## A full implementation of breadth-first search of a graph

In this post I’ll share a full, standalone implementation of breadth-first search of a graph. Breadth-first search (hereafter BFS) is used in many graph algorithms. In particular it is useful for finding the shortest path (in terms of fewest vertices, or “hops”) between two vertices A and B.

There are many pseudocode examples showing breadth-first search of a graph on the interwebs, and lots of textual descriptions. There are also many videos on Youtube talking about this algorithm.

Unfortunately, despite all of the above, I had a really hard time finding the complete source code of a graph traversal that I could read and study — at least, one that was at the right level of complexity for me to actually learn how it works. A lot of the code I found online or in books was too abstract and hid implementation details from me by relying on “helper” libraries which were not really explained. For example, this seemed to be the case with the Java code from Sedgewick. The underlying data structures of most algorithms’ implementations were hidden away in library code which I would have needed to study to understand what was really happening.

On the other hand, the couple of production-quality graph implementations I looked at were very hard to read for someone not very familiar with the code and the algorithm already.

I knew that in order to really learn how this algorithm works, I needed to aim somewhere in the middle. In the end, I decided to implement it myself to make sure I understood it. I’m sure that’s pretty common!

So what do I mean by a full, standalone implementation?

I mean that you can download just the code on this page, and run it on any machine where Perl is installed, and it will work. Other than the language it’s written in, there are no external libraries or other dependencies.

Without further ado here is my take on a full implementation of breadth-first search of a graph. If you download only the code on this page and run it on your machine, it will work – the batteries are included, as they say.

(Incidentally, the statement above is also true of the Scheme code from Simple Network Search in Scheme, if you’d rather look at an implementation in a non-ALGOL language. However, that code is not really “my own creation”, it’s merely ported to Scheme from a Lisp book.)

## Algorithm prose description

At a high level, the way BFS works is:

• 1. Push the starting vertex A onto a queue
• 2. While the queue is not empty, get the next vertex from it (in this case A, since we just added it)
• 3. Look at each neighboring vertex N of A and take the following steps:
• 1. If N has already been marked as visited, skip it and go to the next neighbor of A.
• 2. Add the pair `(N, A)` to a spanning tree (or a reverse linked list). If we find the end node B, we will walk this spanning tree backward to find the path from the end vertex B back to the start vertex A.
• 3. If the currently visited neighbor N is the end vertex B we’re looking for, we walk the spanning tree backward to build the path taken from B back to A.
• 4. If the currently visited neighbor N is not the end vertex, we push N onto the queue to be processed.
• 5. Finally, we mark N as visited.

## Pseudocode

To supplement the prose description above, here is some pseudocode for BFS. It maps pretty directly onto the concrete implementation we will get into in the next section. I did complain a little above about pseudocode, but of course I’ll subject you to mine just the same. :-} I do think this is a reasonably “complete” pseudocode that you could actually use to implement BFS, unlike many examples that I found.

```    function bfs(start, end, graph) {

push start onto queue
mark start as seen
make a spanning tree for storing the path from start to end

while queue is not empty {
vertex = get next item from queue
for each neighbor of vertex in the graph {
if neighbor has been seen {
next
}
add (neighbor, vertex) to the spanning tree
if this neighbor is the end node we want {
walk the spanning tree, printing path from start to end
exit
}
else {
push neighbor onto the queue
}
mark this neighbor as visited
}
}
print "No path found"
exit
}
```

## Implementation

The code below shows a direct implementation of BFS in Perl. The graph it will search is the one shown in the diagram at the top of this post. In code, the data structure for the graph is as shown below. It’s an array of arrays: the first element of each array is the vertex A, followed by an array of its neighbors. This is the classic “adjacency list” representation of a graph.

```    my \$graph = [['s', ['a', 'd']],
['a', ['s', 'b', 'd']],
['b', ['a', 'c', 'e']],
['c', ['b']],
['d', ['s', 'a', 'e']],
['e', ['b', 'd', 'f']]];
```

