{disk.frame} has been soft-deprecated in favor of {arrow}. With the {arrow} 6.0.0 release, it’s now capable of doing larger-than-RAM data analysis quite well see release note. Hence, there is no strong reason to prefer {disk.frame} unless you have very specific feature needs.
For the above reason, I’ve decided to soft-deprecate {disk.frame} which means I will no longer actively develop new features for it but it will remain on CRAN in maintenance mode.
To help with the transition I’ve created a function, disk.frame::disk.frame_to_parquet(df, outdir) to help you convert existing {disk.frame}s to the parquet format so you can use {arrow} with it.
I am working on an reincarnation of {disk.frame} in Julia, so the {disk.frame} will live on!
Thank your for support {disk.frame}. I’ve learnt alot along the way, but time has come to move on!
How do I manipulate tabular data that doesn’t fit into Random Access Memory (RAM)?
Use {disk.frame}!
In a nutshell, {disk.frame} makes use of two simple ideas
{disk.frame} performs a similar role to distributed systems such as Apache Spark, Python’s Dask, and Julia’s JuliaDB.jl for medium data which are datasets that are too large for RAM but not quite large enough to qualify as big data.
You can install the released version of {disk.frame} from CRAN with:
install.packages("disk.frame")
And the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("DiskFrame/disk.frame")
On some platforms, such as SageMaker, you may need to explicitly specify a repo like this
install.packages("disk.frame", repo="https://cran.rstudio.com")
Please see these vignettes and articles about {disk.frame}
{disk.frame} which replicates the sparklyr vignette for manipulating the nycflights13 flights data.{disk.frame} which lists some commons way of creating disk.frames{disk.frame} can be more epic! shows some ways of loading large CSVs and the importance of srckeepdfglm function for fitting generalized linear models{disk.frame} and why create it?
{disk.frame} is an R package that provides a framework for manipulating larger-than-RAM structured tabular data on disk efficiently. The reason one would want to manipulate data on disk is that it allows arbitrarily large datasets to be processed by R. In other words, we go from “R can only deal with data that fits in RAM” to “R can deal with any data that fits on disk”. See the next section.
data.frame and data.table?
A data.frame in R is an in-memory data structure, which means that R must load the data in its entirety into RAM. A corollary of this is that only data that can fit into RAM can be processed using data.frames. This places significant restrictions on what R can process with minimal hassle.
In contrast, {disk.frame} provides a framework to store and manipulate data on the hard drive. It does this by loading only a small part of the data, called a chunk, into RAM; process the chunk, write out the results and repeat with the next chunk. This chunking strategy is widely applied in other packages to enable processing large amounts of data in R, for example, see chunkded arkdb, and iotools.
Furthermore, there is a row-limit of 2^31 for data.frames in R; hence an alternate approach is needed to apply R to these large datasets. The chunking mechanism in {disk.frame} provides such an avenue to enable data manipulation beyond the 2^31 row limit.
{disk.frame} different to previous “big” data solutions for R?
R has many packages that can deal with larger-than-RAM datasets, including ff and bigmemory. However, ff and bigmemory restrict the user to primitive data types such as double, which means they do not support character (string) and factor types. In contrast, {disk.frame} makes use of data.table::data.table and data.frame directly, so all data types are supported. Also, {disk.frame} strives to provide an API that is as similar to data.frame’s where possible. {disk.frame} supports many dplyr verbs for manipulating disk.frames.
Additionally, {disk.frame} supports parallel data operations using infrastructures provided by the excellent future package to take advantage of multi-core CPUs. Further, {disk.frame} uses state-of-the-art data storage techniques such as fast data compression, and random access to rows and columns provided by the fst package to provide superior data manipulation speeds.
{disk.frame} work?
{disk.frame} works by breaking large datasets into smaller individual chunks and storing the chunks in fst files inside a folder. Each chunk is a fst file containing a data.frame/data.table. One can construct the original large dataset by loading all the chunks into RAM and row-bind all the chunks into one large data.frame. Of course, in practice this isn’t always possible; hence why we store them as smaller individual chunks.
{disk.frame} makes it easy to manipulate the underlying chunks by implementing dplyr functions/verbs and other convenient functions (e.g. the cmap(a.disk.frame, fn, lazy = F) function which applies the function fn to each chunk of a.disk.frame in parallel). So that {disk.frame} can be manipulated in a similar fashion to in-memory data.frames.
{disk.frame} different from Spark, Dask, and JuliaDB.jl?
Spark is primarily a distributed system that also works on a single machine. Dask is a Python package that is most similar to {disk.frame}, and JuliaDB.jl is a Julia package. All three can distribute work over a cluster of computers. However, {disk.frame} currently cannot distribute data processes over many computers, and is, therefore, single machine focused.
In R, one can access Spark via sparklyr, but that requires a Spark cluster to be set up. On the other hand {disk.frame} requires zero-setup apart from running install.packages("disk.frame") or devtools::install_github("xiaodaigh/disk.frame").
Finally, Spark can only apply functions that are implemented for Spark, whereas {disk.frame} can use any function in R including user-defined functions.
{disk.frame} works best if it can process multiple data chunks in parallel. The best way to set-up {disk.frame} so that each CPU core runs a background worker is by using
setup_disk.frame() # this allows large datasets to be transferred between sessions options(future.globals.maxSize = Inf)
The setup_disk.frame() sets up background workers equal to the number of CPU cores; please note that, by default, hyper-threaded cores are counted as one not two.
