After recently discovering FauxRPC, I was sufficiently impressed that I decided to use it to test Ackal’s gRPC services using rust.

FauxRPC provides multi-protocol support and so, after successfully implementing the faux gRPC client tests, I was compelled to try gRPC-Web too. For no immediate benefit other than, it’s there, it’s free and it’s interesting. As an aside, the faux REST client tests worked without issue using Reqwest.

Unfortunately, my optimism hit a wall with gRPC-Web and what follows is a summary of my unresolved issue.

After recently discovering FauxRPC, I was sufficiently impressed that I decided to use it to test Ackal’s gRPC services using rust.

FauxRPC provides multi-protocol support and so, after successfully implementing the faux gRPC client tests, I was compelled to try gRPC-Web too. For no immediate benefit other than, it’s there, it’s free and it’s interesting. As an aside, the faux REST client tests worked without issue using Reqwest.

Unfortunately, my optimism hit a wall with gRPC-Web and what follows is a summary of my unresolved issue.

Read FauxRPC + Testcontainers on Hacker News and was intrigued. I spent a little more time “evaluating” this than I’d planned because I’m forcing myself to use rust as much as possible and my ignorance (see below) caused me some challenges.

The technology is interesting and works well. The experience helped me explore Testcontainers too which I’d heard about but not explored until this week.

For my future self:

I started following along with FauxRPC’s Testcontainers example but, being unfamiliar with Connect, I wasn’t familiar with its Eliza service. The service is available on demo.connectrpc.com:443 and is described using eliza.proto as part of examples-go. Had I realized this sooner, I would have used this example rather than the Health Checking protocol.

After recently discovering FauxRPC, I was sufficiently impressed that I decided to use it to test Ackal’s gRPC services using rust.

FauxRPC provides multi-protocol support and so, after successfully implementing the faux gRPC client tests, I was compelled to try gRPC-Web too. For no immediate benefit other than, it’s there, it’s free and it’s interesting. As an aside, the faux REST client tests worked without issue using Reqwest.

Unfortunately, my optimism hit a wall with gRPC-Web and what follows is a summary of my unresolved issue.

Read FauxRPC + Testcontainers on Hacker News and was intrigued. I spent a little more time “evaluating” this than I’d planned because I’m forcing myself to use rust as much as possible and my ignorance (see below) caused me some challenges.

The technology is interesting and works well. The experience helped me explore Testcontainers too which I’d heard about but not explored until this week.

For my future self:

I started following along with FauxRPC’s Testcontainers example but, being unfamiliar with Connect, I wasn’t familiar with its Eliza service. The service is available on demo.connectrpc.com:443 and is described using eliza.proto as part of examples-go. Had I realized this sooner, I would have used this example rather than the Health Checking protocol.

A lazy Sunday afternoon and my interest was piqued by XML-RPC

Client

A very basic XML-RPC client wrapped in a Cloud Functions function:

main.py:

import functions_framework
import os
import xmlrpc.client


endpoint = os.get_env("ENDPOINT")

proxy = xmlrpc.client.ServerProxy(endpoint)

@functions_framework.http
def add(request):
    print(request)
    rqst = request.get_json(silent=True)

    resp = proxy.add(
        {"x":{
            "real":rqst["x"]["real"],
            "imag":rqst["x"]["imag"]
        },
        "y":{
            "real":rqst["y"]["real"],
            "imag":rqst["y"]["imag"]
        }
    })

    return resp

requirements.txt:

functions-framework==3.*

Run it:

python3 -m venv venv
source venv/bin/activate
python3 -m pip install --requirement requirements.txt

export ENDPOINT="..."

python3 main.py

Server

Forcing myself to go Rust first and there’s an (old) xml-rpc crate.

For the first time, I chose Rust to solve a problem. Until this, I’ve been trying to use Rust to learn the language and to rewrite existing code. But, this problem led me to Rust because my other tools wouldn’t cut it.

The question was how to represent oneof fields in Kubernetes Custom Resource Definitions (CRDs).

CRDs use OpenAPI schema and the YAML that results can be challenging to grok.

apiVersion: apiextensions.k8s.io/v1
kind: CustomResourceDefinition
metadata:
  name: deploymentconfigs.example.com
spec:
  group: example.com
  names:
    categories: []
    kind: DeploymentConfig
    plural: deploymentconfigs
    shortNames: []
    singular: deploymentconfig
  scope: Namespaced
  versions:
  - additionalPrinterColumns: []
    name: v1alpha1
    schema:
      openAPIV3Schema:
        description: An example schema
        properties:
          spec:
            properties:
              deployment_strategy:
                oneOf:
                - required:
                  - rolling_update
                - required:
                  - recreate
                properties:
                  recreate:
                    properties:
                      something:
                        format: uint16
                        minimum: 0.0
                        type: integer
                    required:
                    - something
                    type: object
                  rolling_update:
                    properties:
                      max_surge:
                        format: uint16
                        minimum: 0.0
                        type: integer
                      max_unavailable:
                        format: uint16
                        minimum: 0.0
                        type: integer
                    required:
                    - max_surge
                    - max_unavailable
                    type: object
                type: object
            required:
            - deployment_strategy
            type: object
        required:
        - spec
        title: DeploymentConfig
        type: object
    served: true
    storage: true
    subresources: {}

I’ve developed several Kubernetes Operators using the Operator SDK in Go (which builds upon Kubebuilder).

General

You’ll need a Google Cloud project with Cloud Translation (translate.googleapis.com) enabled and a Service Account (and key) with suitable permissions in order to run the following.

BILLING="..." # Your Billing ID (gcloud billing accounts list)
PROJECT="..." # Your Project ID
ACCOUNT="tester"

EMAIL="${ACCOUNT}@${PROJECT}.iam.gserviceaccount.com"

ROLES=(
    "roles/cloudtranslate.user"
    "roles/serviceusage.serviceUsageConsumer"
)

# Create Project
gcloud projects create ${PROJECT}

# Associate Project with your Billing Account
gcloud billing accounts link ${PROJECT} \
--billing-account=${BILLING}

# Enable Cloud Translation
gcloud services enable translate.googleapis.com \
--project=${PROJECT}

# Create Service Account
gcloud iam service-accounts create ${ACCOUNT} \
--project=${PROJECT}

# Create Service Account Key
gcloud iam service-accounts keys create ${PWD}/${ACCOUNT}.json \
--iam-account=${EMAIL} \
--project=${PROJECT}

# Update Project IAM permissions
for ROLE in "${ROLES[@]}"
do
  gcloud projects add-iam-policy-binding ${PROJECT} \
  --member=serviceAccount:${EMAIL} \
  --role=${ROLE}
done

For the code, you’ll need to install protoc and preferably have it in your path.

I’ve written before about Ackal’s use of Firestore and subscribing to Firestore document CRUD events:

• Routing Firestore events to GKE with Eventarc
• Cloud Firestore Triggers in Golang using Firestore triggers

I find Google’s Eventarc documentation to be confusing and, in typical Google fashion, even though open-sourced, you often need to do some legwork to find relevant sources, viz:

• Google’s Protobufs for Eventarc (using cloudevents) google-cloudevents1
• Convenience (since you can generate these using protoc) language-specific types generated from the above e.g. google-cloudevents-go; google-cloudevents-python etc.

1 – IIUC EventArc is the Google service. It carries Google Events that are CloudEvents. These are defined by protocol buffers schemas.

I naively (!) began exploring JSON marshaling of Protobufs in rust. Other protobuf language SDKs include JSON marshaling making the process straightforward. I was to learn that, in rust, it’s not so simple. Unfortunately, for me, this continues to discourage my further use of rust (rust is just hard).

My goal was to marshal an arbitrary protocol buffer message that included a oneof feature. I was unable to JSON marshal the rust generated by tonic for such a message.

I wrote about Navigating Koyeb’s Golang SDK. That client is generated using the OpenAPI Generator project using Koyeb’s Swagger (now OpenAPI) REST API spec.

This post shows how to generate a Rust SDK using the Generator and provides a very basic example of using the SDK.