Next, here is the main loop of BFS. If you know Perl, you can see that it maps pretty much 1:1 to the description in the above pseudocode. The shape of this code would be pretty much the same in another language such as Python or Ruby, with a few small syntactic differences, but hardly any semantic differences.

```    sub find_path_between {
my ( \$start, \$end, \$graph ) = @_;

return () unless defined \$start && defined \$end;

my @path;     # Path so far
my @queue;    # Vertices still to visit.
my %seen;     # Vertices already seen.
my \$found;    # Whether we have found the wanted vertex.
my \$st = {};  # Spanning tree, used to find paths.

if ( \$start eq \$end ) {
push @path, \$start;
return @path;
}

push @queue, \$start;
\$seen{\$start}++;

while (@queue) {
my \$v         = shift @queue;
my \$neighbors = get_neighbors( \$v, \$graph );

for my \$neighbor (@\$neighbors) {
next if \$seen{\$neighbor};
if ( \$neighbor eq \$end ) {
\$found++;
@path = _st_walk( \$start, \$end, \$st );
return @path;
}
else {
push @queue, \$neighbor;
}
\$seen{\$neighbor}++;
}
}
return \$found ? @path : ();
}
```

Once the main loop structure is written so that you are walking the vertices of the graph in the correct (breadth-first) order, one part I found slightly tricky was keeping a “trail of bread crumbs” from the end node back to the start. You may have noticed that the code above uses two helper functions — `_st_add` and `_st_walk` — that hide that complexity away a little bit. We will now look at them in more detail.

`_st_add` is trivial, and could have been written directly as a hash table access. The idea is to keep a logical “reverse linked list” structure in a hash table, where each vertex added to the list has a link pointing back to the previous vertex. I hid it from myself in this function so I could read the logic in the main search loop more easily.

```    sub _st_add {
my ( \$vertex, \$neighbor, \$st ) = @_;
\$st->{\$neighbor}->{prev} = \$vertex;
}
```

`_st_walk` is a little more interesting. As noted above, we kept a trail of bread crumbs from our end vertex (if such exists) back to the start vertex. `_st_walk` walks that trail of crumbs backward and builds an array holding all of the vertices visited along the way, which it returns to the caller.

```    sub _st_walk {
my ( \$start, \$end, \$st ) = @_;

my @path;
push @path, \$end;

my \$prev = \$st->{\$end}->{prev};
while (1) {
if ( \$prev eq \$start ) {
push @path, \$start;
last;
}
push @path, \$prev;
\$prev = \$st->{\$prev}->{prev};
next;
}
return reverse @path;
}
```

Finally, you may have noticed that in order to visit all of the neighbors of some node A we have to be able to list them. The function `get_neighbors` handles that task, along with its helper function `_find_index`. Let’s look at them in turn.

`get_neighbors` looks up some node N in the graph, and returns its list of neighbors, if there are any.

```    sub get_neighbors {
my ( \$k, \$graph ) = @_;

my \$index = _find_index( \$k, \$graph );

if ( defined \$index ) {
return \$graph->[\$index]->[1];
}
else {
return;
}
}
```

`_find_index` looks up the node N‘s index in our array-based graph representation. This is actually not a very performant way to do this, since it’s doing a linear search into an array. There are ways to speed this up, such as using a hash of arrays for faster vertex lookup, or using a cache. However I felt it would be better to keep the graph’s data representation as simple as possible for this example. (Incidentally, the below is exactly the sort of somewhat uninteresting but very necessary bookkeeping code I was having a hard time finding in much of the example code in books or online.)

```    sub _find_index {
my ( \$wanted, \$graph ) = @_;

# Naive linear search, for now.
my \$i = 0;
for my \$elem (@\$graph) {

# Definedness check here is necessary because we delete
# elements from the graph by setting the element's index to
# undef.  In other words, some graph indices can be undef.
if ( defined \$elem->[0] && \$elem->[0] eq \$wanted ) {
return \$i;
}
\$i++;
}
return;
}
```

