Disclaimer: This work was not conducted as part of the HTTPS Everywhere project. My intent when experimenting with rulesets matching was to explore new ways to implement an efficient engine and document my findings. I would of course love it if some of these ideas are used upstream.
Over the last few years, the adoption of HTTPS has continuously increased, reaching 50% of the Web traffic for the first time in 2017 and up to 80% in 2019. Yet, according to the EFF: “Many sites on the web [still] offer some limited support for encryption over HTTPS, but make it difficult to use. For instance, they may default to unencrypted HTTP, or fill encrypted pages with links that go back to the unencrypted site.”
For this reason, the EFF started the HTTPS Everywhere project in 2014, providing users with a browser extension able to automatically upgrade connections to HTTPS whenever possible.
To decide when an upgrade is feasible, the extension relies on a database of rulesets allowing it to know for a given URL if HTTPS is supported. These rules are continuously updated to limit breakage and maximize coverage.
Having spent a fair amount of my time working on content blockers in the last few years—especially on the performance aspect—, I have always been curious about how the rule-matching logic was implemented in HTTPS Everywhere, since the task shares many similarities with adblocking. More recently, I stumbled upon two tickets mentioning high memory usage and slow initialization of the extension and decided to have a closer look.
While experimenting, I was wondering if some of the optimizations implemented as part of modern content blockers would make sense in HTTPS Everywhere and if they would improve the overall efficiency. This blog post presents some of the results of this investigation. The following contributions and improvements are presented:
- A new design, inspired by some of the same optimizations implemented in the fastest content blockers, leading to an increased efficiency:
- ~4.7MB of memory usage (4x less than the current HTTPS Everywhere Rust/WebAssembly implementation), further reduced to ~2.1MB when using an experimental statistical data structure (with ideas to reduce it even more).
- Decision time between 0.0029 and 0.0073 milliseconds when querying the rulesets with a URL to be upgraded to HTTPS.
- Serialization to and deserialization from a compact binary representation in under 25 milliseconds and no memory copy.
- Design and implementation of a compact index data structure allowing to efficiently retrieve a small subset of rules likely to apply to a given input URL.
- A built-in small string compression implementation inspired by SMAZ which allows to reduce memory usage by up to 60%.
- An experimental statistical data structure allowing an even lower memory usage, at the risk of unlikely collisions.
- An experimentation with a compact trie data structure to attempt reducing memory usage further.
Before digging deeper into the design of the matching engine, let’s briefly describe HTTPS Everwhere rulesets.
The database of rules is made of thousands of rulesets (currently about 25k). Each ruleset is an XML file containing information about upgrading requests to HTTPS for a domain or group of domains (e.g. for an organization like Bitly). The file can contain the following entities:
- Targets—define which domains are targeted by this ruleset (e.g.
example.com). They can also make use of wildcards, either to target all subdomains, or multiple top-level domains.
- Exclusions—are regular expressions allowing to prevent some specific domains or URLs from being upgraded to HTTPS (e.g. to prevent breakage).
- Rules—define how insecure requests should be upgraded from insecure to secure (i.e. they encode the URL rewriting logic). They define a
fromregular expression which the input URL should match, associated with a
toattribute describing how the upgraded URL should look like. The most common case being to simply transform
- Secure Cookies—Optionally defined if cookies from one of the targeted domains should be secured as well using hardened flags.
Here is a simple example of how such a ruleset could look like:
<ruleset name="example.com"> <target host="example.com" /> <target host="*.example.com" /> <exclusion pattern="^http://unsecure\.example\.com/" /> <rule from="^http:" to="https:" /> <securecookie host=".+" name=".+" /> </ruleset>
Given a collection of rulesets, the decision of upgrading an insecure request to a secure one relies on the following steps:
- Identifying the subset of rulesets targeting the URL’s domain.
- Eliminating rulesets having at least one matching exclusion rule.
- Evaluating the rules from the candidate rulesets until one matches.
