In Remembrance of Mike Ey

by Chad Austin
Mar 7 15

We hired Mike Ey out of RIT back when IMVU had a Palo Alto office.  My first impression meeting him was that he looked like John Carmack.

My second impression came after completion of a project I can no longer remember.  In the post-project code review, a task came up – he had modified a flexible system to add a feature-specific conditional.  I asked him to fix it, and noticed a month later that nothing had been done.  So I asked him again – still nothing changed.  Finally I had to go to his manager.  Eventually the task was completed.

In hindsight, I see that, like many people fresh out of school, he struggled with the tension between the tasks on the board and the important stuff that has to get done anyway.

Later on, he and I worked a lot more closely.  His background in 3D graphics led us to working on IMVU’s content pipeline together.  He did much of the research into whether COLLADA would be suitable for IMVU’s needs.

I considered Mike a talented but junior engineer when my manager came to me and said “I want to take a chance and make Mike Ey the tech lead of our new engine team.”  I resisted a bit, but eventually we agreed that I would support him in this new role.

And he did great.  Mike brought creativity, positivity, willingness to help, and a constructive attitude, all of which are critical to getting a team off the ground.  His concrete experience building games in Unity3D on the weekends kept us focused on solving for actual customer needs.

Around this time, he and I became friends.  There’s something irreplaceable about working with someone you implicitly trust.  He also made me laugh all the time – we were both fans of Dr. Weird and most days you could hear Dr. Weird quotes in the office.

Mike taught me to solder, inspired me in many of my random woodworking projects, and was always available to chat about what recent games were great.  He even built his own quadcopters.

I was super bummed when he announced he was going to Microsoft to work on a secret project with his childhood friend.  As it turned out, he went to work on HoloLens, and none of us can blame him.  :)  You could tell how proud he was when HoloLens was finally announced.  After a year of being unable to talk about it, he spent days gushing about the team, the coolness of the project, and the pranks he’d played on other team members during development.

The day he died, I couldn’t believe it.  I had just sent him a note asking for a recommendation on something, and while building some shelves in the garage I wondered how his new drill press was working out.  Only then did I realize how much he meant to me, and how much he affected my life.

Mike, we miss you!

Announcing BufferBuilder: Encode JSON in Haskell 2-5x faster than Aeson

by Chad Austin
Feb 23 15

Conclusion

This tale is a little long and detailed, so for those of you who want immediate payoff…

Andy and I wrote a Haskell library called Data.BufferBuilder (including Data.BufferBuilder.Utf8 and Data.BufferBuilder.Json) which makes it easy to safely and efficiently encode Haskell data structures into JSON. In our benchmarks, using Data.BufferBuilder.Json to encode JSON is 4-5x as fast as using Aeson.

Even if you’re already using Aeson, you can benefit somewhat from BufferBuilder’s improved encoding performance. The buffer-builder-aeson package adds a ToJson instance for Aeson’s Value type, which our benchmarks show is 50% to 100% faster than Aeson’s built-in encoder. All you need to do is call Data.BufferBuilder.Json.encodeJson instead of Data.Aeson.encode!

Why did we build BufferBuilder?

Some of IMVU’s backend services are written in Haskell. While Haskell is incredible for many use cases, we ran into an unexpected bottleneck: JSON encoding. Our service response structure produces quite a lot of JSON, and much of that JSON contains URLs encoded into JSON strings.

Amazingly, URL and JSON encoding was showing up as a significant cost center when generating JSON responses. Some services spent over a second encoding JSON!

When faced with a performance problem, my first instinct is to either pound the problem into the ground or unmake the problem so the code isn’t necessary in the first place.

So let’s look at the problem holistically:

  • A JSON response is represented in memory as a collection of varied, in-memory data structures. The response happens to contain many URLs — sometimes more than a hundred.
  • URLs are represented by a data structure consisting of the protocol, hostname, path segments, query string, and so on.
  • Each URL becomes a JSON string.
  • Each in-memory data structure is converted to a JSON object whose properties depend on the type on the corresponding data structure.

Using Aeson to encode all of this results in the following steps:

  • ToJSON instances convert Haskell data types to an AST of Aeson Values.
  • The keys of an Aeson object are Text values. In memory, Text is encoded in UTF-16. Thus, URLs must be translated from their in-memory representation (ours is ASCII) into UTF-16 before they fit into the Aeson AST.
  • Then, the entity bodies are converted into JSON objects, where the keys are known at compile-time, but must be encoded as UTF-16.
  • The entire AST is encoded into a Lazy Text Builder.
  • Then the Lazy Text is encoded into a Lazy ByteString containing UTF-8.
  • Then, to generate the response body, the Lazy ByteString is concatenated into a single strict ByteString.

That’s a lot of steps! To summarize:

(URLs and Custom Data Types) -> Aeson AST -> Lazy Text -> Lazy UTF-8 ByteString -> Strict ByteString

The Design of BufferBuilder

This is where Andy and I sat down to create an API to let us cleanly express JSON encoding without sacrificing type safety OR performance.

