Sync: copying datasets

The “sync” recipe allows you to synchronize one dataset to another. Synchronization can be global or per-partition.

One major use-case of the sync recipe is to copy a dataset between storage backends where different computation are possible. For example:

  • copying a SQL dataset to HDFS to be able to perform Hive recipes on it

  • copying a HDFS dataset to ElasticSearch to be able to query it

  • copying a file dataset to a SQL database for efficient querying.

See also

For more information, see also the Concept | Sync recipe article in the Knowledge Base.

Schema handling

By default, when you create the sync recipe, DSS also creates the output dataset. In that case, DSS automatically copies the schema of the input dataset to the output dataset.

If you modify the schema of the input dataset, you should go to the edition screen for the sync recipe and click the “Resync Schema” button.

Schema mismatch

With DSS streaming engine (see below), the Sync recipe allows the input and output schema to be different. In that case, DSS uses the names of the columns to perform the matching between the input and output columns

Therefore, you can use the Sync recipe to remove or reorder some columns of a dataset. You cannot use the Sync recipe to “rename” a column. To do this, use a Preparation recipe.

Partitioning handling

By default, when syncing a partitioned dataset, DSS creates the output dataset with the same partitioning and puts a “equals” dependency. You can also sync to a non partitioned dataset or change the dependency.

When creating the recipe or clicking “Resync schema”, DSS automatically adds, if needed, the partitioning columns to the output schemas.

More on partitioning in Working with partitions


For optimal performance, the recipe can run over several engines:

Depending on the types and parameters of the input and output, DSS automatically chooses the engine to execute the synchronization.