Assessment Finding Index

This section will help explain UCX Assessment findings and provide a recommended action. The assessment finding index is grouped by:

• The 100 series findings are Databricks Runtime and compute configuration findings.
• The 200 series findings are centered around data related observations.
• The 300 series findings relate to Compute Access mode limitations for Unity Catalog

AF101 - not supported DBR: ##.#.x-scala2.12

Short description: The compute runtime does not meet the requirements to use Unity Catalog. Explanation: Unity Catalog capabilities are fully enabled on Databricks Runtime 13.3 LTS. This is the current recommended runtime for production interactive clusters and jobs. This finding is noting the cluster or job compute configuration does not meet this threshold. recommendation: Upgrade the DBR version to 13.3 LTS or later.

AF102 - not supported DBR: ##.#.x-cpu-ml-scala2.12

Currently, MLR (Machine Learning Runtime) and GPU SHARED clusters are not supported with Unity Catalog. Use Assigned or Job clusters instead.

AF103 - not supported DBR: ##.#.x-gpu-ml-scala2.12

Currently, MLR (Machine Learning Runtime) and GPU SHARED clusters are not supported with Unity Catalog. Use Assigned or Job clusters instead.

AF111 - Uses azure service principal credentials config in cluster.

Azure service principles are replaced by Storage Credentials to access cloud storage accounts. Create a storage CREDENTIAL, then an EXTERNAL LOCATION and possibly external tables to provide data access. If the service principal is used to access additional azure cloud services, convert the cluster to a Assigned cluster type which may work.

AF112 - Uses azure service principal credentials config in Job cluster.

Azure service principles are replaced by Storage Credentials to access cloud storage accounts. Create a storage CREDENTIAL, then an EXTERNAL LOCATION and possibly external tables to provide data access. If the service principal is used to access additional azure cloud services, convert the job cluster to a Assigned cluster type which may work.

AF113 - Uses azure service principal credentials config in pipeline.

Azure service principles are replaced by Storage Credentials to access cloud storage accounts. Create a storage CREDENTIAL, then an EXTERNAL LOCATION and possibly external tables to provide data access.

AF114 - Uses external Hive metastore config: spark.hadoop.javax.jdo.option.ConnectionURL

Spark configurations for External Hive metastore was found in a cluster definition. Unity Catalog is the recommended approach for sharing data across workspaces. The recommendation is to remove the config after migrating the existing tables & views using UCX. As a transition strategy, "No Isolation Shared" clusters or "Assigned" clusters will work.

• spark.hadoop.javax.jdo.option.ConnectionURL an external Hive Metastore is in use. Recommend migrating the these tables and schemas to Unity Catalog external tables where they can be shared across workspaces.
• spark.databricks.hive.metastore.glueCatalog.enabled Glue is used as external Hive Metastore. Recommend migrating the these tables and schemas to Unity Catalog external tables where they can be shared across workspaces.

AF115 - Uses passthrough config: spark.databricks.passthrough.enabled.

Passthrough security model is not supported by Unity Catalog. Passthrough mode relied upon file based authorization which is incompatible with Fine Grained Access Controls supported by Unity Catalog. Recommend mapping your Passthrough security model to an External Location/Volume/Table/View based security model compatible with Unity Catalog.

AF116 - No isolation shared clusters not supported in UC

Unity Catalog data cannot be accessed from No Isolation clusters, they should not be used.

AF117 - cluster type not supported

Only Assigned and Shared access mode are supported in UC. You must change your cluster configuration to match UC compliant access modes.

AF201 - Inplace Sync

Short description: We found that the table or database can be SYNC'd without moving data because the data is stored directly on cloud storage specified via a mount or a cloud storage URL (not DBFS). How: Run the SYNC command on the table or schema. If the tables (or source database) is 'managed' first set this spark setting in your session or in the interactive cluster configuration: spark.databricks.sync.command.enableManagedTable=true

AF202 - Asset Replication Required

We found that the table or database needs to have the data copied into a Unity Catalog managed location or table. Recommendation: Perform a 'deep clone' operation on the table to copy the files

CREATE TABLE [IF NOT EXISTS] table_name
   [SHALLOW | DEEP] CLONE source_table_name [TBLPROPERTIES clause] [LOCATION path]

AF203 - Data in DBFS Root

A table or schema refers to a location in DBFS and not a cloud storage location. The data must be moved from DBFS to a cloud storage location or to a Unity Catalog managed storage.