Finally, we have a `main` function that drives everything. First, we find the shortest path (by number of nodes) from ‘s’ to ‘c’. Then, we find the shortest path from ‘s’ to ‘f’:

```    sub main {

my \$graph = [
[ 's', [ 'a', 'd' ] ],
[ 'a', [ 's', 'b', 'd' ] ],
[ 'b', [ 'a', 'c', 'e' ] ],
[ 'c', ['b'] ],
[ 'd', [ 's', 'a', 'e' ] ],
[ 'e', [ 'b', 'd', 'f' ] ]
];

my \$start = 's';
my \$end   = 'c';

my @path = find_path_between( \$start, \$end, \$graph );

print qq[Path from '\$start' to '\$end' is: @path\n];

# Find a second path.
\$end  = 'f';
@path = find_path_between( \$start, \$end, \$graph );
print qq[Path from '\$start' to '\$end' is: @path\n];
}
```

Putting everything above together and running it will print the following output:

```    Path from 's' to 'c' is: s a b c
Path from 's' to 'f' is: s d e f
```

## References

1. Aho, Ullman, Hopcroft – Data Structures and Algorithms.

2. Sedgewick – Algorithms in Java, Part 5: Graph Algorithms.

3. Orwant, Hietaniemi, Macdonald – Mastering Algorithms with Perl.

## Appendix: The complete program listing

```    #!perl

use strict;
use warnings;

sub find_path_between {
my ( \$start, \$end, \$graph ) = @_;

return () unless defined \$start && defined \$end;

my @path;     # Path so far
my @queue;    # Vertices still to visit.
my %seen;     # Vertices already seen.
my \$found;    # Whether we have found the wanted vertex.
my \$st = {};  # Spanning tree, used to find paths.

if ( \$start eq \$end ) {
push @path, \$start;
return @path;
}

push @queue, \$start;
\$seen{\$start}++;

while (@queue) {
my \$v         = shift @queue;
my \$neighbors = get_neighbors( \$v, \$graph );

for my \$neighbor (@\$neighbors) {
next if \$seen{\$neighbor};
if ( \$neighbor eq \$end ) {
\$found++;
@path = _st_walk( \$start, \$end, \$st );
return @path;
}
else {
push @queue, \$neighbor;
}
\$seen{\$neighbor}++;
}
}
return \$found ? @path : ();
}

sub _st_walk {
my ( \$start, \$end, \$st ) = @_;

my @path;

push @path, \$end;
my \$prev = \$st->{\$end}->{prev};
while (1) {
if ( \$prev eq \$start ) {
push @path, \$start;
last;
}
push @path, \$prev;
\$prev = \$st->{\$prev}->{prev};
next;
}
return reverse @path;
}

my ( \$vertex, \$neighbor, \$st ) = @_;
\$st->{\$neighbor}->{prev} = \$vertex;
}

sub get_neighbors {
my ( \$k, \$graph ) = @_;

my \$index = _find_index( \$k, \$graph );

if ( defined \$index ) {
return \$graph->[\$index]->[1];
}
else {
return;
}
}

sub _find_index {
my ( \$wanted, \$graph ) = @_;

# Naive linear search, for now.
my \$i = 0;
for my \$elem (@\$graph) {

# Definedness check here is necessary because we delete
# elements from the graph by setting the element's index to
# undef.  In other words, some graph indices can be undef.
if ( defined \$elem->[0] && \$elem->[0] eq \$wanted ) {
return \$i;
}
\$i++;
}
return;
}

sub main {
my \$graph = [
[ 's', [ 'a', 'd' ] ],
[ 'a', [ 's', 'b', 'd' ] ],
[ 'b', [ 'a', 'c', 'e' ] ],
[ 'c', ['b'] ],
[ 'd', [ 's', 'a', 'e' ] ],
[ 'e', [ 'b', 'd', 'f' ] ]
];

my \$start = 's';
my \$end   = 'c';

my @path = find_path_between( \$start, \$end, \$graph );

print qq[Path from '\$start' to '\$end' is: @path\n];

# Find a second path.
\$end  = 'f';
@path = find_path_between( \$start, \$end, \$graph );
print qq[Path from '\$start' to '\$end' is: @path\n];
}

main();
```

## Pythagoras Pie

(Image courtesy Tomohiro Tachi under Creative Commons license.)