We know that a fast, if not the fastest, way to build up a buffer of bytes is to allocate a chunk of memory, stream writes to it, and either chunk or realloc() as needed. Obviously, this kind of code can be trivially expressed in C:

void buffer_append(buffer* b, const char* s, size_t length) {
    if (!b->has_room(length)) {
        b->grow(length);
    }
    memcpy(b->data + b->size, s, length);
}

Because I’d been told that bouncing between Haskell and C with the foreign function interface can be slow, my first approach was to attempt to build a Haskell monad that grabbed the RealWorld token out of IO (IO a is basically a newtype around RealWorld -> (RealWorld, a)), augmented it with some extra “local variables” like the output ptr, capacity, and current size, and manually implemented allocation and memory writes with GHC.Prim APIs. GHC did not like this at all. The generated code ran 20 times slower than naive usage of Data.ByteString.Builder. Nonetheless, it was an interesting technique, so maybe I’ll write about it another time.

Surely it was possible to do better. So I tried the foreign function interface after all.

I wrote a tiny C API that allocated the underlying growable buffer. It provided APIs for appending bytes, buffers, UTF-16-to-UTF-8 transcoding, and so on. These FFI calls can only happen within IO actions, but building buffers is fundamentally a pure operation, and the provided Haskell API should be effectively pure. The solution is to offer a restricted state monad like ST which limits the operations within the monad to safe buffer-building operations.

This approach was by the fastest of any that I tried. In fact, in a sequence of appendBS operations, if the arguments are known-strict ByteStrings, GHC will compile the appendBS sequence directly into a series of cheap C function calls. For example, the following code:

data BSTriple = BSTriple !ByteString !ByteString !ByteString

writeBSTriple :: BSTriple -> BufferBuilder ()
writeBSTriple !(BSTriple a b c) = do
    appendBS a
    appendBS b
    appendBS c

compiles into something like:


movq %rbx,%rdi
movq 120(%rsp),%rax
movq %rax,%rsi
movq 96(%rsp),%rax
movq %rax,%rdx
movq 112(%rsp),%rax
addq %rax,%rdx
subq $8,%rsp
movl $0,%eax
call bw_append_bs
addq $8,%rsp
movq %rbx,%rdi
movq 152(%rsp),%rax
movq %rax,%rsi
movq 128(%rsp),%rax
movq %rax,%rdx
movq 144(%rsp),%rax
addq %rax,%rdx
subq $8,%rsp
movl $0,%eax
call bw_append_bs
addq $8,%rsp
movq %rbx,%rdi
movq 216(%rsp),%rax
movq %rax,%rsi
movq 160(%rsp),%rax
movq %rax,%rdx
movq 208(%rsp),%rax
addq %rax,%rdx
subq $8,%rsp
movl $0,%eax
call bw_append_bs
addq $8,%rsp

Obviously GHC’s code generator has some room to improve, but the main thrust is exactly right. An equivalent C++ API would generate much the same kind of code, modulo the poor instruction and register selection, which overall doesn’t matter too much here.

In almost all situations, Haskell isn’t going to be as fast as straightforward C, but with some work and appropriate use of FFI, it’s possible to come close.

Data.BufferBuilder.Utf8

Once we had an API to safely and efficiently build up buffers of bytes, we wanted to build safe APIs on top for constructing valid UTF-8 buffers and valid JSON.

Utf8Builder is a newtype around BufferBuilder with a restricted API. If you only call safe functions in Data.BufferBuilder.Utf8, the result is guaranteed to be valid UTF-8. Unsafe functions are provided for when you know precisely what you’re doing.

Data.BufferBuilder.Json

Data.BufferBuilder.Json is built on top of Data.BufferBuilder.Utf8. Data.BufferBuilder.Json’s Value type is a newtype around Utf8Builder, meaning there’s no Aeson-style AST. Each Value simply knows how to write itself into an output buffer. Just like how the safe Utf8Builder functions guarantee the output is legal UTF-8, the safe JsonBuilder functions guarantee (almost) that the output is a legal JSON document. (There are a couple caveats, see the documentation for details.)

I suspect Data.BufferBuilder.Json is approaching the limit of how fast a JSON encoder can be. And thanks to the beauty of Haskell, it’s convenient and safe!

If you’re using Aeson and encoding performance matters to you, give BufferBuilder a shot!

Code Reviews: Follow the Data

by Chad Austin
Jan 24 15

This is a mirror of the corresponding post at the IMVU Engineering Blog.

After years of reviewing other people’s code, I’d like to share a couple practices that improve the effectiveness of code reviews.

Why Review Code?

First, why review code at all? There are a few reasons:

  • Catching bugs
  • Enforce stylistic consistency with the rest of the code
  • Finding opportunities to share code across systems
  • Helping new engineers spin up with the team and project
  • Helping API authors see actual problems that API consumers face
  • Maintain the health of the system overall

It seems most people reach for one of two standard techniques when reviewing code; they either review diffs as they arrive or they review every change in one large chunk. We’ve used both techniques, but I find there’s a more effective way to spend code review time.

Reviewing Diffs

It seems the default code review technique for most people is to sign up for commit emails or proposed patches and read every diff as it goes by. This has some benefits – it’s nice to see when someone is working in an area or that a library had a deficiency needing correction. However, on a large application, diffs become blinding. When all you see is streams of diffs, you can lose sight of how the application’s architecture is evolving.

I’ve ended up in situations where every diff looked perfectly reasonable but, when examining the application at a higher level, key system invariants had been broken.

In addition, diffs tends to emphasize small, less important details over more important integration and design risks. I’ve noticed that, when people only review diffs, they tend to point out things like whitespace style, how iteration over arrays is expressed, and the names of local variables. In some codebases these are important, but higher-level structure is often much more important over the life of the code.