The Generator will create a Rust library project:

VERS="v7.2.0"

PACKAGE_NAME="koyeb-api-client-rs"
PACKAGE_VERS="1.0.0"

podman run \
--interactive --tty --rm \
--volume=${PWD}:/local \
docker.io/openapitools/openapi-generator-cli:${VERS} \
generate \
-g=rust \
-i=https://developer.koyeb.com/public.swagger.json \
-o=/local/${PACKAGE_NAME} \
--additional-properties=\
packageName=${PACKAGE_NAME},\
packageVersion=${PACKAGE_VERS}

This will create the project in ${PWD}/${PACKAGE_NAME} including the documentation at:

Ackal uses a Kubernetes Operator to orchestrate the lifecycle of its health checks. Ackal’s Operator is written in Go using kubebuilder.

Yesterday, my interest was piqued by a MetalBear blog post Writing a Kubernetes Operator [in Rust]. I spent some time reimplementing one of Ackal’s CRDs (Check) using kube-rs and not only refreshed my Rust knowledge but learned a bunch more about Kubernetes and Operators.

While rummaging around the Kubernetes documentation, I discovered flant’s Shell-operator and spent some time today exploring its potential.

A Microsoft engineer introduced me to pest as a way to introduce service filtering in a ZeroConf plugin that I’m prototyping for Akri. It’s been fun to learn but I worry that, because I won’t use it frequently, I’m going to quickly forget what I’ve done. So, here are my notes.

Here’s the problem, I’d like to be able to provide users of the ZeroConf plugin with a string-based filter that permits them to filter the services discovered when the Akri agent browses a network.

For the past couple of weeks, I’ve been playing around with Akri, a Microsoft (DeisLabs) project for building a connected edge with Kubernetes. Kubernetes, IoT, Rust (and Golang) make this all compelling to me.

Initially, I deployed an Akri End-to-End to MicroK8s on Google Compute Engine (link) and Digital Ocean (link). But I was interested to create me own example and so have proposed a very (!) simple HTTP-based protocol.

This blog summarizes my thoughts about Akri and an explanation of the HTTP protocol implementation in the hope that this helps others.

DigitalOcean launched an App Platform with many Supported Languages and Frameworks. I used Golang first, then wondered how to use non-natively-supported languages, i.e. Rust.

The good news is that Docker is a supported framework and so, you can run pretty much anything.

Repo: https://github.com/DazWilkin/do-apps-rust

Rust

I’m a Rust noob. I’m always receptive to feedback on improvements to the code. I looked to mirror the Golang example. I’m using rocket and rocket-prometheus for the first time:

You will want to install rust nightly (as Rocket has a dependency that requires it) and then you can override the default toolchain for the current project using:

I’ve spent time recently playing around with WebAssembly (WASM) and waPC. Rust and WASM were born at Mozilla and there’s a natural affinity with writing WASM binaries in Rust. In the WASM examples I’ve been using for WASM Transparency, waPC and MsgPack and waPC and Protobufs.

I’ve created 3 WASM binaries: complex.wasm, simplex.wasm and fabcar.wasm and each is about 2.5MB when:

cargo build --target=wasm32-unknown-unknown --release

The Rust and WebAssembly book has an excellent section titled Shrinking .wasm. Code Size. So, let’s see what help that provides.

I’ve been playing around with a proof-of-concept combining WASM and Trillian. The hypothesis was to explore using WASM as a form of chaincode with Trillian. The project works but it’s far from being a chaincode-like solution.

Let’s start with a couple of (trivial) examples and then I’ll explain what’s going on and how it’s implemented.

2020/08/14 18:42:17 [main:loop:dynamic-invoke] Method: mul
2020/08/14 18:42:17 [random:New] Message
2020/08/14 18:42:17 [random:New] Float32
2020/08/14 18:42:17 [random:New] Float32
2020/08/14 18:42:17 [random:New] Message
2020/08/14 18:42:17 [random:New] Float32
2020/08/14 18:42:17 [random:New] Float32
2020/08/14 18:42:17 [Client:Invoke] Metadata: complex.wasm
2020/08/14 18:42:17 [main:loop:dynamic-invoke] Success: result:{real:0.036980484 imag:0.3898267}

After shipping a Rust-sourced WASM solution (complex.wasm) to the WASM transparency server, the client invokes a method mul that’s exposed by it using a dynamically generated request message and outputs the response. Woo hoo! Yes, an expensive way to multiple complex numbers.

As my reader will know (Hey Mom!), I’ve been noodling around with WASM and waPC. I’ve been exploring ways to pass structured messages across the host:guest boundary.

Protobufs was my first choice. @KevinHoffman created waPC and waSCC and he explained to me and that wSCC uses Message Pack.

It’s slightly surprising to me (still) that technologies like this exist with everyone else seemingly using them and I’ve not heard of them. I don’t expect to know everything but I’m surprised I’ve not stumbled upon msgpack until now.

A digression thanks to Sal Rashid who’s exploring WASM filters w/ Envoy.

The documentation is sparse but:

• How to write WASM filters for Envoy…

There is a Rust SDK but it’s not documented:

• proxy-wasm-rust-sdk

I found two useful posts by Rustaceans who were able to make use of it:

• Extending Envoy with WASM and Rust
• Extending Istio with Rust and WebAssembly

Here’s my simple use of the SDK’s examples.

wasme

curl -sL https://run.solo.io/wasme/install | sh
PATH=${PATH}:${HOME}/.wasme/bin
wasme --version

It may be possible to avoid creating an account on WebAssemblyHub if you’re staying local.

I’ve been hacking on a Rust-based transparent application for Google Trillian. As appears to be my fixation, this personality is for another package manager. This time, Rust’s Crates often found in crates.io which is Rust’s Package Registry. I discussed this project earlier this month Rust Crate Transparency && Rust SDK for Google Trillian and and earlier approach for Python’s packages with pypi-transparency.

This time, of course, I’m using Rust. And, by way of a first for me, for the gRPC server implementation (aka “personality”). I’ve been lazy thanks to the excellent gRPCurl and have been using it way of a client. Because I’m more familiar with Golang and because I’ve written (most) other Trillian personalities in Golang, I resorted to quickly implementing Crate Transparency in Golang too in order to uncover bugs with the Rust implementation. I’ll write a follow-up post on the complexity I seem to struggle with when using protobufs and gRPC [in Golang].

I’m noodling the utility of a Transparency solution for Rust Crates. When developers push crates to Cargo, a bunch of metadata is associated with the crate. E.g. protobuf. As with Golang Modules, Python packages on PyPi etc., there appears to be utility in making tamperproof recordings of these publications. Then, other developers may confirm that a crate pulled from cates.io is highly unlikely to have been changed.

On Linux, Cargo stores downloaded crates under ${HOME}/.crates/registry. In the case of the latest version (2.12.0) of protobuf, on my machine, I have:

<3 Google but there’s quite often an assumption that we’re all sitting around the engineering table and, of course, we’re not.

Cloud Endpoints is a powerful offering but – IMO – it’s super confusing to understand and complex to deploy.

If you’re familiar with the motivations behind service meshes (e.g. Istio), Cloud Endpoints fits in a similar niche (“neesh” or “nitch”?). The underlying ambition is that, developers can take existing code and by adding a proxy (or sidecar), general-purpose abstractions, security, logging etc. may be added.

I’ve finally being able to hack my way through to a working Rust gRPC client (for PyPi Transparency).

It’s not very good: poorly structured, hacky etc. but it serves the purpose of giving me a foothold into Rust development so that I can evolve it as I learn the language and its practices.

There are several Rust crates (SDK) for gRPC. There’s no sanctioned SDK for Rust on grpc.io.

I chose stepancheg’s grpc-rust because it’s a pure Rust implementation (not built atop the C implementation).

After recently discovering FauxRPC, I was sufficiently impressed that I decided to use it to test Ackal’s gRPC services using rust.

FauxRPC provides multi-protocol support and so, after successfully implementing the faux gRPC client tests, I was compelled to try gRPC-Web too. For no immediate benefit other than, it’s there, it’s free and it’s interesting. As an aside, the faux REST client tests worked without issue using Reqwest.

