With JSON data type it’s easy to treat Postgres as a document database, which doesn’t need strong schema. One can define a table, that has a field of a type jsonb and insert any valid JSON string (a “document”).
I’ve learned lately, that Postgres’s jsonb prohibits insertion of a valid JSON string if the string contains NULL (U+0000) character. Postgres’s own docs on JSON Types says:
RFC 7159 permits JSON strings to contain Unicode escape sequences denoted by \uXXXX. In the input function for the json type, Unicode escapes are allowed regardless of the database encoding, and are checked only for syntactic correctness. However, the input function for jsonb is stricter: it disallows Unicode escapes for non-ASCII characters (those above U+007F) unless the database encoding is UTF8. The jsonb type also rejects \u0000 (because that cannot be represented in PostgreSQL’s text type), and it insists that any use of Unicode surrogate pairs to designate characters outside the Unicode Basic Multilingual Plane be correct.
In my case, a Go backend inserts tracing logs to Postgres. A trace consists of multiple “spans”, some of which can contain the reply from an external API. As we found out, sometimes, in the event of a failure, the API replies with an empty GIF <facepalm/>. Our backend converts the response to a string, marshals it to a JSON and later tries to insert the JSON into a Postgres table.
Consider the following Go code:
// data is an empty GIF
var data = []byte{
0x47, 0x49, 0x46, 0x38, 0x39, 0x61, 0x01, 0x00,
0x01, 0x00, 0x80, 0x00, 0x00, 0x00, 0x00, 0x00,
0xff, 0xff, 0xff, 0x21, 0xf9, 0x04, 0x01, 0x00,
0x00, 0x00, 0x00, 0x2c, 0x00, 0x00, 0x00, 0x00,
0x01, 0x00, 0x01, 0x00, 0x00, 0x02, 0x01, 0x44,
0x00, 0x3b,
}
func main() {
v, _ := json.Marshal(struct {
Resp interface{} `json:"resp,omitempty"`
}{
Resp: string(data),
})
fmt.Printf("%s\n", v)
// Output (truncated for readability):
// {"resp":"GIF89a\u0001\u0000\u0001\u0000\ufffd\···\u0001D\u0000;"}
}
Above, json.Marshal produces a perfectly valid JSON. But if I try to insert it into a Postgres table as jsonb, the insert fails with “unsupported Unicode escape sequence”:
= CREATE TABLE logs (data jsonb);
= INSERT INTO logs VALUES ('{"resp":"GIF89a\u0001\u0000\u0001\u0000\ufffd\···\u0001D\u0000;"}');
ERROR: unsupported Unicode escape sequence
LINE 1: insert into logs values ('{"resp":"GIF89a\u0001\u0000\u0001\...
^
DETAIL: \u0000 cannot be converted to text.
CONTEXT: JSON data, line 1: {"resp":...
Because in my code, there were only a couple of places where I didn’t control the actual data, that went into a span, the way I’ve chosen to handle that was by introducing a wrapper type, that implements json.Marshaller. The wrapper checks the value is a valid UTF-8 sequence and doesn’t contain NULL character before it marshals the value into a JSON string. If the value is not a valid UTF-8, the marshaller sees it as a binary data and base64-encodes it.
// RawText handles invalid UTF-8 and NULL-bytes, encoding them as base64-string.
// Because we have to make sure the resulting JSON will be compatible with Postgres's jsonb,
// we must use RawText when we don't control the data, e.g. when log the error from an external API.
// Refer to https://www.postgresql.org/docs/10/datatype-json.html
type RawText []byte
func (v RawText) MarshalEasyJSON(w *Writer) {
if utf8.Valid(v) && !bytes.ContainsRune(v, '\u0000') {
// "valid" text is marshalled as string
w.String(string(v))
} else {
// "invalid" text is marshalled as binary data
w.Raw(json.Marshal([]byte(v)))
}
}
Note, the code above is a marshaller for github.com/mailru/easyjson, which we use in the project.
Here is how it looks in practice:
func main() {
v, _ := json.Marshal(struct {
Resp1 interface{} `json:"resp1,omitempty"`
Resp2 interface{} `json:"resp2,omitempty"`
}{
Resp1: RawText(bin), // wrap the bin data into RawText
Rest2: RawText("normal string"), // wrap (copy) a string into RawText
})
fmt.Printf("%s\n", v)
// Output:
// {
// "resp1":"R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7",
// "resp2":"normal string"
// }
}
2020 was (technically, still is) an extraordinary year. For many people, this was the worst, the weirdest, the most depressing year; the year we wish was a bad dream. With all the respect for the terrible things, lots of people went through, for me, it was a year to remember.
In 2020 I learned how to work from home. It’s still not super fun, but it’s possible. As I look at that now, there are obvious benefits: I can start and end my day much more freely; I currently don’t feel much pressure when willing to go for a walk or a run at 11:00, or when I finish my workday at 16:00 (I tend to start around 8:30 while at home). But that being said, I still don’t consider staying full-remote and I want to go back to the office, co-working, or a random coffee bar.
