Apache Spark 使用ipywidgets加载Databricks Unity目录表会引发“缺少凭据范围”异常

z6psavjg  于 2023-01-17  发布在  Apache
关注(0)|答案(1)|浏览(119)

我们正在尝试创建一个数据操作Notebook和ipywidgets，为预定义的函数调用创建一个简单的UI。在执行这些函数时，我们依赖于从Metastore加载数据。下面是我们正在做的一个示例。

def loaddf():
    df = spark.read.table('sandbox.mysamples.sampledata')
    return df

def clickButton(b):
    output.clear_output()
    with output:
        df = loaddf()
        return 
...
search_btnOne = Button(
    ....
)
search_btnOne.on_click(clickButton)

函数loaddf加载一个 Dataframe ，函数clickButton调用该函数以使用下游的 Dataframe ，定义了一个带按钮的ipywidget，它调用函数clickButton。
现在，直接调用函数loaddf()，即简单地作为一个代码单元格，工作正常。数据被加载，并可以使用.display()显示。但点击按钮会出现下面的错误消息。
有人知道什么可能导致这个问题吗？

File <command-2062771599631884>:8, in clickButton(b)
      6 output.clear_output()
      7 with output:
----> 8     df = loaddf()
      9     return

File <command-2062771599631884>:2, in loaddf()
      1 def loaddf():
----> 2     df = spark.read.table('sandbox.mysamples.sampledata')
      3     return df

File /databricks/spark/python/pyspark/instrumentation_utils.py:48, in _wrap_function.<locals>.wrapper(*args, **kwargs)
     46 start = time.perf_counter()
     47 try:
---> 48     res = func(*args, **kwargs)
     49     logger.log_success(
     50         module_name, class_name, function_name, time.perf_counter() - start, signature
     51     )
     52     return res

File /databricks/spark/python/pyspark/sql/readwriter.py:320, in DataFrameReader.table(self, tableName)
    303 def table(self, tableName: str) -> "DataFrame":
    304     """Returns the specified table as a :class:`DataFrame`.
    305 
    306     .. versionadded:: 1.4.0
   (...)
    318     [('name', 'string'), ('year', 'int'), ('month', 'int'), ('day', 'int')]
    319     """
--> 320     return self._df(self._jreader.table(tableName))

File /databricks/spark/python/lib/py4j-0.10.9.5-src.zip/py4j/java_gateway.py:1321, in JavaMember.__call__(self, *args)
   1315 command = proto.CALL_COMMAND_NAME +\
   1316     self.command_header +\
   1317     args_command +\
   1318     proto.END_COMMAND_PART
   1320 answer = self.gateway_client.send_command(command)
-> 1321 return_value = get_return_value(
   1322     answer, self.gateway_client, self.target_id, self.name)
   1324 for temp_arg in temp_args:
   1325     temp_arg._detach()

File /databricks/spark/python/pyspark/sql/utils.py:196, in capture_sql_exception.<locals>.deco(*a, **kw)
    194 def deco(*a: Any, **kw: Any) -> Any:
    195     try:
--> 196         return f(*a, **kw)
    197     except Py4JJavaError as e:
    198         converted = convert_exception(e.java_exception)

File /databricks/spark/python/lib/py4j-0.10.9.5-src.zip/py4j/protocol.py:326, 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)
    329 else:
    330     raise Py4JError(
    331         "An error occurred while calling {0}{1}{2}. Trace:\n{3}\n".
    332         format(target_id, ".", name, value))

