Azure/spark-cdm-connector

[Issue] Issue with connecting to ADSLG2 from Databricks

Closed this issue · 1 comments

Did you read the pinned issues and search the error message?

No, but I will read and search it now before creating an issue.

Summary of issue

I tried reading CDM from Databricks but it fails with error.

There is no documentation how to set up permissions with storage using Databricks. Can you help

Depending on a cluster I see
error 1
java.lang.NoSuchMethodError: com.databricks.backend.daemon.data.client.adl.AdlGen2CredentialContextTokenProvider.getToken()Lshaded/databricks/v20180920_b33d810/org/apache/hadoop/fs/azurebfs/oauth2/AzureADToken;

Depending on a cluster I see
error 2
java.lang.SecurityException: Only default session catalog is supported, for Credential Passthourh or Table ACL enabled cluster. Try to load: com.microsoft.cdm.CDMCatalot

readExplicit = (spark.read.format("com.microsoft.cdm")
  .option("storage", storageAccountName)
  .option("manifestPath", container + "/nestedExplicit/default.manifest.cdm.json")
  .option("entity", "NestedExampleExplicit")
  .load())

Error stack trace

Py4JJavaError Traceback (most recent call last)
File :4
1 container= "raw"
2 storageAccountName = "bronzedatalake.dfs.core.windows.net"
----> 4 readExplicit = (spark.read.format("com.microsoft.cdm")
5 .option("storage", storageAccountName)
6 .option("manifestPath", container + "/nestedExplicit/default.manifest.cdm.json")
7 .option("entity", "NestedExampleExplicit")
8 .load())

File /databricks/spark/python/pyspark/instrumentation_utils.py:48, in _wrap_function..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:309, in DataFrameReader.load(self, path, format, schema, **options)
307 return self._df(self._jreader.load(self._spark._sc._jvm.PythonUtils.toSeq(path)))
308 else:
--> 309 return self._df(self._jreader.load())

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/errors/exceptions.py:228, in capture_sql_exception..deco(*a, **kw)
226 def deco(*a: Any, **kw: Any) -> Any:
227 try:
--> 228 return f(*a, **kw)
229 except Py4JJavaError as e:
230 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 o389.load.
: java.lang.NoSuchMethodError: com.databricks.backend.daemon.data.client.adl.AdlGen2CredentialContextTokenProvider.getToken()Lshaded/databricks/v20180920_b33d810/org/apache/hadoop/fs/azurebfs/oauth2/AzureADToken;
at com.microsoft.cdm.utils.CDMTokenProvider.(CDMTokenProvider.scala:15)
at com.microsoft.cdm.HadoopTables.load(HadoopTables.scala:11)
at com.microsoft.cdm.CDMCatalog.loadTable(CDMCatalog.scala:33)
at com.microsoft.cdm.CDMCatalog.loadTable(CDMCatalog.scala:15)
at org.apache.spark.sql.connector.catalog.CatalogV2Util$.getTable(CatalogV2Util.scala:363)
at org.apache.spark.sql.execution.datasources.v2.DataSourceV2Utils$.loadV2Source(DataSourceV2Utils.scala:135)
at org.apache.spark.sql.DataFrameReader.$anonfun$load$1(DataFrameReader.scala:333)
at scala.Option.flatMap(Option.scala:271)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:331)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:226)
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)

Platform name

Databricks

Spark version

3.3

CDM jar version

spark_cdm_connector_assembly_synapse_spark3_3_1_19_5.jar

What is the format of the data you are trying to read/write?

.csv

The pinned issues mention:

As referenced in #134, credential passthrough is a Synapse specific feature. Use app registration or SAS token auth if you are not using Synapse.