Monday, 26 September 2016

I'll huff, and I'll puff, and I'll blow your house in

Sometimes it really helps to have a different view on a problem and after my recent writings on my Public Suffix List (PSL) library I was fortunate to receive a suggestion from my friend Enrico Zini.

I had asked for suggestions on reducing the size of the library further and Enrico simply suggested Huffman coding. This was a technique I had learned about long ago in connection with data compression and the intervening years had made all the details fuzzy which explains why it had not immediately sprung to mind.

A small subset of the Public Suffix List as stored within libnspslHuffman coding named for David A. Huffman is an algorithm that enables a representation of data which is very efficient. In a normal array of characters every character takes the same eight bits to represent which is the best we can do when any of the 256 values possible is equally likely. If your data is not evenly distributed this is not the case for example if the data was english text then the value is fifteen times more likely to be that for e than k.

every step of huffman encoding tree build for the example string tableSo if we have some data with a non uniform distribution of probabilities we need a way the data be encoded with fewer bits for the common values and more bits for the rarer values. To be efficient we would need some way of having variable length representations without storing the length separately. The term for this data representation is a prefix code and there are several ways to generate them.

Such is the influence of Huffman on the area of prefix codes they are often called Huffman codes even if they were not created using his algorithm. One can dream of becoming immortalised like this, to join the ranks of those whose names are given to units or whole ideas in a field must be immensely rewarding, however given Huffman invented his algorithm and proved it to be optimal to answer a question on a term paper in his early twenties I fear I may already be a bit too late.

The algorithm itself is relatively straightforward. First a frequency analysis is performed, a fancy way of saying count how many of each character is in the input data. Next a binary tree is created by using a priority queue initialised with the nodes sorted by frequency.

The resulting huffman tree and the binary representation of the input symbols
The two least frequent items count is summed together and a node placed in the tree with the two original entries as child nodes. This step is repeated until a single node exists with a count value equal to the length of the input.

To encode data once simply walks the tree outputting a 0 for a left node or 1 for right node until reaching the original value. This generates a mapping of values to bit output, the input is then simply converted value by value to the bit output. To decode the data the data is used bit by bit to walk the tree to arrive at values.

If we perform this algorithm on the example string table *!asiabvcomcoopitamazonawsarsaves-the-whalescomputebasilicata we can reduce the 488 bits (61 * 8 bit characters) to 282 bits or 40% reduction. Obviously in a real application the huffman tree would need to be stored which would probably exceed this saving but for larger data sets it is probable this technique would yield excellent results on this kind of data.

Once I proved this to myself I implemented the encoder within the existing conversion program. Although my perl encoder is not very efficient it can process the entire PSL string table (around six thousand labels using 40KB or so) in less than a second, so unless the table grows massively an inelegant approach will suffice.

The resulting bits were packed into 32bit values to improve decode performance (most systems prefer to deal with larger memory fetches less frequently) and resulted in 18KB of output or 47% of the original size. This is a great improvement in size and means the statically linked test program is now 59KB and is actually smaller than the gzipped source data.

$ ls -alh test_nspsl
-rwxr-xr-x 1 vince vince 59K Sep 25 23:58 test_nspsl
$ ls -al public_suffix_list.dat.gz 
-rw-r--r-- 1 vince vince 62K Sep  1 08:52 public_suffix_list.dat.gz

To be clear the statically linked program can determine if a domain is in the PSL with no additional heap allocations and includes the entire PSL ordered tree, the domain label string table and the huffman decode table to read it.

An unexpected side effect is that because the decode loop is small it sits in the processor cache. This appears to cause the string comparison function huffcasecmp() (which is not locale dependant because we know the data will be limited ASCII) performance to be close to using strcasecmp() indeed on ARM32 systems there is a very modest improvement in performance.

I think this is as much work as I am willing to put into this library but I am pleased to have achieved a result which is on par with the best of breed (libpsl still has a data representation 20KB smaller than libnspsl but requires additional libraries for additional functionality) and I got to (re)learn an important algorithm too.