Py4JJavaError: An error occurred while calling o10344.table.
: org.apache.spark.SparkException: Missing Credential Scope. 
    at com.databricks.unity.UCSDriver$Manager.$anonfun$scope$1(UCSDriver.scala:103)
    at scala.Option.getOrElse(Option.scala:189)
    at com.databricks.unity.UCSDriver$Manager.scope(UCSDriver.scala:103)
    at com.databricks.unity.UCSDriver$Manager.currentScope(UCSDriver.scala:97)
    at com.databricks.unity.UnityCredentialScope$.currentScope(UnityCredentialScope.scala:100)
    at com.databricks.unity.UnityCredentialScope$.getCredentialManager(UnityCredentialScope.scala:128)
    at com.databricks.unity.CredentialManager$.getUnityApiTokenOpt(CredentialManager.scala:456)
    at com.databricks.unity.UnityCatalogClientHelper$.getToken(UnityCatalogClientHelper.scala:35)
    at com.databricks.managedcatalog.ManagedCatalogClientImpl.$anonfun$bulkGetMetadata$1(ManagedCatalogClientImpl.scala:2889)
    at com.databricks.spark.util.FrameProfiler$.record(FrameProfiler.scala:80)
    at com.databricks.managedcatalog.ManagedCatalogClientImpl.$anonfun$recordAndWrapException$1(ManagedCatalogClientImpl.scala:2953)
    at com.databricks.managedcatalog.ErrorDetailsHandler.wrapServiceException(ErrorDetailsHandler.scala:25)
    at com.databricks.managedcatalog.ErrorDetailsHandler.wrapServiceException$(ErrorDetailsHandler.scala:23)
    at com.databricks.managedcatalog.ManagedCatalogClientImpl.wrapServiceException(ManagedCatalogClientImpl.scala:79)
    at com.databricks.managedcatalog.ManagedCatalogClientImpl.recordAndWrapException(ManagedCatalogClientImpl.scala:2952)
    at com.databricks.managedcatalog.ManagedCatalogClientImpl.bulkGetMetadata(ManagedCatalogClientImpl.scala:2882)
    at com.databricks.sql.managedcatalog.NonPermissionEnforcingManagedCatalog.updateCache(NonPermissionEnforcingManagedCatalog.scala:49)
    at com.databricks.sql.managedcatalog.PermissionEnforcingManagedCatalog.getTablesByName(PermissionEnforcingManagedCatalog.scala:244)
    at com.databricks.sql.managedcatalog.ManagedCatalogSessionCatalog.fastGetTablesByName(ManagedCatalogSessionCatalog.scala:1061)
    at com.databricks.sql.transaction.tahoe.catalog.DeltaCatalog.fetchFromCatalog(DeltaCatalog.scala:417)
    at com.databricks.sql.transaction.tahoe.catalog.DeltaCatalog.$anonfun$loadTables$1(DeltaCatalog.scala:362)
    at com.databricks.spark.util.FrameProfiler$.record(FrameProfiler.scala:80)
    at com.databricks.sql.transaction.tahoe.metering.DeltaLogging.recordFrameProfile(DeltaLogging.scala:248)
    at com.databricks.sql.transaction.tahoe.metering.DeltaLogging.recordFrameProfile$(DeltaLogging.scala:246)
    at com.databricks.sql.transaction.tahoe.catalog.DeltaCatalog.recordFrameProfile(DeltaCatalog.scala:80)
    at com.databricks.sql.transaction.tahoe.catalog.DeltaCatalog.loadTables(DeltaCatalog.scala:359)
    at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$$anon$3.$anonfun$submit$1(Analyzer.scala:1819)
    at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
    at com.databricks.spark.util.FrameProfiler$.record(FrameProfiler.scala:80)
    at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.org$apache$spark$sql$catalyst$analysis$Analyzer$ResolveRelations$$record(Analyzer.scala:1878)
    at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$$anon$3.submit(Analyzer.scala:1801)
    at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.apply(Analyzer.scala:1430)
    at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.apply(Analyzer.scala:1370)
    at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$4(RuleExecutor.scala:218)
    at com.databricks.spark.util.FrameProfiler$.record(FrameProfiler.scala:80)
    at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$3(RuleExecutor.scala:218)
    at scala.collection.LinearSeqOptimized.foldLeft(LinearSeqOptimized.scala:126)
    at scala.collection.LinearSeqOptimized.foldLeft$(LinearSeqOptimized.scala:122)
    at scala.collection.immutable.List.foldLeft(List.scala:91)