Reviewing diffs can also result in wasted work. Perhaps someone is iterating towards a solution. The code reviewer may waste time reviewing code that its author is intending to rework anyway.

Reviewing Everything

Less often, I’ve seen another code review approach similar to reviewing diffs, but on entire bodies of work at a time. This approach can work, but it’s often mindnumbing. See, there are two types of code: core, foundational code and leaf code. Foundational code sits beneath and between everything else in the application. It’s important that it be correct, extensible, and maintainable. Leaf code is the specific functionality a feature needs. It is likely to be used in a single place and may never be touched again. Leaf code is not that interesting, so most of your code review energy should go towards the core pieces. Reviewing all the code in a project or user story mixes the leaf code in with the foundational code and makes it harder see exactly what’s going on.

I think there’s a better way to run code reviews. It’s not as boring, tends to catch important changes to core systems, and is fairly efficient in terms of time spent.

Follow the Data

My preferred technique for reviewing code is to trace data as it flows through the system. This should be done after a meaningful, but not TOO large, body of work. You want about as much code as you can review in an hour: perhaps more than a user story, but less than an entire feature. Start with a single piece of data, say, some text entered on a website form. Then, trace that data all the way through the system to the output. This includes any network protocols, transformation functions, text encoding, decoding, storage in databases, caching, and escaping.

Following data through the code makes its high-level structure apparent. After all, code only exists to transform data. You may even notice scenarios where two transformations can be folded into one. Or perhaps eliminated entirely — sometimes abstraction adds no value at all.

This style of code review frequently covers code that wasn’t written by the person or team that initiated the code review. But that’s okay! It helps people get a bigger picture, and if the goal is to maintain overall system health, new code and existing shouldn’t be treated differently.

It’s also perfectly fine for the code review not to cover every new function. You’ll likely hit most of them while tracing the data (otherwise the functions would be dead code) but it’s better to emphasize the main flows. Once the code’s high-level structure is apparent, it’s usually clear which functions are more important than others.

After experimenting with various code review techniques, this approach has been the most effective and reliable over time. Make sure code reviews are somewhat frequent, however. After completion of every “project” or “story” or “module” or whatever, sit down for an hour with the code’s authors and appropriate tech leads and review the code. If the code review takes longer than an hour, people become too fatigued to add value.

Handling Code Review Follow-Up Tasks

At IMVU in particular, as we’re reviewing the code, someone will rapidly take notes into a shared document. The purpose of the review meeting is to review the code, not discuss the appropriate follow-up actions. It’s important not to interrupt the flow of the review meeting with a drawn-out discussion about what to do about one particular issue.

After the meeting, the team leads should categorize follow-up tasks into one of three categories:

  1. Do it right now. Usually tiny tweaks, for example: rename a function, call a different API, delete some commented-out code, move a function to a different file.
  2. Do it at the top of the next iteration. This is for small or medium-sized tasks that are worth doing. Examples: fix a bug, rework an API a bit, change an important but not-yet-ubiquitous file format.
  3. Would be nice someday. Delete these tasks. Don’t put them in a backlog. Maybe mention them to a research or infrastructure team. Example: “It would be great if our job scheduling system could specify dependencies declaratively.”

Nothing should float around on an amorphous backlog. If they are important, they’ll come up again. Plus, it’s very tempting to say “We’ll get to it” but you never will, and even if you have time, nobody will have context. So either get it done right away or be honest with yourself and consciously drop it.

Now go and review some code! :)

My Political Views

by Chad Austin
Dec 30 14

I lean towards empiricism. We use empirical evidence to judge the effectiveness of drugs and the safety of our food. Yet we enact legislation based on wishful thinking, hope, and what feels right. Most of my standpoints stem from what has generally been shown to work — though I also support social experiments like The Kansas Experiment.

Education is critical for the country’s long-term health. Higher education is important but preschool, per dollar, is at least equally valuable. I support public funding of preschool, especially in lower-income settings. College education is also valuable, but I actually think it’s overpriced and trade schools and apprenticeships ought to come back into vogue. In addition, much of the cost of college education appears to stem from the increase in “staff costs” such as health care, as teaching is a lower-leverage activity than factory production or software development.

Science education in particular is critical. There appears to be a growing antiscience movement seeded from the far right that jeopardizes the USA’s future as a world leader in innovation.

Public statements made by politicians should be publicly fact-checked and advertised as such. False advertising is illegal for product advertisements – why is it not illegal for those running for office? It’s disturbing how easily humans are swayed by information that is not true, even after it’s been shown to be untrue. This is my biggest fear about our modern hyper-connected world.

Solving energy is critical for a sustainable world. We should continue investing in fusion just in case it works. I support the development and maintenance of a nuclear power infrastructure as it’s cleaner than coal. It’s a good idea to continue investment into renewable energy, as it’s quite viable and hydrocarbon externalities are not priced into the market like they are with renewables.

I do think that free markets are effective at optimizing efficiency. However, they must be monitored every so often because corporations love to form monopolies and/or capture their regulators. Effective regulation can prevent monopoly formation and enable private markets to succeed. Consider the Swiss healthcare system and UK forcing British Telecom to open its wires.

If legislation has a very small effect, it should not be enacted.

Government systems should maximize transparency and checks and balances to mitigate the inevitable long-term drift towards corruption.