AF204 - Data is in DBFS Mount

A table or schema refers to a location in DBFS mount and not a direct cloud storage location. Mounts are not suppored in Unity Catalog so the mount source location must be de-referenced and the table/schema objects mapped to a UC external location.

AF210 - Non-DELTA format: CSV

Unity Catalog does not support managed CSV tables. Recommend converting the table to DELTA format or migrating the table to an External table.

AF211 - Non-DELTA format: DELTA

This was a known issue of the UCX assessment job. This bug should be fixed with release 0.10.0

AF212 - Non-DELTA format

where format can be any of [PARQUET|JDBC|ORC|XML|JSON|HIVE|deltaSharing|com.databricks.spark.csv|...]

Unity Catalog managed tables only support DELTA format. Recommend converting the table to DELTA lake format, converting the table to an External table. For deltaSharing use Databricks to Databricks Delta Sharing if the provider is also on Databricks. HIVE type tables are not supported.

For JDBC data sources:

Problem (on shared clusters): Accessing third-party databases—other than MySQL, PostgreSQL, Amazon Redshift, Snowflake, Microsoft SQL Server, Azure Synapse (SQL Data Warehouse) and Google BigQuery—will require additional permissions on a shared cluster if the user is not a workspace admin. This is due to the drivers not guaranteeing user isolation, e.g., as the driver writes data from multiple users to a widely accessible temp directory.

Workaround: Granting ANY FILE permissions will allow users to access untrusted databases. Note that ANY FILE will still enforce ACLs on any tables or external (storage) locations governed by Unity Catalog. Upgrade the DBR runtime to 13.3 LTS or higher to avoid cluster level firewall restrictions.

AF221 - Unsupported Storage Type

where storage type can be any of adl://, wasb://, or wasbs://.

ADLS Gen 2 (abfss://) is the only Azure native storage type supported. Use a Deep Clone process to copy the table data.

CREATE TABLE [IF NOT EXISTS] table_name
   [SHALLOW | DEEP] CLONE source_table_name [TBLPROPERTIES clause] [LOCATION path]

AF222 - Explicitly DENYing privileges is not supported in UC

Unity Catalog does not support DENY permissions on securable objects. These permissions will be updated during group migration, but won't be transferred during catalog migration.

AF300 - AF399

The 300 series findings relate to Compute Access mode limitations for Unity Catalog

Resolutions may include:

• Upgrade cluter runtime to latest LTS version (e.g. 14.3 LTS)

• Migrate users with GPU, Distributed ML, Spark Context, R type workloads to Assigned clusters. Implement cluster policies and pools to even out startup time and limit upper cost boundry.

• Upgrade SQL only users (and BI tools) to SQL Warehouses (much better SQL / Warehouse experience and lower cost)

• For users with single node python ML requirements, Shared Compute with %pip install library support or Personal Compute with pools and compute controls may provide a better experience and better manageability.

• For single node ML users on a crowded driver node of a large shared cluster, will get a better experience with Personal Compute policies combined with (warm) Compute pools

AF300.6 - 3 level namespace

The hive_metastore. is used to refer to a 3 level namespace. Most customers will migrate hive_metastore to a workspace catalog (named based on the workspace name). The code should then map the hive_metastore to the appropriate catalog name.

Easier solution is to define a default catalog for your session, job, cluster or workspace.

Setting the Workspace default, use the admin UI or command line:

% databricks settings update-default-workspace-namespace

Setting the Cluster or Job default catalog (in the spark configuration settings):

spark.databricks.sql.initial.catalog.name my_catalog

For JDBC, add to the JDBC connection URL:

• ConnCatalog=my_catalog (preferred)
• databricks.Catalog=my_catalog (rare)

For ODBC .ini file:

[Databricks]
Driver=<path-to-driver>
Host=<server-hostname>
Port=443
HTTPPath=<http-path>
ThriftTransport=2
SSL=1
AuthMech=3
UID=token
PWD=<personal-access-token>
Catalog=my_catalog

AF300.1 - r language support

When using %r command cells, the user will receive Your administrator has only allowed sql and python and scala commands on this cluster. This execution contained at least one disallowed language. message.