In 2020 I bought a bicycle. After three years in Berlin, I’m with “all of those people” now :) Can’t say I’m super excited about the purchase. Mostly because I live in the walking distance from the office. With the bicycle, my “commute” is only five or seven minutes shorter. I have to wait at the traffic lights a lot; plus I have to spend extra minutes to lock/unlock the bicycle.
In 2020 I walked, on average, one kilometre per day more, than in the previous year (5.5 km vs 4.2 km). A bit of surprise but, overall this year, I walked less than I did in 2019. I bet that’s because this year we, obviously, didn’t travel much, so we didn’t have a chance to spend weeks walking around a new city, neither in Spring, nor on Christmas and New Year.
In 2020 we adopted a cat. The CAT, who’s now called Gagarin! He came to us from the middle of “nowhere-Russia” as a frightened little kitten and spent the first month under our bed, avoiding walking under the light. And now, after four months, he comes to have a nap on our pillow (and on my head) at 7:00, as his way to signal that it’s time to feed him.
In 2020 I assembled my four nodes Raspberry Pi Kubernetes cluster, but I haven’t made much use of it yet. As promised to myself in December 2019, during the year I learned and touched some new Kubernetes details, and I have tons of things I still want to get my hands into to understand on a more in-depth level of details.
In 2020 I started to learn Rust. I tried learning it in 2018 and in 2019. But this year, I actually spent a month or two writing code, not only reading/watching/listening to/following the content. It feels refreshing. Programming for embedded devices and game-dev are the most attractive fields that I want to explore further with Rust.
In 2020 I had two job interviews. None of them were successful for me, although I tend to believe that I’ve passed both. I have plans to draft a note about the essential questions one must ask the HR during the first call, somewhere later.
In 2020 I did and learned, and read, and listened to, and watched, and thought about a bunch of things.
In 2021 I will do even better.
This is another thought experiment, this time on the importance of my feedback in the code-reviews.
Providing you’re working on a project maintained by a set team of N people. What would happen with the codebase if, for six months, in code-reviews, you started to accept changes for which, you generally leave feedback?
“Variables, struct or function names are named poorly” — accept. “We already have a package, that solves a similar problem” — accept. “A change brings inconsistency to the codebase” — accept. “The pattern doesn’t belong here” — accept.
Go is a famously opinionated programming language. But does being opinionated help and scale outside of the language and its standard library?
Do you have the answer? Discuss this note on r/golang Reddit or share your thoughts on Twitter.
With Apple’s new M1 Macs showing (reportedly) huge performance improvement, compared to “old”, Intel-based Macs, I wonder what would hold Qualcomm (Snapdragon CPU) and others from doing “the same” and moving into laptop/desktop territory?
Microsoft already has Surface Pro X — an ARM-based Windows computer. They also have a version of Win10 for ARM, that one can even run on Raspberry Pi (still beta quality, I believe). Could 2021 become the year of ARM on desktop?
Even more interesting, Amazon’s Graviton is showing (again, reportedly) an excellent performance, while staying at reasonably low cost. What would stop Google/Microsoft from moving into ARM-based CPU territory for their clouds?
I’m not a big fun of the aesthetics of VS Code, at least not on macOS. In particular, I don’t like the way how its code editor renders fonts. But today I learned!
To make the fonts look better — I’m on macOS — set an alternative font-variant, e.g. “Comic Code Ligatures Medium” instead of “Regular”, in the editor’s settings. To specify font-variant, remove spaces in between the name of the font and pass the variant after the hyphen. That is, instead of 'Comic Code Ligatures', set the following:
"editor.fontFamily": "'ComicCodeLigatures-Medium', monospace"
Full disclosure, my IDE of choice is GoLand, and it has been so since it was only a plugin for IntelliJ IDEA. Even though I can comfortably use VS Code or Vim when I need to make a small change or look something up in the code, I need the IDE to effectively work on a large codebase.
The backstory for this note is that I’m working on a small TypeScript/React application in my spare time this month. For something that small, VS Code works great fine. In fact, I’m writing this particular note in VS Code too ;)
It’s one year since I posted the very first note here. Unbelivable! Despite my own concerns I did published random posts over the course of the previous twelve months.
For the next round I came up with some personal goals:
- Keep posting.
- Work on your grammar.
- Add “Archive” and “Tags” sections.
- Find a better approach for managing drafts.
- Bring back the dark theme but figure out what to do with the illustrations.
- Keep posting ;)
The main component of profefe, a system for continuous profiling, is profefe-collector — a service that receives profiling data, taken from an application and persists the data in collector’s storage (design document describes it in more details). Receiving data from an external source (for example, profefe-agent), indicates that profefe, as an observability system, prefers “push” model. Why not “pulling” the profiles directly from the application?
Both push and pull models have their benefits and drawbacks.