I believe that a solid and practical understanding of systems theory is one of western society’s biggest blind spots. I might go as far as saying systems thinking is a new literacy. As our world becomes increasingly connected and interdependent, systems theory’s relevance will increase.

Empathy is key. In general, people either have good intentions or believe they have good intentions (is there a difference?). Trying to understand their viewpoints is a good idea. (Though there’s a point where you have to give up. Once, during a debate, my counterpart literally said “I’m sorry, I won’t read that information. It’s against my beliefs.”)

The US Department of Defense is dramatically over-funded. It’s totally not clear that meddling in the affairs of other countries has been beneficial to us, especially relative to the enormous cost. I’d love to see a portion of the DOD’s budget redirected towards the NSF and other basic research organizations. Basic research pays long-term dividends.

I think it’s worth preserving the environment. The environment is a broad concern over time and space, thus should be managed at a higher level than individuals, local communities, or corporations. The cost of environmental regulation is peanuts compared to the long-term benefits. [citation needed ;)]

All people should have equal basic rights. This includes people of any race, gender, socioeconomic status, sexual orientation, age, or ability.

Immigration policy should be relaxed, especially to skilled immigrants.

We should strive to demonstrate, through our actions, character, wisdom, and culture, that we are a great nation. I’m not the most patriotic person, but there is value in striving to be great in competition with others.

More Thoughts on Haskell

by Chad Austin
Dec 29 14

I’d like to follow up on my previous post with some points that are more nuanced and probably only make sense to people who’ve written Haskell before.

Syntax

Some syntax elements in Haskell were annoying at first, but I adjusted to them quite easily, and I now believe my annoyance was simply unfamiliarity. Separating arguments by spaces and using the $ operator (that is, a $ b c as shorthand for a (b c) comes naturally now. The only thing I still get wrong is operator precedence.

Terminology

Regarding category theory terminology: when I even mention words like functor or monad, I’ve seen engineers’ eyes glaze over. They instantly think “Oh, that’s too complicated to understand.” I swear, if Functor was named Mappable and Monad was named Chainable or something like that, it would make Haskell seem much less intimidating to beginners. Explaining monads doesn’t require any fancy tutorials or stretch analogies. Similarly, explaining functors doesn’t require using the words “lifting” or “context”. It’s as simple as “Well, you know how you can map across a list in every other language? Well, consider Maybe as a list of zero or one elements. You can map across that too. What about a promise or future? You can map across that too. Functions themselves can be mapped, transforming their return values. Anything that can be mapped is a functor.”

In general, I do think it’s a good idea for programmers to use the same terminology as mathematicians, as they’re generally more rigorous and precise, but… I’ll just quote An Introduction to Category Theory:

In her charming Italian style she asked me (instructed me) to cut out all the jokes. This was quite difficult since some of the official categorical terminology is a joke, but I have done my best.

Modules

This is a relatively minor nit: Haskell module import syntax is cluttered. I always feel like import qualified Data.Vector as Vector would be better written in Python style: from Data import Vector. This would have the side benefit of mitigating a common, and in my opinion unfortunate, pattern you see in Haskell code: importing modules by abbreviations. import qualified Data.Vector as V. For some common modules, like Data.ByteString as BS and Data.ByteString.Char8 as BSC, the abbreviations are common enough that everyone knows in context what module has been imported. However, for your own modules, you should import with explicit, non-abbreviated names, so it’s clear in context which module is being referenced.

Laziness

I’m unsure about laziness by default. Laziness can be really great, and it’s somewhat cheap in Haskell, but there are many situations where, if you’re going to compute anything for a data structure, you might as well compute it all right then, while the caches are still hot. The theoretical benefits of only computing the values necessary have a nontrivial cost: lazy thunked data structures have branching and dereferencing costs that strict languages can avoid.

Streaming and Chunking

I feel like Haskell streaming and chunking libraries, like Conduit, have the same problem as laziness for most reasonably-sized data structures. If your machine has 32 GiB of RAM and 50 GB/s of memory bandwidth, who cares if you allocate 100 MB of data and dump it all on a socket in one go. Chunking only matters much if your peak memory usage times concurrency doesn’t fit. I’ve seen similar performance issues with with Python 2′s xrange() function. range() is frequently faster in context because it avoids the extra iteration overhead.

Partial Functions

Some people have vehemently argued that pattern matching is so superior to conditionals and dereferencing than Java and C++ are fundamentally flawed. That is:

case p of
  Nothing -> bar
  Just v -> foo v

is safer than:

if (ptr) {
  ptr->foo();
} else {
  bar();
}

I have some sympathy for this argument. Pattern matching syntax does mean that the dereferenced value is only visible in the scope where it is valid. However, the real problem with languages like C++ and Java is not the lack of pervasive pattern matching. It’s that Java and C++ don’t have a way to distinguish in the type system between a boxed value (aka T*) and a potentially-null boxed value (aka T*).

Pattern matching by itself is nice, but the real benefit comes from its combination with sum types.

What is the Haskell equivalent of NullPointerException? Partial functions!

Let’s imagine I wrote the above code as follows, except assuming p is not Nothing:

foo $ fromJust p

If p is Nothing, this code throws an error. fromJust is a partial function in that, when the input has an unexpected value, it throws an error. Haskell has many partial functions.

In your code, you should strive to write total functions. However, I am of the opinion that Haskell should distinguish, in the type system, between the two types of bottom. There are two ways that a function in Haskell can have no value. It can either throw an exception or it can enter an infinite loop. Partial functions throw exceptions.