Recommend using Assigned (single user clusters).

AF300.2 - scala language support

Scala is supported on Databricks Runtime 13.3 and above.

Recommend upgrading your shared cluster DBR to 13.3 LTS or greater or using Assigned data security mode (single user clusters).

AF300.3 - Minimum DBR version

The minimum DBR version to access Unity Catalog was not met. The recommendation is to upgrade to the latest Long Term Supported (LTS) version of the Databricks Runtime.

AF300.4 - ML Runtime cpu

The Databricks ML Runtime is not supported on Shared Compute mode clusters. Recommend migrating these workloads to Assigned clusters. Implement cluster policies and pools to even out startup time and limit upper cost boundary.

AF300.5 - ML Runtime gpu

The Databricks ML Runtime is not supported on Shared Compute mode clusters. Recommend migrating these workloads to Assigned clusters. Implement cluster policies and pools to even out startup time and limit upper cost boundary.

AF301.1 - spark.catalog.x

The spark.catalog. pattern was found. Commonly used functions in spark.catalog, such as tableExists, listTables, setCurrentCatalog are not allowed on shared clusters due to security reasons. spark.sql("<sql command>) may be a better alternative. DBR 14.1 and above have made these commands available. Upgrade your DBR version.

AF301.2 - spark.catalog.x (spark._jsparkSession.catalog)

The spark._jsparkSession.catalog pattern was found. Commonly used functions in spark.catalog, such as tableExists, listTables, setCurrentCatalog are not allowed on shared clusters due to security reasons. spark.sql("<sql command>) may be a better alternative. The corresponding spark.catalog.x methods may work on DBR 14.1 and above.

AF302.x - Arbitrary Java

With Spark Connect on Shared clusters it is no longer possible to directly access the host JVM from the Python process. This means it is no longer possible to interact with Java classes or instantiate arbitrary Java classes directly from Python similar to the code below.

Recommend finding the equivalent PySpark or Scala api.

AF302.1 - Arbitrary Java (spark._jspark)

The spark._jspark is used to execute arbitrary Java code.

AF302.2 - Arbitrary Java (spark._jvm)

The spark._jvm is used to execute arbitrary Java code.

AF302.3 - Arbitrary Java (._jdf)

The ._jdf is used to execute arbitrary Java code.

AF302.4 - Arbitrary Java (._jcol)

The ._jcol is used to execute arbitrary Java code.

AF302.5 - Arbitrary Java (._jvm)

The ._jvm is used to execute arbitrary Java code.

AF302.6 - Arbitrary Java (._jvm.org.apache.log4j)

The ._jvm.org.apache.log4j is used to execute arbitrary Java code.

AF303.1 - Java UDF (spark.udf.registerJavaFunction)

The spark.udf.registerJavaFunction is used to register a Java UDF.

AF304.1 - JDBC datasource (spark.read.format("jdbc"))

The spark.read.format("jdbc") pattern was found and is used to read data from a JDBC datasource.

Accessing third-party databases—other than MySQL, PostgreSQL, Amazon Redshift, Snowflake, Microsoft SQL Server, Azure Synapse (SQL Data Warehouse) and Google BigQuery will require additional permissions on a shared cluster if the user is not a workspace admin. This is due to the drivers not guaranteeing user isolation, e.g., as the driver writes data from multiple users to a widely accessible temp directory.

Workaround: Granting ANY FILE permissions will allow users to access untrusted databases. Note that ANY FILE will still enforce ACLs on any tables or external (storage) locations governed by Unity Catalog. This requires DBR 12.2 or later (DBR 12.1 or before is blocked on the network layer)

AF305.1 - boto3

The boto3 library is used.

Instance profiles (AWS) are not supported from the Python/Scala REPL or UDFs, e.g. using boto3 or s3fs, Instance profiles are only set from init scripts and (internally) from Spark. To access S3 objects the recommendation is to use EXTERNAL VOLUMES mapped to the fixed s3 storage location.

Workarounds For accessing cloud storage (S3), use storage credentials and external locations.

(AWS) Consider other ways to authenticate with boto3, e.g., by passing credentials from Databricks secrets directly to boto3 as a parameter, or loading them as environment variables. This page contains more information. Please note that unlike instance profiles, those methods do not provide short-lived credentials out of the box, and customers are responsible for rotating secrets according to their security needs.

