In Databricks Runtime 15.4.3 and above, the following data type changes can no longer be applied to tables with the type widening feature enabled: byte, short, int and long to decimal. byte, short, and int to d
Breaking changesThe PySpark spark.sql() function now fails for invalid argument typesIn Databricks Runtime 15.0 and later, the args parameter for named or positional parameters passed to the spark.sql() function must be a dictionary or list. If any other object type is passed, the following ...
Improving documentation, adding licensing options, and enhancing cluster management features are desired. The need for better predictive analytics integration and more self-service features is highlighted. "In my opinion, areas of Databricks that have room for improvement involve the dashboards. Until ...
Databricks Runtime16.3 and above Changes the default collation of the table for newSTRINGcolumns. Existing columns are not affected by this clause. To change the collation of an existing column, useALTER TABLE ... ALTER COLUMN ... COLLATE collation_name. DROP CONSTRAINT Drops a primary key, f...
Behavior changes Library upgrades Apache Spark Show 2 more Note Support for this Databricks Runtime version has ended. For the end-of-support date, see End-of-support history. For all supported Databricks Runtime versions, see Databricks Runtime release notes versions and compatibility.The...
REORGTABLEtable_nameAPPLY(UPGRADE UNIFORM(ICEBERG_COMPAT_VERSION=2)); -- Update the table schema. ALTERTABLEtable_nameADDCOLUMNS(col_name STRING); Write conflicts with row-level concurrency Row-level concurrency reduces conflicts between concurrent write operations by detecting changes at the row-...
Restart the cluster to apply the changes. You should also ensure that any operations performed on the tables do not unintentionally alter Delta properties that could trigger version changes. For more information, please review theExternal Apache Hive metastore (legacy)documentation....
For streaming tables, only changes to the partitioning currently requires the table be dropped and recreated. For any other supported configuration change, we use CREATE OR REFRESH (plus an ALTER statement for changes to the schedule) to apply the changes. There is currently no mechanism for the...
Changes and Improvements Introduced new vectorized UDFs for PySpark. Capable of limiting the max number of partitions in query watchdog. Improved error message communication when installing CRAN packages using Databricks Library UI and API. MySQL JDBC driver has been replaced by MariaDB JDBC driver. ...
Let's say that you want to track customer transactions in a MySQL table and apply those changes to a Delta Lake table for further analysis. That is, you need to apply the same set of updates, deletes, and inserts made to the MySQL table to the Delta Lake table. You first design and...