Vroom Benchmarks

  collapse = TRUE,
  comment = "#>"


pretty_sec <- function(x) {
  x[!is.na(x)] <- prettyunits::pretty_sec(x[!is.na(x)])

pretty_lgl <- function(x) {
    x == TRUE ~ "TRUE",
    x == FALSE ~ "FALSE",
    TRUE ~ ""

read_benchmark <- function(file, desc) {
  vroom::vroom(file, col_types = c("ccccddddd")) %>%
    filter(op != "setup") %>%
      altrep = case_when(
        grepl("^vroom_no_altrep", reading_package) ~ FALSE,
        grepl("^vroom", reading_package) ~ TRUE,
        TRUE ~ NA
      reading_package = case_when(
        grepl("^vroom", reading_package) ~ "vroom",
        TRUE ~ reading_package
    label = fct_reorder(
        altrep = ifelse(is.na(altrep), "", glue::glue("(altrep = {altrep})"))
      case_when(type == "real" ~ time, TRUE ~ 0),
    op = factor(op, desc)

generate_subtitle <- function(data) {
  rows <- scales::comma(data$rows[[1]])
  cols <- scales::comma(data$cols[[1]])
  size <- fs_bytes(data$size[[1]])
  glue::glue("{rows} x {cols} - {size}B")

plot_benchmark <- function(data, title) {

  subtitle <- generate_subtitle(data)
  data <- data %>%
    filter(reading_package != "read.delim", type == "real")

  p1 <- data %>%
    ggplot() +
    geom_bar(aes(x = label, y = time, fill = op, group = label), stat = "identity") +
    scale_fill_brewer(type = "qual", palette = "Set2") +
    scale_y_continuous(labels = scales::number_format(suffix = "s")) +
    coord_flip() +
    labs(title = title, subtitle = subtitle, x = NULL, y = NULL, fill = NULL) +
    theme(legend.position = "bottom")

  p2 <- data %>%
    group_by(label) %>%
    summarise(max_memory = max(max_memory)) %>%
    ggplot() +
    geom_bar(aes(x = label, y = max_memory / (1024 * 1024)), stat = "identity") +
    scale_y_continuous(labels = scales::number_format(suffix = "Mb")) +
    coord_flip() +
    labs(title = "Maximum memory usage", x = NULL, y = NULL) +
    theme(axis.text.y = element_blank(), axis.ticks.y = element_blank())

  p1 + p2 + plot_layout(widths = c(2, 1))

make_table <- function(data) {
  data %>%
    filter(type == "real") %>%
    select(-label, -size, -type, -rows, -cols) %>%
    spread(op, time) %>%
      total = read + print + head + tail + sample + filter + aggregate,
      max_memory = as.character(bench::as_bench_bytes(max_memory))
    ) %>%
    arrange(desc(total)) %>%
    mutate_if(is.numeric, pretty_sec) %>%
    mutate_if(is.logical, pretty_lgl) %>%
    select(reading_package, manip_package, altrep, max_memory, everything()) %>%
      "reading\npackage" = reading_package,
      "manipulating\npackage" = manip_package,
      memory = max_memory
    ) %>%
    knitr::kable(digits = 2, align = "r", format = "html")

desc <- c("setup", "read", "print", "head", "tail", "sample", "filter", "aggregate")

vroom is a new approach to reading delimited and fixed width data into R.

It stems from the observation that when parsing files reading data from disk and finding the delimiters is generally not the main bottle neck. Instead (re)-allocating memory and parsing the values into R data types (particularly for characters) takes the bulk of the time.

Therefore you can obtain very rapid input by first performing a fast indexing step and then using the Altrep framework available in R versions 3.5+ to access the values in a lazy / delayed fashion.

How it works

The initial reading of the file simply records the locations of each individual record, the actual values are not read into R. Altrep vectors are created for each column in the data which hold a pointer to the index and the memory mapped file. When these vectors are indexed the value is read from the memory mapping.

