403 forbidden error on querying lakehouse table using pyspark

[soumya_cg]

Hi All,

Need a help. I,m trying to run below query using pyspark inside my fabric lakehouse:

----> 1 df = spark.sql("SELECT * FROM Stream_Lakehouse.dbo.FACT_KPIs_LH LIMIT 1000")
2 display(df)

Can someone help. I have truncated the full error log due to issue with space here

I,m getting below error:

Py4JJavaError                             Traceback (most recent call last)

Cell In[47], line 1

----> 1 df = spark.sql("SELECT * FROM Stream_Lakehouse.dbo.FACT_KPIs_LH LIMIT 1000")

      2 display(df)

File /opt/spark/python/lib/pyspark.zip/pyspark/sql/session.py:1440, in SparkSession.sql(self, sqlQuery, args, **kwargs)

   1438 try:

   1439     litArgs = {k: _to_java_column(lit(v)) for k, v in (args or {}).items()}

-> 1440     return DataFrame(self._jsparkSession.sql(sqlQuery, litArgs), self)

   1441 finally:

   1442     if len(kwargs) > 0:

File ~/cluster-env/trident_env/lib/python3.10/site-packages/py4j/java_gateway.py:1322, in JavaMember.__call__(self, *args)

   1316 command = proto.CALL_COMMAND_NAME +\

   1317     self.command_header +\

   1318     args_command +\

   1319     proto.END_COMMAND_PART

   1321 answer = self.gateway_client.send_command(command)

-> 1322 return_value = get_return_value(

   1323     answer, self.gateway_client, self.target_id, self.name)

   1325 for temp_arg in temp_args:

   1326     if hasattr(temp_arg, "_detach"):

File /opt/spark/python/lib/pyspark.zip/pyspark/errors/exceptions/captured.py:169, in capture_sql_exception.<locals>.deco(*a, **kw)

    167 def deco(*a: Any, **kw: Any) -> Any:

    168     try:

--> 169         return f(*a, **kw)

    170     except Py4JJavaError as e:

    171         converted = convert_exception(e.java_exception)

File ~/cluster-env/trident_env/lib/python3.10/site-packages/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 o322.sql.

: java.lang.RuntimeException: Request failed: HTTP/1.1 403 Forbidden

at com.microsoft.fabric.spark.metadata.Helpers$.executeRequest(Helpers.scala:154)

at com.microsoft.fabric.platform.PbiPlatformClient.newGetRequest(PbiPlatformClient.scala:51)

at com.microsoft.fabric.platform.PbiPlatformClient.newGetRequest$(PbiPlatformClient.scala:47)

at com.microsoft.fabric.platform.PbiPlatformInternalApiClient.newGetRequest(PbiPlatformClient.scala:175)

at com.microsoft.fabric.platform.PbiPlatformInternalApiClient.getAllWorkspaces(PbiPlatformClient.scala:199)

at com.microsoft.fabric.platform.InstrumentedPbiPlatformClient.$anonfun$getAllWorkspaces$1(PbiPlatformClient.scala:164)

at com.microsoft.fabric.spark.metadata.Helpers$.timed(Helpers.scala:29)

at com.microsoft.fabric.platform.InstrumentedPbiPlatformClient.getAllWorkspaces(PbiPlatformClient.scala:164)

at com.microsoft.fabric.platform.PbiPlatformCachingClient.$anonfun$workspaceCache$1(PbiPlatformClient.scala:117)

at com.google.common.base.Suppliers$ExpiringMemoizingSupplier.get(Suppliers.java:192)

at com.microsoft.fabric.platform.PbiPlatformCachingClient.getWorkspace(PbiPlatformClient.scala:146)

at com.microsoft.fabric.platform.PbiPlatformCachingClient.getArtifacts(PbiPlatformClient.scala:136)

at com.microsoft.fabric.platform.PbiPlatformCachingClient.$anonfun$artifactCache$1(PbiPlatformClient.scala:130)

at com.github.benmanes.caffeine.cache.LocalLoadingCache.lambda$newMappingFunction$2(LocalLoadingCache.java:145)

at com.github.benmanes.caffeine.cache.BoundedLocalCache.lambda$doComputeIfAbsent$14(BoundedLocalCache.java:2406)

at java.base/java.util.concurrent.ConcurrentHashMap.compute(ConcurrentHashMap.java:1908)

at com.github.benmanes.caffeine.cache.BoundedLocalCache.doComputeIfAbsent(BoundedLocalCache.java:2404)

at com.github.benmanes.caffeine.cache.BoundedLocalCache.computeIfAbsent(BoundedLocalCache.java:2387)

at com.github.benmanes.caffeine.cache.LocalCache.computeIfAbsent(LocalCache.java:108)

at com.github.benmanes.caffeine.cache.LocalLoadingCache.get(LocalLoadingCache.java:56)

at com.microsoft.fabric.platform.PbiPlatformCachingClient.getArtifact(PbiPlatformClient.scala:151)

at com.microsoft.fabric.spark.metadata.SchemaPathResolver.getArtifactRoot(pathResolvers.scala:127)

at com.microsoft.fabric.spark.metadata.SchemaPathResolver.getSchemaRoot(pathResolvers.scala:144)