A collector that pulls profiling data from running applications could simplify integration into existing infrastructure because there would be no need in making changes in the applications that already exposed pprof HTTP endpoint. Making sure that every application integrated and configured profefe-agent would be a challenging job in a large organisation.
On the other hand, pull model requires pprof servers to be exposed and available for the collector, so it could fetch (pull) profiling data. That can also be challenging in the deployments, where applications are collocated on the bare-metal machines. Every application (application’s instance) would have to communicate a unique TCP port for its pprof server.
To work as a pull-system, the collector must be able to discover the pprof servers, thus it requires a mechanism for service discovery (SD), to be usable at scale. Unfortunately, there isn’t a universal SD protocol or a provider, an observability system could be built upon.
Prometheus, the best example of an open-source system, which uses pull model for data collection, have to support several different SD systems in their code base. At some point they ended up introducing their own general protocol, that expects a “middle-man-service”, which translates the data from a SD system into a list of Prometheus targets (Update, this comment from u/bbrazil does a better job explaining the state of SD in Prometheus). There is no clear way for an open-source system to be both flexible and don’t end up being a pile of “plugins”, that no one is willing to maintain or break.
From the start of profefe project, several years ago, I had the idea that translating push into pull would be easier for an end-user. That is if a small deployment already exposes a pprof server, writing an external job that pulls the profiles from the applications, annotates them with meta-data, and pushes the data into the collector, can be as easy as spawning a cronjob in a sidecar. kube-profefe solves that nicely for deployments running in Kubernetes. At some point, I hoped to come up with something similar for Nomad+Consul if the experiments ended successfully.
Translating pull into push is a similarly possible but because profefe didn’t have to support any SD mechanisms from the start, that simplified the overall code base and allowed us to focus on the collector and the API for profiles quering.
profefe-collector does uses push model. But one can deploy profefe so it reflected the use cases your organisation has.
Do you use continuous profiling? Let me know about your experience. Share your thoughts on Twitter or discuss on r/golang.
A good API is designed around the use-case. A poorly designed, around the API’s implementation details.
Sometimes I write small services in Go from scratch. And every time main.go ends up looking almost the same:
func main() {
ctx, cancel := context.WithCancel(context.Background())
defer cancel()
sigs := make(chan os.Signal, 2)
signal.Notify(sigs, os.Interrupt, syscall.SIGTERM)
go func() {
<-sigs
signal.Stop(sigs)
cancel()
}()
if err := run(ctx, os.Args[1:]); err != nil {
log.Fatalln(err)
}
}
type Config struct {
HTTPAddr string
HTTPShutdownTimeout time.Duration
}
func run(ctx context.Context, args []string) error {
flags := flag.NewFlagSet("", flag.ExitOnError)
var conf Config
flags.StringVar(&conf.HTTPAddr, "http-addr", "127.0.0.1:10080", "address to listen on")
flags.DurationVar(&conf.HTTPShutdownTimeout, "http-shutdown-timeout", 5*time.Second, "server shutdown timeout")
if err := flags.Parse(args); err != nil {
return err
}
// TODO: define the handler, the routing, and wire the dependencies with the main context
mux := http.NewServeMux()
server := &http.Server{
Addr: conf.HTTPAddr,
Handler: mux,
}
errs := make(chan error, 1)
go func() {
log.Printf("starting: addr %s", server.Addr)
errs <- server.ListenAndServe()
}()
select {
case <-ctx.Done():
log.Println("exiting...")
case err := <-errs:
return err
}
// create new context because top-most one is already canceled
ctx, cancel := context.WithTimeout(context.Background(), conf.HTTPShutdownTimeout)
defer cancel()
return server.Shutdown(ctx)
}
Of course, not every service requires an HTTP server, but the general idea stands.
When Go will introduce signal.NotifyContext(), the signals handling in main() function will be much smaller (we’re at go1.15rc1 as I’m writing that and the change hasn’t landed into the release yet).
I love how transparent is the flow here and how everything is scoped inside run() function. This structure forces you to eliminate global state, making unit or integration testing almost trivial — at least, in theory ;)
It might feel like too much of boilerplate code for a “small” service. In practice, though, I don’t recall any time this caused any real troubles to me. The beauty of Go is in explicitness.
I’ve been playing with an e-paper ESP866 Driver Board, and a 2.7" E-Ink display from Waveshare. Arduino C++ looks manageable. One strange thing, though. In both board’s documentation and GxEPD2 library’s examples, they say the display is connected to pins as BUSY → GPIO16, RST → GPIO5, DC → GPIO4, CS → GPIO15. This mapping seems wrong.