Unfortunately, my optimism hit a wall with gRPC-Web and what follows is a summary of my unresolved issue.

Read FauxRPC + Testcontainers on Hacker News and was intrigued. I spent a little more time “evaluating” this than I’d planned because I’m forcing myself to use rust as much as possible and my ignorance (see below) caused me some challenges.

The technology is interesting and works well. The experience helped me explore Testcontainers too which I’d heard about but not explored until this week.

For my future self:

I started following along with FauxRPC’s Testcontainers example but, being unfamiliar with Connect, I wasn’t familiar with its Eliza service. The service is available on demo.connectrpc.com:443 and is described using eliza.proto as part of examples-go. Had I realized this sooner, I would have used this example rather than the Health Checking protocol.

An interesting question on Stack overflow prompted me to understand how to use Visual Studio Code and Delve to remotely debug a Golang app running on Kubernetes (MicroK8s).

The OP is using Gin which was also new to me so the question gave me an opportunity to try out several things.

Sources

A simple healthz handler:

package main

import (
	"flag"
	"log/slog"
	"net/http"

	"github.com/gin-gonic/gin"
)

var (
	addr = flag.String("addr", "0.0.0.0:8080", "HTTP server endpoint")
)

func healthz(c *gin.Context) {
	c.String(http.StatusOK, "ok")
}
func main() {
	flag.Parse()

	router := gin.Default()
	router.GET("/fib", handler())
	router.GET("healthz", healthz)

	slog.Info("Server starting")
	slog.Info("Server error",
		"err", router.Run(*addr),
	)
}

Containerfile:

General

You’ll need a Google Cloud project with Cloud Translation (translate.googleapis.com) enabled and a Service Account (and key) with suitable permissions in order to run the following.

BILLING="..." # Your Billing ID (gcloud billing accounts list)
PROJECT="..." # Your Project ID
ACCOUNT="tester"

EMAIL="${ACCOUNT}@${PROJECT}.iam.gserviceaccount.com"

ROLES=(
    "roles/cloudtranslate.user"
    "roles/serviceusage.serviceUsageConsumer"
)

# Create Project
gcloud projects create ${PROJECT}

# Associate Project with your Billing Account
gcloud billing accounts link ${PROJECT} \
--billing-account=${BILLING}

# Enable Cloud Translation
gcloud services enable translate.googleapis.com \
--project=${PROJECT}

# Create Service Account
gcloud iam service-accounts create ${ACCOUNT} \
--project=${PROJECT}

# Create Service Account Key
gcloud iam service-accounts keys create ${PWD}/${ACCOUNT}.json \
--iam-account=${EMAIL} \
--project=${PROJECT}

# Update Project IAM permissions
for ROLE in "${ROLES[@]}"
do
  gcloud projects add-iam-policy-binding ${PROJECT} \
  --member=serviceAccount:${EMAIL} \
  --role=${ROLE}
done

For the code, you’ll need to install protoc and preferably have it in your path.

I’ve written before about Ackal’s use of Firestore and subscribing to Firestore document CRUD events:

• Routing Firestore events to GKE with Eventarc
• Cloud Firestore Triggers in Golang using Firestore triggers

I find Google’s Eventarc documentation to be confusing and, in typical Google fashion, even though open-sourced, you often need to do some legwork to find relevant sources, viz:

• Google’s Protobufs for Eventarc (using cloudevents) google-cloudevents1
• Convenience (since you can generate these using protoc) language-specific types generated from the above e.g. google-cloudevents-go; google-cloudevents-python etc.

1 – IIUC EventArc is the Google service. It carries Google Events that are CloudEvents. These are defined by protocol buffers schemas.

I naively (!) began exploring JSON marshaling of Protobufs in rust. Other protobuf language SDKs include JSON marshaling making the process straightforward. I was to learn that, in rust, it’s not so simple. Unfortunately, for me, this continues to discourage my further use of rust (rust is just hard).

My goal was to marshal an arbitrary protocol buffer message that included a oneof feature. I was unable to JSON marshal the rust generated by tonic for such a message.

Ackal deploys gRPC Health Checking clients in locations around the World in order to health check services that are representative of customer need.

Koyeb offers multiple locations and I spent time today writing a client for Ackal to integrate with Koyeb using the Golang client for the Koyeb API.

The SDK is generated from Koyeb’s OpenAPI (nee Swagger) endpoint using openapi-generator-cli. This is a smart, programmatic solution to ensuring that the SDK always matches the API definition but I found the result is idiosyncratic and therefore a little gnarly.

Google publishes interface definitions of Google APIs (services) that support REST and gRPC in a repo called Google APIs. Google’s SDKs uses gRPC to access these services but, how to do this using e.g. gRPCurl?

I wanted to debug Cloud Profiler and its agent makes UpdateProfile RPCs to cloudprofiler.googleapis.com. Cloud Profiler is more challenging service to debug because (a) it’s publicly “write-only”; and (b) it has complex messages. UpdateProfile sends UpdateProfileRequest messages that include Profile messages that include profile_bytes which are gzip compressed serialized protos of pprof’s Profile.

As I contemplate moving my “thing” into production, I’m anticipating aspects of the application that need maintenance and how this can be automated.

I’d been negligent in the maintenance of some of my container images.

I’m using mostly Go and some Rust as the basis of static(ally-compiled) binaries that run in these containers but not every container has a base image of scratch. scratch is the only base image that doesn’t change and thus the only base image that doesn’t require that container images buit FROM it, be maintained.

I’d not used Google’s Domain-wide Delegation from Golang and struggled to find example code.

Google provides Java and Python samples.

Google has a myriad packages implementing its OAuth security and it’s always daunting trying to determine which one to use.

As it happens, I backed into the solution through client.Options

ctx := context.Background()

// Google Workspace APIS don't use IAM do use OAuth scopes
// Scopes used here must be reflected in the scopes on the
// Google Workspace Domain-wide Delegate client
scopes := []string{ ... }

// Delegates on behalf of this Google Workspace user
subject := "a@google-workspace-email.com"

creds, _ := google.FindDefaultCredentialsWithParams(
    ctx,
    google.CredentialsParams{
        Scopes: scopes,
        Subject: subject,
    },
)
opts := option.WithCredentials(creds)
service, _ := admin.NewService(ctx, opts)

In this case NewService applies to Google’s Golang Admin SDK API although the pattern of NewService(ctx) or NewService(ctx, opts) where opts is a option.ClientOption is consistent across Google’s Golang libraries.

It’s been a while!

I’ve been spending time writing Bash scripts and a web site but neither has been sufficiently creative that I’ve felt worth a blog post.

As I’ve been finalizing the web site, I needed an Issue Tracker and decided to leverage GitHub(’s Issues).

As a former Googler, I’m familiar with Google’s (excellent) internal issue tracking tool (Buganizer) and it’s public manifestation Issue Tracker. Google documents Issue Tracker and its Issue type which I’ve mercilessly plagiarized in my implementation.

UPDATE There’s an issue with my naive implementation of RenderValuesHook as described in this post. I summarized the problem in this issue where I’ve outlined (hopefully) a more robust solution.

Recently, I described how to configure Golang logging so that user-defined key-values applied to the logs are parsed when ingested by Google Cloud Logging.

Here’s an example of what we’re trying to achieve. This is an example Cloud Logging log entry that incorporates user-defined labels (see dog:freddie and foo:bar) and a readily-querable jsonPayload:

I’ve multiple components in an app and these are deployed across multiple Google Cloud Platform (GCP) services: Kubernetes Engine, Cloud Functions, Cloud Run, etc. Almost everything is written in Golang and I started the project using go-logr.

logr is in two parts: a Logger that you use to write log entries; a LogSink (adaptor) that consumes log entries and outputs them to a specific log implementation.

Initially, I defaulted to using stdr which is a LogSink for Go’s standard logging implementation. Something similar to the module’s example:

This is very useful:

I am building an application that comprises multiple repos. I continue to procrastinate on whether using multiple repos vs. a monorepo was a good idea but, an issue that I have (had) is the need to ensure that the repos’ contents are using current|latest modules. GitHub can help.