AF305.2 - s3fs

The s3fs library is used which provides posix type sematics for S3 access. s3fs is based on boto3 library and has similar restrictions. The recommendation is to use EXTERNAL VOLUMES mapped to the fixed s3 storage location or MANAGED VOLUMES.

AF306.1 - dbutils...getContext (.toJson())

The dbutils.notebook.entry_point.getDbutils().notebook().getContext().toJson() pattern was found. This function may trigger a security exception in DBR 13.0 and above.

The recommendation is to explore alternative APIs:

from dbruntime.databricks_repl_context import get_context
context = get_context()
context.__dict__

AF306.2 - dbutils...getContext

The dbutils.notebook.entry_point.getDbutils().notebook().getContext() pattern was found. This function may trigger a security exception in DBR 13.0 and above.

from dbruntime.databricks_repl_context import get_context
context = get_context()
context.__dict__

AF310.1 - credential passthrough (dbutils.credentials.)

The dbutils.credentials. is used for credential passthrough. This is not supported by Unity Catalog.

AF311.x - dbutils (dbutils)

DBUtils and other clients that directly read the data from cloud storage are not supported. Use Volumes or use Assigned clusters.

AF311.1 - dbutils.fs (dbutils.fs.)

The dbutils.fs. pattern was found. DBUtils and other clients that directly read the data from cloud storage are not supported. Please note that dbutils.fs calls with /Volumes (Volumes) will work.

AF311.2 - dbutils mount(s) (dbutils.fs.mount)

The dbutils.fs.mount pattern was found. This is not supported by Unity Catalog. Use instead EXTERNAL LOCATIONS and VOLUMES.

AF311.3 - dbutils mount(s) (dbutils.fs.refreshMounts)

The dbutils.fs.refreshMounts pattern was found. This is not supported by Unity Catalog. Use instead EXTERNAL LOCATIONS and VOLUMES.

AF311.4 - dbutils mount(s) (dbutils.fs.unmount)

The dbutils.fs.unmount pattern was found. This is not supported by Unity Catalog. Use instead EXTERNAL LOCATIONS and VOLUMES.

AF311.5 - mount points (dbfs:/mnt)

The dbfs:/mnt is used as a mount point. This is not supported by Unity Catalog. Use instead EXTERNAL LOCATIONS and VOLUMES.

AF311.6 - dbfs usage (dbfs:/)

The dbfs:/ pattern was found. DBFS is not supported by Unity Catalog. Use instead EXTERNAL LOCATIONS and VOLUMES. There may be false positives with this pattern because dbfs:/Volumes/mycatalog/myschema/myvolume is legitimate usage.

Please Note: dbfs:/Volumes/<catalog>/<schema>/<volume> is a supported access pattern for spark.

AF311.7 - dbfs usage (/dbfs/)

The /dbfs/ pattern was found. DBFS is not supported by Unity Catalog. Use instead EXTERNAL LOCATIONS and VOLUMES.

AF313.x - SparkContext

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 with Shared Compute access mode due to security restrictions. In Shared Compute mode, these methods do not support strict data isolation.

To run legacy workloads without modification, use access mode "Single User" type clusters.

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

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

AF313.1 - SparkContext (spark.sparkContext)

The spark.sparkContext pattern was found, use the spark variable directly.

AF313.2 - SparkContext (from pyspark.sql import SQLContext)

The from pyspark.sql import SQLContext and import org.apache.spark.sql.SQLContext are used. These are not supported in Unity Catalog. Possibly, the spark variable will suffice.

AF313.3 - SparkContext (.binaryFiles)

The .binaryFiles pattern was found, this is not supported by Unity Catalog. Instead, please consider using spark.read.format('binaryFiles').

AF313.4 - SparkContext (.binaryRecords)

The .binaryRecords pattern was found, which is not supported by Unity Catalog Shared Compute access mode.

AF313.5 - SparkContext (.emptyRDD)

The .emptyRDD pattern was found, which is not supported by Unity Catalog Shared Compute access mode. Instead use:

%python
from pyspark.sql.types import StructType, StructField, StringType

schema = StructType([StructField("k", StringType(), True)])
spark.createDataFrame([], schema)

AF313.6 - SparkContext (.getConf)

The .getConf pattern was found. There may be significant false positives with this one as .getConf is a common API pattern. In the case of sparkContext.getConf or sc.getConf, use spark.conf instead.