In practice, infinite loops are rare (except through accidental recursive references). However, it would be useful to disallow certain classes of pure code from throwing an exceptions. This would make it illegal for pure code to call partial functions. However, certain types of errors could still occur, especially out of memory errors and stack overflows. Thus, the language would have to draw a distinction between synchronous errors such as partial matches and asynchronous errors such as resource exhaustion.

I can understand why the language adopted a simpler model, even though it would be nice to guarantee that some pure functions cannot throw non-resource-exhaustion errors.

Purity is a vague concept. Some useful attributes can be applied to a call graph: “has no observable side effects”. “does not allocate heap memory”. “fits within a defined resource budget”. “does not throw exceptions.” I’m not aware of any mainstream languages that distinguish between these notions of purity.

Thoughts on Haskell

by Chad Austin
Dec 28 14

My colleague Andy recently wrote about what it’s like to use Haskell at IMVU. Since then, I’ve started writing some Haskell, and I wanted to share my thoughts on the language and ecosystem.

I am by no means a language expert. But I’ve gotten over the beginner’s hump and have written a fair amount of real code, so I have a decent sense of what the idioms are.

Haskell is Awesome

Overall, I find Haskell to be great. It’s not that fun to write — the common complaint that Haskell makes you think a lot up front is totally true — but once the code is written it is so easy to safely refactor. This is likely due to type inference, Haskell’s brevity, and the ease of creating new, lightweight types. At the technology strategy level, I value long-term maintainability and evolvability over ease of writing new code, so Haskell has a big advantage here.

With Warp and Wai, Haskell is particularly excellent for writing HTTP services. (Michael Snoyman generally has solid design taste and performance sense.) In just a few lines of code, you can write a scalable, concurrent, low-latency HTTP server. Better yet, you can deploy this HTTP server to your production servers as a static binary: no fancy configuration or package management necessary!

The GHC runtime is both innovative and mature. It has excellent concurrency support. With STM, avoiding deadlocks in concurrent algorithm is easy. Green threads and sparks enable spinning off hundreds of thousands of lightweight jobs, soaking up any available CPU capacity. The parallel I/O manager makes it efficient to concurrently handle multiple requests in a single process.

GHC’s benefits go beyond the language and runtime: with GHC’s foreign function interface, it’s easy to call into C functions from Haskell.

Enough about the runtime, let me discuss the language a bit.

When digging into an unfamiliar codebase, the hardest part is building an understanding of the possible states and data flows. That is, understanding the big picture. Sum types help a lot here. They explicitly denote the possible states. Pattern matching, plus warnings for partial matches, make sure that all states are properly handled, largely preventing any kind of accidental null pointer errors. (Remember: Maybe a is a sum type: Just a | Nothing.)

Haskell draws a sharp line between pure code and effectful code. This makes it much easier to understand how information flows through the code. When you see a pure function, you know it is only calculating a result, and thus only need to read the type signature to understand its effect. Also, separating pure and effectful code prevents a common class of performance regression: accidentally adding expensive I/O to otherwise pure data transformation code. (The compiler also benefits from knowledge of which functions are pure: it can evaluate in arbitrary order, lift common function calls to outer scopes, and apply rewrite rules.)

In addition, through custom Monad types, Haskell makes it easy to restrict effects to specific contexts. For example, you can enforce rules like “No MySQL queries in Redis transactions” or “No arbitrary I/O in HTTP request handlers, only specific restricted actions.” By limiting your code to a restricted interface, it’s even possible to guarantee that your unit tests will be deterministic. This is a real problem in languages like Python, C++, and JavaScript, where a single unmocked call to “get the current time” can make your tests intermittent.

Monads, in general, let you use the same syntax for imperative code, asynchronous code, code with automatic error handling. You don’t need generators or new keywords to have convenient syntax for asynchronous programs. Monads and ‘do sugar’ by themselves are sufficient. See slide 121 and beyond in Scott Wlaschin’s excellent Functional Design Patterns slides.

Type classes are awesome too! Traditional OO languages allow polymorphism through the first argument (this pointer, vtable). But type classes allow polymorphism on single values (see Data.Default) and across multiple arguments, even return values. See Eq.

Type classes can be created independently from their resident types. Types can be specified independently from type class implementations. This allows layering concepts into third-party libraries that weren’t designed with them in mind.

Type classes are especially powerful when you start building application frameworks with them. For example, consider the type of HTTP request handlers:

myRequestHandler :: ToHTTPResponse a => HTTPRequest -> a

That is, a request handler is a function that takes an HTTP request and returns any value that can be converted to an HTTP response. This means request handlers don’t themselves need to build the HTTP response: just return any compatible object and let the framework convert.

Haskell is Not Awesome

Haskell is not all roses. In fact, I’m not sure it can even become the Next Big Language. And for a rather sad, superficial reason: the syntax is a too idiosyncratic compared to every other mainstream language. I’m not talking about lets, operators, or separating functions from arguments by whitespace. Those things can be learned pretty quickly.

My biggest syntax problem with Haskell is that there is too much variety. I’m a strong believer in the Style is Substance thesis, which is that our precious brain cells and decision-making units shouldn’t be spent on stylistic choices — instead, we should all have a fairly consistent style and spend our energy on more important decisions.

Haskell, on the other hand, has WAY too many nerd knobs. You can use “let … in …” syntax, “expr … where …” syntax, do syntax (even in non-monadic contexts). You can write functions with explicit arguments or in point-free style.

