What is the fastest way to read the header of a delim?

colnames_delim.R

readr_lazy_colnames <- function(path, delim = "|") {
  readr::read_delim(
    path,
    delim = delim,
    lazy = TRUE,
    progress = FALSE,
    show_col_types = FALSE
  ) |>
    colnames()
}

arrow_colnames <- function(path, delim = "|") {
  arrow::open_delim_dataset(path, delim = delim) |>
    colnames()
}

arrow_schema_colnames <- function(path, delim = "|") {
  arrow::open_delim_dataset(path, delim = delim)$schema |>
    names()
}

arrow_nodf_colnames <- function(path, delim = "|") {
  arrow::read_delim_arrow(path, delim = delim, as_data_frame = FALSE) |>
    colnames()
}

readr_nmax_colnames <- function(path, delim = "|") {
  readr::read_delim(
    path,
    delim = delim,
    n_max = 0,
    progress = FALSE,
    show_col_types = FALSE
  ) |>
    colnames()
}

readlines_colnames <- function(path, delim = "|") {
  strsplit(readLines(path, n = 1), split = delim, fixed = TRUE)[[1]]
}

vroom_colnames <- function(path, delim = "|") {
  vroom::vroom(
    path,
    delim = delim,
    n_max = 0 ,
    show_col_types = FALSE,
    progress = FALSE
  ) |>
    colnames()
}

dt_colnames <- function(path, delim = "|") {
  data.table::fread(
    nrow = 0,
    sep = delim,
    header = TRUE,
    cmd = sprintf("head -n 1 %s", path)
  ) |>
    colnames()
}

# Big csv file (pipe separated in my case, not provided! 
big_csv <- "~/Downloads/wcvp_taxon.csv"

bench::mark(
  readr_lazy_colnames(big_csv),
  readr_nmax_colnames(big_csv),
  arrow_colnames(big_csv),
  arrow_schema_colnames(big_csv),
  arrow_nodf_colnames(big_csv),
  readlines_colnames(big_csv),
  vroom_colnames(big_csv),
  dt_colnames(big_csv)
)

Using readlines is by far the fastest way to do this