20th of April, 2022
For some weird reason I've always enjoyed the topic of performance and
optimizations tremendously. Especially trying to understand why some compiler
does various optimizations on an instruction level to the hardware. It's quite
a trip to see years of expertise on hardware design and how it works in modern
computing. But recently that got me wondering is there really a point to that?
Now don't get me wrong, less instructions usually means slightly faster
computation, and that's a good thing, right? But considering modern hardware,
is that necessary? Should we be concerned about the fact that our compilers
work that hard to minimize the amount of instructions in the output of the
code? Of course that would make sense if we would be living in a world where
computation would still be slow (I don't know, 50s? 70s?).
These kind of actions to minimize the amount of instructions can easily lead
up to some funky situations where familiar operations start behaving
unintuitively. For example, common operations like '+' or '<'. In these kind
of situations, if the program happens to behave incorrectly, often it's
considered to be programmer's fault.
In modern hardware, computations are more or less free, and we almost flirt
with the idea of concrete Turing machine with infinite amount of
memory. Shouldn't the fact that we mostly use this kind of hardware be
reflected in the programming languages also? Especially if we consider the
fact that one cache miss can easily lead up to a way bigger run-time compared
to hundreds of add instructions. If those extra instructions don't increase
the size of the data or the program itself, what's wrong with these extra
instructions? Especially considering the fact that we could add quite a bit of
run-time computation to the program without affecting too much the total
running time of the program.
So instead of focusing on minimizing instructions in the output of the
language, we could focus on improving the semantics of the language and pretty
much completely remove these common hard-to-find errors from our
software. This is especially present in many language where we have multiple
different features that does more or less the same thing but they might have
slight difference when it comes to the performance.
When we start having multiple of these different features that work pretty
much the same way to each other, languages easily start having excess amount
of features. Using the large amount various features in one code base can
easily lead to complex and hard to understand programs. This then often leads
that the used features are limited in one code base, so that programmers in
the project only use common subset of the language.
Great example in "modern" programming languages of this is C++ regular
vs. virtual functions. These kind of features easily lead to a fact that
programmers start wasting their precious time on different micro-optimizations
which usually in the grand scheme of things aren't really
worthwhile. Especially considering the fact that when we start to focus on
these kind of optimizations, we can easily loose focus from the stuff that
really matters, large-scale behavior of the program.
Can we fix this anyway? Probably no, since we are already so invested in these
kind of languages. We can point fingers to various places and blame them that
we are in this situation. New programming language doesn't really solve this
issue since we just can't rewrite everything in it, and the migration would be
a really slow process. Can we fix existing languages? Probably no, which is
why we rely on various external tools to analyze and check our programs and
various conventions to follow so that we are able to write the best code
possible in these languages.
So modern computing is very exciting but it also can be a mess…
6th of February, 2022
First, I would like to say I think Kubernetes is an excellent platform for its
intended purposes. It provides excellent fault-tolerance all over the cluster,
a fast and easy way to run updates on your deployments, great tools for
managing services, volumes, metrics, and more, each having its own lifecycle
to manage. Also, implementing your tooling by extending the Kubernetes' API is
a trivial task, so you can easily leverage the great tooling to make your own
for whatever you might need.
Today it's also effortless to spin up a Kubernetes cluster with various
installers and different managed options. While being very complex, it's still
a step closer to the idea of "just run my code and make it work". Also, with
containers in the picture, we are pretty close to the magical situation where
we actually can run the same application similarly on the laptop and in one
For me, issues start rising when we begin using Kubernetes for something other
than its intended purposes. While I don't have any statistics on this, I have
a pretty strong gut feeling that most of the people running Kubernetes are
using it as a glorified scheduler for placing containers on nodes as fast as
possible. While it's an excellent and overall pretty easy tool to use for
orchestrating containers, its fundamental purpose is to orchestrate anything
crucial to your infrastructure like network, storage, and other dependencies.
Kubernetes allows complete user freedom to run your infrastructure as you see
fit. Despite sounding like a cliche, this kind of freedom can bear huge
responsibilities. I would dare to say that most developers and system
administrators don't want to make these decisions. What if, at some point in
the development, you would wish to change your networking interface or maybe
some dynamic storage provider? Can you even do such a thing in that stage of
development if the decision was made before you even had anything running in
Kelsey Hightower put it nicely a while back when he described Kubernetes isn't
meant for being a developer platform but a framework for creating
platforms. So while it definitely can work as a developer platform, and
overall it's pretty easy to get started,
kubectl run and
and your good to go. That being said, all the API designs in Kubernetes are
created for clusters and how to manage these. So while containers are part of
this, there are so much more to be leveraged. So should application
developers, startups or small businesses use something like this? Probably
not, unless they are developing a platform product.
