Elmord's Magic Valley

Software, lingüística e rock'n'roll. Às vezes em Português, sometimes in English.

From Thunderbird to Liferea as a feed reader

2019-09-20 18:04 -0300. Tags: comp, unix, mundane, in-english

I've recently switched from Thunderbird to Liferea as my RSS feed reader. Thunderbird was randomly failing to update feeds at times*, and I thought it might be a good idea to use separate programs for e-mail and RSS for a change, so I went for Liferea. (I considered Elfeed too, but Elfeed does not support folders, only tags. In principle, tags can do everything folders can and more; the problem is that Elfeed cannot show a pane with all tags and the number of unread articles with each tag, the way Thunderbird or Liferea (or your average mail client) can do with folders.)

Liferea is pretty good, although I miss some shortcuts from Thunderbird, and sometimes shortcuts don't work (because focus is on some random widget). Here are some tips and tricks.

Importing feeds from Thunderbird to Liferea

Thunderbird can export the feed list in OPML format (right click on the feed folder, click Subscribe…, then Export). You can then import that on Liferea (Subscriptions > Import Feed List). No surprises here.

The tray icon

Liferea comes with a number of plugins (Tools > Plugins). By default, it comes with the Tray Icon (GNOME Classic) plugin enabled, which, unsurprisingly, creates a tray icon for Liferea. The problem with this for me is that whenever the window is 'minimized', Liferea hides the window entirely; you can only bring it back by clicking on the tray icon. I believe the idea is so that the window does not appear in the taskbar and the tray, but this interacts badly with EXWM, where switching workspaces or replacing Liferea with another buffer in the same Emacs 'window' counts as minimizing it, and after that it disappears from the EXWM buffer list. The solution I used is to disable the tray icon plugin.

Playing media

Liferea has a Media Player plugin to play media attachments/enclosures (such as in podcast feeds). To use it on Debian, you must have the gir1.2-gstreamer-1.0 package installed (it is a 'Recommends' dependency, not a mandatory one).

Alternatively, you can set Liferea to run an arbitrary command to open a media enclosure; the command will receive the enclosure URL as an argument. You can use VLC for that. The good thing about it is that VLC will start playing the stream immediately; you don't have to wait for it to download completely before playing it. The bad thing is that once it finishes playing the stream, the stream is gone; if you play it again, it will start downloading again. Maybe there is a way to configure this in VLC, but the solution I ended up using was to write a small script to start the download, wait a bit, and start VLC on the partially downloaded file. This way, the file will be fully downloaded and can be replayed (and moved elsewhere if you want to preserve it), but you don't have to wait for the download to finish.

# download-and-play-media.sh

# Save file in a temporary place.
file="/tmp/$(date "+%Y%m%d-%H%M%S").media"
# Start download in a terminal so we can see the progress.
x-terminal-emulator -e wget "$1" -O "$file" &
# Wait for the file to be non-empty (i.e, for the download to start).
until [[ -s "$file" ]]; do
    sleep 1
# Wait a bit for the file to fill.
sleep 2
# Play it.
vlc "$file"

Miscellaneous tips


So far I had two UI-related problems with Liferea:


Overall, I'm pretty satisfied with Liferea. There are a few problems, but so far I like it better than Thunderbird for feed reading.

Update (2020-03-23): After a few months using Liferea, I have to say that Thunderbird is better to use from the keyboard. Liferea is way too sensitive to which invisible thing has focus at a given moment. Were it not for Thunderbird not handling well hundreds of feeds, I think I would switch back.

Update (2020-07-10): I ended up switching to Elfeed.


* I suspect the problem was that Thunderbird was trying to DNS-resolve the domains for a huge number (perhaps all) of feeds at the same time, and some of the requests were being dropped by the network. I did not do a very deep investigation, though.