In your own code, or even within an organization, you can define a coding style and aggressively follow it. But if Haskell syntax was as consistent and tight as, for example, Java or Python, it would have a better shot at being mainstream.

I have some other syntax nits too: the verbosity of module imports is annoying and some common libraries (Aeson) use too many custom operators.

Some extensions should definitely be promoted to the core language by now. String should basically never be used in production code (more on that later), and ByteString and Text aren’t usable without the OverloadedStrings extension, which allows specifying ByteString and Text values with string literals. OverloadedLists should also be enabled. Really, any literal should be overloaded. It’s very convenient to be able to use existing literal syntax to initialize values of many different types.

“ScopedTypeVariables simplifies real code. TupleSections are commonly-used. There’s little risk in folding both into the core language. I feel like RecordWildCards should also be enabled by default, but records are a mess in Haskell and that part of the language is actively evolving. ExistentialQuantification is also critical in most real codebases.

Now let’s talk about the Prelude. The Prelude probably made sense in the 80s and 90s when Haskell was a simple research language, but parts of it are showing its age. String should never be used in production code. A lazy linked list of 32-bit Unicode code points is… elegant? … but pretty much the least efficient data structure for text. Sadly, a lot of underlying APIs in Haskell use String, just because it’s standard and the original data type for text.

The Num typeclass is a mess. Just because a value can be added does not also mean it can be multiplied or has an absolute value or can be converted from a numeric program literal. Generally, type classes should be small and precise, and Num is none of those. The problems with Num are well-documented.

The Prelude in general should use more type classes. This is a point of contention within the Haskell community. My opinion, however, falls squarely on the “more polymorphic functions!” side of the argument. It really sucks to have to teach people about both map and fmap when they’re the same function. It generally goes like this:

“What is fmap?”

“It’s generalized map. It works on Maybe and Vector and HashMap.”

“Why doesn’t map work on those?”

“Well, some people in the community believe that type classes make learning harder, so the Prelude should stick to less general function definitions.”

I haven’t seen any actual human factors research that says type classes make learning the language harder. And you can’t really learn Haskell without understanding type classes, so a monomorphic Prelude simply delays some education that is necessary anyway.

The C++ standard library is a good counterexample: you can use std::vector without understanding how to write templates. Python too. You can call len on any container without having to know how to implement the __len__ protocol. Haskell could have this same exact feature if people would stop inventing usability reasons not to. See Michael Snoyman’s work on ClassyPrelude.

My coworker, Jonathan Fischoff, makes a similar point on Reddit.

This brings up a higher-level issue with the design of most Haskell APIs: little regard is paid to the number of symbols exposed. Ideal APIs are memorable and predictable. Once you know how to map across any collection, you should know how to map across all collections. Once you know how to calculate the length of a list, you should know how to calculate the length of a vector or map. In Haskell, today, you need separate functions for each of those (e.g. Prelude.length vs. Data.Vector.length vs. Data.HashMap.Strict.size). There is so much to remember that you need about six browser tabs open to get any useful work done.

In summary: type classes are an excellent language feature. But they’re not used enough or well enough in the base Haskell libraries. And that’s all I’ll say on the subject for now. :)

Now I will discuss integrating with libraries.

I think it’s time that certain libraries get promoted into the standard: ByteString, Data.Maybe, Data.Map, Data.Set, Data.Text. It’s not realistic to write a real application without using them, so they should be made ubiquitous.

Finally, I have one more major complaint about the Haskell ecosystem. Fortunately, it’s a solvable one. Cabal sucks. I don’t know enough about it to say specifically why, but literally every time I come back to our build system after time away, something goes horribly wrong. I end up having to manually deleted generated files, or manually uninstall some packages. The error messages are always inscrutable. Sometimes I have to pull in one of the Cabal experts at the company and it’s a coin flip as to whether they’ll be able to figure it out.

I assume Cabal is fixable, but it’s somewhat boggling to me that a community that thinks so much about precision and mathematical laws and reliability can produce a tool so flaky.

Haskell is Worth Using

That was a long list of complaints – longer than I spent on the upsides. :) But I want to be clear: I still think Haskell is pretty great. It’s certainly worth learning. It’s also a great platform for writing HTTP services. Unless someone with GvR-esque taste attacks the syntax, Prelude, and standard libraries with gusto, Haskell likely won’t become as popular as Java or PHP or Python. Nonetheless, Haskell’s on a rapid upward trajectory, and as the community becomes less academic and more practical, many of these problems will get addressed. Even if the community is slow to adapt, as with any language, you can define your own internal conventions and base libraries, including your own Prelude if desired.

Update on Sorting Translucent Triangles

by Chad Austin
Dec 20 14

In my last article on the subject, I searched for an algorithm to sort translucent triangle meshes that ran in less than quadratic time. Sadly, on large organic meshes, the proposed algorithm didn’t perform very well. In particular, when triangles in one continuous patch of geometry sort differently, the individual triangles of the mesh become visible, which is visually worse than not sorting at all.

Thus, I gave up on that approach and tried some others. If the algorithm has to be quadratic, then we have some options:

Topological Sort

First, build a digraph that represents whether a triangle should be rendered before another.
A triangle A should be rendered before triangle B if A is in front of B and B is facing A but A is not facing B.

