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Introduction

How can I manipulate structured 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

1) split up a larger-than-RAM dataset into chunks and store each chunk in a separate file inside a folder and 2) provide a convenient API to manipulate these chunks

{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 that require distributing processing over many computers to be effective.

Sponsors & Backers

I would like to thank our backer and sponsor:

Installation

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("xiaodaigh/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")

Vignettes and articles

Please see these vignettes and articles about {disk.frame}

Interested in learning {disk.frame} in a structured course?

Please register your interest at:

https://leanpub.com/c/taminglarger-than-ramwithdiskframe

Common questions

a) What is {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.

b) How is it different to 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.

c) How is {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.

d) How does {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 map.disk.frame(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.

e) How is {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.

f) How is {disk.frame} different from multidplyr, partools and distributedR?

The packages multidplyr doesn’t seem to be disk-focused and hence does not allow arbitrarily large dataset to be manipulated; the focus on parallel processing is similar to disk.frame though. For partools [https://matloff.wordpress.com/2015/08/05/partools-a-sensible-r-package-for-large-data-sets/], it seems to use it’s own verbs for aggregating data instead of relying on existing verbs provided by data.table and dplyr. The package distributedR hasn’t been updated for a few years and also seems to require its own functions and verbs.

Example usage

Set-up {disk.frame}

{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).

Quick-start

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)

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\\Rtmp2Ps66a\\file287c4e97742d.df"

A number of data.frame functions are implemented for disk.frame

# get first few rows
head(flights.df)
#>    year month day dep_time sched_dep_time dep_delay arr_time
#> 1: 2013     1   1      517            515         2      830
#> 2: 2013     1   1      533            529         4      850
#> 3: 2013     1   1      542            540         2      923
#> 4: 2013     1   1      544            545        -1     1004
#> 5: 2013     1   1      554            600        -6      812
#> 6: 2013     1   1      554            558        -4      740
#>    sched_arr_time arr_delay carrier flight tailnum origin dest air_time
#> 1:            819        11      UA   1545  N14228    EWR  IAH      227
#> 2:            830        20      UA   1714  N24211    LGA  IAH      227
#> 3:            850        33      AA   1141  N619AA    JFK  MIA      160
#> 4:           1022       -18      B6    725  N804JB    JFK  BQN      183
#> 5:            837       -25      DL    461  N668DN    LGA  ATL      116
#> 6:            728        12      UA   1696  N39463    EWR  ORD      150
#>    distance hour minute           time_hour
#> 1:     1400    5     15 2013-01-01 05:00:00
#> 2:     1416    5     29 2013-01-01 05:00:00
#> 3:     1089    5     40 2013-01-01 05:00:00
#> 4:     1576    5     45 2013-01-01 05:00:00
#> 5:      762    6      0 2013-01-01 06:00:00
#> 6:      719    5     58 2013-01-01 05:00:00
# get last few rows
tail(flights.df)
#>    year month day dep_time sched_dep_time dep_delay arr_time
#> 1: 2013     9  30       NA           1842        NA       NA
#> 2: 2013     9  30       NA           1455        NA       NA
#> 3: 2013     9  30       NA           2200        NA       NA
#> 4: 2013     9  30       NA           1210        NA       NA
#> 5: 2013     9  30       NA           1159        NA       NA
#> 6: 2013     9  30       NA            840        NA       NA
#>    sched_arr_time arr_delay carrier flight tailnum origin dest air_time
#> 1:           2019        NA      EV   5274  N740EV    LGA  BNA       NA
#> 2:           1634        NA      9E   3393    <NA>    JFK  DCA       NA
#> 3:           2312        NA      9E   3525    <NA>    LGA  SYR       NA
#> 4:           1330        NA      MQ   3461  N535MQ    LGA  BNA       NA
#> 5:           1344        NA      MQ   3572  N511MQ    LGA  CLE       NA
#> 6:           1020        NA      MQ   3531  N839MQ    LGA  RDU       NA
#>    distance hour minute           time_hour
#> 1:      764   18     42 2013-09-30 18:00:00
#> 2:      213   14     55 2013-09-30 14:00:00
#> 3:      198   22      0 2013-09-30 22:00:00
#> 4:      764   12     10 2013-09-30 12:00:00
#> 5:      419   11     59 2013-09-30 11:00:00
#> 6:      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

Example: dplyr verbs

Group by

Group-by in disk.frame are performed within each chunk, hence a two-stage group by is required to obtain the correct group by results. The two-stage approach is preferred for performance reasons too.