Most of the application is written in Golang with a smattering of Rust and some JavaScript.

For… reasons, I wanted to pre-filter gRPC requests to check for authorization. Authorization is implemented as a ‘micro-service’ and I wanted the authorization server to run in the same process as the gRPC client.

TL;DR:

• Shiju’s “Writing gRPC Interceptors in Go” is great
• This Stack overflow answer ostensibly for writing unit tests for gRPC got me an in-process server

What follows stands on these folks’ shoulders…

A key motivator for me to write blog posts is that helps me ensure that I understand things. Writing this post, I realized I’d not researched gRPC Interceptors and, as luck would have it, I found some interesting content, not on grpc.io but on the grpc-ecosystem repo, specifically Go gRPC middleware. But, I refer again to Shiju’s clear and helpful “Writing gRPC Interceptors in Go”

It’s been almost a month since my last post. I’ve been occupied learning Stripe and integrating it into an application that I’m developing. The app benefits from a billing mechanism for prospective customers and, as far as I can tell, Stripe is the solution. I’d be interested in hearing perspectives on alternatives.

As with any platform, there’s good and bad and I’ll summarize my perspective on Stripe here. It’s been some time since I developed in JavaScript and this lack of familiarity has meant that the solution took longer than I wanted to develop. That said, before this component, I developed integration with Firebase Authentication and that required JavaScript’ing too and that was much easier (and more enjoyable).

Julia’s post Blog about what you’ve struggled with resonates because I’ve been struggling with Golang structs in a project. Not the definitions of structs but seemingly needing to reproduce them across the project. I realize that each instance of these resources differs from the others but I’m particularly concerned by having to duplicate method implementations on them.

I’m kinda hoping that I see the solution to my problem by writing it out. If you’re reading this, I didn’t :-(

I’m using Google’s Golang SDK for Firestore. The experience is excellent and I’m quickly becoming a fan of Firestore. However, as a Golang Firestore developer, I’m feeling less loved and some of the concepts in the database were causing me a conundrum.

I’m still not entirely certain that I have Timestamps nailed but… I learned an important lesson on the auto-creation of Timestamps in documents and how to retain these values.

I was pleased to discover that Google provides a non-Node.JS mechanism to subscribe to and act upon Firestore triggers, Google Cloud Firestore Triggers. I’ve nothing against Node.JS but, for the project i’m developing, everything else is written in Golang. It’s good to keep it all in one language.

I’m perplexed that Cloud Functions still (!) only supports Go 1.13 (03-Sep-2019). Even Go 1.14 (25-Feb-2020) was released pre-pandemic and we’re now running on 1.16. Come on Google!

This works great but it wasn’t clearly documented for non-Firebase users. I assume it will work, as well, for any of the client libraries (not just Golang).

Assuming you have some (Golang) code (in this case using the Google Cloud Client Library) that interacts with a Firestore database. Something of the form:

package main

import (
  "context"
  "crypto/sha256"
  "fmt"
  "log"
  "os"
  "time"

  "cloud.google.com/go/firestore"
)

func hash(s string) string {
  h := sha256.New()
  h.Write([]byte(s))
  return fmt.Sprintf("%x", h.Sum(nil))
}

type Dog struct {
  Name    string                 `firestore:"name"`
  Age     int                    `firestore:"age"`
  Human   *firestore.DocumentRef `firestore:"human"`
  Created time.Time              `firestore:"created"`
}

func NewDog(name string, age int, human *firestore.DocumentRef) Dog {
  return Dog{
    Name:    name,
    Age:     age,
    Human:   human,
    Created: time.Now(),
  }
}

func (d *Dog) ID() string {
  return hash(d.Name)
}

type Human struct {
  Name string `firestore:"name"`
}

func (h *Human) ID() string {
  return hash(h.Name)
}

func main() {
  ctx := context.Background()
  project := os.Getenv("PROJECT")

  client, err := firestore.NewClient(ctx, project)

  if value := os.Getenv("FIRESTORE_EMULATOR_HOST"); value != "" {
    log.Printf("Using Firestore Emulator: %s", value)
  }

  if err != nil {
    log.Fatal(err)
  }
  defer client.Close()

  me := Human{
    Name: "me",
  }
  meDocRef := client.Collection("humans").Doc(me.ID())
  if _, err := meDocRef.Set(ctx, me); err != nil {
    log.Fatal(err)
  }

  freddie := NewDog("Freddie", 2, meDocRef)
  freddieDocRef := client.Collection("dogs").Doc(freddie.ID())
  if _, err := freddieDocRef.Set(ctx, freddie); err != nil {
    log.Fatal(err)
  }
}

Then you can interact instead with the Firestore Emulator.

Phew! Programmitcally deploying Cloud Run services should be easy but it didn’t find it so.

My issues were that the Cloud Run Admin (!) API is poorly documented and it uses non-standard endpoints (thanks Sal!). Here, for others who may struggle with this, is how I got this to work.

Goal

Programmatically (have Golang, Python, want Rust) deploy services to Cloud Run.

i.e. achieve this:

gcloud run deploy ${NAME} \
--image=${IMAGE} \
--platform=managed \
--no-allow-unauthenticated \
--region=${REGION} \
--project=${PROJECT}

TRICK --log-http is your friend

It’s a good name, I read it as “dapper” but I frequently type “darp” :-(

Was interested to read that Dapr is now v1.0 and decided to check it out. I was initially confused between Dapr and service mesh functionality. But, having used Dapr, it appears to be more focused in aiding the development of (cloud-native) (distributed) apps by providing developers with abstractions for e.g. service discovery, eventing, observability whereas service meshes feel (!) more oriented towards simplifying the deployment of existing apps. Both use the concept of proxies, deployed alongside app components (as sidecars on Kubernetes) to provide their functionality to apps.

I spent some time last week writing my first admission webhook for Kubernetes. I wrote the handler in Golang because I’m most familiar with Golang and because, as Kubernetes’ native language, I was more confident that the necessary SDKs would exist and that the documentation would likely use Golang by default. I struggled to find useful documentation and so this post is to help you (and me!) remember how to do this next time!

For the past couple of weeks, I’ve been playing around with Akri, a Microsoft (DeisLabs) project for building a connected edge with Kubernetes. Kubernetes, IoT, Rust (and Golang) make this all compelling to me.

Initially, I deployed an Akri End-to-End to MicroK8s on Google Compute Engine (link) and Digital Ocean (link). But I was interested to create me own example and so have proposed a very (!) simple HTTP-based protocol.

This blog summarizes my thoughts about Akri and an explanation of the HTTP protocol implementation in the hope that this helps others.

You know when you start something and then regret it!? I think I’ll be sticking with Google Cloud Build; GitHub Actions appears functional and useful but I found the documentation to be confusing and limited and struggled to get a simple container image build|push working.

I’ve long used Docker Hub but am planning to use it less as a result of the planned changes. I want to see Docker succeed and to do so it needs to find a way to make money but, there are free alternatives including the new GitHub Container Registry and the very very cheap Google Container Registry.

Chatting with one of Google’s Trillian team, I was encouraged to explore Trillian’s Map Mode. The following is the result of some spelunking through this unfamiliar cave. I can’t provide any guarantee that this usage is correct or sufficient.

Here’s the repo: https://github.com/DazWilkin/go-trillian-map

I’ve written about Trillian Log Mode elsewhere.

I uncovered use of Trillian Map Mode through Trillian’s integration tests. I’m unclear on the distinction between TrillianMapClient and TrillianMapWriteClient but the latter served most of my needs.

I’ve been playing around with a proof-of-concept combining WASM and Trillian. The hypothesis was to explore using WASM as a form of chaincode with Trillian. The project works but it’s far from being a chaincode-like solution.

Let’s start with a couple of (trivial) examples and then I’ll explain what’s going on and how it’s implemented.