If a matching
rule is found following the previous steps, then the URL is rewritten to a secure version using the rewriting logic defined by this
rule. For more information about the exact semantic of matching rulesets, check this page of the official documentation.
The naive approach to writing a matching algorithm would be to iteratively inspect all rulesets for each input URL, checking their targets, exclusions and rules until a match is found. This would not be very efficient and we can do better (to be clear, this is not the approach taken by HTTPS Everywhere and I only describe it to get a sense of the most naive solution).
In the following few sections we are going to explore some of the biggest ideas contributing to the speed and memory efficiency of the new matching engine. Firstly, I will present the central indexing data structure which allows to drastically reduce the amount of work required to find relevant rulesets. Secondly, I will walk you through how this index can be represented in a very compact way, as a single typed array. Thirdly, we will see how we can further reduce the memory usage by implementing a built-in string compression capability to this compact index. Lastly I will briefly describe two other attempts at reducing the size of the index using a trie data structure and an experimental probabilistic data structure based on hashing.
Instead of iterating through all rulesets for each input URL, we want to quickly identify a small subset of candidates which will be evaluated against the input URL. To achieve this goal, we rely on a reverse index which groups targets, exclusions and rules into buckets indexed by a common substring (or token) that they contain. This allows us to collect candidates for a given URL by querying the index with tokens found in the URL. Each candidate retrieved is thus guaranteed to share at least a common substring with the URL. In practice, this drastically reduces the amount of work required to take a decision. This technique is used as part of content blockers to identify lists of filters indicating that a network request should be canceled.
We create a separate index for targets, exclusions, rules and secure cookies. To minimize the number of candidates retrieved for each URL, we make sure that each target and secure cookie is indexed using its rarest token, whereas exclusions and rules are indexed only using the ID of the ruleset they belong to. In practice, each index is created using the following algorithm:
- Each element is tokenized using
\w+(alpha-numeric characters) or the ruleset ID is used as a token. For example target
example.comwould be tokenized into
- We keep track of the number of occurrences of each token with a global counter.
- We then select the best (i.e. least seen) token for each element, and use it as a key in the reverse index.
As a result, most buckets of the index will contain a single element (meaning that we found a token which is unique globally to index the element). To get a better idea of the dispatching capabilities brought by this technique, consider the following statistics collected by matching a datasets containing 240k URLs from the most popular domains against the HTTPS Everywhere rulesets:
- The median number of targets candidates evaluated for a given URL is: 7—from a total of 163k; which means we only need to look at 0.004% of all targets on average. And out of these targets, most only cost a look-up in a
Setsince we often get multiple candidates from the same ruleset. By keeping track of which rulesets we are already considering, we only need to evaluate the first target from a given ruleset. The median number of targets candidates requiring a string comparison is: 5.
- The median number of rulesets considered is: 1, with a maximum of: 2 in the rare case where a given domain is targeted by more than one ruleset (from a total of 25k).
- For each ruleset, we then retrieve a combined exclusion (all regular expressions aggregated into one, joined with
|characters), resulting in one or two
RegExpevaluations (from the one or two rulesets considered).
- Finally, we inspect the rules from each ruleset not already excluded, until we find a match. The median number of rules considered is 2.
This graph depicts the average time it takes to rewrite a request to HTTPS, based on the latest snapshots of rulesets, evaluated against the dataset of 240k URLs mentioned above. Please note that internal caching of HTTPS Everywhere was disabled for these measurements, to only take into account the raw speed of the engine.
While the indexing technique described in the previous section speeds-up matching drastically, it is not optimal in terms of memory usage and initialization time. If the index is represented as a
Map, it means that on each initialization (when the extension starts) we need to either re-create the index from scratch (using the raw XML rulesets or a JSON version of it), or load it from a textual representation of the
Map (i.e. from cache), like an array of key, value pairs.