Once the graph is built, cycles must be broken, ideally using a heuristic that minimizes visual errors. (If a set of triangles form a cycle, there is no ordering that has no errors.) Finally, once all cycles are broken, the graph can be topologically sorted using a depth-first traversal.

This algorithm is O(n^2) in CPU and O(n^2) in memory.

Topologically sorting works well on geometric meshes with a clear triangle ordering. But on organic meshes, there are a great number of cycles, and the heuristic cycle breaking produces visual artifacts where individual triangles become visible.

Coverage Sort

For all pairs of triangles (A, B), if A is in front of B, calculate the form factor of A onto B. Form factor between triangles is fairly hard to calculate, so I tried a simple approximation using area of covering triangle, distance between triangle centers, and incident angles. Then, using a comparison sort, order the triangles such that triangles covered the most are first in the mesh.

This algorithm is O(n^2) in CPU and O(n) in memory.

I am not sure whether my heuristic was that good, but this also produced somewhat weak results on large organic meshes.

Sort by Distance and Angle to Center

This is a cheapo algorithm that works pretty well on convex shapes. Sort all triangles by the dot product of the triangle’s normal vector and the vector from the mesh’s center to the triangle’s center.

It’s pretty easy to come up with cases where this algorithm will sort incorrectly, but for most convex meshes it does well. More importantly, errors occur across entire slabs, which looks better than errors showing individual triangles.

Jon Watte’s “Coverage Peeling” Algorithm

Jon’s original algorithm works by building a graph of which triangles are in front of others, just like the topological sort above. It also accumulates a per-triangle-pair coverage metric, and accumulates them into a per-triangle coverage sum.

Then it peels triangles off, one at a time, by finding the triangle with the lowest coverage value. After peeling a triangle off, it reduces coverage values by the corresponding amount.

This algorithm works well, even in the presence of triangle cycles, but it has a fairly high constant factor. However, it runs out of memory on large meshes where the topological sort doesn’t.

I still plan to see if I can get an implementation to complete on a 60,000 triangle mesh in a few seconds and under a few GB of memory.

Summary

Algorithm Time Complexity Memory Constant Factors Errors
Topological Sort O(n^2) O(n^2) Low Per-triangle
Coverage Sort O(n^2) O(n) Low Per-triangle
Distance and Angle Sort O(n lg n) O(n) Low Consistent regions of the mesh
Coverage Peeling O(n^2) O(n^2) High Per-triangle, but fewer than the other algorithms

The Parsing Problem

by Chad Austin
Nov 20 14

Computers are insanely wide these days.  Not only can out-of-order cores issue and retire several instructions per clock cycle, but vector instructions get wider and wider.  SSE has 128-bit registers.  AVX widened that to 256 bits.  AVX2 widens the vector registers further — to 512 bits!  On top of that, modern CPUs have several cores, and some of those CPUs support running a couple hardware threads per core.

Given a parallelizable problem and a bit of work, it’s amazing how much computation you can achieve per clock.

However, there’s a class of problems that don’t benefit much, if at all, from instruction-level parallelism or vectorization.  I call this “the parsing problem”.

Consider a high-performance JSON parser like sajson or rapidjson.  Fundamentally, a JSON parser converts a stream of bytes into a higher-level structure.  The algorithm looks something like:

read a byte
switch on byte value to see whether we're opening an object, closing an object, opening or closing a string, etc.

This kind of byte-by-byte, branch-heavy code is not amenable to vectorization, especially for particularly dense structure like JSON.  It IS possible to accelerate XML parsers with SIMD: see Parabix.

Parabix uses SIMD to rapidly scan characters for the next XML closing tag.  Similar techniques can be applied to parsing JSON, but minified JSON has a higher ratio of structural characters to text than XML.

In my sajson benchmarks, my fancy new Haswell 4771 (3.9 GHz during benchmark run) is only a few times faster than my years-old, in-order Atom server, clocked at 2.1 GHz.

The only point of this article is that some problems lend themselves to taking advantage of wide vector units, instruction-level parallelism, and multithreading.  Others, like JSON parsing, don’t.  :)

The Web is a Frustrating Application Platform

by Chad Austin
Nov 19 14

Preface: In this article, I am not talking about the web as the document-based hypermedia system. I’m talking about the web platform as a potential replacement for desktop and mobile applications.

I’ve spent the last few years trying to bring a rich client application to the web platform. The web has enormous promise. It runs almost everywhere. It’s secure. It’s seamless – customers click a link and land in your application. Deployment doesn’t require any installers or code signing tools. HTML and CSS are fantastic for UIs, flows, transitions, typography, and layout. HTTP has excellent tooling.

At the same time, the web is enormously frustrating. I cut my teeth on Windows and Linux. I love having rich control over the quality of the experience delivered to customers. For comparison, on Windows and Linux:

  • APIs are low-level. Higher-level functionality is built by the community into user-mode libraries. The platform vendor provides capabilities and the community provides convenience. A web analogue would be jQuery on top of the DOM, or Three.js on top of WebGL.
  • APIs are capable. With few exceptions, the entire functionality of the machine is accessible to programs. There are a few security concerns here, but in many cases, it’s possible to provide capability without sacrificing security.
  • For the sake of performance and flexibility, native platform vendors are somewhat willing to leave a bit of undefined behavior on the table. The specific behavior of an allocator, for example, is left undefined between releases.

