Unable to load databricks data into Power BI Desktop

[ChayDommeti]

I am trying for several days to load the data i have into Power Bi desktop from Databricks using spark connector and also databricks connector but it keeps failing. Earlier i used to get maxresultsize error and i have added that in spark config in databricks but now i am getting different error as below. Any help is appreciated

database_labels_view

Failed to save modifications to the server. Error returned: 'OLE DB or ODBC error: [DataSource.Error] ODBC: ERROR [HY000] [Microsoft][Hardy] (35) Error from server: error code: '0' error message: 'Error running query: org.apache.spark.SparkException: Job aborted due to stage failure: Task 8 in stage 23.0 failed 4 times, most recent failure: Lost task 8.3 in stage 23.0 (TID 474, 172.26.186.39, executor 6): java.lang.OutOfMemoryError at java.io.ByteArrayOutputStream.hugeCapacity(ByteArrayOutputStream.java:123) at java.io.ByteArrayOutputStream.grow(ByteArrayOutputStream.java:117) at java.io.ByteArrayOutputStream.ensureCapacity(ByteArrayOutputStream.java:93) at java.io.ByteArrayOutputStream.write(ByteArrayOutputStream.java:153) at org.apache.spark.util.ByteBufferOutputStream.write(ByteBufferOutputStream.scala:41) at java.io.ObjectOutputStream$BlockDataOutputStream.drain(ObjectOutputStream.java:1877) at java.io.ObjectOutputStream$BlockDataOutputStream.setBlockDataMode(ObjectOutputStream.java:1786) at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1189) at java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:348) at org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:43) at org.apache.spark.serializer.JavaSerializerInstance.serialize(JavaSerializer.scala:100) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:585) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) at java.lang.Thread.run(Thread.java:748) Driver stacktrace:'.. '.

kpi_infra azure_resources_costs

Failed to save modifications to the server. Error returned: 'OLE DB or ODBC error: [DataSource.Error] ODBC: ERROR [HY000] [Microsoft][Hardy] (35) Error from server: error code: '0' error message: 'Error running query: org.apache.spark.SparkException: Job aborted due to stage failure: Task 8 in stage 23.0 failed 4 times, most recent failure: Lost task 8.3 in stage 23.0 (TID 474, 172.26.186.39, executor 6): java.lang.OutOfMemoryError at java.io.ByteArrayOutputStream.hugeCapacity(ByteArrayOutputStream.java:123) at java.io.ByteArrayOutputStream.grow(ByteArrayOutputStream.java:117) at java.io.ByteArrayOutputStream.ensureCapacity(ByteArrayOutputStream.java:93) at java.io.ByteArrayOutputStream.write(ByteArrayOutputStream.java:153) at org.apache.spark.util.ByteBufferOutputStream.write(ByteBufferOutputStream.scala:41) at java.io.ObjectOutputStream$BlockDataOutputStream.drain(ObjectOutputStream.java:1877) at java.io.ObjectOutputStream$BlockDataOutputStream.setBlockDataMode(ObjectOutputStream.java:1786) at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1189) at java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:348) at org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:43) at org.apache.spark.serializer.JavaSerializerInstance.serialize(JavaSerializer.scala:100) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:585) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) at java.lang.Thread.run(Thread.java:748) Driver stacktrace:'.. '.

[v-shex-msft]

Hi @ChayDommeti,

What changes that you apply to your data source configuration properties?

How to Get Your Question Answered Quickly

According to your error messages, it mentioends the error code '0' with 'Job aborted due to stage failure' and 'out of memory' error message.  I'd like to suggest you check these parts of the settings to confirm if they cause the issues.

Regards,

Xiaoxin Sheng