After digging through the code examples from Waveshare’s Wiki, the correct mapping is the following:
BUSY → GPIO5,
RST → GPIO2,
DC → GPIO4,
CS → GPIO15
That’s how the initialisation of the main GxEPD2 class for my 2.7" display looks like now:
#define ENABLE_GxEPD2_GFX 0
#include <GxEPD2_BW.h>
// mapping of Waveshare e-Paper ESP8266 Driver Board
GxEPD2_BW<GxEPD2_270, GxEPD2_270::HEIGHT> display(GxEPD2_270(/*CS=15*/ SS, /*DC=4*/ 4, /*RST=2*/ 2, /*BUSY=5*/ 5));
Sticky (or “fixed") headers are everywhere. It feels that, nowadays, every web designer’s first attempt to site’s navigation starts with a sticky header. I hate this.
Keep reading, the note has images...
There’s the late-night dilemma…
Who should be in charge of logging context: a component’s owner or the component itself?
type Logger interface {
With(...kvpairs) Logger
}
type Storage struct {
logger Logger
}
// OPTION 1: component's owner defines the context of component's logger
func main() {
_ = NewStorage(logger.With("component", "storage"))
}
// OPTION 2: component itself is in charge of its logging context
func NewStorage(logger Logger) (st *Storage) {
return &Storage{
logger: logger.With("component", "storage"),
}
}
Fun fact: a couple months back, we ruined the team’s Friday, by debating about a similar topic in the context of (Graphite) metrics namespaces. It has become even more intricate since then :/
Update (2020-04-15)
Many people on Twitter suggest that Option 1 is an obvious choice because only application knows how to name the components. I totally agree with that.
As I wrote later, the real dilemma is not about “application and component” but about “owner of the component”. Function main, in the example above, was a silly example, that tried (and failed) to illustrate the question in a code.
Let’s try another (silly) example:
// there are buch of different handlers (maybe ten) in this application
type Handler1 struct { logger Logger }
func (h *Handler1) ServeHTTP(w http.ResponseWriter, r *http.Request) {
// OPTION 1
req := NewRequst(h.logger.With("component", "request"), r)
}
type Handler2 struct { logger Logger }
func (h *Handler2) ServeHTTP(w http.ResponseWriter, r *http.Request) {
// OPTION 1, still
req := NewRequst(h.logger.With("component", "request"), r)
}
type Request struct {
logger Logger
}
// OPTION 2
func NewRequst(logger Logger, *r http.Request) *Request {
return &Request{
logger: logger.With("component", "request"),
}
}
We want to have a consistent nomenclature across the application’s logs.
Is the choice still obvious? ;)
Do you have an opinion? Share it with me on Twitter.
Participating in self-isolation is more fun when you have toys to play. As a fun weekend project, I wanted to look at how one accesses macOS Location Services and get the geographic location of the device from Go.
To obtain the geographic location of a device on macOS, we use Apple’s Core Location framework. The framework is part of the OS, but it requires writting Objective-C (or Swift). Thanks to Go’s cgo and because Objective-C is from the family of C languages, we can write a bridge between Objective-C and Go.
Continue reading...
This is a lengthy note. If you don’t quite feel reading and only need the working example, go directly to the Travis CI build file.
The more I delve into the world of Raspberry Pi, the more I notice that “regular and boring” things on ARM are harder than I expected.
People build and distribute software exclusively for amd64. You read another “Kubernetes something” tutorial, that went viral on Twitter, and is fancy to try it out. Still, all helm charts, or whatever the author prefered, use Docker images built exclusively for amd64.
Docker toolchain has added the support for building multi-platform images in 19.x. However, it’s available only under the “experimental” mode. The topic of building multi-platform Docker images yet feels underrepresented.
When a client, e.g. Docker client, tries to pull an image, it must negotiate the details about what exactly to pull with the registry. The registry provides a manifest that describes the digest of the requested image, the volumes the image consists of, the platform this image can run on, etc. Optionally, the registry can provide a manifests list, which, as the name suggests, is a list of several manifests bundled into one. With the manifests list in hands, the client can figure out the particular digest of the image it needs to pull.
So multi-platform Docker images are just several images, whose manifests are bundled into the manifests list.
Imagine we want to pull the image golang:1.13.6-alpine3.10. Docker client will get the manifests list from Dockerhub. This list includes digests of several images, each built for the particular platform. If we’re on Raspberry Pi, running the current Raspbian Linux, which is arm/v7, the client will pick the corresponding image’s digest. Alternatively, we could choose to pull the image arm32v7/golang:1.13.6-alpine3.10 instead, and we ended up with the same image with the digest d72fa60fb5b9. Of course, to use a single universal image name, i.e. golang, on every platform is way more convenient.
You can read more about manifests in Docker registry documentation.
Well, yes. This is how, official images are built.
For every platform, the image is built and pushed to the registry under the name <platform>/<image>:<tag>, e.g. amd64/golang:1-alpine. And next, a manifests list, that combines all those platform-specific images, is built and pushed with the simple name <image>:<tag>.
Docker’s BuildKit provides a toolkit that, among other nice things, allows building multi-platform images on a single host. BuildKit is used inside Docker' buildx project, that is part of the recent Docker version.
