multidplyr is a backend for dplyr that partitions a data frame across multiple cores. You tell multidplyr how to split the data up with
partition() and then the data stays on each node until you explicitly retrieve it with
collect(). This minimises the amount of time spent moving data around, and maximises parallel performance. This idea is inspired by partools by Norm Matloff and distributedR by the Vertica Analytics team.
Due to the overhead associated with communicating between the nodes, you won’t see much performance improvement on basic dplyr verbs with less than ~10 million observations, and you may want to try dtplyr, which uses data.table instead. multidplyr’s strength is conveniently parallelising the type of more complex operation often found in
(Note that unlike other packages in the tidyverse, multidplyr requires R 3.5 or greater. We hope to relax this requirement in the future.)
To install from GitHub:
To use multidplyr, you first create a cluster of the desired number of workers. Each one of these workers is a separate R process, and the operating system will spread their execution across multiple cores:
There are two primary ways to use multidplyr. The first, and most efficient, way is to read different files on each worker:
# Create a filename vector containing different values on each worker cluster_assign_each(cluster, filename = c("a.csv", "b.csv", "c.csv", "d.csv")) # Use vroom to quickly load the csvs cluster_send(cluster, my_data <- vroom::vroom(filename)) # Create a party_df using the my_data variable on each worker my_data <- party_df(cluster, "my_data")
Alternatively, if you already have the data loaded in the main session, you can use
partition() to automatically spread it across workers. Use
group_by() to ensure that all of the observations belonging to a group end up on the same worker.
library(nycflights13) flight_dest <- flights %>% group_by(dest) %>% partition(cluster) flight_dest #> Source: party_df [336,776 x 19] #> Groups: dest #> Shards: 4 [81,594--86,548 rows] #> #> year month day dep_time sched_dep_time dep_delay arr_time #> <int> <int> <int> <int> <int> <dbl> <int> #> 1 2013 1 1 544 545 -1 1004 #> 2 2013 1 1 558 600 -2 923 #> 3 2013 1 1 559 600 -1 854 #> 4 2013 1 1 602 610 -8 812 #> 5 2013 1 1 602 605 -3 821 #> 6 2013 1 1 611 600 11 945 #> # … with 3.368e+05 more rows, and 12 more variables: sched_arr_time <int>, #> # arr_delay <dbl>, carrier <chr>, flight <int>, tailnum <chr>, #> # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>, #> # minute <dbl>, time_hour <dttm>
Now you can work with it like a regular data frame, but the computations will be spread across multiple cores. Once you’ve finished computation, use
collect() to bring the data back to the host session:
flight_dest %>% summarise(delay = mean(dep_delay, na.rm = TRUE), n = n()) %>% collect() #> # A tibble: 105 x 3 #> dest delay n #> <chr> <dbl> <int> #> 1 ABQ 13.7 254 #> 2 AUS 13.0 2439 #> 3 BQN 12.4 896 #> 4 BTV 13.6 2589 #> 5 BUF 13.4 4681 #> 6 CLE 13.4 4573 #> 7 CMH 12.2 3524 #> 8 DEN 15.2 7266 #> 9 DSM 26.2 569 #> 10 DTW 11.8 9384 #> # … with 95 more rows
Note that there is some overhead associated with copying data from the worker nodes back to the host node (and vice versa), so you’re best off using multidplyr with more complex operations. See
vignette("multidplyr") for more details.