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Showing content from https://docs.databricks.com/aws/en/compute/access-mode-limitations below:

Compute access mode limitations for Unity Catalog

note

Access modes have been renamed. Shared access mode is now Standard. Single user access mode is now Dedicated and can be assigned to a single user or group. Group access is in Public Preview.

Databricks recommends using standard access mode (formerly shared access mode) for most workloads. This article outlines limitations and requirements 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.

For demos of updating compute for Unity Catalog, see:

• All-purpose compute
• SQL warehouses

note

No-isolation shared and credential passthrough are legacy access modes that do not support Unity Catalog.

Dedicated 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.

note

To take advantage of the data filtering available on Databricks Runtime 15.4 LTS and above, your workspace must be enabled for serverless compute.

• Databricks Runtime 15.4 LTS and above supports fine-grained access control for read operations.

• Databricks Runtime 16.3 and above supports writes into tables with row and column filters using MERGE INTO and the DataFrame.write.mode("append") API. See Support for write operations.

• On Databricks Runtime 15.3 and below, fine-grained access control on dedicated compute is not supported. Specifically:

  • You cannot access a table that has a row filter or column mask.
  • You cannot access dynamic views.
  • To read from any view, you must have SELECT on all tables and views that are referenced by the view.

On Databricks Runtime 15.3 and below, you cannot use dedicated compute to query tables that were created using Lakeflow Declarative Pipelines, including streaming tables and materialized views, if those tables are owned by other users. The user who creates a table is the owner.

To query streaming tables and materialized views created by Lakeflow Declarative Pipelines and owned by other users, use one of the following:

• A SQL warehouse.
• Compute with standard access mode on Databricks Runtime 13.3 LTS or above.
• Compute with dedicated access mode on Databricks Runtime 15.4 LTS or above.

Your workspace must also be enabled for serverless compute. For more information, see Fine-grained access control on dedicated compute.

• Asynchronous checkpointing is not supported in Databricks Runtime 11.3 LTS and below.
• StreamingQueryListener requires Databricks Runtime 15.1 or above to use credentials or interact with objects managed by Unity Catalog on dedicated compute.

• If your workspace was deployed with a firewall or has outbound network restrictions, you must open ports 8443 and 8444 to enable fine-grained access control on dedicated compute. See Security groups.

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

• Databricks Runtime ML is not supported.
• Spark ML is not supported in Databricks Runtime 16.4 and below. Databricks Runtime 17.0 has added support for Spark ML.
• Spark-submit job tasks are not supported. Use a JAR task instead.
• 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.
• In Databricks Runtime 13.3 and above, individual rows must not exceed 128MB.
• DBFS root and mounts do not support FUSE.

• Custom containers are not supported.

• R is not supported.
• Scala is supported in Databricks Runtime 13.3 and above.
  • In Databricks Runtime 15.4 LTS and above, all Java or Scala libraries (JAR files) bundled with Databricks Runtime are available on compute in Unity Catalog access modes.
  • For Databricks Runtime 15.3 or below on compute that uses standard access mode, set the Spark config spark.databricks.scala.kernel.fullClasspath.enabled to true.

• RDD APIs are not supported.
• 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.
• The following Scala Dataset API operations require Databricks Runtime 15.4 LTS or above: map, mapPartitions, foreachPartition, flatMap, reduce and filter.
• The Spark configuration property spark.executor.extraJavaOptions is not supported.

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

• Hive UDFs are not supported.
• applyInPandas and mapInPandas require Databricks Runtime 14.3 or above.
• PySpark UDFs cannot access Git folders, workspace files, or volumes to import modules in Databricks Runtime 14.2 and below.
• Scala scalar UDFs and Scala UDAFs require Databricks Runtime 14.2 LTS or above.
• In Databricks Runtime 14.2 and below, using a custom version of grpc, pyarrow, or protobuf in a PySpark UDF through notebook-scoped or cluster-scoped libraries is not supported because the installed version is always preferred. To find the version of installed libraries, see the System Environment section of the specific Databricks Runtime version release notes.

• Python scalar UDFs and Pandas UDFs require Databricks Runtime 13.3 LTS or above.

• Non-scalar Python and Pandas UDFs, including UDAFs, UDTFs, and Pandas on Spark, require Databricks Runtime 14.3 LTS or above.

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

• You cannot use the formats statestore and state-metadata to query state information for stateful streaming queries.
• transformWithState and associated APIs are not supported.
• transformWithStateInPandas requires Databricks Runtime 16.3 and above.
• For Scala, foreach requires Databricks Runtime 16.1 or above. foreachBatch, and flatMapGroupsWithState require Databricks Runtime 16.2 or above.
• For Python, foreachBatch has the following behavior changes in Databricks Runtime 14.0 and above:
  • print() commands write output to the driver logs.
  • You cannot access the dbutils.widgets submodule inside the function.
  • Any files, modules, or objects referenced in the function must be serializable and available on Spark.
• For Scala, from_avro requires Databricks Runtime 14.2 or above.
• applyInPandasWithState requires Databricks Runtime 14.3 LTS or above.
• 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 not supported:
  • 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 13.3 LTS and above 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
• For Scala, StreamingQueryListener requires Databricks Runtime 16.1 and above.
• For Python, StreamingQueryListener requires Databricks Runtime 14.3 LTS or above to use credentials or interact with objects managed by Unity Catalog on compute with standard access mode.

• Instance profiles to configure access to external sources such as Kafka or Kinesis for streaming workloads are not supported.

The following limitations apply when using the scala kernel on standard access mode compute.

• Certain classes cannot be used in your code if they conflict with the internal almond kernel library, most notably Input. For a list of almond's defined imports, see almond imports.
• Logging directly to log4j is not supported.
• In the UI, the dataframe schema dropdown is not supported.
• If your driver hits OOM, the Scala REPL will not terminate.
• //connector/sql-aws-connectors:sql-aws-connectors is not in the Scala REPL's bazel target, use results in ClassNotFoundException.
• Scala streaming is not supported.
• The Scala kernel is incompatible with SQLImplicits.

• You must run commands on compute nodes as a low-privilege user forbidden from accessing sensitive parts of the filesystem.
• POSIX-style paths (/) for DBFS are not supported.
• Only workspace admins and users with ANY FILE permissions can directly interact with files using DBFS.
• In Databricks Runtime 11.3 LTS and below, you can only create network connections to ports 80 and 443.

• You cannot connect to the instance metadata service (IMDS) or any other services running in the Databricks VPC. To access cloud services using boto3, use service credentials.

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

Graviton instance support for UDFs on Unity Catalog-enabled clusters is available in Databricks Runtime 15.2 and above. Additional limitations exist for standard access mode. See UDF limitations and requirements for Unity Catalog standard access mode.

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

See also Streaming limitations for Unity Catalog dedicated access mode and Streaming limitations and requirements for Unity Catalog standard access mode.

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

RDD APIs are not supported.

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