One can use buildx, but, for this post, I wanted to try out, what would it look like to use BuildKit directly. For profefe, the system for continuous profiling of Go services, I set up Travis CI, that builds a multi-platform Docker image and pushes them to Dockerhub.
profefe is written in Go. That simplifies things, because, thanks to Go compiler, I don’t have to think about how to compile code for different platforms. The same Dockerfile will work fine on every platform.
Here’s how “deploy” stage of the build job looks like (see travis.yml on profefe’s GitHub).
dist: bionic
language: go
go:
- 1.x
jobs:
include:
- stage: deploy docker
services: docker
env:
- PLATFORMS="linux/amd64,linux/arm64,linux/arm/v7"
install:
- docker container run --rm --privileged multiarch/qemu-user-static --reset -p yes
- docker container run -d --rm --name buildkitd --privileged moby/buildkit:latest
- sudo docker container cp buildkitd:/usr/bin/buildctl /usr/local/bin/
- export BUILDKIT_HOST="docker-container://buildkitd"
script: skip
deploy:
- provider: script
script: |
buildctl build \
--progress=plain \
--frontend=dockerfile.v0 \
--local context=. --local dockerfile=. \
--opt filename=contrib/docker/Dockerfile \
--opt platform=$PLATFORMS \
--opt build-arg:VERSION=\"master\" \
--opt build-arg:GITSHA=\"$TRAVIS_COMMIT\" \
--output type=image,\"name=profefe/profefe:git-master\",push=true
on:
repo: profefe/profefe
branch: master
before_deploy:
- echo "$DOCKER_PASSWORD" | docker login --username "$DOCKER_USERNAME" --password-stdin
after_failure:
- buildctl debug workers ls
- docker container logs buildkitd
It’s a lot happening here, but I’ll describe the most critical parts.
Let’s start with dist: bionic.
We run the builds under Ubuntu 18.04 (Bionic Beaver). To be able to build multi-platform images on a single amd64 host, BuildKit uses QEMU to emulate other platforms. That requires Linux kernel 4.8, so even Ubuntu 16.04 (Xenial Xerus) should work.
The top-level details on how the emulation works are very well described in https://www.kernel.org/doc/html/latest/admin-guide/binfmt-misc.html
In short, we tell the component of the kernel (binfmt_misc) to use QEMU when the system executes a binaries built for a different platform. The following call in the “install” step is what’s doing that:
- docker container run --rm --privileged multiarch/qemu-user-static --reset -p yes
Under the hood, the container runs a shell script from QEMU project, that registers the emulator as an executor of binaries from the external platforms.
If you think, that running a docker container to do the manipulations with the host’s OS looks weird, well… I can’t agree more. Probably, a better approach would be to install qemu-user-static, which would do the proper setup. Unfortunately, the current package’s version for Ubuntu Bionic doesn’t do the registration as we need it. I.e. its post-install doesn’t add the "F" flag (“fix binaries”), which is crucial for our goal. Let’s just agree,that docker-run will do ok for the demonstrational purpose.
- docker container run -d --rm --name buildkitd --privileged moby/buildkit:latest
- sudo docker container cp buildkitd:/usr/bin/buildctl /usr/local/bin/
- export BUILDKIT_HOST="docker-container://buildkitd"
This is another “docker-run’ism”. We start BuildKit’s buildkitd daemon inside the container, attaching it to the Docker daemon that runs on the host (“privileged” mode). Next, we copy buildctl binary from the container to the host system and set BUILDKIT_HOST environment variable, so buildctl knew where its daemon runs.
Alternatively, we could install BuildKit from GitHub and run the daemon directly on the build host. YOLO.
before_deploy:
- echo "$DOCKER_PASSWORD" | docker login --username "$DOCKER_USERNAME" --password-stdin
To be able to push the images to the registry, we need to log in providing Docker credentials to host’s Docker daemon. The credentials are set as Travis CI’s encrypted environment variables ([refer to Travis CI docs])](https://docs.travis-ci.com/user/environment-variables/)).
buildctl build \
--progress=plain \
--frontend=dockerfile.v0 \
--local context=. --local dockerfile=. \
--opt filename=contrib/docker/Dockerfile \
--opt platform=$PLATFORMS \
--opt build-arg:VERSION=\"master\" \
--opt build-arg:GITSHA=\"$TRAVIS_COMMIT\" \
--output type=image,\"name=profefe/profefe:git-master\",push=true
This is the black box where everything happens. Magically!
We run buildctl stating that it must use the specified Dockerfile; it must build the images for defined platforms (I specified linux/amd64,linux/arm64,linux/arm/v7), create a manifests list tagged as the desired image (profefe/profefe:<version>), and push all the images to the registry.
buildctl debug workers ls shows what platforms does BuildKit on this host support. I listed only those I’m currently intrested with.
And that’s all. This setup automatically builds and pushes multi-platform Docker images for profefe (https://hub.docker.com/p/profefe/profefe) on a commit to project’s “master” branch on GitHub.