2020/08/14 18:42:17 [main:loop:dynamic-invoke] Method: mul
2020/08/14 18:42:17 [random:New] Message
2020/08/14 18:42:17 [random:New] Float32
2020/08/14 18:42:17 [random:New] Float32
2020/08/14 18:42:17 [random:New] Message
2020/08/14 18:42:17 [random:New] Float32
2020/08/14 18:42:17 [random:New] Float32
2020/08/14 18:42:17 [Client:Invoke] Metadata: complex.wasm
2020/08/14 18:42:17 [main:loop:dynamic-invoke] Success: result:{real:0.036980484 imag:0.3898267}

After shipping a Rust-sourced WASM solution (complex.wasm) to the WASM transparency server, the client invokes a method mul that’s exposed by it using a dynamically generated request message and outputs the response. Woo hoo! Yes, an expensive way to multiple complex numbers.

As my reader will know (Hey Mom!), I’ve been noodling around with WASM and waPC. I’ve been exploring ways to pass structured messages across the host:guest boundary.

Protobufs was my first choice. @KevinHoffman created waPC and waSCC and he explained to me and that wSCC uses Message Pack.

It’s slightly surprising to me (still) that technologies like this exist with everyone else seemingly using them and I’ve not heard of them. I don’t expect to know everything but I’m surprised I’ve not stumbled upon msgpack until now.

Google has a new Golang Protobuf API, APIv2 (google.golang.org/protobuf) superseding APIv1 (github.com/golang/protobuf). If your code is importing github.com/golang/protobuf, you’re using APIv2. Otherwise, you should consult with the docs because Google reimplemented APIv1 atop APIv2. One challenge this caused me, as someone who does not use protobufs and gRPC on a daily basis, is that gRPC (code-generation) is being removed from the (Golang) protoc-gen-go, the Golang plugin that generates gRPC service bindings.

Well, this became more of an adventure that I’d originally wanted but, after learning some BLE and, with the help of others (Thanks Jonatha, JsBergbau), I’ve sample code that connects to 4 Xiaomi 2nd gen. Thermometers, subscribes to readings and publishes the data to MQTT. From there, I’m scraping it using Inuits MQTTGateway into Prometheus.

Repo: https://github.com/DazWilkin/gomijia2

Thanks|Credit:

• Jonathan McDowell for gomijia and help
• JsBergbau for help

Background

I’ve been playing around with ESPHome and blogged around my very positive experience ESPHome, MQTT, Prometheus and almost Cloud IoT. I’ve ordered a couple of ESP32-DevKitC and hope this will enable me to connect to Google Cloud IoT.

<3 Google but there’s quite often an assumption that we’re all sitting around the engineering table and, of course, we’re not.

Cloud Endpoints is a powerful offering but – IMO – it’s super confusing to understand and complex to deploy.

If you’re familiar with the motivations behind service meshes (e.g. Istio), Cloud Endpoints fits in a similar niche (“neesh” or “nitch”?). The underlying ambition is that, developers can take existing code and by adding a proxy (or sidecar), general-purpose abstractions, security, logging etc. may be added.

Update: 2020-03-24: Since writing this post, I’ve contributed Golang and Rust samples to Google’s project. I recommend you start there.

Google explained how to run gRPC servers with Cloud Run. The examples are good but only Python and Node.JS:

• gRPC comes to Cloud Run
• gRPC in Google Cloud Run

Missing Golang…. until now ;-)

I had problems with 1.14 and so I’m using 1.13.

Project structure

I’ll tidy up my repo but the code may be found:

Google has released a new Golang SDK for protobuf. In the [announcement], a useful tool to redact properties is described. If like me, this is somewhat novel to you, here’s a mashup using Google’s Protocol Buffer Basics w/ redaction.

To be very clear, as it’s an important distinction:

Project

Here’s my project structure:

.
├── protoc-3.11.4-linux-x86_64
│   ├── bin
│   │   └── protoc
│   ├── include
│   │   └── google
│   └── readme.txt
└── src
    ├── go.mod
    ├── go.sum
    ├── main.go
    ├── protos
    │   ├── addressbook.pb.go
    │   └── addressbook.proto
    └── README.md

You may structure as you wish.

Recently, I wrote about some initial adventures with OriginStamp

Using OriginStamp’s UI or API, submitting a hash results in transactions being submitted to Bitcoin, Ethereum and a German newspaper.

Using the API, it’s possible to query OriginStamp’s service for a proof. This post explains how to verify such a proof.

The diligent reader among you (Hey Mom!) will recall that I submitted a hash for the message:

Frederik Jack is a bubbly Border Collie

The SHA-256 hash of this message is:

I wrote recently about some exploration of Timestamping with OriginStamp. Since writing that post, I had some supportive feedback from the helpful folks at OriginStamp and plan to continue exploring that solution.

Meanwhile, OriginStamp exposed me to timestamping and trusted timestamping and I discovered freeTSA.org.

What’s the point? These services provide authoritative proof of the existence of a digital asset before some point in time; OriginStamp provides a richer service and uses multiple timestamp authorities including Bitcoin, Ethereum and rather interestingly a German Newspaper’s Trusted Timestamp.

A friend mentioned OriginStamp to me.

NB There are 2 sites: originstamp.com and originstamp.org.

It’s an interesting project.

It’s a solution for providing auditable proof that you had a(ccess to) some digital thing before a certain date. OriginStamp provides user-|developer-friendly means to submit files|hashes (of your content) and have these bundled into transactions that are submitted to e.g. bitcoin.

I won’t attempt to duplicate the narrative here, review OriginStamp’s site and some of its content.

I think it would be valuable if Google were to provide volumes in Cloud Build of package registries (e.g. Go Modules; PyPi; Maven; NPM etc.).

Google provides a mirror of a subset of Docker Hub. This confers several benefits: Google’s imprimatur; speed (latency); bandwidth; and convenience.

The same benefits would apply to package registries.

In the meantime, there’s a hacky way to gain some of the benefits of these when using Cloud Build.

In the following example, I’ll show an approach using Golang Modules and Google’s module proxy aka proxy.golang.org.

The prolific Alex Ellis has a new project, Inlets.

Here’s a quick tutorial using Google Compute Platform’s (GCP) Compute Engine (GCE).

NB I’m using one of Google’s “Always free” f1-micro instances but you may still pay for network *gress and storage

Assumptions

I’m assuming you’ve a Google account, have used GCP and have a billing account established, i.e. the following returns at least one billing account:

gcloud beta billing accounts list

If you’ve only one billing account and it’s the one you wish to use, then you can:

I’ve spent a few days exploring [Google Fit SDK] as I try to wean myself from my obsession with metrics (of all forms). A quick Googling got me to Robert’s Exporter Google Fit Daily Steps, Weight and Distance to a Google Sheet. This works and is probably where I should have stopped… avoiding the rabbit hole that I’ve been down…

I threw together a simple Golang implementation of the SDK using Google’s Golang API Client Library. Thanks to Robert’s example, I was able to infer some of the complexity this API particularly in its use of data types, data sources and datasets. Having used Stackdriver in my previous life, Google Fit’s structure bears more than a passing resemblance to Stackdriver’s data model and its use of resource types and metric types.

I’m obsessing over Prometheus exporters. First came Linode Exporter, then GCP Exporter and, on Sunday, I stumbled upon a reverse-engineered API for Google Home devices and so wrote a very basic Google Home SDK and a similarly basic Google Home Exporter:

The SDK only implements /setup/eureka_info and then only some of the returned properties. There’s not a lot of metric-like data to use besides SignalLevel (signal_level) and NoiseLevel (noise_level). I’m not clear on the meaning of some of the properties.

Earlier this week I discussed a Linode Prometheus Exporter.

I added metrics for Digital Ocean’s Managed Kubernetes service to @metalmatze’s Digital Ocean Exporter.

This left, metrics for Google Cloud Platform (GCP) which has, for many years, been my primary cloud platform. So, today I wrote Prometheus Exporter for Google Cloud Platform.

All 3 of these exporters follow the template laid down by @metalmatze and, because each of these services has a well-written Golang SDK, it’s straightforward to implement an exporter for each of them.