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Emacs performance, profiling, and garbage collection

2019-09-13 00:13 -0300. Tags: comp, emacs, in-english

This week I finally got around to upgrading my system and my Emacs packages, including EXWM. Everything went fine, except for one problem: every time I loaded a XKB keymap, EXWM would hang for 10–20 seconds, with CPU usage going up. I opened an issue on the EXWM repository, but I decided to investigate a bit more.

After learning the basic commands for profiling Emacs Lisp code, I started the profiler (M-x profiler-start), loaded a new keymap, and generated a report (M-x profiler-report). It turned out that 73% of the CPU time during the hangup was spent on garbage collection. I tried the profiler again, now starting it in cpu+mem mode rather than the standard cpu mode. From the memory report, I learned that Emacs/EXWM was allocating around ~500MB of memory during the keyboard loading (!), apparently handling X MapNotify events.

I did not go far enough to discover why so much memory was being allocated. What I did discover though is that Emacs has a couple of variables that control the behavior of the garbage collector.

gc-cons-threshold determines how many bytes can be allocated without triggering a garbage collection. The default value is 800000 (i.e., ~800kB). For testing, I set it to 100000000 (i.e., ~100MB). After doing that, the keyboard loading freeze fell from 10–20s to about 2–3s. Not only that, but after setting it near the top of my init.el, Emacs startup time fell by about half.

Now, I've seen people warn that if you set gc-cons-threshold too high, Emacs will garbage collect less often, but each garbage collection will take longer, so it may cause some lag during usage, whereas the default setting will cause more frequent, but less noticeable garbage collections (unless you run code causing an unusually large number of allocations, as in this case with EXWM). However, I have been using it set to 100MB for a couple of days now, and I haven't noticed any lag; I just got a faster startup and less EXWM hangup. It may well depend on your Emacs usage patterns; you may try different values for this setting and see how it works for you.

Another recomendation I have seen elsewhere is to set gc-cons-threshold high and then set an idle timer to run garbage-collect, so Emacs would run it when idle rather than when you're using it, or setting a hook so it would run when unfocused. I did not try that, and I suspect it wouldn't work for my use case: since Emacs runs my window manager, I'm pretty much always using it, and it's never unfocused anyway. Yet another recommendation is to bind gc-cons-threshold temporarily around the allocation-intensive code (that comes from the variable's own documentation), or to set it high on startup and back to the original value after startup is finished. Those don't work easily for the XKB situation, since Emacs does not know when a XKB keymap change will happen (unless I wrote some Elisp to raise gc-cons-threshold, call XKB, and set it back after a while, which is more complicated than necessary).

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More blogging, less twittering

2019-09-09 22:33 -0300. Tags: life, mind, about, in-english

It's been a while since I last posted on this blog. I've been travelling, and also interviewing for a new job; I'll have more to say about that in the future. Now that things have settled down a bit, I intend to start blogging more again.

Aside from that, I intend to start spending less time on Twitter. I've long been in a love/hate relationship with Twitter, and the hate side of it is starting to win out, for a variety of reasons:

Mastodon isn't much better. It does not have some of Twitter's misfeatures, such as trying to push a non-chronological timeline onto users, and it has the advantages of being decentralized and community-oriented, but the experience is very similar to that of Twitter. I think these problems are related to the media of microblogging, rather than a specific platform.

Traditional blogs can be addictive too. I follow hundreds of blogs via RSS, and sometimes I feel like refreshing my feeds every now and then to see if there is something new. But blog posts typically take more time to write, so updates are not so frequent, so the incentive to keep refreshing them several times a day is not so strong. It's also easier to keep track of unread posts and read them at a later time. Finally, blog posts tend to be much more informationally nutritious than tweets or toots.

At the same time I start to use Twitter less, I would like to start posting more here. I still have to figure out what to do with content that seems too short for a blog post – for example, if I just want to share a link to a video, or a little command I learned. One solution is to accumulate those and post them in weekly installments. Another is just to go ahead and post short posts, but I don't really want to pollute the blog with a plethora of micro-posts. Yet another is to create a separate page for the micro-posts. I'm currently more inclined towards the first option.

