CONVERT TO DELTA
Applies to: Databricks SQL Databricks Runtime
Converts an existing Parquet table to a Delta table in-place. This command lists all the files in the directory, creates a Delta Lake transaction log that tracks these files, and automatically infers the data schema by reading the footers of all Parquet files. The conversion process collects statistics to improve query performance on the converted Delta table. If you provide a table name, the metastore is also updated to reflect that the table is now a Delta table.
This command supports converting Iceberg tables whose underlying file format is Parquet. In this case, the converter generates the Delta Lake transaction log based on Iceberg table's native file manifest, schema and partitioning information.
Syntax
CONVERT TO DELTA table_name [ NO STATISTICS ] [ PARTITIONED BY clause ]
Parameters
-
Either an optionally qualified table identifier or a path to a
parquet
oriceberg
file directory. The name must not include a temporal specification. For Iceberg tables, you can only use paths, as converting managed iceberg tables is not supported. NO STATISTICS
Bypass statistics collection during the conversion process and finish conversion faster. After the table is converted to Delta Lake, you can use
OPTIMIZE ZORDER BY
to reorganize the data layout and generate statistics.-
Partition the created table by the specified columns. When
table_name
is a path, thePARTITIONED BY
is required for partitioned data. When thetable_name
is a qualified table identifier,PARTITIONED BY
clause is optional and the partition specification are loaded from the metastore. In either approach, the conversion process aborts and throw an exception if the directory structure does not conform to the provided or loadedPARTITIONED BY
specification.Note
In Databricks Runtime 11.1 and below,
PARTITIONED BY
is a required argument for all partitioned data.
Examples
Note
You do not need to provide partitioning information for Iceberg tables or tables registered to the metastore.
CONVERT TO DELTA database_name.table_name; -- only for Parquet tables
CONVERT TO DELTA parquet.`abfss://container-name@storage-account-name.dfs.core.chinacloudapi.cn/path/to/table`
PARTITIONED BY (date DATE); -- if the table is partitioned
CONVERT TO DELTA iceberg.`abfss://container-name@storage-account-name.dfs.core.chinacloudapi.cn/path/to/table`; -- uses Iceberg manifest for metadata
Caveats
Any file not tracked by Delta Lake is invisible and can be deleted when you run VACUUM
. You should avoid updating or appending data files during the conversion process. After the table is converted, make sure all writes go through Delta Lake.
It is possible that multiple external tables share the same underlying Parquet directory. In this case, if you run CONVERT
on one of the external tables, then you will not be able to access the other external tables because their underlying directory has been converted from Parquet to Delta Lake. To query or write to these external tables again, you must run CONVERT
on them as well.
CONVERT
populates the catalog information, such as schema and table properties, to the Delta Lake transaction log. If the underlying directory has already been converted to Delta Lake and its metadata is different from the catalog metadata, a convertMetastoreMetadataMismatchException
is thrown.
While using Databricks Runtime, if you want CONVERT
to overwrite the existing metadata in the Delta Lake transaction log, set the SQL configuration spark.databricks.delta.convert.metadataCheck.enabled
to false.