As I hope you’ve seen, support for multi-platform is getting easier and things that were hard a year ago are only mildly annoying now :)
If you have any comments or suggestions, reach out to me on Twitter or discuss this note on r/docker Reddit.
Some more reading on the topic:
As I’ve twitted recently, I’m updating one of my Raspberry Pis to Ubuntu Server 19.10 (arm64).
“One of Raspberry Pis”?
My home cluster is four Raspberry Pis 4 (2GB); all connected to my internet router through ethernet and
powered with 60W 6 USB-ports charger. All Pis build a small kubernetes cluster that runs with k3s.
All by one Pis run on Raspbian Buster Lite and this setup’s been working pretty well until I’ve found out,
Aerospike, a database I required to run for a testing lab, only works on a 64-bit OS.
Luckily, Ubuntu Server has an arm64 version built for Raspberry Pi. Thus, my working plan is to switch one Pi
to Ubuntu, compile and run a single-instance Aerospike server (and any other components, that require a 64-bit OS) on this Pi, and provide a kubernetes service in front of the DB, so other components in the cluster could access it as if it was
fully managed by kubernetes.
The Setup
Setting up Ubuntu Server on a Pi was smooth. All I did was flushing the image with 19.10 OS to an SD card, as described in Ubuntu wiki. That is, the headless setup worked out of the box, and after I inserted the card into the PI and connected it to the router, I managed to SSH into the system:
$ ssh ubuntu@192.168.10.18
The default password for ubuntu user is ubuntu. The system asks to change the password on the first login.
The first thing to do after installing the system:
$ sudo apt-get update
$ sudo apt-get upgrade -y
Disable “message of the day” (motd) to speed SSH login.
For that I commented out the following lines in /etc/pam.d/login and /etc/pam.d/sshd:
#session optional pam_motd.so motd=/run/motd.dynamic
#session optional pam_motd.so noupdate
Reduce GPU memory split. I truly don’t know if that even makes sense, tbh; read about memory split on Raspberry PI config-txt wiki. I added the following to /boot/firmware/usercfg.txt:
gpu_mem=16
To run Kubernetes or Docker, the kernel needs some cgroup options. On Ubuntu Server, the configuration is in /boot/firmware/nobtcmd.txt (refer to cmdline=nobtcmd.txt in /boot/firmware/nobtcfg.txt). Add the following to the end of the file:
cgroup_enable=cpuset cgroup_enable=memory cgroup_memory=1
Reboot the Pi, re-login, and all is ready to install k3s-agent:
$ curl -sfL https://get.k3s.io | K3S_URL="https://<k3s-master-pi>:6443" K3S_TOKEN="<k3s-token>" sh -
After the agent installed and running, check the Pi was added to kubernetes cluster:
pi@pi-1:~ $ sudo kubectl get node -o wide
NAME ROLES AGE VERSION INTERNAL-IP OS-IMAGE KERNEL-VERSION CONTAINER-RUNTIME
pi-1 master 30d v1.17.0+k3s.1 192.168.10.16 Raspbian GNU/Linux 10 (buster) 4.19.75-v7l+ containerd://1.3.0-k3s.5
pi-2 <none> 47h v1.17.0+k3s.1 192.168.10.14 Raspbian GNU/Linux 10 (buster) 4.19.75-v7l+ containerd://1.3.0-k3s.5
pi-3 <none> 47h v1.17.0+k3s.1 192.168.10.15 Raspbian GNU/Linux 10 (buster) 4.19.75-v7l+ containerd://1.3.0-k3s.5
pi-4 <none> 10h v1.17.0+k3s.1 192.168.10.18 Ubuntu 19.10 5.3.0-1014-raspi2 containerd://1.3.0-k3s.5
That is for today. The next steps are to figure out how to build Aerospike on arm64, but this is a story for another day.
Update (2020-01-15)
I’ve managed to build and run Aerospike server for arm64! See make-arm64v8 branch
in my fork of aerospike-server and the gist with my systemd services and configs.
Update (2020-02-13)
A week ago I tried to install Ubuntu Server 18.04.3 on Pi 4 and didn’t even get to the login shell in the headless mode.