This means initial reading is extremely fast, in the real world dataset below it is ~ 1/4 the time of the multi-threaded data.table::fread(). Sampling operations are likewise extremely fast, as only the data actually included in the sample is read. This means things like the tibble print method, calling head(), tail() x[sample(), ] etc. have very low overhead. Filtering also can be fast, only the columns included in the filter selection have to be fully read and only the data in the filtered rows needs to be read from the remaining columns. Grouped aggregations likewise only need to read the grouping variables and the variables aggregated.

Once a particular vector is fully materialized the speed for all subsequent operations should be identical to a normal R vector.

This approach potentially also allows you to work with data that is larger than memory. As long as you are careful to avoid materializing the entire dataset at once it can be efficiently queried and subset.

Reading delimited files

The following benchmarks all measure reading delimited files of various sizes and data types. Because vroom delays reading the benchmarks also do some manipulation of the data afterwards to try and provide a more realistic performance comparison.

Because the read.delim results are so much slower than the others they are excluded from the plots, but are retained in the tables.

Taxi Trip Dataset

This real world dataset is from Freedom of Information Law (FOIL) Taxi Trip Data from the NYC Taxi and Limousine Commission 2013, originally posted at https://chriswhong.com/open-data/foil_nyc_taxi/. It is also hosted on archive.org.

The first table trip_fare_1.csv is 1.55G in size.

#> Observations: 14,776,615
#> Variables: 11
#> $ medallion       <chr> "89D227B655E5C82AECF13C3F540D4CF4", "0BD7C8F5B...
#> $ hack_license    <chr> "BA96DE419E711691B9445D6A6307C170", "9FD8F69F0...
#> $ vendor_id       <chr> "CMT", "CMT", "CMT", "CMT", "CMT", "CMT", "CMT...
#> $ pickup_datetime <chr> "2013-01-01 15:11:48", "2013-01-06 00:18:35", ...
#> $ payment_type    <chr> "CSH", "CSH", "CSH", "CSH", "CSH", "CSH", "CSH...
#> $ fare_amount     <dbl> 6.5, 6.0, 5.5, 5.0, 9.5, 9.5, 6.0, 34.0, 5.5, ...
#> $ surcharge       <dbl> 0.0, 0.5, 1.0, 0.5, 0.5, 0.0, 0.0, 0.0, 1.0, 0...
#> $ mta_tax         <dbl> 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0...
#> $ tip_amount      <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...
#> $ tolls_amount    <dbl> 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.8, 0.0, 0...
#> $ total_amount    <dbl> 7.0, 7.0, 7.0, 6.0, 10.5, 10.0, 6.5, 39.3, 7.0...

Taxi Benchmarks

code: bench/taxi

All benchmarks were run on a Amazon EC2 m5.4xlarge instance with 16 vCPUs and an EBS volume type.

The benchmarks labeled vroom_base uses vroom with base functions for manipulation. vroom_dplyr uses vroom to read the file and dplyr functions to manipulate. data.table uses fread() to read the file and data.table functions to manipulate and readr uses readr to read the file and dplyr to manipulate. By default vroom only uses Altrep for character vectors, these are labeled vroom(altrep: normal). The benchmarks labeled vroom(altrep: full) instead use Altrep vectors for all supported types and vroom(altrep: none) disable Altrep entirely.

The following operations are performed.

taxi <- read_benchmark(path_package("vroom", "bench", "taxi.tsv"), desc)

plot_benchmark(taxi, "Time to analyze taxi trip data")


(N.B. Rcpp used in the dplyr implementation fully materializes all the Altrep numeric vectors when using filter() or sample_n(), which is why the first of these cases have additional overhead when using full Altrep.).

All numeric data

All numeric data is really a worst case scenario for vroom. The index takes about as much memory as the parsed data. Also because parsing doubles can be done quickly in parallel and text representations of doubles are only ~25 characters at most there isn't a great deal of savings for delayed parsing.

For these reasons (and because the data.table implementation is very fast) vroom is a bit slower than fread for pure numeric data.

