You can create a connection to Azure Databricks tables from the . This section describes how to create connections of this type.
NOTE: For job execution on Spark, the connection must use the Spark instance on the Azure Databricks cluster. No other Spark instance is supported. You can run jobs from this connection through the Photon running environment. For more information, see Running Environment Options.
This connection interacts with Databricks Tables through the Hive metastore that has been installed in Azure Databricks.
NOTE: External Hive metastores are not supported.
Each user must insert a Databricks Personal Access Token into the user profile. For more information, see Databricks Personal Access Token Page.
To enable Databricks Tables connections, please complete the following:
NOTE: Typically, you need only one connection to Databricks Tables, although you can create multiple connections.
NOTE: This connection is created with SSL automatically enabled.
Locate the following parameter and set it to
To allow for direct publishing of job results to Databricks tables from the Run Job page, you must enable the following parameters. For more information on these settings, see Run Job Page.
|feature.databricks.enableDeltaTableWrites||Set this value to |
|feature.databricks.enableExternalTableWrites||Set this value to |
Save your changes and restart the platform.
This connection can also be created via API. For details on values to use when creating via API, see Connection Types.
Please create an Databricks connection and then specify the following properties with the listed values:
NOTE: Host and port number connection information is taken from Azure Databricks and does not need to be re-entered here. See Configure for Azure Databricks.
|Connect String options|
Please insert any connection string options that you need. Connect String options are not required for this connection.
Click this button to test the specified connection.
|Default Column Data Type Inference|
For more information, see Using Databricks Tables.
For more information on how values are converted during input and output with this database, see Databricks Tables Data Type Conversions.
If you are attempting to import a table containing a large number of columns (>200), you may encounter an error message similar to the following:
2019-11-26T10:17:11.439Z [XNIO-3 task-15] ERROR com.trifacta.dataservice.DataServiceController - [sid=de7f0e78-087d-4922-9251-337c0cc6da71] - [rid=4851532f-3fb1-4746-9884-a1f71c8209ee] - [method=POST] - [url=/jdbc/datastream] - Stack trace: com.trifacta.dataservice.connect.exception.UnknownJdbcException: A system error occurred: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 408.0 failed 4 times, most recent failure: Lost task 0.3 in stage 408.0 (TID 1342, 10.139.64.11, executor 11): org.apache.spark.SparkException: Kryo serialization failed: Buffer overflow. Available: 0, required: 1426050. To avoid this, increase spark.kryoserializer.buffer.max value.
The problem is that the serializer ran out of memory.
To address this issue, you can increase the Kyroserializer buffer size.
spark.props section and add the following setting. Modify
2000 (2GB) depending on whether your import is successful:
For more information on passing property values into Spark, see Configure for Spark.