I enjoy using Prometheus and have toyed around with it for some time particularly in combination with Kubernetes. I signed up with Linode [referral] compelled by the addition of a managed Kubernetes service called Linode Kubernetes Engine (LKE). I have an anxiety that I’ll inadvertently leave resources running (unused) on a cloud platform. Instead of refreshing the relevant billing page, it struck me that Prometheus may (not yet proven) help.

The hypothesis is that a combination of a cloud-specific Prometheus exporter reporting aggregate uses of e.g. Linodes (instances), NodeBalancers, Kubernetes clusters etc., could form the basis of an alert mechanism using Prometheus’ alerting.

I’ve been noodling around with another Trillian personality.

Another in a theme that interests me in providing tamperproof logs for the packages in the popular package management registries.

The Golang team recently announced Go Module Mirror which is built atop Trillian. It seems to me that all the package registries (Go Modules, npm, Maven, NuGet etc.) would benefit from tamperproof logs hosted by a trusted 3rd-party.

As you may have guessed, PyPi Transparency is a log for PyPi packages. PyPi is comprehensive, definitive and trusted but, as with Go Module Mirror, it doesn’t hurt to provide a backup of some of its data. In the case of this solution, Trillian hosts a log of self-calculated SHA-256 hashes for Python packages that are added to it.

Tweaked yesterday’s solution so that it will randomly select one from several hosts with which it’s configured.

package proxy

import (
	"log"
	"math/rand"
	"net/http"
	"net/url"
	"os"
	"strings"
	"time"
)

func robin() {
	hostsList := os.Getenv("PROXY_HOST")
	if hostsList == "" {
		log.Fatal("'PROXY_HOST' environment variable should contain comma-separated list of hosts")
	}
    // Comma-separated lists of hosts
    hosts := strings.Split(hostsList, ",")
    
    urls := make([]*url.URL, len(hosts))
    for i, host := range hosts {
		var origin = Endpoint{
			Host: host,
			Port: os.Getenv("PROXY_PORT"),
		}
		url, err := origin.URL()
		if err != nil {
			log.Fatal(err)
		}
		urls[i] = url
	}

	s := rand.NewSource(time.Now().UnixNano())
	q := rand.New(s)

	Handler = func(w http.ResponseWriter, r *http.Request) {
		// Pick one of the URLs at random
		url := urls[q.Int31n(int32(len(urls)))]
		log.Printf("[Handler] Forwarding: %s", url.String())
        // Forward to it
        reverseproxy(url, w, r)
	}
}

This requires a minor tweak to the deployment to escape the commas within the PROXY_HOST string to disambiguate these for gcloud:

I’m investigating the use of LetsEncrypt for gRPC services. I found this straightforward post by Scott Devoid and am going to try this approach.

Before I can do that, I need to be able to publish services (make them Internet-accessible) and would like to try to continue to use GCP for free.

Some time ago, I wrote about using the excellent Microk8s on GCP. Using an f1-micro, I’m hoping (!) to stay within the Compute Engine free tier. I’ll also try to be diligent and delete the instance when it’s not needed. This gives me a runtime platform and I can expose services to the Instance’s (Node)Ports but, I’d prefer to not be billed for a simple proxy.

The goal of pypi-transparency is very similar to the underlying motivation for the Golang team’s Checksum Database (also built with Trillian).

Even though, PyPi provides hashes of the content of packages it hosts, the developer must trust that PyPi’s data is consistent. One ambition with pypi-transparency is to provide a companion, tamperproof log of PyPi package files in order to provide a double-check of these hashes.

It is important to understand what this does (and does not) provide. There’s no validation of a package’s content. The only calculation is that, on first observation, a SHA-256 hash is computed of the package’s content and the hash is recorded. If the package is subsequently altered, it’s very probable that the hash will change and this provides a signal to the user that the package’s contents has changed. Because pypi-transparency uses a tamperproof log, it’s very difficult to update the hash recorded in the tamperproof log, to reflect this change. Corrolary: pypi-transparency will record the hashes of packages that include malicious code.

Read FauxRPC + Testcontainers on Hacker News and was intrigued. I spent a little more time “evaluating” this than I’d planned because I’m forcing myself to use rust as much as possible and my ignorance (see below) caused me some challenges.

The technology is interesting and works well. The experience helped me explore Testcontainers too which I’d heard about but not explored until this week.

For my future self:

I started following along with FauxRPC’s Testcontainers example but, being unfamiliar with Connect, I wasn’t familiar with its Eliza service. The service is available on demo.connectrpc.com:443 and is described using eliza.proto as part of examples-go. Had I realized this sooner, I would have used this example rather than the Health Checking protocol.

General

You’ll need a Google Cloud project with Cloud Translation (translate.googleapis.com) enabled and a Service Account (and key) with suitable permissions in order to run the following.

BILLING="..." # Your Billing ID (gcloud billing accounts list)
PROJECT="..." # Your Project ID
ACCOUNT="tester"

EMAIL="${ACCOUNT}@${PROJECT}.iam.gserviceaccount.com"

ROLES=(
    "roles/cloudtranslate.user"
    "roles/serviceusage.serviceUsageConsumer"
)

# Create Project
gcloud projects create ${PROJECT}

# Associate Project with your Billing Account
gcloud billing accounts link ${PROJECT} \
--billing-account=${BILLING}

# Enable Cloud Translation
gcloud services enable translate.googleapis.com \
--project=${PROJECT}

# Create Service Account
gcloud iam service-accounts create ${ACCOUNT} \
--project=${PROJECT}

# Create Service Account Key
gcloud iam service-accounts keys create ${PWD}/${ACCOUNT}.json \
--iam-account=${EMAIL} \
--project=${PROJECT}

# Update Project IAM permissions
for ROLE in "${ROLES[@]}"
do
  gcloud projects add-iam-policy-binding ${PROJECT} \
  --member=serviceAccount:${EMAIL} \
  --role=${ROLE}
done

For the code, you’ll need to install protoc and preferably have it in your path.

Referring to Accessing Google Services using gRPC, I wanted to query a project’s Cloud Logging for log-based metrics using gRPC.

In summary:

ENDPOINT="logging.googleapis.com:443"

ROOT="/path/to/googleapis" # https://github.com/googleapis/googleapis
PACKAGE="google/logging/v2"

# NB Not logging.proto
PROTO="${ROOT}/${PACKAGE}/logging_metrics.proto"

TOKEN=$(gcloud auth print-access-token)

PROJECT="..."

PACKAGE="google.logging.v2"
SERVICE="MetricsServiceV2"
METHOD="${PACKAGE}.${SERVICE}/ListLogMetrics"

# ListLogMetricsRequest fields
PARENT="projects/${PROJECT}"

grpcurl \
--import-path=${ROOT} \
--proto=${PROTO} \
-H "Authorization: Bearer ${TOKEN}" \
-d "{\"parent\": \"${PARENT}\"}" \
${ENDPOINT} ${METHOD}

From APIs Explorer, Cloud Logging API v2, instead of the REST reference, browse the gRPC reference specifically the package google.logging.v2 which includes MetricsServiceV2. We’re interested in the ListLogMetrics method (which unfortunately isn’t directly hyperlinkable) but is defined to be:

Google publishes interface definitions of Google APIs (services) that support REST and gRPC in a repo called Google APIs. Google’s SDKs uses gRPC to access these services but, how to do this using e.g. gRPCurl?

I wanted to debug Cloud Profiler and its agent makes UpdateProfile RPCs to cloudprofiler.googleapis.com. Cloud Profiler is more challenging service to debug because (a) it’s publicly “write-only”; and (b) it has complex messages. UpdateProfile sends UpdateProfileRequest messages that include Profile messages that include profile_bytes which are gzip compressed serialized protos of pprof’s Profile.

I’ve written previously (Google Trillian for Noobs) about Google’s interesting project Trillian and about some of the “personalities” (e.g. PyPi Transparency) that I’ve build using it.

Having gone slight cra-cra on Cloud Run and gRPC this week with Golang gRPC Cloud Run and gRPC, Cloud Run & Endpoints, I thought it’d be fun to deploy Trillian and a personality to Cloud Run.

