🏎💨vroom

The fastest delimited reader for R, 1.23 GB/sec.

But that’s impossible! How can it be so fast?

vroom doesn’t stop to actually read all of your data, it simply indexes where each record is located so it can be read later. The vectors returned use the Altrep framework to lazily load the data on-demand when it is accessed, so you only pay for what you use. This lazy access is done automatically, so no changes to your R data-manipulation code are needed.

vroom also uses multiple threads for indexing, materializing non-character columns, and when writing to further improve performance.

package	version	time (sec)	speedup	throughput
vroom	1.5.1	1.36	53.30	1.23 GB/sec
data.table	1.14.0	5.83	12.40	281.65 MB/sec
readr	1.4.0	37.30	1.94	44.02 MB/sec
read.delim	4.1.0	72.31	1.00	22.71 MB/sec

Features

vroom has nearly all of the parsing features of readr for delimited and fixed width files, including

• delimiter guessing*
• custom delimiters (including multi-byte* and Unicode* delimiters)
• specification of column types (including type guessing)
  • numeric types (double, integer, big integer*, number)
  • logical types
  • datetime types (datetime, date, time)
  • categorical types (characters, factors)
• column selection, like dplyr::select()*
• skipping headers, comments and blank lines
• quoted fields
• double and backslashed escapes
• whitespace trimming
• windows newlines
• reading from multiple files or connections*
• embedded newlines in headers and fields**
• writing delimited files with as-needed quoting.
• robust to invalid inputs (vroom has been extensively tested with the afl fuzz tester)*.

* these are additional features not in readr.

** requires num_threads = 1.

Installation

Install vroom from CRAN with:

install.packages("vroom")

Alternatively, if you need the development version from GitHub install it with:

# install.packages("pak")
pak::pak("tidyverse/vroom")

Usage

See getting started to jump start your use of vroom!

vroom uses the same interface as readr to specify column types.

vroom::vroom("mtcars.tsv",
  col_types = list(cyl = "i", gear = "f",hp = "i", disp = "_",
                   drat = "_", vs = "l", am = "l", carb = "i")
)
#> # A tibble: 32 × 10
#>   model           mpg   cyl    hp    wt  qsec vs    am    gear   carb
#>   <chr>         <dbl> <int> <int> <dbl> <dbl> <lgl> <lgl> <fct> <int>
#> 1 Mazda RX4      21       6   110  2.62  16.5 FALSE TRUE  4         4
#> 2 Mazda RX4 Wag  21       6   110  2.88  17.0 FALSE TRUE  4         4
#> 3 Datsun 710     22.8     4    93  2.32  18.6 TRUE  TRUE  4         1
#> # ℹ 29 more rows

Reading multiple files

vroom natively supports reading from multiple files (or even multiple connections!).

First we generate some files to read by splitting the nycflights dataset by airline. For the sake of the example, we’ll just take the first 2 lines of each file.

library(nycflights13)
purrr::iwalk(
  split(flights, flights$carrier),
  ~ { .x$carrier[[1]]; vroom::vroom_write(head(.x, 2), glue::glue("flights_{.y}.tsv"), delim = "\t") }
)

Then we can efficiently read them into one tibble by passing the filenames directly to vroom. The id argument can be used to request a column that reveals the filename that each row originated from.

files <- fs::dir_ls(glob = "flights*tsv")
files
#> flights_9E.tsv flights_AA.tsv flights_AS.tsv flights_B6.tsv flights_DL.tsv 
#> flights_EV.tsv flights_F9.tsv flights_FL.tsv flights_HA.tsv flights_MQ.tsv 
#> flights_OO.tsv flights_UA.tsv flights_US.tsv flights_VX.tsv flights_WN.tsv 
#> flights_YV.tsv
vroom::vroom(files, id = "source")
#> Rows: 32 Columns: 20
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: "\t"
#> chr   (4): carrier, tailnum, origin, dest
#> dbl  (14): year, month, day, dep_time, sched_dep_time, dep_delay, arr_time, ...
#> dttm  (1): time_hour
#> 
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
#> # A tibble: 32 × 20
#>   source          year month   day dep_time sched_dep_time dep_delay arr_time
#>   <chr>          <dbl> <dbl> <dbl>    <dbl>          <dbl>     <dbl>    <dbl>
#> 1 flights_9E.tsv  2013     1     1      810            810         0     1048
#> 2 flights_9E.tsv  2013     1     1     1451           1500        -9     1634
#> 3 flights_AA.tsv  2013     1     1      542            540         2      923
#> # ℹ 29 more rows
#> # ℹ 12 more variables: sched_arr_time <dbl>, arr_delay <dbl>, carrier <chr>,
#> #   flight <dbl>, tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>,
#> #   distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>

Learning more

• Getting started with vroom
• 📽 vroom: Because Life is too short to read slow - Presentation at UseR!2019 (slides)
• 📹 vroom: Read and write rectangular data quickly - a video tour of the vroom features.

Benchmarks

The speed quoted above is from a real 1.53G dataset with 14,388,451 rows and 11 columns, see the benchmark article for full details of the dataset and bench/ for the code used to retrieve the data and perform the benchmarks.

Environment variables

In addition to the arguments to the vroom() function, you can control the behavior of vroom with a few environment variables. Generally these will not need to be set by most users.

• VROOM_TEMP_PATH - Path to the directory used to store temporary files when reading from a R connection. If unset defaults to the R session’s temporary directory (tempdir()).
• VROOM_THREADS - The number of processor threads to use when indexing and parsing. If unset defaults to parallel::detectCores().
• VROOM_SHOW_PROGRESS - Whether to show the progress bar when indexing. Regardless of this setting the progress bar is disabled in non-interactive settings, R notebooks, when running tests with testthat and when knitting documents.
• VROOM_CONNECTION_SIZE - The size (in bytes) of the connection buffer when reading from connections (default is 128 KiB).
• VROOM_WRITE_BUFFER_LINES - The number of lines to use for each buffer when writing files (default: 1000).

There are also a family of variables to control use of the Altrep framework. For versions of R where the Altrep framework is unavailable (R < 3.5.0) they are automatically turned off and the variables have no effect. The variables can take one of true, false, TRUE, FALSE, 1, or 0.

• VROOM_USE_ALTREP_NUMERICS - If set use Altrep for all numeric types (default false).

There are also individual variables for each type. Currently only VROOM_USE_ALTREP_CHR defaults to true.

• VROOM_USE_ALTREP_CHR
• VROOM_USE_ALTREP_FCT
• VROOM_USE_ALTREP_INT
• VROOM_USE_ALTREP_BIG_INT
• VROOM_USE_ALTREP_DBL
• VROOM_USE_ALTREP_NUM
• VROOM_USE_ALTREP_LGL
• VROOM_USE_ALTREP_DTTM
• VROOM_USE_ALTREP_DATE
• VROOM_USE_ALTREP_TIME

RStudio caveats

RStudio’s environment pane calls object.size() when it refreshes the pane, which for Altrep objects can be extremely slow. RStudio 1.2.1335+ includes the fixes (RStudio#4210, RStudio#4292) for this issue, so it is recommended you use at least that version.

Thanks

• Gabe Becker, Luke Tierney and Tomas Kalibera for conceiving, Implementing and maintaining the Altrep framework
• Romain François, whose Altrepisode package and related blog-posts were a great guide for creating new Altrep objects in C++.
• Matt Dowle and the rest of the Rdatatable team, data.table::fread() is blazing fast and great motivation to see how fast we could go faster!