Tuesday, 20 September 2016

If I see an ending, I can work backward.

Now while I am sure Arthur Miller was referring to writing a play when he said those words they have an oddly appropriate resonance for my topic.

In the early nineties Lou Montulli applied the idea of magic cookies to HTTP to make the web stateful, I imagine he had no idea of the issues he was going to introduce for the future. Like most of the web technology it was a solution to an immediate problem which it has never been possible to subsequently improve.

Chocolate chip cookie are much tastier than HTTP cookiesThe HTTP cookie is simply a way for a website to identify a connecting browser session so that state can be kept between retrieving pages. Due to shortcomings in the design of cookies and implementation details in browsers this has lead to a selection of unwanted side effects. The specific issue that I am talking about here is the supercookie where the super prefix in this context has similar connotations as to when applied to the word villain.

Whenever the browser requests a resource (web page, image, etc.) the server may return a cookie along with the resource that your browser remembers. The cookie has a domain name associated with it and when your browser requests additional resources if the cookie domain matches the requested resources domain name the cookie is sent along with the request.

As an example the first time you visit a page on you might receive a cookie with the domain so next time you visit a page on your browser will send the cookie along. Indeed it will also send it along for any page on

A supercookies is simply one where instead of being limited to one sub-domain ( the cookie is set for a top level domain (foo.invalid) so visiting any such domain (I used the invalid name in my examples but one could substitute com or your web browser gives out the cookie. Hackers would love to be able to set up such cookies and potentially control and hijack many sites at a time.

This problem was noted early on and browsers were not allowed to set cookie domains with fewer than two parts so example.invalid or were allowed but invalid or com on their own were not. This works fine for top level domains like .com, .org and .mil but not for countries where the domain registrar had rules about second levels like the uk domain (uk domains must have a second level like

NetSurf cookie manager showing a supercookieThere is no way to generate the correct set of top level domains with an algorithm so a database is required and is called the Public Suffix List (PSL). This database is a simple text formatted list with wildcard and inversion syntax and is at time of writing around 180Kb of text including comments which compresses down to 60Kb or so with deflate.

A few years ago with ICANN allowing the great expansion of top level domains the existing NetSurf supercookie handling was found to be wanting and I decided to implement a solution using the PSL. At this point in time the database was only 100Kb source or 40Kb compressed.

I started by looking at limited existing libraries. In fact only the regdom library was adequate but used 150Kb of heap to load the pre-processed list. This would have had the drawback of increasing NetSurf heap usage significantly (we still have users on 8Mb systems). Because of this and the need to run PHP script to generate the pre-processed input it was decided the library was not suitable.

Lacking other choices I came up with my own implementation which used a perl script to construct a tree of domains from the PSL in a static array with the label strings in a separate table. At the time my implementation added 70Kb of read only data which I thought reasonable and allowed for direct lookup of answers from the database.

This solution still required a pre-processing step to generate the C source code but perl is much more readily available, is a language already used by our tooling and we could always simply ship the generated file. As long as the generated file was updated at release time as we already do for our fallback SSL certificate root set this would be acceptable.

wireshark session shown NetSurf sending a supercookie to
I put the solution into NetSurf, was pleased no-one seemed to notice and moved on to other issues. Recently while fixing a completely unrelated issue in the display of session cookies in the management interface and I realised I had some test supercookies present in the display. After the initial "thats odd" I realised with horror there might be a deeper issue.

It quickly became evident the PSL generation was broken and had been for a long time, even worse somewhere along the line the "redundant" empty generated source file had been removed and the ancient fallback code path was all that had been used.

This issue had escalated somewhat from a trivial display problem. I took a moment to asses the situation a bit more broadly and came to the conclusion there were a number of interconnected causes, centered around the lack of automated testing, which could be solved by extracting the PSL handling into a "support" library.

