Most simple iterative programming tasks can be rephrased in terms of a 'map' or 'reduce' operation. The advantage here is that if we can work our problem into a map - reduce problem, we can easily increase parallelism.

A simple example

Let's check a couple Wikipedia pages for mentions of 'Unix'. The most straight forward, imperative way, to do this is something along these lines:

for url in Linux ANSI_escape_code Shell; do

  page="$( curl -s "$url" )"

  if [[ $page =~ Unix ]]; then
    echo "$url mentions Unix!"

If you're familiar with shell scripts, you may already see some immediate improvements we could make. If all we care about is whether the page contains 'Unix', we could just pipe the curl output to grep.

for url in Linux ANSI_escape_code Shell; do

  if curl -s "$url" | grep -q 'Unix'; then
    echo "$url mentions Unix!"

With this approach, it's not even necessary to download the rest of the article if we encounter 'Unix' early. grep will return success and stop reading the input from curl, so then curl will stop downloading any more of the article. If we allow error reporting from curl, we can see this is the case:

$ curl -sS "" | grep -q Unix && echo 'Found it!'
curl: (23) Failed writing body (161 != 1300)
Found it!

We can hit this even sooner with the -N 'no buffering' option.

$ curl -sSN "" | grep -q Unix && echo 'Found it!'
curl: (23) Failed writing body (0 != 397)
Found it!


This works for a single article at a time, but what about all of them?

It would be nice to break this down to a single Pipeline, and we can with xargs. echo allows multi line strings, xargs expects separate inputs to come on separate lines, so we can use them together to pass an arbitrary list of elements.

$ echo 'Linux
ANSI_escape_code' | xargs -i echo curl "{}"

Let's clean this up and search for mentions of 'Unix' again.

list() {
  for item in "[email protected]"; do
    echo "$item"

map() {
  xargs -i "[email protected]"

list Linux ANSI_escape_code Shell \
  | map curl -s "{}" \
  | grep -q 'Unix' \
  && echo 'Found it!'

This looks nice, and is completing the same task as everything above. Helper functions aside, this solution is half as many lines as the original solution, and gives us the option of early stopping. It also gives us the option to parallelize - all of the articles could be downloaded concurrently since grep can easily handle more input at once.

A more interesting example

Let's try something more interesting. On these three pages, what are the most common words around mentions of 'Unix'? The steps we need to take are:

  1. Download the files
  2. Pick out the letters around mentions of 'Unix', using space as a delimiter
  3. Remove duplicates and count instances
  4. Sort on the number of instances
  5. Grab the top 5
list Linux ANSI_escape_code Shell \
  | map curl -s "{}" \
  | grep -o '[^ ]\+Unix[^ ]\+' \
  | sort | uniq -c \
  | sort -r -n -k 1 \
  | head -n 5
$ bash
      6 href="/wiki/Unix-like"
      5 title="Unix-like">Unix-like</a>
      4 href="/wiki/Unix"
      3 title="Unix">Unix</a>
      2 href="/wiki/File:Unix_timeline.en.svg"

How about the most linked articles on the Linux page?

  • Download the article
download-article() {
  curl -s ""$1"
  • Grab references to other Wikipedia pages. Clean them up.
page-to-links() {
  # page -> href=/wiki/Slackware, href=/wiki/Austrumi_Linux

  grep -o 'href="/wiki/[^"]\+"' \
    | tr -d '"'
  • Break up the URL, take the last element, which is the name
links-to-articles() {
  # href=/wiki/Slackware -> Slackware

  tr '/' ' ' \
    | awk '{print $NF}'
  • Get the top 'n' unique instances
top-unique() {
  sort | uniq -c \
    | sort -r -n -k 1 \
    | head -n "$1"

Assemble the pieces, and we have an answer!

download-article "Linux" \
  | page-to-links \
  | links-to-articles \
  | top-unique 15
$ bash
     13 Linux_kernel
     10 Android_(operating_system)
      9 Free_software
      8 Ubuntu_(operating_system)
      8 Linux_distribution
      7 Linux
      7 GNU
      7 Debian
      7 C_(programming_language)
      6 Linus_Torvalds
      6 IBM
      6 Chrome_OS
      5 Wayland_(display_server_protocol)
      5 Unix-like
      5 Smartphone


Let's write our own version xargs with parallelism built in. We can either let each subprocess write to stdout whenever it has a result, which will get us results more quickly and without blocking, but will also interleave results from other processes.

Alternatively, we can redirect the output of each subprocess somewhere else, wait for them all to end, then write the results to stdout in order. Either way, writing our own xargs function in our shell lets us 'map' our own functions. xargs can't use shell functions.

faster-map() {

  while read -r line; do
    "[email protected]" "$line" &


strict-map() {


  while read -r line; do
    tmp="$( mktemp /dev/shm/strict-map.XXXXXX )"
    output+=( "$tmp" )

    # could keep separate files for stdout + stderr if we wanted
    # but not necessary here
    "[email protected]" "$line" >"$tmp" 2>&1 &


  for item in "${output[@]}"; do
    cat "$item"
    rm -f "$item"

faster-map should be slightly faster than strict-map, but both will be much faster than running each command + argument pair serially like xargs does.


Treating input data as a list of items and then progressively filtering or transforming the data atomically is a powerful way to describe a problem. It allows for easy extension or refinement, and parallelism.