Instead, the reverse index described above is implemented as a compact binary data structure stored in a single
Uint8Array (typed array), where the data is organized in a way that allows for efficient look-ups. In-memory instances of targets, exclusions, rules and secure cookies along with the instances of
RegExp required to match against input URLs and domains are only lazily loaded and compiled from their binary representation stored in the typed array, when there is a chance they will match, thanks to the reverse index. These instances can also be (optionally) cached into a
Map so that subsequent look-ups do not need to hit the binary index (which is a bit slower than
Since the number of rulesets really considered in practice is more or less proportional to the number of unique domains visited by a user during a browsing session, the additional memory usage required for the caching mechanism is fairly small.
More implementation details are given in the section “Going low-level with typed arrays” from this other article. To summarize the benefits of this data structure:
- It allows to encode all rulesets into a very compact, binary format, stored in a single
Uint8Array. The total memory usage of the extension using such an engine is therefore fairly predictable, and close to the size of this typed array.
- Serialization and deserialization are extremely efficient since the look-ups can be performed directly on this
Uint8Arrayinstance without the need to first copy the data into a more convenient data structure such as a
Map. Serialization thus consists in storing the same typed array locally (e.g. in IndexedDB), and deserialization consists in reading it back.
- This binary data structure can be created once on the server-side and hosted on a CDN, so that clients can fetch it directly, speeding-up initialization further (The following binary file is updated automatically using a GitHub Workflow triggered using
- In-memory instances of targets, exclusions, rules and secure cookies along with the instances of
RegExprequired to match against input URLs and domains are only lazily loaded and compiled from the binary representation, when there is a chance they will match, thanks to the reverse index.
The drawback of this approach, though, is that updating (adding or deleting elements from the index), currently requires to recreate the index completely, which is relatively costly (it takes around 500 milliseconds). But since updates can be performed backend-side, and are relatively infrequent, this is not a road-blocker.
Up to this point, we have shown how we can efficiently query rulesets and how the indexing data structures can be represented in a compact way, friendly to serialization and deserialization to allow faster initializations. The size of the final typed array is roughly of 7MB. When looking closer, it appears that a big proportion of this data consists of the raw strings from targets (
host), exclusions (
pattern), rules (
to), as well as secure cookies (
name): about 3MB, or a bit more than 40% of the total size.
Looking at these strings, it does not take long to notice that some values are very frequent, like
.+ in secure cookies, or
https: in rules. One way to take advantage of these patterns would be to hard-code the detection of some of the common strings and replace them by opcodes, or perform some kind of string interning, to avoid having many times the same data in memory (or in the compact reverse index).
Applying this codebook compression idea to rulesets, we are able to compress strings by 40 to 60%, further reducing the total size of the serialized engine to 5MB (i.e. a 2MB, or 30%, reduction). Applying this optimization can be done transparently in the custom DataView-like abstraction used to serialize data to the binary representation and back.
A drawback of relying on codebooks is that they need to be re-generated when the rulesets are updated so that they remain relevant. The prototype hosted on GitHub is relying on a GitHub Workflow to update the codebooks based on the latest snapshot of the rules and open a PR with the updated assets. The codebooks are also shipped as part of the binary representation of the matching engine, which means that clients downloading a new version from the CDN (i.e. GitHub) always get the best compression, without needing to update the source code.
Do. Or do not. There is no Trie.
Although the codebook-based compression is very effective at reducing the memory usage of raw strings needed for matching rulesets, there might be more efficient approaches depending on the nature of the data. In particular, targets are domain names, most of which are not using wildcards at all; they also represent the bulk of the strings. A trie is commonly used to represent this kind of data. We expect suffixes of domains to be repeated among many targets (some top-level domains are very common).
I already knew it was possible to encode a trie in a very compact way—using only one 32-bit number to represent each node when storing ASCII strings. Before putting the work to implement this new data structure, I started by estimating the expected final size to make sure it was worth it.