Recently I came across a fun programming challenge called the Pythagoras Pie, which was described as:

At a party a pie is to be shared by 100 guests. The first guest gets 1% of the pie, the second guest gets 2% of the remaining pie, the third gets 3% of the remaining pie, the fourth gets 4% and so on.

Write a script that figures out which guest gets the largest piece of pie.

I sat down for a few minutes, and wrote the obvious code. It iterates over the list of guests. For each guest, it calculates how large a piece of pie the guest will get. All the while, it stores size of the largest piece of pie it has seen so far.

Here is a solution in Perl.

```sub slice_pie {
my \$iters   = shift;
my \$pie     = 1;
my \$largest = 0;
my \$winner  = 0;
for ( 0 .. \$iters ) {
my \$iter_value = \$_ * .01;
my \$portion    = ( \$iter_value * \$pie );
\$pie = \$pie - \$portion;

if ( \$portion >= \$largest ) {
\$largest = \$portion;
\$winner  = \$_;
}
}
print qq[Winner is guest # \$winner with the largest portion: \$largest\n];
}

slice_pie(100);
```

The answer, as it turns out, is that the 10th guest gets the largest piece of the pie: 0.0628156509555295, or about 6%.

Just for fun, I wrote almost the same exact code once again, except this time in Perl 6. Even though this is a straightforward translation using the same basic loop structure, it has a few nice improvements:

• No need for argument unpacking (saves a horizontal line — vertical compactness is good)
• Nice type annotations mean we can call an integer an `Int`, which also helps the compiler
• No need for parens around the `for` and `if` checks
```sub slice-pie(Int \$iters) {
my \$pie = 1;
my \$largest = 0;
my Int \$winner;

for 0 .. \$iters {
my \$iter_value = \$_ * .01;
my \$portion = \$iter_value * \$pie;
\$pie -= \$portion;

if \$portion >= \$largest {
\$largest = \$portion;
\$winner = \$_;
}
}
say qq[Winner is guest number \$winner with the largest portion: \$largest ];
}

slice-pie(100);
```

## A Perl one liner to generate passwords

I’ve noticed that browsers like Safari and Chrome are helpfully offering to generate secure passwords for me when I create a new login somewhere.

Sometimes this is really nice! Certainly it’s better than having to take a few minutes to compose a new password, especially since the quality of passwords I can easily generate on the spot is … shall we say of questionable quality sometimes, depending on time of day, blood sugar levels, etc.

So just for fun I decided to type out a Perl one liner to generate passwords for me in situations where I don’t necessarily have access to (or want) Safari and friends to do it for me.

I make no claims nor warranties about the “security” of the passwords generated by the following code, but I sure did enjoy writing it.  Just for fun, I did paste an output string into Kaspersky’s online password strength tester, and according to the tester it’s … actually not bad?  (Again: not an expert here)

Anyway, here’s the code.  It loops over an alphanumeric array with some special characters thrown in, grabbing one character at random for each iteration.  It also folds the case of the character if the number of the current iteration is even (assuming the character is of the sort whose case can be folded, which some aren’t).

```\$ perl -E '@vals = split "", "0x1234567890ABCDEFGHIJKLMNOPQRSTUVWXYZ-?!@^&*()"; \$_ % 2 == 0 ? print \$vals[ rand(\$#vals) ] : print fc \$vals[ rand(\$#vals) ] for 0 .. 24; say;'
```

To give you a sense of the output of this script, here’s the password I typed into the Kaspersky checker for reference:

```Vp8vJmNnN*8(CrE8*30*4@JlC
```

## Announcing flycheck-pod.el, Flycheck support for Perl 5 POD files

Recently I was writing some Plain Old Documentation (aka POD) for one of my Perl modules, and I thought it would be convenient to have Flycheck support while writing docs.