That's it, folks. Have a nice week!

1 comentário / comment

As flautas do Céu

2019-07-07 02:25 -0300. Tags: philosophy, translation, em-portugues

Esses dias eu traduzi uma passagem do Zhuangzi (a primeira história do segundo capítulo), combinando elementos de diversas traduções para o inglês. Posto-a aqui para a posteridade.

Zi-Qi da Fronteira Sul estava sentado, debruçado sobre sua mesa baixa. Olhou para o céu e exalou lentamente – ausente e distante, como se tivesse perdido sua companhia. Yan Cheng Zi-You, que aguardava de pé diante dele, perguntou: "O que é isso? Pode o corpo tornar-se como madeira seca? Pode a mente tornar-se como cinzas extinguidas? O homem debruçado sobre a mesa agora não é aquele que estava debruçado antes!"

Zi-Qi disse: "Fazes bem em perguntar, Yan. Eu acabo de perder a mim mesmo. Entendes isso? Podes ter ouvido as flautas dos homens, mas não ouviste as flautas da Terra; podes ter ouvido as flautas da Terra, mas não ouviste as flautas do Céu."

Zi-You disse: "Aventuro-me a perguntar o significado disto."

Zi-Qi disse: "A Grande Massa [da natureza] emite um sopro vital, e seu nome é vento. Se ele não sopra, nada acontece; mas quando ele sopra, então as dez mil fendas ressoam ferozmente. Já não o ouviste soprar e soprar? Na floresta de uma montanha que se agita e chacoalha, há imensas árvores de cem palmos de largura com cavidades e aberturas, como narizes, como bocas, como ouvidos, como jarros, como copos, como vasos, como fissuras, como sulcos. O vento que sopra nelas ruge como ondas, assovia como flechas, berra, suspira, grita, ronca, geme, uiva. O vento à frente canta "yiii", o vento que segue canta "wuuu". Uma brisa leve produz uma pequena resposta; um vendaval produz uma grande resposta. E quando a ventania se acalma, a multidão de fendas torna-se vazia. Já não viste esse sacudir, esse chacoalhar?"

Zi-You disse: "As flautas da Terra são estas que acabaste de descrever; as flautas dos homens são tubos de bambu dispostos lado a lado. Aventuro-me a perguntar sobre as flautas do Céu."

Zi-Qi disse: "Os sons do sopro sobre as dez mil coisas são diferentes, mas ele apenas traz à tona as propensões naturais das próprias coisas, cada uma tomando para si o que lhe é apropriado; quem é que as sopra?"

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Functional record updates in Fenius, and other stories

2019-06-16 17:33 -0300. Tags: comp, prog, pldesign, fenius, in-english

Fenius now has syntax for functional record updates! Records now have a with(field=value, …) method, which allows creating a new record from an existing one with only a few fields changed. For example, if you have a record:

fenius> record Person(name, age)
<class `Person`>
fenius> let p = Person("Hildur", 22)
Person("Hildur", 22)

You can now write:

fenius> p.with(age=23)
Person("Hildur", 23)

to obtain a record just like p but with a different value for the age field. The update is functional in the sense that the p is not mutated; a new record is created instead. This is similar to the with() method in dictionaries.

Another new trick is that now records can have custom printers. Printing is now performed by calling the repr_to_port(port) method, which can be overridden by any class. Fenius doesn't yet have much of an I/O facility, but we can cheat a bit by importing the functions from the host Scheme implementation:

fenius> record Point(x, y)
<class `Point`>
fenius> import chezscheme

# Define a new printing function for points.
fenius> method Point.repr_to_port(port) = {
            chezscheme.fprintf(port, "<~a, ~a>", this.x, this.y)

# Now points print like this:
fenius> Point(1, 2)
<1, 2>

A native I/O API is coming Real Soon Now™.

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Questions, exclamations, and binaries

2019-06-03 21:39 -0300. Tags: comp, prog, pldesign, fenius, in-english

I'm a bit tired today, so the post will be short.

ready? go!