• spark.conf.get() # retrieves a single value
• spark.conf.set() # sets a single value (If allowed)
• spark.conf.getAll() unfortunately does not exist

AF313.7 - SparkContext ( .hadoopFile )

The .hadoopFile pattern was found. Use "Assigned" access mode compute or make permanent file type changes.

AF313.8 - SparkContext ( .hadoopRDD )

The .hadoopRDD pattern was found. Use "Assigned" access mode compute or make permanent file type changes.

AF313.9 - SparkContext ( .init_batched_serializer )

The .init_batched_serializer pattern was found. No suggestions available at this time.

AF313.10 - SparkContext ( .newAPIHadoopFile )

The .newAPIHadoopFile pattern was found. Use "Assigned" access mode compute or make permanent file type changes.

AF313.11 - SparkContext ( .newAPIHadoopRDD )

The .newAPIHadoopRDD pattern was found. Use "Assigned" access mode compute or make permanent file type changes.

AF313.12 - SparkContext ( .parallelize )

The .parallelize pattern was found. Instead of:

json_content1 = "{'json_col1': 'hello', 'json_col2': 32}"
json_content2 = "{'json_col1': 'hello', 'json_col2': 'world'}"

json_list = []
json_list.append(json_content1)
json_list.append(json_content2)

df = spark.read.json(sc.parallelize(json_list))
display(df)

use:

from pyspark.sql import Row
import json
# Sample JSON data as a list of dictionaries (similar to JSON objects)
json_data_str = response.text
json_data = [json.loads(json_data_str)]

# Convert dictionaries to Row objects
rows = [Row(**json_dict) for json_dict in json_data]
# Create DataFrame from list of Row objects
df = spark.createDataFrame(rows)
# Show the DataFrame
df.display()

AF313.13 - SparkContext ( .pickleFile )

The .pickleFile pattern was found. Use "Assigned" access mode compute or make permanent file type changes.

AF313.14 - SparkContext ( .range )

The .range pattern was found. Use spark.range() instead of sc.range()

AF313.15 - SparkContext ( .rdd )

The .rdd pattern was found. Use "Assigned" access mode compute or upgrade to the faster Spark DataFrame api.

AF313.16 - SparkContext ( .runJob )

The .runJob pattern was found. Use "Assigned" access mode compute or upgrade to the faster Spark DataFrame api.

AF313.17 - SparkContext ( .sequenceFile )

The .sequenceFile pattern was found. Use "Assigned" access mode compute or make permanent file type changes.

AF313.18 - SparkContext ( .setJobGroup )

The .setJobGroup pattern was found. spark.addTag() can attach a tag, and getTags() and interruptTag(tag) can be used to act upon the presence/absence of a tag. These APIs only work with Spark Connect (Shared Compute Mode) and will not work in "Assigned" access mode.

AF313.19 - SparkContext ( .setLocalProperty )

The .setLocalProperty pattern was found.

AF313.20 - SparkContext ( .setSystemProperty )

The .setSystemProperty pattern was found. Use "Assigned" access mode compute or find alternative within the spark API.

AF313.21 - SparkContext ( .stop )

The .stop pattern was found. Use "Assigned" access mode compute or find alternative within the spark API.

AF313.22 - SparkContext ( .textFile )

The .textFile pattern was found. Use "Assigned" access mode compute or find alternative within the spark API.

AF313.23 - SparkContext ( .uiWebUrl)

The .uiWebUrl pattern was found. Use "Assigned" access mode compute or find alternative within the spark API.

AF313.24 - SparkContext (.union)

The .union pattern was found, Use "Assigned" access mode compute or find alternative within the spark API.

AF313.25 - SparkContext (.wholeTextFiles)

The .wholeTextFiles pattern was found. Use "Assigned" access mode compute or use the spark.read().text("file_name") API.

AF314.x - Distributed ML

Databricks Runtime ML and Spark Machine Learning Library (MLlib) are not supported on shared Unity Catalog compute. The recommendation is to use Assigned mode cluster; Use cluster policies and (warm) compute pools to improve compute and cost management.

AF314.1 - Distributed ML (sparknlp)

The sparknlp pattern was found. Use "Assigned" access mode compute.