NetSurf has several of these support libraries which could be used separately to the main browser project but are principally oriented towards it. These libraries are shipped and built in releases alongside the main browser codebase and mainly serve to make API more obvious and modular. In this case my main aim was to have the functionality segregated into a separate module which could be tested, updated and monitored directly by our CI system meaning the embarrassing failure I had found can never occur again.

Before creating my own library I did consider a library called libpsl had been created since I wrote my original implementation. Initially I was very interested in using this library given it managed a data representation within a mere 32Kb.

Unfortunately the library integrates a great deal of IDN and punycode handling which was not required in this use case. NetSurf already has to handle IDN and punycode translations and uses punycode encoded domain names internally only translating to unicode representations for display so duplicating this functionality using other libraries requires a great deal of resource above the raw data representation.

I put the library together based on the existing code generator Perl program and integrated the test set that comes along with the PSL. I was a little alarmed to discover that the PSL had almost doubled in size since the implementation was originally written and now the trivial test program of the library was weighing in at a hefty 120Kb.

This stemmed from two main causes:
  1. there were now many more domain label strings to be stored
  2. there now being many, many more nodes in the tree.
To address the first cause the length of the domain label strings was moved into the unused padding space within each tree node removing a byte from each domain label saving 6Kb. Next it occurred to me that while building the domain label string table that if the label to be added already existed as a substring within the table it could be elided.

The domain labels were sorted from longest to shortest and added in order searching for substring matches as the table was built this saved another 6Kb. I am sure there are ways to reduce this further I have missed (if you see them let me know!) but a 25% saving (47Kb to 35Kb) was a good start.

The second cause was a little harder to address. The structure representing nodes in the tree I started with was at first look reasonable.

struct pnode {
    uint16_t label_index; /* index into string table of label */
    uint16_t label_length; /* length of label */
    uint16_t child_node_index; /* index of first child node */
    uint16_t child_node_count; /* number of child nodes */

I examined the generated table and observed that the majority of nodes were leaf nodes (had no children) which makes sense given the type of data being represented. By allowing two types of node one for labels and a second for the child node information this would halve the node size in most cases and requiring only a modest change to the tree traversal code.

The only issue with this would be that a way to indicate a node has child information. It was realised that the domain labels can have a maximum length of 63 characters meaning their length can be represented in six bits so a uint16_t was excessive. The space was split into two uint8_t parts one for the length and one for a flag to indicate child data node followed.

union pnode {
    struct {
        uint16_t index; /* index into string table of label */
        uint8_t length; /* length of label */
        uint8_t has_children; /* the next table entry is a child node */
    } label;
    struct {
        uint16_t node_index; /* index of first child node */
        uint16_t node_count; /* number of child nodes */
    } child;

static const union pnode pnodes[8580] = {
    /* root entry */
    { .label = { 0, 0, 1 } }, { .child = { 2, 1553 } },
    /* entries 2 to 1794 */
    { .label = {37, 2, 1 } }, { .child = { 1795, 6 } },


    /* entries 8577 to 8578 */
    { .label = {31820, 6, 1 } }, { .child = { 8579, 1 } },
    /* entry 8579 */
    { .label = {0, 1, 0 } },


This change reduced the node array size from 63Kb to 33Kb almost a 50% saving. I considered using bitfields to try and reduce the label length and has_children flag into a single byte but such packing will not reduce the length of a node below 32bits because it is unioned with the child structure.

A possibility of using the spare uint8_t derived by bitfield packing to store an additional label node in three other nodes was considered but added a great deal of complexity to node lookup and table construction for saving around 4Kb so was not incorporated.

With the changes incorporated the test program was a much more acceptable 75Kb reasonably close to the size of the compressed source but with the benefits of direct lookup. Integrating the libraries single API call into NetSurf was straightforward and resulted in correct operation when tested.

This episode just reminded me of the dangers of code that can fail silently. It exposed our users to a security problem that we thought had been addressed almost six years ago and squandered the limited resources of the project. Hopefully a lesson we will not have to learn again any time soon. If there is a positive to take away it is that the new implementation is more space efficient, automatically built and importantly tested