In Scheme, it is conventional for procedures returning booleans to have names ending in ? (e.g., string?, even?), and for procedures which mutate their arguments to have names ending in ! (e.g., set-car!, reverse!). This convention has also been adopted by other languages, such as Ruby, Clojure and Elixir.

I like this convention, and I've been thinking of using it in Fenius too. The problem is that ? and ! are currently operator characters. ? does not pose much of a problem: I don't use it for anything right now. !, however, is a bit of a problem: it is part of the != (not-equals) operator. So if you write a!=b, it would be ambiguous whether the ! should be interpreted as part of an identifier a!, or as part of the operator !=. So my options are:

What do you think? Which of these you like best? Do you have other ideas? Feel free to comment.

Binaries available

In other news, I started to make available a precompiled Fenius binary (amd64 Linux), which you can try out without having to install Chez Scheme first. You should be aware that the interpreter is very brittle at this stage, and most error messages are in terms of the underlying implementation rather than something meaningful for the end user, so use it at your own peril. But it does print stack traces in terms of the Fenius code, so it's not all hopeless.

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Pattern matching and AST manipulation in Fenius

2019-05-30 19:40 -0300. Tags: comp, prog, pldesign, fenius, in-english

Fenius has pattern matching! This means you can now write code like this:

record Rectangle(width, height)
record Triangle(base, height)
record Circle(radius)

let pi = 355/113    # We don't have float syntax yet :(

let area(shape) = {
    match shape {
        Rectangle(width, height) => width * height
        Triangle(base, height) => base * height / 2
        Circle(radius) =>  pi * radius * radius

print(area(Rectangle(4, 5)))
print(area(Triangle(3, 4)))

More importantly, you can now pattern match over ASTs (abstract syntax trees). This is perhaps the most significant addition to Fenius so far. It means that the code for the for macro from this post becomes:

# Transform `for x in items { ... }` into `foreach(items, fn (x) { ... })`.
let for = Macro(fn (ast) {
    match ast {
        ast_match(for _(var) in _(items) _(body)) => {
            ast_gen(foreach(_(items), fn (_(var)) _(body)))

This is a huge improvement over manually taking apart the AST and putting a new one together, and it basically makes macros usable.

It still does not handle hygiene: it won't prevent inserted variables from shadowing bindings in the expansion site, and will break if you shadow the AST constructors locally. But that will come later. (The AST constructors will move to their own module eventually, too.)

The _(var) syntax is a bit of a hack. I wanted to use some operator, like ~var or $var, but the problem is that all operators in Fenius can be interpreted as either infix or prefix depending on context, so in for $var would be interpreted as an infix expression for $ var, and you would have to parenthesize everything. One solution to this is to consider some operators (like $) as exclusively prefix. I will think about that.

How does it work?

I spent a good while hitting my head against the whole meta-ness of the ast_match/ast_gen macros. In fact I'm still hitting my head against it even though I have already implemented them. I'll try to explain them here (to you and to myself).

ast_match(x) is a macro that generates a pattern that would match the AST for x. So, for example, ast_match(f(x)) generates a pattern that would match the AST for f(x). Which pattern is that? Well, it's:

Call(_, Identifier(_, `f`), [Identifier(_, `x`)])

That's what you would have to write on the left-hand side of the => in a match clause to match the AST for f(x). (The _ patterns are to discard the location information, which is the first field of every AST node. ast_gen is just like ast_match but does not discard location information.) So far, so good.