Before I get into what I think has to improve at a high level, let me describe some of my specific complaints about the web platform:

  • On WebGL, there’s no way to detect available VRAM. The current recommendation appears to be to use a “reasonable amount” of VRAM and back off if you run into problems…
  • Web Workers had the same problem – there was no way to query the number of available cores. I believe an API is coming that finally exposes this, however.
  • Lack of direct access to the browser’s message loop. For some bizarre reason, both Mozilla and Google were highly resistant to implementing setImmediate, resulting in some… interesting… polyfills.
  • STILL no fast typed array memcpy! This is just ridiculous. The best option I’ve seen allocates a new aliasing Uint8Array object so it can call .set(). The proposed .copyWithin() doesn’t allow copying between two TypedArrays, which means you can’t use it for copying XMLHttpRequest data into the asm.js heap.
  • CORS, thanks to preflight OPTIONS requests, turns out to be quite expensive to use in practice. For every cross-origin request with credentials or using custom headers, you pay an extra network round-trip for the preflight OPTIONS request. For high performance, you’ll want to keep your JSON services on the same domain as your site and bypass CORS entirely. If you don’t need credentials, avoid custom headers and stick with query parameters.
  • I can’t find the mailing list thread, but there was a discussion where it came up that ES6 Promises cannot be implemented in user code, and es-discuss folks considered that acceptable.
  • JavaScript stack traces are not standardized and may never be so, as there are apparently security concerns with allowing JavaScript code to inspect the names of functions higher on the stack. In general, handling and reporting application errors on the web is a disaster.
  • No weak references. The web API community is resistant to anything that could possibly expose garbage collection implementation details.
  • No way to specify priority on XMLHttpRequests, for any supported protocols (HTTP, SPDY, HTTP/2). If you read this blog, you already know the sordid details.
  • vector minimum and maximum in SIMD.js will likely sacrifice performance for the sake of cross-platform consistent behavior in the presence of NaNs. Personally, I’d prefer this be left as undefined behavior for maximum performance, but JavaScript convention is that every operation is exactly defined, to the point that all browsers have, in practice, attempted to standardize the order of iteration through hash tables.
  • No efficient way to encode and decode between Strings and ArrayBuffer. See the Twitter thread.

Why is the web like this? Why isn’t the web more like a typical native platform? My hypothesis follows.

Software and standards are a product of time and the pressures acting upon them. Browser vendors have been burned in the past by trying to advance the platform in a backwards-incompatible way. Thus, there is enormous fear of exposing any undefined behavior. Couple that with security concerns (some well-founded, some not), and you have a recipe for APIs that don’t actually expose the full set of functionality required for rich, high-performance applications.

Moreover, there is widespread ambiguity on whether the web should provide high-level or low-level APIs. In mailing list discussions, it seems generally expected that web programmers will call JavaScript APIs directly. Yet, to expose maximum functionality and performance, the APIs should be low-level. This tension was a large source of the discussion around my request to get direct access to protocol priorities in XMLHttpRequest.

Couple all of this with some strong personalities, and you end up with something that’s actually rather hostile to systems programmers like myself.

I feel the web standards community is disempowering to developers. Paraphrasing: “You don’t want text encodings.” “Let’s not even bother supporting HTTP/1.” “Weak references are too dangerous to put in peoples’ hands.” [private communication]

Part of me wishes browser vendors would stop playing nice with each other and instead court developers as aggressively as possible, even if that means new incompatible APIs. For example, perhaps asm.js would have never existed without Native Client. :)

I am not alone in this point of view! I recently discovered the Extensible Web Manifesto which aims to correct many of my gripes by focusing on low-level APIs that advance the capabilities of the web platform.

I still think the web has promise. But now I see that the problem isn’t that browsers have to catch up with native platform capabilities. Instead, it’s politics. The web standards authors need to focus on exposing raw functionality, even if it allows programmers to shoot themselves in the foot.

It’s up to you to decide whether the web is the right platform for your app. But I never thought I would say “I miss Microsoft’s low-level APIs.” :)

Update on HTTP/2 Priority

by Chad Austin
Nov 16 14

After some great offline discussions with various folks involved in the specification of HTTP/2 priority, I wrote an email to the list expressing my concern about the design as written.

In response, Martin Thomson agreed with my concerns and proposed a small tweak to the spec that allows for the allocation of dummy nodes, which enables rough approximation of a priority hierarchy.

The tweak makes it possible to send a PRIORITY frame to a stream in any state. Given two streams {A, B} at a higher priority than streams {C, D}, this allows construction of a tree as follows:

A{w=256} -> 0
B{w=256} -> 0
Dummy{w=1} -> 0
C{w=256} -> Dummy
D{w=256} -> Dummy

We can probably assume that a well-behaved server would not spend any resources on the Dummy subtree — since it has weight=1 — until A and B are completed, which places C and D at a lower priority.

This protocol tweak appears to be headed for acceptance. Here are the relevant meeting minutes.

Offline, several people expressed general nervousness about HTTP/2 priority, as there is no production experience with a tree-based priority model. I was told that, should HTTP/2 priority not work out in practice, it will be deprecated and some kind of alternate protocol or extension will be proposed instead.

Ultimately, I’m thrilled that, with Martin’s tweak, it’s plausible that HTTP/2 priority can solve most use cases. I do wish I’d gotten involved in this topic nine months ago, as I would have had a realistic shot at getting Microsoft’s proposal adopted. At this point, the standard will ship with only minor changes.