Write Spark DataFrame to RDS files

spark_write_rds

Description

Write Spark dataframe to RDS files. Each partition of the dataframe will be exported to a separate RDS file so that all partitions can be processed in parallel.

Usage

spark_write_rds(x, dest_uri)

Arguments

Arguments Description

x A Spark DataFrame to be exported

dest_uri Can be a URI template containing partitionId (e.g., "hdfs://my_data_part_{partitionId}.rds") where partitionId will be substituted with ID of each partition using glue, or a list of URIs to be assigned to RDS output from all partitions (e.g., "hdfs://my_data_part_0.rds", "hdfs://my_data_part_1.rds", and so on) If working with a Spark instance running locally, then all URIs should be in "file://<local file path>" form. Otherwise the scheme of the URI should reflect the underlying file system the Spark instance is working with (e.g., “hdfs://”). If the resulting list of URI(s) does not contain unique values, then it will be post-processed with make.unique() to ensure uniqueness.

Arguments	Description
x	A Spark DataFrame to be exported
dest_uri	Can be a URI template containing `partitionId` (e.g., `"hdfs://my_data_part_{partitionId}.rds"`) where `partitionId` will be substituted with ID of each partition using `glue`, or a list of URIs to be assigned to RDS output from all partitions (e.g., `"hdfs://my_data_part_0.rds"`, `"hdfs://my_data_part_1.rds"`, and so on) If working with a Spark instance running locally, then all URIs should be in `"file://<local file path>"` form. Otherwise the scheme of the URI should reflect the underlying file system the Spark instance is working with (e.g., “hdfs://”). If the resulting list of URI(s) does not contain unique values, then it will be post-processed with `make.unique()` to ensure uniqueness.

Value

A tibble containing partition ID and RDS file location for each partition of the input Spark dataframe.