When we get into cluster management, we need to start thinking about managing
the lifecycle of everything running inside the cluster. Unfortunately, this is
also when things start to get hard. What to do if something inside the cluster
dies? What if I need to provision something dynamically? Kubernetes is pretty
good at simplifying many of these topics, but due to the complexity of things
happening behind the scenes, all this complexity cannot be simplified
Kubernetes has a high entry threshold, and it's a very complex project, but
still, way too often, I see it marketed as a simple solution for many
problems. While you can use Kubernetes in a very simple manner and get lots of
stuff done, eventually, you will hit a wall. Deploying fault-tolerant
distributed applications that are scalable against a pool of machines with
dependencies in networking, storage, and more, that's a hard
Kubernetes is built for production workloads and running infrastructure beyond
your demo application. For this reason, complexity in Kubernetes is justified
and should be approached with that mindset.
19th of January, 2022
Some time ago I wrote a short post about my my feelings towards web analytics
which were sparked due to a spike in visitors on my site (mainly coming from
Hacker News). Due to that surge, I decided to part ways completely from any
sort of tracking, since for me it was mainly a unnecessary dopamine fix rather
than anything useful.
Today I stumbled upon big news on the front of legitimacy of web analytics
from the point of view of privacy. Turns out, as most suspected, it's not so
good, at least according to Austria's data protection authority.
Basically this case dates back to invalidation of Privacy Shield data sharing
system between the EU and the US, because of overreaching US
surveillance. Turns out that many companies in US have largely ignored this
invalidation, which happened in 2020, and despite this they have still
continued to transfer data from EU to US. The Austrian DPA held that the use
of Google Analytics by an Austrian website provider led to transfers of
personal data to Google LLC in the U.S. in violation of Chapter V. of the
Future of Google Analytics in EU
In the long run, there will be two options: Either the US changes its
surveillance laws to strengthen their tech businesses, or US providers will
have to host data of European users in Europe. This kind of transcontinental
transfer is currently (as the time of writing this) only illegal Austria, but
Dutch's DPA (data protection authority) has stated that Google Analytics "may
may soon no longer be allowed".
Any case, this is great thing for privacy in EU and hopefully many more
countries would join Austria in this effort. You can follow what countries
have started to follow this at Is Google Analytics ILLEGAL in your country?
9th of January, 2022
Lately, I have dedicated a large part of my free time to audio software. I
have done this mainly out of interest in the subject due to my history in
music. But at the same time, I also thought writing audio software could be a
fun passion project or even a small business that I could work on alongside my
day job. I don't see myself replacing my current job with this, but maybe I
could dedicate 20% of my work time to it.
The world of audio software is a pretty exciting place. It involves a lot of
low-level systems stuff like signals and real-time operations, complex math at
times, and something that you can feel or at least hear. And what's great, I
don't have any background in this stuff!
Now I have programmed most of my life and played around with RTOS, but when it
comes to writing algorithms for manipulating digital signals, that's new stuff
for me. However, I have experience with the topic from the user point of view
since I have been making music for almost as long as I have programmed. This
experience involves playing instruments, how effects affect the sound, how
mixing and mastering works etc. But what do linear types have to do with any
Signals in the wild
Like I said earlier, signal processing (not necessarily just audio) is very
low-level stuff. So when it comes to working with signals in software, you
often need to work with C or C++. This is mainly due to the performant and
close to hardware nature of the languages required to handle and manipulate
signals optimally and efficiently.
Digital signal processing is also full of algorithms. Standard workflow for
people working in this industry seems to be that these applications are
prototyped on some high-level language before being produced. Often in
languages/tools like MATLAB, Octave, Mathematica, and similar very heavily
math-oriented languages and tools. Julia has appeared to grow in popularity
also in this world. These high-level languages are mainly used due to the
speed of development.
It is also not uncommon to see FPGA being used in these applications. For
several reasons: they are reconfigurable hardware, so you can tailor and
deploy on them computation units and data buses specifically designed to your
particular needs. So if you're working with digital hardware, you can't go
wrong with FPGAs. In this world, VHDL or Verilog comes in handy.
As you can see, overall, the applications tend to involve a lot of different
low-level concepts, but at the same time, high-level topics in terms of
prototyping. But, as the post's header might hint, I'm not interested in the
prototyping aspects of signal processing since I think those are all well and
good. Instead, I'm interested in having a small thought experiment on whether
the low-level elements could be improved somehow.
