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.
Either an optionally qualified table identifier or a path to a
icebergfile 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.
Bypass statistics collection during the conversion process and finish conversion faster. After the table is converted to Delta Lake, you can use
OPTIMIZE ZORDER BYto reorganize the data layout and generate statistics.
Partition the created table by the specified columns. When
table_nameis a path, the
PARTITIONED BYis required for partitioned data. When the
table_nameis a qualified table identifier,
PARTITIONED BYclause 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 loaded
In Databricks Runtime 11.1 and below,
PARTITIONED BYis a required argument for all partitioned data.
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.`s3://my-bucket/path/to/table` PARTITIONED BY (date DATE); -- if the table is partitioned CONVERT TO DELTA iceberg.`s3://my-bucket/path/to/table`; -- uses Iceberg manifest for metadata
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.