    at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$2(RuleExecutor.scala:215)
    at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
    at com.databricks.spark.util.FrameProfiler$.record(FrameProfiler.scala:80)
    at org.apache.spark.sql.catalyst.rules.RuleExecutor.executeBatch$1(RuleExecutor.scala:207)
    at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$8(RuleExecutor.scala:277)
    at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$8$adapted(RuleExecutor.scala:277)
    at scala.collection.immutable.List.foreach(List.scala:431)
    at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$1(RuleExecutor.scala:277)
    at com.databricks.spark.util.FrameProfiler$.record(FrameProfiler.scala:80)
    at org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:194)
    at org.apache.spark.sql.catalyst.analysis.Analyzer.executeSameContext(Analyzer.scala:353)
    at org.apache.spark.sql.catalyst.analysis.Analyzer.$anonfun$execute$1(Analyzer.scala:346)
    at org.apache.spark.sql.catalyst.analysis.AnalysisContext$.withNewAnalysisContext(Analyzer.scala:253)
    at org.apache.spark.sql.catalyst.analysis.Analyzer.execute(Analyzer.scala:346)
    at org.apache.spark.sql.catalyst.analysis.Analyzer.execute(Analyzer.scala:274)
    at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$executeAndTrack$1(RuleExecutor.scala:186)
    at org.apache.spark.sql.catalyst.QueryPlanningTracker$.withTracker(QueryPlanningTracker.scala:153)
    at org.apache.spark.sql.catalyst.rules.RuleExecutor.executeAndTrack(RuleExecutor.scala:186)
    at org.apache.spark.sql.catalyst.analysis.Analyzer.$anonfun$executeAndCheck$1(Analyzer.scala:326)
    at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.markInAnalyzer(AnalysisHelper.scala:331)
    at org.apache.spark.sql.catalyst.analysis.Analyzer.executeAndCheck(Analyzer.scala:325)
    at org.apache.spark.sql.execution.QueryExecution.$anonfun$analyzed$1(QueryExecution.scala:163)
    at com.databricks.spark.util.FrameProfiler$.record(FrameProfiler.scala:80)
    at org.apache.spark.sql.catalyst.QueryPlanningTracker.measurePhase(QueryPlanningTracker.scala:319)
    at org.apache.spark.sql.execution.QueryExecution.$anonfun$executePhase$3(QueryExecution.scala:353)
    at org.apache.spark.sql.execution.QueryExecution$.withInternalError(QueryExecution.scala:789)
    at org.apache.spark.sql.execution.QueryExecution.$anonfun$executePhase$1(QueryExecution.scala:353)
    at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:1003)
    at org.apache.spark.sql.execution.QueryExecution.executePhase(QueryExecution.scala:350)
    at org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:144)
    at org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:144)
    at org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:136)
    at org.apache.spark.sql.Dataset$.$anonfun$ofRows$1(Dataset.scala:98)
    at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:1003)
    at org.apache.spark.sql.SparkSession.$anonfun$withActiveAndFrameProfiler$1(SparkSession.scala:1010)
    at com.databricks.spark.util.FrameProfiler$.record(FrameProfiler.scala:80)
    at org.apache.spark.sql.SparkSession.withActiveAndFrameProfiler(SparkSession.scala:1010)
    at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:96)
    at org.apache.spark.sql.DataFrameReader.table(DataFrameReader.scala:802)
    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:306)
    at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
    at py4j.commands.CallCommand.execute(CallCommand.java:79)
    at py4j.ClientServerConnection.waitForCommands(ClientServerConnection.java:195)
    at py4j.ClientServerConnection.run(ClientServerConnection.java:115)
    at java.lang.Thread.run(Thread.java:750)
  • 共享群集和单用户群集上确实会出现此问题。
  • MANAGED和EXTERNAL表出现此问题。
  • 在启用UC且数据位于配置单元元存储区之前，问题未出现。
  • 用户对storage-credential和外部位置（对于EXTERNAL情况）以及使用的目录具有完全访问权限。
apache-spark

1条答案

按热度按时间
dced5bon

dced5bon1#

可能是ipywidgets（前端库），数据库无法识别哪个用户触发了函数（loaddf()），因此在这种情况下无法进行身份验证。但这是我的猜测

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