However because vroom is multi-threaded it is a bit quicker than readr and read.delim for this type of data.


code: bench/all_numeric-long

all_num <- read_benchmark(path_package("vroom", "bench", "all_numeric-long.tsv"), desc)

plot_benchmark(all_num, "Time to analyze long all numeric data")



code: bench/all_numeric-wide

all_num_wide <- read_benchmark(path_package("bench", "all_numeric-wide.tsv", package = "vroom"), desc)

plot_benchmark(all_num_wide, "Time to analyze wide all numeric data")


All character data

code: bench/all_character-long

All character data is a best case scenario for vroom when using Altrep, as it takes full advantage of the lazy reading.


all_chr <- read_benchmark(path_package("vroom", "bench", "all_character-long.tsv"), desc)

plot_benchmark(all_chr, "Time to analyze long all character data")



code: bench/all_character-wide

all_chr_wide <- read_benchmark(path_package("vroom", "bench", "all_character-wide.tsv"), desc)

plot_benchmark(all_chr_wide, "Time to analyze wide all character data")


Reading multiple delimited files

code: bench/taxi_multiple

mult <- read_benchmark(path_package("vroom", "bench", "taxi_multiple.tsv"), desc)

The benchmark reads all 12 files in the taxi trip fare data, totaling r scales::comma(mult$rows[[1]]) rows and r mult$cols[[1]] columns for a total file size of r format(fs_bytes(mult$size[[1]])).

plot_benchmark(mult, "Time to analyze multiple file data")


Reading fixed width files

United States Census 5-Percent Public Use Microdata Sample files

fwf <- read_benchmark(path_package("vroom", "bench", "fwf.tsv"), desc)

This fixed width dataset contains individual records of the characteristics of a 5 percent sample of people and housing units from the year 2000 and is freely available at https://web.archive.org/web/20150908055439/https://www2.census.gov/census_2000/datasets/PUMS/FivePercent/California/all_California.zip. The data is split into files by state, and the state of California was used in this benchmark.

The data totals r scales::comma(fwf$rows[[1]]) rows and r fwf$cols[[1]] columns with a total file size of r format(fs_bytes(fwf$size[[1]])).

Census data benchmarks

code: bench/fwf

plot_benchmark(fwf, "Time to analyze fixed width data")


Writing delimited files

code: bench/taxi_writing

The benchmarks write out the taxi trip dataset in a few different ways.

taxi_writing <- read_benchmark(path_package("vroom", "bench", "taxi_writing.tsv"), c("setup", "writing")) %>%
    package = reading_package,
    compression = manip_package
  ) %>% mutate(
    package = factor(package, c("base", "readr", "data.table", "vroom")),
    compression = factor(compression, rev(c("gzip", "multithreaded_gzip", "zstandard", "uncompressed")))
  ) %>% filter(type == "real")

subtitle <- generate_subtitle(taxi_writing)

taxi_writing %>%
  ggplot(aes(x = compression, y = time, fill = package)) +
  geom_bar(stat = "identity", position = position_dodge2(reverse = TRUE, padding = .05)) +
  scale_fill_brewer(type = "qual", palette = "Set2") +
  scale_y_continuous(labels = scales::number_format(suffix = "s")) +
  theme(legend.position = "bottom") +
  coord_flip() +
  labs(title = "Writing taxi trip data", subtitle = subtitle, x = NULL, y = NULL, fill = NULL)

taxi_writing %>%
  select(-size, -op, -rows, -cols, -type, -altrep, -label, -max_memory) %>%
  mutate_if(is.numeric, pretty_sec) %>%
  pivot_wider(names_from = package, values_from = time) %>%
  arrange(desc(compression)) %>%
  knitr::kable(digits = 2, align = "r", format = "html")

Session and package information

si <- vroom::vroom(path_package("vroom", "bench", "session_info.tsv"))
class(si) <- c("packages_info", "data.frame")
select(as.data.frame(si), package, version = ondiskversion, date, source) %>%

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vroom documentation built on Oct. 2, 2023, 5:07 p.m.