But here's the thing: that's not what the macro has to output. That's what you would have to write in the source code. The macro has to output the AST for the pattern. This means that where the pattern has, say, Identifier, the macro actually has to output the AST for that, i.e., Identifier(nil, `Identifier`). And for something like:

Identifier(_, `f`)

i.e., a call to the Identifier constructor, the macro has to output:

Call(nil, Identifier(nil, `Identifier`),
          [Identifier(nil, `_`), Constant(nil, `f`)])

and for the whole AST of f(x), i.e.:

Call(_, Identifier(_, `f`), [Identifier(_, `x`)])

the macro has to output this monstrosity:

Call(nil, Identifier(nil, `Call`),
     [Identifier(nil, `_`),
      Call(nil, Identifier(nil, `Identifier`),
                [Identifier(nil, `_`), Constant(nil, `f`)]),
      Array(nil, [Call(nil, Identifier(nil, `Identifier`),
                            [Identifier(nil, `_`), Constant(nil, `x`)])])])

All of this is to match f(x). It works, is all encapsulated inside the ast_* macros (so the user doesn't have to care about it), and the implementation is not even that much code, but it's shocking how much complexity is behind it.

Could it have been avoided? Perhaps. I could have added a quasiquote pattern of sorts, which would be treated especially by match; when matching quasiquote(ast), the matching would happen against the constructors of ast itself, rather than the code it represents. Then I would have to implement separate logic for quasiquote outside of a pattern (e.g., on the right-hand side). In the end, I think it would require much more code. ast_match/ast_gen actually share all the code (they call the same internal meta-expand function, with a different value for a "keep location information" boolean argument), and requires no special-casing in the match form: from match's perspective, it's just a macro that expands to a pattern. You can write macros that expand to patterns and use them in the left-hand side of match too.

(I think I'll have some observations on how all of this relates/contrasts to Lisp in the future, but I still have not finished digesting them, and I'm tracking down some papers/posts I read some time ago which were relevant to this.)

Missing things

The current pattern syntax has no way of matching against a constant. That is:

match false {
    true => "yea"
    false => "nay"

binds true (as a variable) to false and returns "yea". I still haven't found a satisfactory way of distinguishing variables from constants (which are just named by identifiers anyway). Other languages do various things:

One thing that occurred to me is to turn all constructors into calls (i.e., you'd write true() and false(), not only in patterns but everywhere), which would make all patterns unambiguous, but that seems a bit annoying.

Rust's solution seems the least intrusive, but Fenius does not really have a syntactically separate class of "constructors" (as opposed to just variables bound to a constant value), and considering all bound variables as constants in patterns makes patterns too fragile (if you happen to add a global variable – or worse, a new function in the base library – with the same name as a variable currently in use in a pattern, you break the pattern). I'll have to think more about it. Suggestions and comments, as always, are welcome.

Another missing thing is a way to debug patterns: I would like to be able to activate some kind of 'debug mode' for match which showed why a pattern did not match. I think this is feasible, but we'll see in the future.

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Partial goals, and other rambles

2019-05-25 22:33 -0300. Tags: comp, prog, pldesign, fenius, ramble, in-english

Designing a programming language is a huge undertaking. The last few days I have been thinking about types and classes and interfaces/traits and I got the feeling I was getting stuck in an analysis paralysis stage again. There is also lots of other questions I have to thinking about, such as: How will I handle mutability, and concurrency, and how those things interact? Can I add methods to classes at runtime? Can I implement interfaces to arbitrary existing types? How do I get a trait implementation 'in scope' / available? How static and dynamic typing will interact? How does this get implemented under the hood? And so on, and so forth…

At the same time, it may seem that not solving those problems undermine the whole point of designing a new language in the first place. If you don't solve the hard problems, why not keep using an existing language?

And that's the recipe for paralysis.

But the thing is, even if you solve part of the problems, you can already have something valuable. If Fenius 0.3 gets to be just a 'better' Scheme with nicer syntax and fewer namespacing problems*, that would be already a language I would like to use.

So what would a Minimum Viable Fenius need?

These would go a long way already. These are all pretty doable and don't involve much hard thinking. The greater problems can be tackled later, after the language is usable for basic tasks.

I would also love to have basic sockets to try to write web stuff in Fenius, but these will have to wait, especially given that Chez does not come with sockets natively. Thunderchez has a socket library; maybe I can use that. But this will come after the items above are solved. I can write CGI apps in it, anyway, or make a wrapper in another language to receive the connections and pass the data via stdin/stdout.