Map in each node to link a parent to its children. Storing all targets resulted in a trie of
1,654,430 nodes, which would result in about 6.6MB of memory with our compact representation. Not very encouraging…
I then realized that it would probably make more sense to store the domains in reverse, to benefit from compression of top-level domains. After reversing the order of targets on insertion, the number of nodes went down to
878,251, which would result in 3.5MB of memory. This already seemed more reasonable. But we also need to factor-in the extra information about which ruleset each target belongs to (information needed when matching). Given that we have
163,486 targets, and assuming we find a way to encode the ruleset membership with an extra 32-bit number for each target, a back-of-the-envelope calculation tells us that we would need an extra 650KB, resulting in a total of 4.1MB memory usage. Even assuming a very optimistic 16-bit overhead per target, we would still need more memory to store targets than with the codebook-compression approach described above.
This concluded the experimentation with tries. Unfortunately, it does not seem like using a trie would yield any significant savings compared to the string compression method already implemented. It might be a viable option if string compression is not to be implemented at all. Also, it could be that better results can be obtained using a more advanced trie structure such as a patricia or adaptive (ART) trie, which would allow to store multiple characters into a single node.
At this point it seemed like the different new ideas to improve the memory representation of rulesets were hitting diminishing returns. As a last trick, I thought of implementing a data structure which allows to trade space for uncertainty (a.k.a. probabilistic data structure). I had already experimented with a similar approach when working on the
tldts-experiment package. You are probably familiar with Bloom filters, which allow to perform membership tests on a potentially large collection of elements, while keeping the memory usage under control (by adjusting the probability of false positives).
Instead of going for full-blown Bloom filters, I decided to experiment with a simpler method, also based on hashing. The idea is fairly simple, each domain in the collection is stored in a bucket alongside domains having the same number of labels. Each bucket is a sorted array of 32-bit hashes of these domains. We could also store all hashes in a single array regardless of the number of labels, but this increases the probability of collisions by a factor of 2. Each bucket is followed by a second array of same size, containing ids of rulesets to which the targets belong. The final data structure is then composed of all buckets concatenated into a single
Using this trick allows to reduce the size of the final serialized engine to 2.1MB (i.e. a 2MB, or 40%, reduction). This comes at the cost of a
0.000017 probability of collision when looking-up targets in the index (estimated using a list of 20M popular domains). For this reason, this feature is turned-off by default but can be enabled using a config flag when building the binary engine.
Note that an extra 320KB of memory could be saved by using a 16-bit identifier for ruleset IDs instead of the current 32-bit identifier (which could work because we only have 25k rulesets at the moment and this can be represented using 16-bit numbers). This would reduce the total memory usage to 1.8MB of memory (i.e. a 10x improvement over the memory usage of the current HTTPS Everywhere implementation in Rust compiled to WebAssembly).
Conclusion and Future Work
In this article I have presented the current state of an experiment aiming at implementing a more efficient matching engine for HTTPS Everywhere rulesets. Using a radically different design, matching can be made between 4x and 10x more memory-efficient, initialization of the engine reduced to less than 25 milliseconds, and HTTPS upgrades performed in 0.0029 to 0.0073 milliseconds, without relying on the Rust/WebAssembly combo.
The source code can be found on GitHub. You can also install a simple WebExtension and try out the new engine locally in Firefox or Chromium.
There are currently a few known limitations compared to the official HTTPS Everywhere:
- No way for users to add custom rules. This can be implemented as a second, smaller engine which would be stored separately from the main rulesets.
- Rulesets need to have a unique 32-bit identifier known when creating the index. For built-in rules, this ID is determined whenever the engine is created. To handle user-defined rules, we could either rely on a counter maintained client-side, or a 32-bit hash of the ruleset name.
- Metadata for rulesets (i.e. the name and default state) are currently discarded at build-time. It would be fairly easy to include them in the engine for an estimated overhead of 350KB in the final size. It should be noted, though, that the
nameinformation is currently not needed in the prototype.
- Rulesets marked as mixedcontent—only supported in the Tor browser—are discarded at build time. They could easily be included, either enabled by default (if we ship two different engines, fetched by clients depending on their browser support), or side-by-side with the other rules and enabled dynamically.
I hope this work will be helpful to the community and I would be glad to discuss these findings in more details with people directly working on the HTTPS Everywhere extension.