at com.microsoft.fabric.spark.metadata.DefaultSchemaMetadataManager.listSchemas(DefaultSchemaMetadataManager.scala:218)

at com.microsoft.fabric.spark.metadata.DefaultSchemaMetadataManager.$anonfun$defaultSchemaPathResolver$1(DefaultSchemaMetadataManager.scala:30)

at com.microsoft.fabric.spark.metadata.NamespaceResolver.$anonfun$decodedSchemaNameCache$1(pathResolvers.scala:46)

at com.github.benmanes.caffeine.cache.LocalLoadingCache.lambda$newMappingFunction$2(LocalLoadingCache.java:145)

at com.github.benmanes.caffeine.cache.BoundedLocalCache.lambda$doComputeIfAbsent$14(BoundedLocalCache.java:2406)

at java.base/java.util.concurrent.ConcurrentHashMap.compute(ConcurrentHashMap.java:1908)

at com.github.benmanes.caffeine.cache.BoundedLocalCache.doComputeIfAbsent(BoundedLocalCache.java:2404)

at com.github.benmanes.caffeine.cache.BoundedLocalCache.computeIfAbsent(BoundedLocalCache.java:2387)

at com.github.benmanes.caffeine.cache.LocalCache.computeIfAbsent(LocalCache.java:108)

at com.github.benmanes.caffeine.cache.LocalLoadingCache.get(LocalLoadingCache.java:56)

at com.microsoft.fabric.spark.metadata.Helpers$.forceLoadIfRequiredInCachedMap(Helpers.scala:61)

at com.microsoft.fabric.spark.metadata.NamespaceResolver.inferNamespace(pathResolvers.scala:87)

at com.microsoft.fabric.spark.metadata.NamespaceResolver.$anonfun$toNamespace$1(pathResolvers.scala:79)

at java.base/java.util.concurrent.ConcurrentHashMap.computeIfAbsent(ConcurrentHashMap.java:1705)

at com.microsoft.fabric.spark.metadata.NamespaceResolver.toNamespace(pathResolvers.scala:79)

at com.microsoft.fabric.spark.metadata.DefaultSchemaMetadataManager.getSchema(DefaultSchemaMetadataManager.scala:73)

at com.microsoft.fabric.spark.metadata.MetadataManager.getSchema(MetadataManager.scala:192)

at com.microsoft.fabric.spark.metadata.InstrumentedMetadataManager.super$getSchema(MetadataManager.scala:321)

at com.microsoft.fabric.spark.metadata.InstrumentedMetadataManager.$anonfun$getSchema$1(MetadataManager.scala:321)

at com.microsoft.fabric.spark.metadata.Helpers$.timed(Helpers.scala:29)

at com.microsoft.fabric.spark.metadata.InstrumentedMetadataManager.getSchema(MetadataManager.scala:321)

at com.microsoft.fabric.spark.catalog.OnelakeExternalCatalog.getDatabase(OnelakeExternalCatalog.scala:78)

at com.microsoft.fabric.spark.catalog.OnelakeExternalCatalog.databaseExists(OnelakeExternalCatalog.scala:84)

at com.microsoft.fabric.spark.catalog.InstrumentedExternalCatalog.$anonfun$databaseExists$1(OnelakeExternalCatalog.scala:417)

at scala.runtime.java8.JFunction0$mcZ$sp.apply(JFunction0$mcZ$sp.java:23)

at com.microsoft.fabric.spark.metadata.Helpers$.timed(Helpers.scala:29)

at com.microsoft.fabric.spark.catalog.InstrumentedExternalCatalog.databaseExists(OnelakeExternalCatalog.scala:417)

at org.apache.spark.sql.internal.SharedState.externalCatalog$lzycompute(SharedState.scala:169)

at org.apache.spark.sql.internal.SharedState.externalCatalog(SharedState.scala:142)

at org.apache.spark.sql.hive.HiveSessionStateBuilder.externalCatalog(HiveSessionStateBuilder.scala:54)

at org.apache.spark.sql.hive.HiveSessionStateBuilder.$anonfun$catalog$1(HiveSessionStateBuilder.scala:69)

at org.apache.spark.sql.catalyst.catalog.SessionCatalog.externalCatalog$lzycompute(SessionCatalog.scala:140)

[Anonymous]

Hi @soumya_cg 

The issue seems to be partially fixed. I can read data from a schema enabled lakehouse now but I need to pin it as the default lakehouse of my notebook. Otherwise it returns me a "[REQUIRES_SINGLE_PART_NAMESPACE] spark_catalog requires a single-part namespace“ exception. 

I will keep tracking this issue and update if there is any further progress. 

Best Regards,
Jing

[Anonymous]

Hi @soumya_cg 

This is a known issue as frithjof_v has linked. The engineers are working on the fix now. But we haven't received any definite ETA yet. I will keep you updated once there is any progress. We appreciate your understanding and patience. 

Best Regards,
Jing

[frithjof_v · Accepted solution]

Are you using schema enabled Lakehouse?

https://community.fabric.microsoft.com/t5/Data-Engineering/403-Forbidden-when-reading-the-tables-of-...

[soumya_cg]

Yes I,m using

[frithjof_v]

Okay, see if the link in my previous comment helps you.

In general I would recommend not to use the schema enabled Lakehouse as long as it's only a preview feature.

I would just use normal Lakehouse without schemas.