Also, I sometimes wonder if it wouldn't be more profitable to implement Fenius on the top of Guile instead of Chez, since it has more libraries, and now also has a basic JIT compiler. But at the same time, the ultimate goal is to rewrite the implementation in itself and leave the Scheme dependency behind, so maybe it's better not to depend too much on the host ecosystem. Although the Chez compiler is so good, maybe it would make more sense to compile to Scheme and run on the top of Chez rather than compile to native code. If only it did not have the annoying startup time… we'll see in the future!


* Of course, 'better' is subjective; what I want is for Fenius 0.3 to be a better Scheme for me.

Addendum: And by 'better syntax' I don't even mean the parentheses; my main annoyance when programming in Scheme is the syntax for accessors: the constant repeating of type names, like (person-name p) rather than p.name, (vector-ref v i) rather than v[i], (location-line (AST-location ast)) rather than ast.location.line, and so on. This goes hand in hand with the namespacing issue: because classes define a namespace for their methods, you don't have to care about name conflicts so often.

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Persistent hashmaps

2019-05-23 21:36 -0300. Tags: comp, prog, pldesign, fenius, in-english

Fenius got dictionaries! Now you can say:

fenius> let d = dict("foo" => 1, "bar" => 2)
dict("foo" => 1, "bar" => 2)
fenius> d["foo"]
fenius> d.with("foo" => 3, "quux" => 4)
dict("foo" => 3, "bar" => 2, "quux" => 4)
fenius> d.without("foo")
dict("bar" => 2)

Dictionaries are immutable: the with and without methods return new dictionaries, rather than modifying the original. I plan to add some form of mutable dictionary in the future, but that will probably wait until I figure Fenius's mutability story. (Right now, you can mutate the variables themselves, so you can write d := d.with("foo" => 42), for example.)

Dictionaries are persistent hashmaps, much like the ones in Clojure. Fenius's implementation of hashmaps is simpler than Clojure's, because it does less: beside persitent/immutable hashmaps, Clojure also supports transient hashmaps, a mutable data structure onto which entries can be inserted and removed efficiently, which then can be turned into a persistent hashmap. I want to implement something like this in Fenius at some point. On the flip side, the current Fenius implementation is easier to understand than its Clojure counterpart.

The underlying data structure is based on hash array mapped tries (HAMTs). The standard reference for it is Bagwell (2000). The persistent variant used by Clojure, Fenius and other languages is slightly different, but the basic idea is the same. In this post, I intend to explain how it works.

I will begin by explaining an auxiliary data structure used by the hashmap, which I will call a sparse vector here.

Sparse vectors

Suppose you want to create vectors of a fixed size (say, 8 elements), but you know most of the positions of the vector will be empty much of the time. If you create lots of these vectors in the naive way, you will end up wasting a lot of memory with the empty positions.

23 42

A cleverer idea is to split this information into two parts:

00001010 23 42

(Note that the first position is represented by the lowest bit.) If we know beforehand that the number of positions is limited (say, 32), we can fit the bitmap into a single integer, which makes for a quite compact representation.

That takes care of representing the vectors. But how do we fetch a value from this data structure given its position index i? First, we need to know if the position i is filled or empty. For that, we bitwise-and the bitmap with a mask containing only the ith bit set, i.e., bitmap & (1 << i) (or, equivalently, bitmap & 2i). If the result is non-zero, then the ith bit was set in the bitmap, and the element is present. (If it's not, we return a default empty value.)

