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(This is part 2 of n “quick hit” posts, each walking through some approaches to speeding up components of an iterative operation. Go here for part 1).

Thanks to the aforementioned previous post, we now have a super fast way of reading individual text files containing HTTP headers from HEAD requests into a character vector:

library(Rcpp)

vapply(
X = fils,
FUN = cpp_read_file, # see previous post for the source for this C++ Rcpp function
FUN.VALUE = character(1),
USE.NAMES = FALSE
) -> hdrs

## [1] "HTTP/1.1 200 OK\r\nDate: Mon, 08 Jun 2020 14:40:45 GMT\r\nServer: Apache\r\nLast-Modified: Sun, 26 Apr 2020 00:06:47 GMT\r\nETag: \"ace-ec1a0-5a4265fd413c0\"\r\nAccept-Ranges: bytes\r\nContent-Length: 967072\r\nX-Frame-Options: SAMEORIGIN\r\nContent-Type: application/x-msdownload\r\n\r\n"
## [2] "HTTP/1.1 200 OK\r\nDate: Mon, 08 Jun 2020 14:43:46 GMT\r\nServer: Apache\r\nLast-Modified: Wed, 05 Jun 2019 03:52:22 GMT\r\nETag: \"423-d99a0-58a8b864f8980\"\r\nAccept-Ranges: bytes\r\nContent-Length: 891296\r\nX-XSS-Protection: 1; mode=block\r\nX-Frame-Options: SAMEORIGIN\r\nContent-Type: application/x-msdownload\r\n\r\n"


However, I need the headers and values broken out so I can eventually get to the analysis I need to do, and a data frame of name/value columns would be the most helpful format. We’ll use {stringi} to help us build a function (explanation of what it’s doing is in comment annotations) that turns each unkempt string into a very kempt data frame:

library(stringi)

# split lines from into a character vector
split_hdrs <- stri_split_lines(x, omit_empty = TRUE)

lapply(split_hdrs, function(lines) {

# we don't care about the HTTP x/x ...
lines <- lines[-1]

# make a matrix out of found NAME: VALUE
hdrs <- stri_match_first_regex(lines, "^([^:]*):\\s*(.*)$") if (nrow(hdrs) > 0) { # if we have any data.frame( name = stri_replace_all_fixed(stri_trans_tolower(hdrs[,2]), "-", "_"), value = hdrs[,3] ) } else { # if we don't have any NULL } }) } parse_headers(hdrs[1:3]) ## [[1]] ## name value ## 1 date Mon, 08 Jun 2020 14:40:45 GMT ## 2 server Apache ## 3 last_modified Sun, 26 Apr 2020 00:06:47 GMT ## 4 etag "ace-ec1a0-5a4265fd413c0" ## 5 accept_ranges bytes ## 6 content_length 967072 ## 7 x_frame_options SAMEORIGIN ## 8 content_type application/x-msdownload ## ## [[2]] ## name value ## 1 date Mon, 08 Jun 2020 14:43:46 GMT ## 2 server Apache ## 3 last_modified Wed, 05 Jun 2019 03:52:22 GMT ## 4 etag "423-d99a0-58a8b864f8980" ## 5 accept_ranges bytes ## 6 content_length 891296 ## 7 x_xss_protection 1; mode=block ## 8 x_frame_options SAMEORIGIN ## 9 content_type application/x-msdownload ## ## [[3]] ## name value ## 1 date Mon, 08 Jun 2020 14:23:53 GMT ## 2 server Apache ## 3 content_type text/html; charset=iso-8859-1 parse_header(hdrs[1]) ## name value ## 1 date Mon, 08 Jun 2020 14:40:45 GMT ## 2 server Apache ## 3 last_modified Sun, 26 Apr 2020 00:06:47 GMT ## 4 etag "ace-ec1a0-5a4265fd413c0" ## 5 accept_ranges bytes ## 6 content_length 967072 ## 7 x_frame_options SAMEORIGIN ## 8 content_type application/x-msdownload  Unfortunately, this takes almost 16 painful seconds to crunch through the ~75K text entries: system.time(tmp <- parse_headers(hdrs)) ## user system elapsed ## 15.033 0.097 15.227  as each call can be near 150 microseconds: microbenchmark( ph = parse_headers(hdrs[1]), times = 1000, control = list(warmup = 100) ) ## Unit: microseconds ## expr min lq mean median uq max neval ## ph 143.328 146.8995 154.8609 148.361 158.121 415.332 1000  A big reason it takes so long is the data frame creation. If you’ve never looked at the source for data.frame() have a go at it — https://github.com/wch/r-source/blob/86532f5aa3d9880f4c1c9e74a417005616846a34/src/library/base/R/dataframe.R#L435-L603 — before continuing. Back? Great! The {base} data.frame() has tons of guard rails to make sure you’re getting what you think you asked for across a myriad of use cases. I learned about a trick to make data frame creation faster when I started playing with {ggplot2} source. Said trick has virtually no guard rails — it just adds a class, and row.names attribute to a list — so you really should only use it in cases like this where you have a very good idea of the structure and values of the data frame you’re making. Here’s an even more simplified version of the function in the {ggplot2} source: fast_frame <- function(x = list()) { lengths <- vapply(x, length, integer(1)) n <- if (length(x) == 0 || min(lengths) == 0) 0 else max(lengths) class(x) <- "data.frame" attr(x, "row.names") <- .set_row_names(n) # help(.set_row_names) for info x }  Now, we’ll change parse_headers() a bit to use that function instead of data.frame(): parse_headers <- function(x) { # split lines from into a character vector split_hdrs <- stri_split_lines(x, omit_empty = TRUE) lapply(split_hdrs, function(lines) { # we don't care about the HTTP x/x ... lines <- lines[-1] # make a matrix out of found NAME: VALUE hdrs <- stri_match_first_regex(lines, "^([^:]*):\\s*(.*)$")

if (nrow(hdrs) > 0) { # if we have any
fast_frame(
list(
name = stri_replace_all_fixed(stri_trans_tolower(hdrs[,2]), "-", "_"),
value = hdrs[,3]
)
)
} else { # if we don't have any
NULL
}

})

}


Note that we had to pass in a list() to it vs bare name/value vectors.

How much faster is it? Quite a bit:

microbenchmark(
times = 1000,
control = list(warmup = 100)
)
## Unit: microseconds
##  expr   min      lq     mean median      uq      max neval
##    ph 27.94 28.7205 34.66066 29.024 29.3785 4144.402  1000


This speedup means the painful ~15s is now just a tolerable ~3s:

system.time(tmp <- parse_headers(hdrs))
##  user  system elapsed
## 2.901   0.011   2.918


### FIN

Normally, guard rails are awesome, and you can have even more safe code (which means safer and more reproducible analyses) when using {tidyverse} functions. As noted in the previous post, I’m doing a great deal of iterative work, have more than one set of headers I’m crunching on, and am testing out different approaches/theories, so going from 16 seconds to 3 seconds does truly speed up my efforts and has an even bigger impact when I process around 3 million raw header records.

I think I promised {future} work in this post (asynchronous pun not intended), but we’ll get to that eventually (probably the next post).

If you have your own favorite way to speedup data frame creation (or extracting target values from raw text records) drop a note in the comments!