Now Ubuntu Server 18.04.4 LTS is out and it works exactly as described in this note:
$ kubectl get node -o wide
NAME ROLES AGE VERSION INTERNAL-IP OS-IMAGE KERNEL-VERSION CONTAINER-RUNTIME
pi-1 master 63d v1.17.2+k3s1 192.168.10.16 Raspbian GNU/Linux 10 (buster) 4.19.93-v7l+ containerd://1.3.3-k3s1
pi-2 <none> 35d v1.17.2+k3s1 192.168.10.14 Raspbian GNU/Linux 10 (buster) 4.19.93-v7l+ containerd://1.3.3-k3s1
pi-3 <none> 14m v1.17.2+k3s1 192.168.10.20 Ubuntu 18.04.4 LTS 5.3.0-1017-raspi2 containerd://1.3.3-k3s1
pi-4 <none> 4d23h v1.17.2+k3s1 192.168.10.19 Ubuntu 19.10 5.3.0-1017-raspi2 containerd://1.3.3-k3s1
Update (2020-06-01)
Ubuntu Server 20.04 is the recommended version of Ubuntu for Ri 4. It works perfectly fine:
$ kubectl get node -o wide
NAME ROLES AGE VERSION INTERNAL-IP OS-IMAGE KERNEL-VERSION CONTAINER-RUNTIME
pi-1 master 172d v1.18.2+k3s1 192.168.10.41 Raspbian GNU/Linux 10 (buster) 4.19.118-v7l+ containerd://1.3.3-k3s2
pi-2 <none> 57d v1.17.4+k3s1 192.168.10.42 Raspbian GNU/Linux 10 (buster) 4.19.118-v7l+ containerd://1.3.3-k3s2
pi-3 <none> 38d v1.17.4+k3s1 192.168.10.43 Ubuntu 20.04 LTS 5.4.0-1011-raspi containerd://1.3.3-k3s2
pi-4 <none> 3d19h v1.18.2+k3s1 192.168.10.44 Ubuntu 20.04 LTS 5.4.0-1011-raspi containerd://1.3.3-k3s2
Also Raspberry Pi OS (ex-Raspbian) for arm64 is currently in beta.
After listening to the “Fireside edition” of Go Time, I questioned myself, how would I answered the questions the hosts discussed.
Because no one has asked, you are very welcome:
1. If you had two weeks to spend on a personal Go project, what would you work on?
I really want to invest more time for profefe. Specifically,
on implementing an analyser of stored profiles: the thing that would help to make sense of the data,
showing how the performance of an instance, a node, or a cluster had changed over the period of time;
how different parts of the codebase had influenced the performance of the application.
Recently Amazon has announced CodeGuru profiler (currently Java-only). From the description it feels
exactly what I pictured in my head when I started the project.
Another topic that I would like to invest more time on is the understanding of the ecosystem around/inside Kubernetes.
During the past two years, I slowed down the consumption of the DevOps/SRE topics, mostly due to the specific
state of the infrastructure in our company. But, “k8s is the new linux”, regardless of what one’s opinion on that.
Even profefe recently has ended up having a kube-profefe (a bridge between profefe and Kubernetes), contributed and maintained by other people.
2. What annoys you about Go of 2019?
The same small things that annoyed me in Go 1.4: var, new, make and “naked return”. Sure, I understand that they all ended up in the language for a reason. But I simply don’t like the “magic” of make, which works only with particular types;
the two ways of defining a variable (var or :=), or a pointer to an instance of a type (new or &T{}).
One new thing, though. Go modules' semver imports. But I can’t say anything new about that. Probably, I just need to embrace them. Go 1.14 looks like a version where I might completely switch to modules, thanks to better handling of vendored dependencies.
3. What’s your ideal working environment?
That always surprises me. Lots of people keep saying that working from home is their ideal environment or even a factor
that influence their job offers choice. I don’t like to work at home. The only time when I feel productive when stay home is in the nights. A café or a co-working works sometimes. But I like working in a big office space. I don’t know why.
Of course, open-plan offices can be very different. Yandex’s “Red Rose” is still the best space I ever worked in. I heard they do excursions around the Moscow’s office now.
3.1. Something on pair-programming?
Since I wrote about Yandex.
Some people think pair-programming is a sort of super-power. It’s, and it’s not.
You can’t just put yourself in an environment, where someone is watching how you write the code while trying to hold a conversation about the code architecture. Pair-programming is a skill to master. But it pays off.
The pair-(trio actually)-programming sessions we did in Yandex, when we worked on bem-core, was the most significant skill boost I had during the five-plus years there.
Of course, the positive experience comes from your peers. In my case, they were people with huge baggage of knowledge and practice of working, talking, debating with each other. Like, out of nowhere, you get the understanding of what
types of questions you must ask; when it is important to spend more time on thinking and when you can make a small hack.
4. Your advice to you junior-developer self?
Don’t overthink and afraid of starting anything. Trying something by making a raw, dirty, barely-working prototype will give you way more knowledge than thinking about how to do that.
Go has an old wiki page, titled “Compiler And Runtime Optimizations”.
The part I like most there is different cases where compiler doesn’t allocate memory for string to []byte conversions:
For a map m of type map[string]T and []byte b, m[string(b)] doesn’t allocate (the temporary string copy of the byte slice isn’t made)
Turned out, since this wiki page was written, more similar optimisations were added to the compiler.
As it’s in Go 1.12+ the following cases are also listed in runtime/string.go:
For the case "<" + string(b) + ">", where b is []byte no extra copying of b is needed.
if string(b) == "foo" { ··· }
In the code above, b []byte also won’t be copied.