Once we know the element is there, we need to find its place in the content vector. To do that, we need to know how many elements are there before the one we want, and skip those. For that:

The result of all this is the number of filled positions before the one we want. Because positions are counted from 0, it also happens to be the position of the wanted element in the content vector (e.g., if there is 1 element before the one we want, the one we want is at position 1 (which is the second position of the vector)). In summary:

def sparsevec_get(sparsevec, position):
    if sparsevec.bitmap & (1 << position):
        actual_position = bit_count(sparsevec.bitmap & ((1 << position) - 1))
        return sparsevec.content[actual_position]
        return SOME_EMPTY_VALUE

To insert an element at position i into the vector, we first figure if position i is already filled. If it is, we find its actual position in the content vector (using the same formula above) and replace its contents with the new value. If it is currently empty, we figure out how many positions are filled before i (again, same formula), and insert the new element just after that (and update the bitmap setting the ith bit). Analogously, to remove the ith element, we check if it is filled, and if it is, we find its position in the content vector, remove it, and clear the ith bit in the bitmap.

In our real implementation, sparse vectors will be persistent, so updates are performed by creating a new vector that is just like the original except for the added or removed elements.

Enough with sparse vectors. Let's get back to HAMTs.

Hash Array-Mapped Tries

A trie ("retrieval tree") is a tree where the path from the root to the desired element is determined by the symbols of the element's key. More specifically:

A hash function is a function f such that if two elements x and y are equal, then f(x) = f(y). (The opposite is not true: there are typically infinitely many elements x for which f(x) produces the same result.) The result of f(x) is called a hash of x. A hash is typically a finite sequence of bits, or symbols... from an alphabet. You can see where this is going.

A hash trie is a trie indexed by hashes. To find an element with key x in the trie, you first compute the hash f(x), and then consume successive bits (or symbols) from the hash to decide the path from the root of the trie to the desired element.

In principle, this would mean that the depth of the trie would be the same as the length of the hash. For example, if our hashes have 32 bits, we would have to traverse 32 children until we reach the element, which would be pretty wasteful in both storage and time. However, we don't need to use the entire hash: we can use just enough bits to distinguish the element from all other elements in the trie. For example, if the trie has elements x with hash 0010, y with hash 0100, and z with hash 1000, we build a trie like:

                  0 / \ 1
                   /   \
                  *     z
                0/ \1
                /   \
               x     y

That is: z is the only element whose hash starts with 1, so we can store it right below the root as the child labelled 1. Both x and y have hashes beginning with 0, so we create a subtree under the root's 0 for them; the second bit of the hash is 0 for x and 1 for y, so we store them below the subtree under the appropriate label. We don't need to go any deeper to disambiguate them.

To insert an element in the tree, we compute its hash and start traversing the tree until either:

If our hashes have a finite number of bits, it may happen that two distinct elements end up having the same hash. There are two ways of handling this problem:

The only problem with this scheme is that our trees can still get pretty deep as we add more elements to them. For example, if we add 1024 elements (210) to the tree, we need at least 10 bits of hash to distinguish them, which means we need to go at least 10 levels deep in the tree to find them; the deeper the tree is, the slower it is to find an element in it. We can reduce the depth if, instead of branching on single bits of the hash, we use groups of bits of a fixed size, say, 5 bits. Then instead of each node having 2 children, each node has 25 = 32 children, labelled {00000, 00001, ..., 11110, 11111}, and we consume 5 bits of the hash for each level we traverse. Now a tree of 210 elements will typically be 2 levels deep rather than 10, which makes it much faster to traverse.

The only problem with this scheme is that each node now needs to have space for 32 children, even when the children are empty. For example, if I store two elements, x with hash 00000 00001, and y with hash 00000 00100, the root of the tree will be a node with 32 children, of which only the 00000 child will be non-empty. This child will contain a subtree containing x at position 00001, y at position 00100, and all other 30 positions empty. If only we had a way to only store those positions that are actually filled. A sparse vector, if you will...

Congratulations, we have just invented hash array-mapped tries, or HAMTs. A HAMT is a hash trie in which each non-leaf node is a sparse vector, indexed by a fixed-length chunk of bits from the hash. To find an element in the HAMT, we traverse the sparse vectors, consuming successive chunks from the hash, until we either find the element we want, or we consume the entire hash and reach a collision list (in which case we look for the element inside the list), or we reach an empty child (in which case the element is not found). Because the sparse vector only allocates space for the elements actually present, each node is compact, and because each level is indexed by a large-ish chunk of bits, the tree is shallow. Win win.