It mostly (!) works :-)

At the end of the post, I’ve summarized creating a Cloud SQL instance to host the Trillian data(base).

<3 Google but there’s quite often an assumption that we’re all sitting around the engineering table and, of course, we’re not.

Cloud Endpoints is a powerful offering but – IMO – it’s super confusing to understand and complex to deploy.

If you’re familiar with the motivations behind service meshes (e.g. Istio), Cloud Endpoints fits in a similar niche (“neesh” or “nitch”?). The underlying ambition is that, developers can take existing code and by adding a proxy (or sidecar), general-purpose abstractions, security, logging etc. may be added.

Update: 2020-03-24: Since writing this post, I’ve contributed Golang and Rust samples to Google’s project. I recommend you start there.

Google explained how to run gRPC servers with Cloud Run. The examples are good but only Python and Node.JS:

• gRPC comes to Cloud Run
• gRPC in Google Cloud Run

Missing Golang…. until now ;-)

I had problems with 1.14 and so I’m using 1.13.

Project structure

I’ll tidy up my repo but the code may be found:

Cloud Run supports gRPC startup|liveness probes which I’d not used before.

I’m using Cloud Run v2 and specifically projects.locations.services.create and Service

PROJECT="..."
REGION="..."
REPO=".."

# Must be in an Artifact Registry repo
IMAGE="${REGION}-docker.pkg.dev/${PROJECT}/${REPO}/..."

# Run v2
ENDPOINT="https://run.googleapis.com/v2"

PARENT="projects/${PROJECT}/locations/${REGION}"

SERVICE="..."

I like to use Jsonnet (specifically go-jsonnet) to help templating Kubernetes(-like) deployments.

cloudrun.jsonnet:

local project = std.extVar("project");
local region = std.extVar("region");
local service = std.extVar("service");
local image = std.extVar("image");

local port = 8080;
local health_checking_service = "foo";

{
    "labels":{
        "type": "test"
    },
    "annotations": {
        "type": "test"
    },
    "template":{
        "containers": {
            "name": service,
            "image": image,
            "args": [],
            "resources": {
                "limits": {
                    "cpu": "1000m",
                    "memory": "512Mi"
                }
            },
            "ports": [
                {
                    "name": "http1",
                    "containerPort": port
                }
            ],
            "startupProbe": {
                "grpc": {
                    "port": port,
                    "service": health_checking_service
                }
            }
        },
        "scaling": {
            "maxInstanceCount": 1
        }
    }
}

And deploy it using:

I have several Cloud Run services that I want to map to a domain.

During development, I create a Google Cloud Platform (GCP) project each day into which everything is deployed. This means that, every day, the Cloud Run services have newly non-inferable (to me) URLs. I thought this would be tedious to manage because:

1. My DNS service isn’t programmable (I know!)
2. Cloud Run services have non-inferable (by me) URLs

i.e. I thought I’d have to manually update the DNS entries each day.

Some time ago, I wrote about using Prometheus Service Discovery w/ Consul for Cloud Run and also Scraping metrics exposed by Google Cloud Run services that require authentication. Both solutions remain viable but they didn’t address another use case for Prometheus and Cloud Run services that I have with a “thing” that I’ve been building.

In this scenario, I want to:

1. Configure Prometheus to scrape Cloud Run service metrics
2. Discover Cloud Run services dynamically
3. Authenticate to Cloud Run using Firebase Auth ID tokens

These requirements and – one other – present several challenges:

I’m using Firebase Authentication in a project to authenticate users of various OAuth2 identity systems. Firebase Authentication requires a set of Authorized Domains.

The (web) app that interacts with Firebase Authentication is deployed to Cloud Run. The Authorized Domains list must include the app’s Cloud Run service URL.

Cloud Run service URLs vary by Project (ID). They are a combination of the service name, a hash (?) of the Project (ID) and .a.run.app.

I’ve written a solution (gcp-oidc-token-proxy) that can be used in conjunction with Prometheus OAuth2 to authenticate requests so that Prometheus can scrape metrics exposed by e.g. Cloud Run services that require authentication. The solution resulted from my question on Stack overflow.

Problem #1: Endpoint requires authentication

Given a Cloud Run service URL for which:

ENDPOINT="my-server-blahblah-wl.a.run.app"

# Returns 200 when authentication w/ an ID token
TOKEN="$(gcloud auth print-identity-token)"
curl \
--silent \
--request GET \
--header "Authorization: Bearer ${TOKEN}" \
--write-out "%{response_code}" \
--output /dev/null \
https://${ENDPOINT}/metrics

# Returns 403 otherwise
curl \
--silent \
--request GET \
--write-out "%{response_code}" \
--output /dev/null \
https://${ENDPOINT}/metrics

Problem #2: Prometheus OAuth2 configuration is constrained

TL;DR I’m working on a project that includes multiple Cloud Run services. I’ve been putting my gcloud head on to deploy these services thinking that it’s curious there’s no way to write the specs as YAML configs. Today, I learned that there is: gcloud beta run services replace.

What prompted the discovery was some frustration trying to deploy a JSON-valued environment variable to Cloud Run:

  local FIREBASE_CONFIG="{
    apiKey: ${FIREBASE_API_KEY},
    authDomain: ${FIREBASE_AUTH_DOMAIN},
    projectId: ${FIREBASE_PROJECT},
    storageBucket: ${FIREBASE_STORAGE_BUCKET},
    messagingSenderId: ${FIREBASE_MESSAGING_SENDER},
    appId: ${FIREBASE_APP}}"

  gcloud run deploy ${SRV_NAME} \
  --image=${IMAGE} \
  --command="/server" \
  --args="--endpoint=:${PORT}" \
  --set-env-vars=FIREBASE_CONFIG="${FIREBASE_CONFIG}" \
  --max-instances=1 \
  --memory=256Mi \
  --ingress=all \
  --platform=managed \
  --port=${PORT} \
  --allow-unauthenticated \
  --region=${REGION} \
  --project=${PROJECT}

gcloud balks at this.

See:

• Multiplexing gRPC and HTTP endpoints with Cloud Run
• gRPC, Cloud Run & Endpoints
• ESPv2: Configure Cloud Endpoints to proxy traffic to a Cloud Run multiplexed (gRPC|HTTP) service

Challenges:

• Cloud Run permits single port
• Cloud Run services publishing e.g. gRPC and Prometheus, must multiplex transports
• Cloud Run services publishing multiplexed transports are challenging to expose using Cloud Endpoints

Hypothesis #1: Multiplexed transports work with Cloud Run

See: Multiplexing gRPC and HTTP endpoints with Cloud Run

I’ve written a basic discoverer of Google Cloud Run services. This is for a project and it extends work done in some previous posts to Multiplex gRPC and Prometheus with Cloud Run and to use Consul for Prometheus service discovery.

This solution:

• Accepts a set of Google Cloud Platform (GCP) projects
• Trawls them for Cloud Run services
• Assumes that the services expose Prometheus metrics on :443/metrics
• Relabels the services
• Surfaces any discovered Cloud Run services’ metrics in Prometheus

You’ll need Docker and Docker Compose.

Google Cloud Run is useful but, each service is limited to exposing a single port. This caused me problems with a gRPC service that serves (non-gRPC) Prometheus metrics because customarily, you would serve gRPC on one port and the Prometheus metrics on another.

Fortunately, cmux provides a solution by providing a mechanism that multiplexes both services (gRPC and HTTP) on a single port!

TL;DR See the cmux Limitations and use:

grpcl := m.MatchWithWriters(
   cmux.HTTP2MatchHeaderFieldSendSettings("content-type", "application/grpc"))

Extending the example from the cmux repo:

I’m working on a project that will programmatically create Google Cloud Run services and I want to be able to dynamically discover these services using Prometheus.

This is one solution.

NOTE Google Cloud Run is the service I’m using, but the principle described herein applies to any runtime service that you’d wish to use.

