Compute access mode limitations for Unity Catalog

Databricks recommends using Unity Catalog and shared access mode for most workloads. This article outlines various limitations for each access mode with Unity Catalog. For details on access modes, see Access modes.

Databricks recommends using compute policies to simplify configuration options for most users. See Create and manage compute policies.

Note

No-isolation shared is a legacy access mode that does not support Unity Catalog.

Important

Init scripts and libraries have different support across access modes and Databricks Runtime versions. See Where can init scripts be installed? and Cluster-scoped libraries.

Single user access mode limitations on Unity Catalog

Single user access mode on Unity Catalog has the following limitations. These are in addition to the general limitations for all Unity Catalog access mode. See General limitations for Unity Catalog.

Fine-grained access control limitations for Unity Catalog single user access mode

  • Dynamic views are not supported.

  • To read from a view, you must have SELECT on all referenced tables and views.

  • You cannot access a table that has a row filter or column mask.

Streaming limitations for Unity Catalog single user access mode

  • Asynchronous checkpointing is not supported in Databricks Runtime 11.3 LTS and below.

Shared access mode limitations on Unity Catalog

Shared access mode on Unity Catalog has the following limitations. These are in addition to the general limitations for all Unity Catalog access mode. See General limitations for Unity Catalog.

  • Databricks Runtime ML and Spark Machine Learning Library (MLlib) are not supported.

  • Spark-submit jobs are not supported.

  • On Databricks Runtime 13.3 and above, individual rows must not exceed the maximum size of 128MB.

Language support for Unity Catalog shared access mode

  • R is not supported.

  • Scala is supported on Databricks Runtime 13.3 and above.

Spark API limitations for Unity Catalog shared access mode

  • RDD APIs are not supported.

  • DBUtils and other clients that directly read the data from cloud storage are only supported when you use an external location to access the storage location. See Create an external location to connect cloud storage to Databricks.

  • Spark Context (sc),spark.sparkContext, and sqlContext are not supported for Scala in any Databricks Runtime and are not supported for Python in Databricks Runtime 14.0 and above.

    • Databricks recommends using the spark variable to interact with the SparkSession instance.

    • The following sc functions are also not supported: emptyRDD, range, init_batched_serializer, parallelize, pickleFile, textFile, wholeTextFiles, binaryFiles, binaryRecords, sequenceFile, newAPIHadoopFile, newAPIHadoopRDD, hadoopFile, hadoopRDD, union, runJob, setSystemProperty, uiWebUrl, stop, setJobGroup, setLocalProperty, getConf.

UDF limitations for Unity Catalog shared access mode

Preview

Support for Scala UDFs on Unity Catalog-enabled compute with shared access mode is in Public Preview.

User-defined functions (UDFs) have the following limitations with shared access mode:

  • Hive UDFs are not supported.

  • applyInPandas and mapInPandas are not supported.

  • In Databricks Runtime 14.2 and above, Scala scalar UDFs are supported. Other Scala UDFs and UDAFs are not supported.

  • In Databricks Runtime 14.1 and above, Python scalar UDFs and Pandas UDFs are supported. Other Python UDFs, including UDAFs, UDTFs, and Pandas on Spark are not supported.

See User-defined functions (UDFs) in Unity Catalog.

Streaming limitations for Unity Catalog shared access mode

Note

Some of the listed Kafka options have limited support when used for supported configurations on Databricks. See Stream processing with Apache Kafka and Databricks.

  • For Scala, foreach and foreachBatch are not supported.

  • For Python, foreachBatch has new behavior in Databricks Runtime 14.0 and above. See Behavior changes for foreachBatch in Databricks Runtime 14.0.

  • For Scala, from_avro requires Databricks Runtime 14.2 or above.

  • applyInPandasWithState is not supported.

  • Working with socket sources is not supported.

  • The sourceArchiveDir must be in the same external location as the source when you use option("cleanSource", "archive") with a data source managed by Unity Catalog.

  • For Kafka sources and sinks, the following options are unsupported:

    • kafka.sasl.client.callback.handler.class

    • kafka.sasl.login.callback.handler.class

    • kafka.sasl.login.class

    • kafka.partition.assignment.strategy

  • The following Kafka options are supported in Databricks Runtime 13.0 but unsupported in Databricks Runtime 12.2 LTS. You can only specify external locations managed by Unity Catalog for these options:

    • kafka.ssl.truststore.location

    • kafka.ssl.keystore.location

Network and file system access limitations for Unity Catalog shared access mode

  • Must run commands on compute nodes as a low-privilege user forbidden from accessing sensitive parts of the filesystem.

  • In Databricks Runtime 11.3 LTS and below, you can only create network connections to ports 80 and 443.

  • Cannot connect to the instance metadata service or any services running in the Databricks VPC.

General limitations for Unity Catalog

The following limitations apply to all Unity Catalog-enabled access modes.

Streaming limitations for Unity Catalog

  • Apache Spark continuous processing mode is not supported. See Continuous Processing in the Spark Structured Streaming Programming Guide.

  • StreamingQueryListener cannot use credentials or interact with objects managed by Unity Catalog.

See also Streaming limitations for Unity Catalog single user access mode and Streaming limitations for Unity Catalog shared access mode.

For more on streaming with Unity Catalog, see Using Unity Catalog with Structured Streaming.