The sparse vectors are immutable, and so is our tree. To add an element to the tree, we have to change the nodes from the root to the final place of the element in the tree, which means making copies of them with the desired parts changed. But the nodes that are not changed (i.e., those that are not part of the path from the root to the new element) are not copied: the new tree will just point to the same old nodes (which we can do because we know they won't change). So adding an element to the tree does not require making a full copy of it.

Removing an element from a HAMT requires some care. Basically, we have to replace the node where the element is with an empty child. But if the subtree where the element was had only two elements, and after removal is left with only one, that one element takes the place of the whole subtree (you never have a subtree with a single leaf child in a HAMT, because the purpose of a subtree is to disambiguate multiple elements with the same hash prefix; if there is no other element sharing the same prefix with the element, there is no point in having a subtree: the element could have been stored directly in the level above instead).

Miscellaneous notes

When profiling my implementation in a benchmark inserting a million elements in a HAMT, I discovered that most of the time was spent on an auxiliary function I wrote to copy sequences of elements between vectors (when updating sparse vectors). This would probably be more efficient if R6RS (or Chez) had an equivalent of memcpy for vectors. It does have bytevector-copy!, but not a corresponding vector-copy!. Go figure.

R7RS does have a vector-copy!, but I'm using Chez, which is an R6RS implementation. Moreover, R7RS(-small) does not have bitwise operations. But it totally has gcd (greatest common divisor), lcm (least common multiple) and exact-integer-sqrt. I mean, my idea of minimalism is a bit different. Also, it has a timestamp function which is based on TAI instead of UTC and thus requires taking account of leap seconds, except it's not really guaranteed to return accurate time anyway ("in particular, returning Coordinated Universal Time plus a suitable constant might be the best an implementation can do"). Yay. [/rant]

Implementing transient/mutable HAMTs efficiently is a bit more complicated. For it to be efficient, you need to be able to insert new elements in a sparse vector in-place, but you can only do that if you pre-allocate them with more space than they actually need, so you have room for growing. Finding a proper size and grow factor is left as an exercise to the reader.

Comparing performance with Clojure HAMTs is not a very exact benchmark, because the implementations are not in the same language (Clojure's is in Java and Fenius's is in Chez Scheme). In my tests doing 10M insertions, Clojure sometimes did much faster than Chez, sometimes much slower, with times varying between 16s and 48s; the JVM works in mysterious ways. Chez's fastest was not as fast as Clojure, but performance was consistent across runs (around ~35s). Note that this is the time for using the hashmap implementation from ChezScheme, not Fenius; doing the benchmark directly in Fenius would be much slower because the language is currently interpreted and the interpreter is very naive. Note also that in actual Clojure, you would do all the insertions on a transient hashmap, and then turn it into a persistent hashmap after all the insertions, so the benchmark is not very representative of actual Clojure usage.

End of file

That's all for now, folks. I wanted to discuss some other aspects of Fenius dictionaries (such as syntax), but this post is pretty big already. Enjoy your hashmaps!

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Hel is now Fenius, and other notes

2019-05-13 16:41 -0300. Tags: hel, fenius, in-english

After feedback from a number of people (1) in the last post, and consulting privately with a number of other people (1), and some consideration on the merits of each naming option (including possibility of confusion with other projects and searchability in your favorite search engine), I'm going with Fenius as the new name for Hel. That will also give me an excuse to get more acquainted with Irish mythology and legends to be able to give cool names to Fenius-related tools.

As for the question of licensing, I'll keep everything under the GPLv3 for now; I can worry about library licensing after I actually have libraries to license. But I have reached the following conclusions about the matter:

Stay tuned for a post about persistent hashmaps Real Soon Now™.

P.S.: My coding activity is still a bit limited right now due to RSI, which is getting gradually better.

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