3.22
但这是我收到的错误消息:
java.lang.NoSuchMethodError: org.apache.spark.sql.AnalysisException.<init>(Ljava/lang/String;Lscala/Option;Lscala/Option;Lscala/Option;Lscala/Option;)V
---------------------------------------------------------------------------
Py4JJavaError Traceback (most recent call last)
<command-37526120346879> in <module>
5 # write feature_df into our EventHub
6
----> 7 send_to_eventhub(feature_df)
8
9 # implement reading data from EventHub through a loop in print statement
<command-2498519353602292> in send_to_eventhub(df)
34 # .format("org.apache.spark.sql.eventhubs.EventHubsSourceProvider")\
35 # .format("org.apache.spark.sql.eventhubs.EventHubsSourceProvider")
---> 36 ds = df.select(struct(*[c for c in df.columns]).alias("body"))\
37 .select("body")\
38 .write.format("eventhubs")\
/databricks/spark/python/pyspark/sql/readwriter.py in save(self, path, format, mode, partitionBy, **options)
736 self.format(format)
737 if path is None:
--> 738 self._jwrite.save()
739 else:
740 self._jwrite.save(path)
/databricks/spark/python/lib/py4j-0.10.9.1-src.zip/py4j/java_gateway.py in __call__(self, *args)
1302
1303 answer = self.gateway_client.send_command(command)
-> 1304 return_value = get_return_value(
1305 answer, self.gateway_client, self.target_id, self.name)
1306
/databricks/spark/python/pyspark/sql/utils.py in deco(*a, **kw)
115 def deco(*a, **kw):
116 try:
--> 117 return f(*a, **kw)
118 except py4j.protocol.Py4JJavaError as e:
119 converted = convert_exception(e.java_exception)
/databricks/spark/python/lib/py4j-0.10.9.1-src.zip/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
324 value = OUTPUT_CONVERTER[type](answer[2:], gateway_client)
325 if answer[1] == REFERENCE_TYPE:
--> 326 raise Py4JJavaError(
327 "An error occurred while calling {0}{1}{2}.\n".
328 format(target_id, ".", name), value)
Py4JJavaError: An error occurred while calling o1187.save.
: java.lang.NoSuchMethodError: org.apache.spark.sql.AnalysisException.<init>(Ljava/lang/String;Lscala/Option;Lscala/Option;Lscala/Option;Lscala/Option;)V
at org.apache.spark.sql.eventhubs.EventHubsWriter$.validateQuery(EventHubsWriter.scala:58)
at org.apache.spark.sql.eventhubs.EventHubsWriter$.write(EventHubsWriter.scala:70)
at org.apache.spark.sql.eventhubs.EventHubsSourceProvider.createRelation(EventHubsSourceProvider.scala:124)
at org.apache.spark.sql.execution.datasources.SaveIntoDataSourceCommand.run(SaveIntoDataSourceCommand.scala:47)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$lzycompute(commands.scala:80)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult(commands.scala:78)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.executeCollect(commands.scala:89)
at org.apache.spark.sql.execution.QueryExecution$$anonfun$$nestedInanonfun$eagerlyExecuteCommands$1$1.$anonfun$applyOrElse$1(QueryExecution.scala:160)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withCustomExecutionEnv$8(SQLExecution.scala:239)
at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:386)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withCustomExecutionEnv$1(SQLExecution.scala:186)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:968)
at org.apache.spark.sql.execution.SQLExecution$.withCustomExecutionEnv(SQLExecution.scala:141)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:336)
at org.apache.spark.sql.execution.QueryExecution$$anonfun$$nestedInanonfun$eagerlyExecuteCommands$1$1.applyOrElse(QueryExecution.scala:160)
at org.apache.spark.sql.execution.QueryExecution$$anonfun$$nestedInanonfun$eagerlyExecuteCommands$1$1.applyOrElse(QueryExecution.scala:156)
at org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$transformDownWithPruning$1(TreeNode.scala:575)
at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:167)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformDownWithPruning(TreeNode.scala:575)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.org$apache$spark$sql$catalyst$plans$logical$AnalysisHelper$$super$transformDownWithPruning(LogicalPlan.scala:30)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.transformDownWithPruning(AnalysisHelper.scala:268)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.transformDownWithPruning$(AnalysisHelper.scala:264)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.transformDownWithPruning(LogicalPlan.scala:30)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.transformDownWithPruning(LogicalPlan.scala:30)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:551)
at org.apache.spark.sql.execution.QueryExecution.$anonfun$eagerlyExecuteCommands$1(QueryExecution.scala:156)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.allowInvokingTransformsInAnalyzer(AnalysisHelper.scala:324)
at org.apache.spark.sql.execution.QueryExecution.eagerlyExecuteCommands(QueryExecution.scala:156)
at org.apache.spark.sql.execution.QueryExecution.commandExecuted$lzycompute(QueryExecution.scala:141)
at org.apache.spark.sql.execution.QueryExecution.commandExecuted(QueryExecution.scala:132)
at org.apache.spark.sql.execution.QueryExecution.assertCommandExecuted(QueryExecution.scala:186)
at org.apache.spark.sql.DataFrameWriter.runCommand(DataFrameWriter.scala:959)
at org.apache.spark.sql.DataFrameWriter.saveToV1Source(DataFrameWriter.scala:427)
at org.apache.spark.sql.DataFrameWriter.saveInternal(DataFrameWriter.scala:396)
at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:258)
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)
问题之一是不太清楚没有找到什么方法。
我运行笔记本的集群详细信息是:
最佳答案
要写入的数据帧需要具有以下架构:
Column | Type
----------------------------------------------
body (required) | string or binary
partitionId (*optional) | string
partitionKey (*optional) | string
这对我有用。
df.withColumn('body', F.to_json(
F.struct(*df.columns),
options={"ignoreNullFields": False}))\
.select('body')\
.write\
.format("eventhubs")\
.options(**ehconf)\
.save()
关于python - 从 Databricks 笔记本向 Azure Eventhubs 发送 Spark 数据帧时出现错误 (java.lang.NoSuchMethodError)，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/73962665/