Since I couldn’t find the Elisp code already written to do this, I whipped something up: flycheck-pod.

As of right now, it supports error checking in Emacs’ various POD and Perl modes using podchecker in the background. I doubt it covers all of the features of POD::Checker though — patches are certainly welcome. See the source code for details.

(Image courtesy Kenneth Lu via Creative Commons license.)

## A Trivial Utility: Prepend

Recently at work I needed to add a timestamp to the top of a bunch of Markdown files. There are plenty of ways to skin this particular cat. As you probably know, the semantics of how you navigate UNIX file contents mean it’s easy to add something to the end of a file, but it’s not as easy to add something to the beginning.

This is a pretty trivial task that other people have solved in lots of ways. In my case, I decided against a shell script using `sh` or the like because I use Windows a bunch, too, and I wanted something cross-platform. As usual for me, this meant breaking out Perl.

I decided to name the tool `prepend`, on the grounds that that’s what it does: it adds text content to the beginning of a file.

Since I like to design top-down, let’s look at how it’s meant to be used:

```\$ prepend STRING_OR_FILE FILES
```

There are two ways to use it:

1. Add a string to the beginning of one or more files
2. Add the contents of a file to the beginning of one or more files

Let’s say I wanted to add a timestamp to every Markdown file in a directory. In such a case I’d add a string like so:

```\$ prepend '<!-- Converted on: 1/26/2017 -->' *.md
```

If I had some multi-line text I wanted to add to the beginning of every Markdown file, I’d say

```\$ prepend /tmp/multiline-preamble.md *.md
```

The code is shown below. I could have written it using a more low-level function such as seek but hey, why fiddle with details when memory is cheap and I can just read the entire file into an array using Tie::File?

```#!/usr/bin/env perl

use strict;
use warnings;
use experimentals;
use autodie;
use IO::All;
use Tie::File;

my \$usage = <<"EOF";
Usage:
\\$ \$0 MESSAGE_OR_FILE FILE(S)
e.g.,
\\$ \$0 '<!-- some text for the opening line -->' *.md
OR
\\$ \$0 /tmp/message.txt *.txt
EOF
die "\$usage\n" unless scalar @ARGV >= 2;
my @files = @ARGV;

my \$maybe_file = shift;
my \$content;

if (-f \$maybe_file) {
\$content = io(\$maybe_file)->slurp;
}
else {
\$content = \$maybe_file;
}

for my \$file (@files) {
my @lines;
tie @lines, 'Tie::File', \$file;
unshift @lines, \$content;
untie @lines;
}
```

## Announcing cpan.el

The CPAN shell is just another shell, so why not drive it from Emacs?

If you write Perl code in Emacs, you may have wondered why we don’t have a simple mode for driving the CPAN shell (at least I couldn’t find one!).

Well, I finally stopped wondering. It wasn’t that hard to rip out the `sh`-specific parts of `shell.el` and make a new mode for the CPAN shell.

Here’s the code:

https://github.com/rmloveland/cpan-el

It’s easy to load up and drive from Emacs:

```(add-to-list 'load-path (expand-file-name "/path/to/cpan-el/"))
(setq cpan-file-name "cpan")

(require 'cpan)
```

To run it, type `M-x cpan`.

There aren’t too any bells and whistles yet (completion, etc.), but you it’s pretty small so feel free to hack away.

(Image courtesy NASA Goddard Photo and Video under Creative Commons License.)

## Scripting JIRA

JIRA is everybody’s favorite enterprise issue management system. It’s the system many of us just love to hate.