AF314.2 - Distributed ML (xgboost.spark)

The xgboost.spark pattern was found. Use "Assigned" access mode compute.

AF314.3 - Distributed ML (catboost_spark)

The catboost_spark pattern was found. Use "Assigned" access mode compute.

AF314.4 - Distributed ML (ai.catboost:catboost-spark)

The ai.catboost:catboost-spark pattern was found. Use "Assigned" access mode compute.

AF314.5 - Distributed ML (hyperopt)

The hyperopt pattern was found. Use "Assigned" access mode compute.

AF314.6 - Distributed ML (SparkTrials)

The SparkTrials pattern was found. Use "Assigned" access mode compute.

AF314.7 - Distributed ML (horovod.spark)

The horovod.spark pattern was found. Use "Assigned" access mode compute.

AF314.8 - Distributed ML (ray.util.spark)

The ray.util.spark pattern was found. Use "Assigned" access mode compute.

AF314.9 - Distributed ML (databricks.automl)

The databricks.automl pattern was found. Use "Assigned" access mode compute.

AF308.1 - Graphframes (from graphframes)

The from graphframes pattern was found. Use "Assigned" access mode compute.

AF309.1 - Spark ML (pyspark.ml.)

The pyspark.ml. pattern was found. Use "Assigned" access mode compute.

AF315.1 - UDAF scala issue (UserDefinedAggregateFunction)

The UserDefinedAggregateFunction pattern was found. Use "Assigned" access mode compute.

AF315.2 - applyInPandas (applyInPandas)

The applyInPandas pattern was found. Use "Assigned" access mode compute.

AF315.3 - mapInPandas (mapInPandas)

The mapInPandas pattern was found. Use "Assigned" access mode compute.

AF330.x - Streaming

Streaming limitations for Unity Catalog shared access mode documentation should be consulted for more details.

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

The assessment patterns and specifics are as follows:

AF330.1 - Streaming (.trigger(continuous)

The .trigger(continuous pattern was found. Continuous processing mode is not supported in Unity Catalog shared access mode. Apache Spark continuous processing mode is not supported. See Continuous Processing in the Spark Structured Streaming Programming Guide.

AF330.2 - Streaming (kafka.sasl.client.callback.handler.class)

The kafka.sasl.client.callback.handler.class pattern was found. SASL features are not supported in Unity Catalog shared access mode.

AF330.3 - Streaming (kafka.sasl.login.callback.handler.class)

The kafka.sasl.login.callback.handler.class pattern was found. SASL features are not supported in Unity Catalog shared access mode.

AF330.4 - Streaming (kafka.sasl.login.class)

The kafka.sasl.login.class pattern was found. SASL features are not supported in Unity Catalog shared access mode.

AF330.5 - Streaming (kafka.partition.assignment.strategy)

The kafka.partition.assignment.strategy pattern was found. Kafka features are not supported in Unity Catalog shared access mode.

AF330.6 - Streaming (kafka.ssl.truststore.location)

The kafka.ssl.truststore.location pattern was found. SSL features are not supported in Unity Catalog shared access mode.

AF330.7 - Streaming (kafka.ssl.keystore.location)

The kafka.ssl.keystore.location pattern was found. SSL features are not supported in Unity Catalog shared access mode.

AF330.8 - Streaming (cleanSource)

The cleanSource pattern was found. The cleanSource operation is not supported in Unity Catalog shared access mode.

AF330.9 - Streaming (sourceArchiveDir)

The sourceArchiveDir pattern was found. The sourceArchiveDir operation is not supported in Unity Catalog shared access mode.

AF330.10 - Streaming (applyInPandasWithState)

The applyInPandasWithState pattern was found. The applyInPandasWithState operation is not supported in Unity Catalog shared access mode.

AF330.11 - Streaming (.format("socket"))

The .format("socket") pattern was found. Socket source is not supported in Unity Catalog shared access mode.

AF330.12 - Streaming (StreamingQueryListener)

The StreamingQueryListener pattern was found. StreamingQueryListener is not supported in Unity Catalog shared access mode.

AF330.13 - Streaming (applyInPandasWithState)

The applyInPandasWithState pattern was found. The applyInPandasWithState operation is not supported in Unity Catalog shared access mode.