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Serverless compute limitations | Databricks Documentation

This article explains the current limitations of serverless compute for notebooks and jobs. It starts with an overview of the most important considerations and then provides a comprehensive reference list of limitations.

Before creating new workloads or migrating workloads to serverless compute, first consider the following limitations:

• Python and SQL are the only supported languages.
• Only Spark connect APIs are supported. Spark RDD APIs are not supported.
• JAR libraries are not supported. For workarounds, see Best practices for serverless compute.
• Serverless compute is available to all workspace users.
• Notebook tags are not supported. Use serverless budget policies to tag serverless usage.
• For streaming, only incremental batch logic can be used. There is no support for default or time-based trigger intervals. See Streaming limitations.

The following sections list the current limitations of serverless compute.

Serverless compute is based on Databricks standard access mode compute architecture (formerly called shared access mode). The most relevant limitations inherited from standard access mode are listed below, along with additional serverless-specific limitations. For a full list of standard access mode limitations, see Compute access mode limitations for Unity Catalog.

• Scala and R are not supported.
• ANSI SQL is the default when writing SQL. Opt-out of ANSI mode by setting spark.sql.ansi.enabled to false.
• Spark RDD APIs are not supported.
• Spark Context (sc), spark.sparkContext, and sqlContext are not supported.

• DBFS root is supported, but DBFS mounts with AWS instance profiles are not supported.
• Serverless compute is not supported with the Databricks cloud setup free trial.

• Databricks Container Services are not supported.
• By default, no Spark query in a serverless notebook can run longer than 9000 seconds. This is configurable using the spark.databricks.execution.timeout property. For more details, see Configure Spark properties for serverless notebooks and jobs. This limit does not apply to serverless jobs.
• You must use Unity Catalog to connect to external data sources. Use external locations to access cloud storage.
• User-defined functions (UDFs) cannot access the internet. Because of this, the CREATE FUNCTION (External) command is not supported. Databricks recommends using CREATE FUNCTION (SQL and Python) to create UDFs.
• Individual rows must not exceed the maximum size of 128MB.
• The Spark UI is not available. Instead, use the query profile to view information about your Spark queries. See Query profile.
• Spark logs are not available when using serverless notebooks and jobs. Users only have access to client-side application logs.
• Cross-workspace access is allowed only if the workspaces are in the same region and the destination workspace does not have an IP ACL or front-end PrivateLink configured.
• Global temporary views are not supported. Databricks recommends using session temporary views or creating tables where cross-session data passing is required.
• Maven coordinates are not supported.

• There is no support for default or time-based trigger intervals. Only Trigger.AvailableNow is supported. See Configure Structured Streaming trigger intervals.
• All limitations for streaming on standard access mode also apply. See Streaming limitations and requirements for Unity Catalog standard access mode.

• Databricks Runtime for Machine Learning and Apache Spark MLlib are not supported.
• GPUs are not supported.

• Notebook-scoped libraries are not cached across development sessions.
• Sharing TEMP tables and views when sharing a notebook among users is not supported.
• Autocomplete and Variable Explorer for dataframes in notebooks are not supported.
• By default, new notebooks are saved in .ipynb format. If your notebook is saved in source format, serverless metadata might not be captured correctly, and some features might not function as expected.

• The driver size for serverless compute for jobs is currently fixed and cannot be changed.
• Task logs are not isolated per task run. Logs will contain the output from multiple tasks.
• Task libraries are not supported for notebook tasks. Use notebook-scoped libraries instead. See Notebook-scoped Python libraries.

The following compute-specific features are not supported:

• Compute policies
• Compute-scoped init scripts
• Compute-scoped libraries, including custom data sources and Spark extensions. Use notebook-scoped libraries instead.
• Instance pools
• Compute event logs
• Most Apache Spark compute configurations. For a list of supported configurations, see Configure Spark properties for serverless notebooks and jobs.
• Environment variables. Instead, Databricks recommends using widgets to create job and task parameters.

Dataframe and SQL cache APIs are not supported on serverless compute. Using any of these APIs or SQL commands will result in an exception.

• df.cache(), df.persist()
• df.unpersist()
• spark.catalog.cacheTable()
• spark.catalog.uncacheTable()
• spark.catalog.clearCache()
• CACHE TABLE
• UNCACHE TABLE
• REFRESH TABLE
• CLEAR CACHE

• Hive SerDe tables are not supported. Additionally, the corresponding LOAD DATA command which loads data into a Hive SerDe table is not supported. Using the command will result in an exception.

  Support for data sources is limited to AVRO, BINARYFILE, CSV, DELTA, JSON, KAFKA, ORC, PARQUET, ORC, TEXT, and XML.

• Hive variables (for example ${env:var}, ${configName}, ${system:var}, and spark.sql.variable) or config variable references using the ${var} syntax are not supported. Using Hive variables will result in an exception.

  Instead, use DECLARE VARIABLE, SET VARIABLE, and SQL session variable references and parameter markers ('?', or ':var') to declare, modify, and reference session state. You can also use the IDENTIFIER clause to parameterize object names in many cases.

Serverless compute supports the following data sources for DML operations (write, update, delete):

• CSV
• JSON
• AVRO
• DELTA
• KAFKA
• PARQUET
• ORC
• TEXT
• UNITY_CATALOG
• BINARYFILE
• XML
• SIMPLESCAN
• ICEBERG

Serverless compute supports the following data sources for read operations:

• CSV
• JSON
• AVRO
• DELTA
• KAFKA
• PARQUET
• ORC
• TEXT
• UNITY_CATALOG
• BINARYFILE
• XML
• SIMPLESCAN
• ICEBERG
• MYSQL
• POSTGRESQL
• SQLSERVER
• REDSHIFT
• SNOWFLAKE
• SQLDW (Azure Synapse)
• DATABRICKS
• BIGQUERY
• ORACLE
• SALESFORCE
• SALESFORCE_DATA_CLOUD
• TERADATA
• WORKDAY_RAAS
• MONGODB

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