Unlike some vocal people on the interwebs, I don’t “hate” JIRA, but I like to keep it at arm’s length. My job is not to be a JIRA jockey (although I do know me some JQL, about which more below) — my job is to get shit done.

And getting shit done quickly, for me at least, is usually a function of being able to control my tools from the command line, including via scripts.

That’s why I have written several scripts for interacting with JIRA from the command line (they’re explained below). You can get them from Github.

Note that they use some hard-coded values that match the JIRA server where I work, which you’ll have to change. However they do at least use your `.netrc` for passwords, etc., so you should be able to change them for your own use with a quick `sed` one-liner.

The functionality isn’t there to fully replace your web browser, especially if you work at a company that insists on enterprise-level JIRA jockeying, with crazy themes and labels and stuff, but here’s what’s included as of this writing — for each command, I’ve marked whether it is “plumbing” or “porcelain”:

• `jira-get-issue` (plumbing): View an issue’s JSON, which you can pipe through tools such as `jq` to build other more generic tools you can control from your text editor, scripts, etc.
• `jira-create-issue` (porcelain): This one is “end-user-ready”, in the sense that you call it and it pops up your text editor of choice so you can write the issue description, and when you close your editor the issue is created for you!
• `jira-search-issues` (plumbing): Uses JQL to search your JIRA, and returns a bunch of issues as JSON for you to fiddle with however you prefer.
• `jira-add-comment` (porcelain): Like `jira-create-issue`, this one pops open your text editor to add a comment to an issue.
• `jira-get-issue-status` (plumbing): Gets back JSON describing an issue’s status. Used to check the status to transition an issue forwards or backwards using `jira-set-issue-status` (below).
• `jira-set-issue-status` (porcelain-ish): Depending on an issue’s status, bump it forwards or backwards to an adjacent status. Help output describes the statuses and their ordering. (I’m not so sure about this one; more design work is needed, it’s still not really “porcelain”.)

As an example of using one of the plumbing tools to make a more user-friendly CLI tool, I have one I call `jli` that just gives me a list of my currently open issues:

```#!/usr/bin/env sh

jira-search-issues "assignee = rloveland AND status not in \
(Closed, Resolved)" | jq '.key + " " + .fields.summary' | sed -e 's/\"//g';
```

Since it’s an arbitrary JQL query returning JSON, it’s easy to imagine how you might extend this to give separate lists per-project, ordered by status, etc. You can make your own little terminal- or text-file-based dashboard, send yourself a daily digest email using a larger script, and so forth.

There are some others, but I only have them on my work computer. For example, I have one that checks JIRA for new tickets by diffing the list of my assigned tickets and my local TODO list, which is in a text file.

Obviously these tools could use further development to be more modular and general-purpose, and they don’t come anywhere near covering the whole JIRA API, but they’re pretty small and easy to modify for your own use.

Most importantly, I use them every day at my job, so I know they work and are useful.

Hopefully they are useful to you too!

(Image courtesy Terry Robinson via Flickr under a Creative Commons license.)

# Emacs Compilation Mode Regexp for Perl 6

Stick this in your `.emacs` if you want Perl 6 support in Emacs’ compilation mode:

```  (add-to-list 'compilation-error-regexp-alist 'perl6-stack-trace)
'(perl6-stack-trace .
("at \\([A-Za-z-_]+\\(\.p[l6m]\\)?\\):\\([[:digit:]]+\\)"
1 3)))
```

I don’t know if this would be a good addition to perl6-mode, or if compilation mode regexps are supposed to live elsewhere.

## How to install Perlbrew on Fedora 20 and use it with (m)ksh

This post details how to go about installing perlbrew on a Fedora 20 Linux system for a user whose default shell is in the ksh family, specifically `mksh`, which is the shell I use.

The instructions in each section can be used separately if you are just a Fedora user or just a (m)ksh user.

