我们正在尝试与ipywidgets一起创建一个数据操作笔记本，为预定义的函数调用创建一个简单的UI。在这些函数中，我们依赖于从Metastore加载数据。下面是我们正在做的一个例子。
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加载一个数据帧。函数clickButton调用该函数以使用下游的数据帧。定义一个带有按钮的ipywidget，它调用clickButton函数。
现在，直接调用函数loaddf()，即简单地作为代码单元，工作得很好。通过.display()加载并显示数据。但是点击这个按钮会显示下面的错误信息。
有人知道是什么原因导致这个问题吗?
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)
- 这个问题出现在共享和单用户集群上。 管理表和外部表出现问题。
- 问题没有出现，在UC启用之前，数据生活在hive-metastore中。
- 用户可以完全访问存储凭据和外部位置(对于external情况)以及使用的目录。
我们正在为UC数据添加ipywidgets支持。请查看Databricks ipywidgets用户指南以获取更新。
23年2月13日更新:现在你可以从Databricks Runtime 12.1使用ipywidgets访问Unity Catalog。详见https://docs.databricks.com/notebooks/ipywidgets.html#uc-support
这可能是由于ipywidgets(前端库)，数据块无法识别哪个用户触发函数(loaddf())，因此在这种情况下无法验证。但这是我的猜测