I would consider myself a functional programmer first and foremost, even
though I mainly write imperative and/or object-oriented code, at least
professionally. Now in my free time and in non-trivial side projects (that are
not signal processing related), I like to work with weird languages like
Haskell or Common Lisp. Unfortunately, as I mentioned above, almost all the
work in this signal processing world is written in C or C++, emphasizing the
latter. However, I completely understand why these languages are used since we
talk about real-time programming, so latency needs to be minimized.
"Real-time" can be understood that the program has to produce the correct
result but also on a certain amount of time (which varies between systems).
If we use audio processing as an example, typically, you would have some sort
of processing function in your code that would work in the audio callback:
process :: BufferRef -> ()
This function would get its callback from either a sound card or either some
input device, e.g. microphone. After it has received its callback, this block
of code (whatever might be inside it) would write the corresponding audio data
into the given buffer. Which would then be played at the speakers or
vice-versa when recording.
This procedure is basically what should happen in real-time lots of times when
we are doing audio processing. Audio software is often set up to send these
audio callbacks from a high priority "real-time" thread with a very short
latency between the callbacks, ~1-10ms (varies between systems).
To achieve this minimal latency between callbacks, you often can't rely on
stuff like garbage collection since you can't be sure when your program
launches it. I dare to say that most of the software benefits from GC
significantly, but in the audio making GC right is very hard. What makes it
hard is that if GC launches at the wrong time or the latency between callbacks
gets too large, garbage data will leak into the buffer, causing unwanted
Most other software might only see a slight latency in their computations if
they do profiling, so that might not be the end of the world, of course,
depending on your context. But in audio, you cannot let that happen since you
can literally hear that glitch, which is unforgivable.
When it comes to C or C++, I think everyone knows their foot guns that involve
memory management. Thankfully in modern C++, it's not that bad (as long as you
follow core guidelines), but there is still a lot of unnecessary baggage when
it comes to safe code in these languages.
Could there be any way we could use garbage collected language while doing
"real-time" operations and how that could be achieved?
GHC 9.0 introduced support for Linear Haskell, which can be enabled with
-XLinearTypes. One of the significant use cases for linear types is
implementing latency-sensitive real-time services and analytics jobs. As I
mentioned earlier, a major issue in this use case is GC pauses, which can
happen at arbitrary points for reasonably long periods. The problem is
exacerbated as the size of the working set increases. The goal here is to
partially or entirely eliminate garbage collection by controlling aliasing and
making memory management explicit but safe.
So what then are linear types. Henry Baker described linear types and their
benefits in his paper Lively Linear Lisp — 'Look Ma, No Garbage!' and also on
"Use-once" variables and linear objects: storage management, reflection and
multi-threading. As you can see, we are not talking about a new
topic. Basically, we are talking about types whose instances must be held in a
linear variable. A variable is linear if it's accessed exactly once in its
scope. Same for a linear object, their reference count is always 1. When we
have this safety guarantee on type-level, we can avoid synchronization and GC
and also, we could update linear objects in place since that would be
Avoiding garbage collection
So why we can avoid synchronization and GC with linear types? If we would
consider the following function as an example:
linearFunc :: a %1-> b
On their own, linear types only gives a type to functions that consume their
argument exactly once when their result is consumed precisely once. So alone,
they don't make your programs any faster or more safe for resources. Still,
they allow you to write many different optimizations and resource-safe
abstractions that weren't possible before.
First, since linear values can only be used once, these values cannot be
shared. This means that, in principle, they shouldn't be a subject to GC. But
this is very dependant on the consumer of the values since that may very well
do some sort of de-allocation on the spot. One way to mitigate this could be
to store these values to heap outside of GC's control.
While utilizing heap for these values alone would diminish the GC, it would
introduce some overhead to your program, which could increase the total
running time of your application. But if we continue to use real-time systems
as an example, this isn't necessarily a bad thing.
In real-time systems, optimizations often happen only to the worst-case
scenarios. This is because you don't really care about your latencies as long
as they stay within the particular window. But you do care that those
latencies should never go above your maximum limit, and this is primarily
where optimizations utilizing linear types could come in handy.
Practical linear types
Linear types are a blessing in GC languages if you intend to do anything
safely in the low-level world. I would like to continue this post with some
practical examples of how Haskell utilizes these types and how they can make
low-level optimizations and resources safer in your Haskell code, but that
deserves their own post.