## Installing Perlbrew on Fedora 20

If you try to install Perlbrew using the instructions on its website, Fedora barfs. Fortunately, the workaround you need is detailed in this Github issue. (Hint: read the whole thread and then do what wumpus says.)

## Using Perlbrew with ksh or mksh

The perlbrew `bashrc` file uses a few “bashisms” (unnecessarily, I think, but it is a “bash” rc after all). I managed to hack them out and, instead of sourcing perlbrew’s default `bashrc` file, I now source a `kshrc` built by applying the patch below.

It’s working well for me so far under `mksh`, but feel free to leave a comment if it doesn’t work for you and I’ll try to help.

Happy Perlbrewing!

```--- bashrc	2014-12-30 12:49:14.110446784 -0500
+++ kshrc	2014-12-30 13:15:58.750454519 -0500
@@ -1,6 +1,5 @@
export PERLBREW_BASHRC_VERSION=0.72

-
__perlbrew_reinit() {
if [[ ! -d "\$PERLBREW_HOME" ]]; then
mkdir -p "\$PERLBREW_HOME"
@@ -13,7 +12,9 @@
}

__perlbrew_purify () {
-    local path patharray outsep
+    path=
+    patharray=
+    outsep=
if [[ -n "\$BASH_VERSION" ]]; then
IFS=: read -ra patharray <<< "\$1"
fi
@@ -30,13 +31,13 @@
}

__perlbrew_set_path () {
-    export MANPATH=\$PERLBREW_MANPATH\${PERLBREW_MANPATH:+:}\$(__perlbrew_purify "\$(manpath)")
-    export PATH=\${PERLBREW_PATH:-\$PERLBREW_ROOT/bin}:\$(__perlbrew_purify "\$PATH")
+    export MANPATH=\$PERLBREW_MANPATH:\$MANPATH
+    export PATH=\${PERLBREW_PATH:-\$PERLBREW_ROOT/bin}:\$PATH
hash -r
}

__perlbrew_set_env() {
-    local code
+    code=
code="\$(\$perlbrew_command env \$@)" || return \$?
eval "\$code"
}
@@ -64,8 +65,8 @@
}

perlbrew () {
-    local exit_status
-    local short_option
+    exit_status=
+    short_option=
export SHELL

if [[ \$1 == -* ]]; then
```

(Image courtesy Japanexperterna.se under Creative Commons License.)

## Announcing confluence2html

A tool for converting Confluence to HTML.

If you use (or used to use) a Confluence wiki, you may need to deliver content that was written in wiki markup to HTML. Confluence does have the ability to export an entire space to HTML, but not a single page (or section of a page). To overcome this limitation, I’ve written a script called `confluence2html` which can convert a subset of Confluence’s wiki markup to HTML. (You can check it out at my Github page – For examples of the supported subset of wiki syntax, see the `t/files` directory).

Unfortunately, the new versions of Confluence use a hacky “not-quite-XML” storage format that is terrible for writers and for which there are basically no existing XML tools either. If you are trying to get your content out of a newer version of Confluence and back into wiki markup, check out Graham Hannington’s site (search the page for “wikifier”). His page also has tools to help you wrangle the new format, if you care to (I don’t).

With a bit of editing, you should be able to get the output of Graham’s tool to conform to the subset of Confluence wiki markup supported by this script. It supports some common macros (including tables!), and I’ve found it really useful.

Right now, it’s a command line tool only. You can use it like so:

\$ confluence2html < wiki-input.txt > html-output.html

If you don’t know how to run commands from the shell, you can read about it at LinuxCommand.org. If you are on Windows, you can run shell commands using Cygwin. If you’re on a Mac, open Utilities > Terminal in Finder.

At this point I’ll quit blathering and point you to the Github page. Again: for examples of the supported subset of wiki syntax, see the `t/files` directory. For documentation on the supported wiki syntax, see the `README`.

Happy writing!

(Image courtesy of caribb under a Creative Commons license.)