There are still cases where compiler can’t optimise the code for us. In some of those cases
it’s fine to do string to bytes conversion using a so called “unsafe trick” (accessing string’s underling
data directly, with out copying the data from string to bytes and vice versa). One can find several
ways of performing the trick, but none of them seems “the one that must be used”.
After years of episodic discussions, a collegue of mine assembled the list of different conserns and about
the proper way of doing it (see “unsafe conversion between string <-> []byte” topic on golang-nuts forum).
Thanks to replies from Go team, our most valid way of doing it is following:
// Refer to github.com/fmstephe/unsafeutil
type stringHeader struct {
data unsafe.Pointer
stringLen int
}
type sliceHeader struct {
data unsafe.Pointer
sliceLen int
sliceCap int
}
func StringToBytes(s string) (b []byte) {
stringHeader := (*stringHeader)(unsafe.Pointer(&s))
sliceHeader := (*sliceHeader)(unsafe.Pointer(&b))
sliceHeader.data = stringHeader.data
sliceHeader.sliceLen = len(s)
sliceHeader.sliceCap = len(s)
return b
}
func BytesToString(b []byte) (s string) {
sliceHeader := (*sliceHeader)(unsafe.Pointer(&b))
stringHeader := (*stringHeader)(unsafe.Pointer(&s))
stringHeader.data = sliceHeader.data
stringHeader.stringLen = len(b)
return s
}
The other day I received my beta access to GitHub Actions. To try them out I picked an existing pet project and created a workflow using a Go project template provided by GitHub. As it’s in September 2019, their template defines the sequence of steps: setup Go, checkout code, get dependencies, build. This is not exactly how I used to do it.
My project is a classic Go service ;) meaning: it uses vendoring and doesn’t use Go modules. So no need for “get dependencies” step. And it requires to be inside the GOPATH. With that, the provided workflow needed some adjustment.
After some trials and errors, I’ve managed to make checkout step to clone the repo into the correct destination inside the GOPATH. Here is the final workflow:
name: Run Go test
on: [pull_request]
jobs:
test:
strategy:
matrix:
go-version: [1.12.9]
runs-on: ubuntu-latest
steps:
- uses: actions/setup-go@v1
with:
go-version: ${{ matrix.go-version }}
- uses: actions/checkout@v1
with:
path: ./src/github.com/${{ github.repository }}
fetch-depth: 5
- run: make test
env:
GOPATH: ${{ runner.workspace }}
Note, how actions/checkout@v1 above uses custom path input parameter. I set the path to ./src/github.com/${{ github.repository }}, so the project is checked out to src directory in the runners’s workspace, which I later pass as the value of GOPATH to the “make test” step. The leading dot in ./src seems very important — I’ve spent the majority of the time trying to figure out that part — refer to this issue.
See the workflow in action.
To learn more about those ${{ ··· }} “macroses” I suggest looking at the Actions' “Contexts and expression syntax” documentation.
One probably knows that net/http.Headers is no more than map[string][]string with extra specific methods. A usual way to initialize and populate such data-structure from an external representation is something like that:
type Header map[string][]string
func (h Header) Add(key, val string) {
if val == "" {
return
}
h[key] = append(h[key], val)
}
func main() {
h := make(Header)
h.Add("Host", "example.com")
h.Add("Via", "a.example.com")
h.Add("Via", "b.example.com")
}
From the code above, one can notice that we allocated a new slice of strings for every unique key that we added to headers. For things like HTTP headers, that’re automatically parsed for every incoming request, this bunch of tiny allocations is something we’d like to avoid.
I was curious to know if Go’s standard library cares about that.
Looking at the implementation of net/textproto.Reader.ReadMIMEHeader(), which is used in the standard
HTTP server, or Go 1.13’s new net/http.Header.Clone(), it turned out they solve the problem quite elegantly.
We know that for a majority of cases, HTTP headers are an immutable key-value pair, where most of the keys have a single value. Instead of allocating a separate slice for a unique key, Go pre-allocates a continues slice for values and refers to a sub-slice of this slice for all keys.
Knowing that, we can refactor the initial Header.Add as the following:
type Header map[string][]string
func (h Header) add(vv []string, key, val string) []string {
if val == "" { ··· }
// fast path for KV pair of a single value
if h[key] == nil {
vv = append(vv, value)
h[key] = vv[:1:1]
return vv[1:]
}
// slow path, when KV pair has two or more values
h[key] = append(h[key], val)
return vv
}
func main() {
h := make(Header)
// net/textprotocol pre-counts total number of request's headers
// to allocate the slice of known capacity
vv := make([]string, 0)
vv = h.add(vv, "Host", "example.com")
vv = h.add(vv, "Via", "a.example.com")
}
Note that we use vv[:1:1] to create a sub-slice of a fixed capacity (length 1, capacity 1).
If there is a KV-pair that has several values, e.g. “Via” header, Add will allocate a separate slice for that key, doubling its capacity.
Let’s create a blog. But let’s call them “notes”.
Because sometimes there are thoughts I want to share with you. Some of them might even be larger than a tweet.
