Execute an rquery pipeline with data.table in parallel, partitioned by a given column. Note: usually the overhead of partitioning and distributing the work will by far overwhelm any parallel speedup. Also data.table itself already seems to exploit some thread-level parallelism (one often sees user time > elapsed time). Requires the parallel package. For a worked example with significant speedup please see https://github.com/WinVector/rqdatatable/blob/master/extras/Parallel_rqdatatable.md.

ex_data_table_parallel(optree, partition_column, cl = NULL, ...,
  tables = list(), source_limit = NULL, debug = FALSE,
  env = parent.frame())

Arguments

optree

relop operations tree.

partition_column

character name of column to partition work by.

cl

a cluster object, created by package parallel or by package snow. If NULL, use the registered default cluster.

...

not used, force later arguments to bind by name.

tables

named list map from table names used in nodes to data.tables and data.frames.

source_limit

if not null limit all table sources to no more than this many rows (used for debugging).

debug

logical if TRUE use lapply instead of parallel::clusterApplyLB.

env

environment to look for values in.

Value

resulting data.table (intermediate tables can sometimes be mutated as is practice with data.table).

Details

Care must be taken that the calculation partitioning is course enough to ensure a correct calculation. For example: anything one is joining on, aggregating over, or ranking over must be grouped so that all elements affecting a given result row are in the same level of the partition.