Alternatively, one may specify the number of workers using setup_disk.frame(workers = n).
suppressPackageStartupMessages(library(disk.frame)) library(nycflights13) # this will setup disk.frame's parallel backend with number of workers equal to the number of CPU cores (hyper-threaded cores are counted as one not two) setup_disk.frame() #> The number of workers available for disk.frame is 6 # this allows large datasets to be transferred between sessions options(future.globals.maxSize = Inf) # convert the flights data.frame to a disk.frame # optionally, you may specify an outdir, otherwise, the flights.df <- as.disk.frame(nycflights13::flights)
{disk.frame} aims to support as many dplyr verbs as possible. For example
flights.df %>% filter(year == 2013) %>% mutate(origin_dest = paste0(origin, dest)) %>% head(2) #> year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time arr_delay #> 1: 2013 1 1 517 515 2 830 819 11 #> 2: 2013 1 1 533 529 4 850 830 20 #> carrier flight tailnum origin dest air_time distance hour minute time_hour #> 1: UA 1545 N14228 EWR IAH 227 1400 5 15 2013-01-01 05:00:00 #> 2: UA 1714 N24211 LGA IAH 227 1416 5 29 2013-01-01 05:00:00 #> origin_dest #> 1: EWRIAH #> 2: LGAIAH
Starting from {disk.frame} v0.3.0, there is group_by support for a limited set of functions. For example:
result_from_disk.frame = iris %>%
as.disk.frame %>%
group_by(Species) %>%
summarize(
mean(Petal.Length),
sumx = sum(Petal.Length/Sepal.Width),
sd(Sepal.Width/ Petal.Length),
var(Sepal.Width/ Sepal.Width),
l = length(Sepal.Width/ Sepal.Width + 2),
max(Sepal.Width),
min(Sepal.Width),
median(Sepal.Width)
) %>%
collect
The results should be exactly the same as if applying the same group-by operations on a data.frame. If not, please report a bug.
List of supported group-by functionsIf a function you like is missing, please make a feature request here. It is a limitation that function that depend on the order a column can only be obtained using estimated methods.
Function Exact/Estimate Notesmin Exact max Exact mean Exact sum Exact length Exact n Exact n_distinct Exact sd Exact var Exact var(x) only cor, cov support planned any Exact all Exact median Estimate quantile Estimate One quantile only IQR Estimate Example: data.table syntax
library(data.table)
suppressWarnings(
grp_by_stage1 <-
flights.df[
keep = c("month", "distance"), # this analysis only required "month" and "dist" so only load those
month <= 6,
.(sum_dist = sum(distance)),
.(qtr = ifelse(month <= 3, "Q1", "Q2"))
]
)
#> data.table syntax for disk.frame may be moved to a separate package in the future
grp_by_stage1
#> qtr sum_dist
#> 1: Q1 27188805
#> 2: Q1 953578
#> 3: Q1 53201567
#> 4: Q2 3383527
#> 5: Q2 58476357
#> 6: Q2 27397926
The result grp_by_stage1 is a data.table so we can finish off the two-stage aggregation using data.table syntax
grp_by_stage2 = grp_by_stage1[,.(sum_dist = sum(sum_dist)), qtr] grp_by_stage2 #> qtr sum_dist #> 1: Q1 81343950 #> 2: Q2 89257810
To find out where the disk.frame is stored on disk:
# where is the disk.frame stored attr(flights.df, "path") #> [1] "C:\\Users\\RTX2080\\AppData\\Local\\Temp\\RtmpeygI4C\\file4e9c4ab6775c.df"
A number of data.frame functions are implemented for disk.frame
# get first few rows head(flights.df, 1) #> year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time arr_delay #> 1: 2013 1 1 517 515 2 830 819 11 #> carrier flight tailnum origin dest air_time distance hour minute time_hour #> 1: UA 1545 N14228 EWR IAH 227 1400 5 15 2013-01-01 05:00:00
# get last few rows tail(flights.df, 1) #> year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time arr_delay #> 1: 2013 9 30 NA 840 NA NA 1020 NA #> carrier flight tailnum origin dest air_time distance hour minute time_hour #> 1: MQ 3531 N839MQ LGA RDU NA 431 8 40 2013-09-30 08:00:00
# number of rows nrow(flights.df) #> [1] 336776
# number of columns ncol(flights.df) #> [1] 19
This project exists thanks to all the people who contribute.
The work priorities at this stage are
{disk.frame} in a structured course?
Please register your interest at:
https://leanpub.com/c/taminglarger-than-ramwithdiskframe
If you like {disk.frame} and want to speed up its development or perhaps you have a feature request? Please consider sponsoring {disk.frame} on Open Collective
Thank you to all our backers!
Sponsor and back{disk.frame}
Support {disk.frame} development by becoming a sponsor. Your logo will show up here with a link to your website.
Do you need help with machine learning and data science in R, Python, or Julia? I am available for Machine Learning/Data Science/R/Python/Julia consulting! Email me
Non-financial ways to contributeDo you wish to give back the open-source community in non-financial ways? Here are some ways you can contribute
{disk.frame} usage or experience. I would love to learn more about how {disk.frame} has helped you{disk.frame} to help promote it{disk.frame} Github repo{disk.frame} depends on e.g. {fst} and {future}https://github.com/DiskFrame/disk.frame-fannie-mae-example https://github.com/DiskFrame/disk.frame-vs https://github.com/DiskFrame/disk.frame.ml
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