Shuffles data to produce a specified amount of Spark partitions by using the specified columns to determine the partitioning. This operation is useful for caching carefully repartitioned data before multiple join operations.
mappings: repartitioned_customer_basket: kind: repartition input: customer_basket partitions: 200 columns: - customer_id - product_id sort: true
broadcast(optional) (type: boolean) (default: false): Hint for broadcasting the result of this mapping for map-side joins.
cache(optional) (type: string) (default: NONE): Cache mode for the results of this mapping. Supported values are
NONE- Disables caching of teh results of this mapping
DISK_ONLY- Caches the results on disk
MEMORY_ONLY- Caches the results in memory. If not enough memory is available, records will be uncached.
MEMORY_ONLY_SER- Caches the results in memory in a serialized format. If not enough memory is available, records will be uncached.
MEMORY_AND_DISK- Caches the results first in memory and then spills to disk.
MEMORY_AND_DISK_SER- Caches the results first in memory in a serialized format and then spills to disk.
input(mandatory) (string): The name of the input mapping
columns(mandatory) (list:string): The list of column names used for partitioning the data
partitions(optional) (integer): The number of output partitions
sort(optional) (boolean): Specifies if the records within each partition should also be sorted
filter(optional) (type: string) (default: empty): An optional SQL filter expression that is applied before the repartition operation itself.
main- the only output of the mapping
This transformation can be used as part of a processing optimization where you want to repartition the result of some mapping because you perform multiple join operations on the same columns afterward.