To perform group-by one needs to do it in two-stage approach as the group-by’s are performed within each chunk. This will be addressed in a future package called disk.frame.db, but for now two-stage aggregation is the best to do group-bys in {disk.frame}.

flights.df = as.disk.frame(nycflights13::flights)

flights.df %>%
  srckeep(c("year","distance")) %>%  # keep only carrier and distance columns
  chunk_group_by(year) %>% 
  chunk_summarise(sum_dist = sum(distance)) %>% # this does a count per chunk
  collect
#> # A tibble: 6 x 2
#>    year sum_dist
#>   <int>    <dbl>
#> 1  2013 57446059
#> 2  2013 59302212
#> 3  2013 56585094
#> 4  2013 58476357
#> 5  2013 59407019
#> 6  2013 59000866

This is two-stage group-by in action

# need a 2nd stage to finalise summing
flights.df %>%
  srckeep(c("year","distance")) %>%  # keep only carrier and distance columns
  chunk_group_by(year) %>% 
  chunk_summarise(sum_dist = sum(distance)) %>% # this does a count per chunk
  collect %>% 
  group_by(year) %>% 
  summarise(sum_dist = sum(sum_dist))
#> # A tibble: 1 x 2
#>    year  sum_dist
#>   <int>     <dbl>
#> 1  2013 350217607

Here an example of using filter

# filter
pt = proc.time()
df_filtered <-
  flights.df %>% 
  filter(month == 1)
cat("filtering a < 0.1 took: ", data.table::timetaken(pt), "\n")
#> filtering a < 0.1 took:  0.020s elapsed (0.010s cpu)
nrow(df_filtered)
#> [1] 336776

You can mix group-by with other dplyr verbs as below

pt = proc.time()
res1 <- flights.df %>% 
  srckeep(c("month", "dep_delay")) %>% 
  filter(month <= 6) %>% 
  mutate(qtr = ifelse(month <= 3, "Q1", "Q2")) %>% 
  chunk_group_by(qtr) %>% 
  chunk_summarise(sum_delay = sum(dep_delay, na.rm = TRUE), n = n()) %>% 
  collect %>%
  group_by(qtr) %>% 
  summarise(sum_delay = sum(sum_delay), n = sum(n)) %>% 
  mutate(avg_delay = sum_delay/n)
cat("group by took: ", data.table::timetaken(pt), "\n")
#> group by took:  0.390s elapsed (0.180s cpu)

collect(res1)
#> # A tibble: 2 x 4
#>   qtr   sum_delay     n avg_delay
#>   <chr>     <dbl> <int>     <dbl>
#> 1 Q1       892053 80789      11.0
#> 2 Q2      1319941 85369      15.5

However, a one-stage group_by is possible with a hard_group_by to first rechunk the disk.frame. This not recommended for performance reasons, as it can quite slow.

pt = proc.time()
res1 <- flights.df %>% 
  srckeep(c("month", "dep_delay")) %>% 
  filter(month <= 6) %>% 
  mutate(qtr = ifelse(month <= 3, "Q1", "Q2")) %>% 
  hard_group_by(qtr) %>% # hard group_by is MUCH SLOWER but avoid a 2nd stage aggregation
  chunk_summarise(avg_delay = mean(dep_delay, na.rm = TRUE)) %>% 
  collect
#> Hashing...
#> Hashing...
#> Hashing...
#> Hashing...
#> Hashing...
#> Hashing...
#> Appending disk.frames:
cat("group by took: ", data.table::timetaken(pt), "\n")
#> group by took:  1.700s elapsed (0.270s cpu)

collect(res1)
#> # A tibble: 2 x 2
#>   qtr   avg_delay
#>   <chr>     <dbl>
#> 1 Q1         11.4
#> 2 Q2         15.9

Example: data.table syntax

library(data.table)
#> 
#> Attaching package: 'data.table'
#> The following object is masked from 'package:purrr':
#> 
#>     transpose
#> The following objects are masked from 'package:dplyr':
#> 
#>     between, first, last

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"))
    ]

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

Contributors

This project exists thanks to all the people who contribute.

Current Priorities

The work priorities at this stage are

  1. Bugs
  2. Urgent feature implementations that can improve an awful user-experience
  3. More vignettes covering every aspect of disk.frame
  4. Comprehensive Tests
  5. Comprehensive Documentation
  6. More features

Blogs and other resources

Title Author Date Description
{disk.frame} is epic Bruno Rodriguez 20190903 It’s about loading a 30G file into {disk.frame}
My top 10 R packages for data analytics Jacky Poon 20190903 {disk.frame} was number 3
useR! 2019 presentation video Dai ZJ 20190803
useR! 2019 presentation slides Dai ZJ 20190803
Split-apply-combine for Maximum Likelihood Estimation of a linear model Bruno Rodriguez 20191006 {disk.frame} used in helping to create a maximum likelihood estimation program for linear models
Emma goes to useR! 2019 Emma Vestesson 20190716 The first mention of {disk.frame} in a blog post

Open Collective

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

Backers

Thank you to all our backers! [Become a backer]

Sponsors

Support {disk.frame} development by becoming a sponsor. Your logo will show up here with a link to your website. [Become a sponsor]

Contact me for consulting

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