Why is this challenging? IIUC, it’s primarily because Prometheus has a limited set of plugins for service discovery, see the sections that include _sd_ in Prometheus Configuration documentation. Unfortunately, Cloud Run is not explicitly supported. The alternative appears to be to use file-based discovery but this seems ‘challenging’; it requires, for example, reloading Prometheus on file changes.

Phew! Programmitcally deploying Cloud Run services should be easy but it didn’t find it so.

My issues were that the Cloud Run Admin (!) API is poorly documented and it uses non-standard endpoints (thanks Sal!). Here, for others who may struggle with this, is how I got this to work.

Goal

Programmatically (have Golang, Python, want Rust) deploy services to Cloud Run.

i.e. achieve this:

gcloud run deploy ${NAME} \
--image=${IMAGE} \
--platform=managed \
--no-allow-unauthenticated \
--region=${REGION} \
--project=${PROJECT}

TRICK --log-http is your friend

I’ve written previously (Google Trillian for Noobs) about Google’s interesting project Trillian and about some of the “personalities” (e.g. PyPi Transparency) that I’ve build using it.

Having gone slight cra-cra on Cloud Run and gRPC this week with Golang gRPC Cloud Run and gRPC, Cloud Run & Endpoints, I thought it’d be fun to deploy Trillian and a personality to Cloud Run.

It mostly (!) works :-)

At the end of the post, I’ve summarized creating a Cloud SQL instance to host the Trillian data(base).

<3 Google but there’s quite often an assumption that we’re all sitting around the engineering table and, of course, we’re not.

Cloud Endpoints is a powerful offering but – IMO – it’s super confusing to understand and complex to deploy.

If you’re familiar with the motivations behind service meshes (e.g. Istio), Cloud Endpoints fits in a similar niche (“neesh” or “nitch”?). The underlying ambition is that, developers can take existing code and by adding a proxy (or sidecar), general-purpose abstractions, security, logging etc. may be added.

Update: 2020-03-24: Since writing this post, I’ve contributed Golang and Rust samples to Google’s project. I recommend you start there.

Google explained how to run gRPC servers with Cloud Run. The examples are good but only Python and Node.JS:

• gRPC comes to Cloud Run
• gRPC in Google Cloud Run

Missing Golang…. until now ;-)

I had problems with 1.14 and so I’m using 1.13.

Project structure

I’ll tidy up my repo but the code may be found:

Cloud Run supports gRPC startup|liveness probes which I’d not used before.

I’m using Cloud Run v2 and specifically projects.locations.services.create and Service

PROJECT="..."
REGION="..."
REPO=".."

# Must be in an Artifact Registry repo
IMAGE="${REGION}-docker.pkg.dev/${PROJECT}/${REPO}/..."

# Run v2
ENDPOINT="https://run.googleapis.com/v2"

PARENT="projects/${PROJECT}/locations/${REGION}"

SERVICE="..."

I like to use Jsonnet (specifically go-jsonnet) to help templating Kubernetes(-like) deployments.

cloudrun.jsonnet:

local project = std.extVar("project");
local region = std.extVar("region");
local service = std.extVar("service");
local image = std.extVar("image");

local port = 8080;
local health_checking_service = "foo";

{
    "labels":{
        "type": "test"
    },
    "annotations": {
        "type": "test"
    },
    "template":{
        "containers": {
            "name": service,
            "image": image,
            "args": [],
            "resources": {
                "limits": {
                    "cpu": "1000m",
                    "memory": "512Mi"
                }
            },
            "ports": [
                {
                    "name": "http1",
                    "containerPort": port
                }
            ],
            "startupProbe": {
                "grpc": {
                    "port": port,
                    "service": health_checking_service
                }
            }
        },
        "scaling": {
            "maxInstanceCount": 1
        }
    }
}

And deploy it using:

This is so useful that it’s worth its own post.

I write many gRPC services. As these generally run securely, it’s best to test them that way too but, even with e.g. Let’s Encrypt, it can be challenging to generate appropriate TLS certs.

Tailscale makes this trivial.

Assuming there’s a gRPC service running on localhost:50051, we want to avoid -plaintext:

PORT="50051"

grpcurl \
-plaintext 0.0.0.0:${PORT} \
list

NOTE I’m using list and assuming your service has reflection enabled but you can, of course, use relevant methods.

General

You’ll need a Google Cloud project with Cloud Translation (translate.googleapis.com) enabled and a Service Account (and key) with suitable permissions in order to run the following.

BILLING="..." # Your Billing ID (gcloud billing accounts list)
PROJECT="..." # Your Project ID
ACCOUNT="tester"

EMAIL="${ACCOUNT}@${PROJECT}.iam.gserviceaccount.com"

ROLES=(
    "roles/cloudtranslate.user"
    "roles/serviceusage.serviceUsageConsumer"
)

# Create Project
gcloud projects create ${PROJECT}

# Associate Project with your Billing Account
gcloud billing accounts link ${PROJECT} \
--billing-account=${BILLING}

# Enable Cloud Translation
gcloud services enable translate.googleapis.com \
--project=${PROJECT}

# Create Service Account
gcloud iam service-accounts create ${ACCOUNT} \
--project=${PROJECT}

# Create Service Account Key
gcloud iam service-accounts keys create ${PWD}/${ACCOUNT}.json \
--iam-account=${EMAIL} \
--project=${PROJECT}

# Update Project IAM permissions
for ROLE in "${ROLES[@]}"
do
  gcloud projects add-iam-policy-binding ${PROJECT} \
  --member=serviceAccount:${EMAIL} \
  --role=${ROLE}
done

For the code, you’ll need to install protoc and preferably have it in your path.

I responded to a question Prometheus metric protocol buffer in gRPC on Stackoverflow and it piqued my curiosity and got me yak shaving.

Prometheus used to support two exposition formats including Protocol Buffers, then dropped Protocol Buffer and has since re-added it (see Protobuf format). The Protobuf format has returned to support the experimental Native Histograms feature.

I’m interested in adding Native Histogram support to Ackal so thought I’d learn more about this metric.

This Stackoverflow question piqued my interest:

retry policy configuration for grpc not working

Service Config in gRPC is new to me but, my initial suspicion (albeit incorrect) was that the JSON types were incorrect.

I decided to try using the Protocol Buffer source service_config.proto to verify the JSON.

To do so I needed to compile the source…. it was gnarly.

There are 2 repos used:

• googleapis
• grpc-proto

The service_config.proto includes options for java_package but no go_package.

Referring to Accessing Google Services using gRPC, I wanted to query a project’s Cloud Logging for log-based metrics using gRPC.

In summary:

ENDPOINT="logging.googleapis.com:443"

ROOT="/path/to/googleapis" # https://github.com/googleapis/googleapis
PACKAGE="google/logging/v2"

# NB Not logging.proto
PROTO="${ROOT}/${PACKAGE}/logging_metrics.proto"

TOKEN=$(gcloud auth print-access-token)

PROJECT="..."

PACKAGE="google.logging.v2"
SERVICE="MetricsServiceV2"
METHOD="${PACKAGE}.${SERVICE}/ListLogMetrics"

# ListLogMetricsRequest fields
PARENT="projects/${PROJECT}"

grpcurl \
--import-path=${ROOT} \
--proto=${PROTO} \
-H "Authorization: Bearer ${TOKEN}" \
-d "{\"parent\": \"${PARENT}\"}" \
${ENDPOINT} ${METHOD}

From APIs Explorer, Cloud Logging API v2, instead of the REST reference, browse the gRPC reference specifically the package google.logging.v2 which includes MetricsServiceV2. We’re interested in the ListLogMetrics method (which unfortunately isn’t directly hyperlinkable) but is defined to be:

The majority of Ackal’s components are deployed to Google Cloud. However, by its nature, Ackal benefits from deployments that span cloud platforms. I’ve deployed Ackal’s gRPC health checks to Fly, and managed Kubernetes services on Linode and Vultr.

Today, I decided to revisit¹ Azure. Ackal uses Azure (Active Directory) for one of its OAuth providers. This time, I wanted to deploy a containerized gRPC service. Azure provides several container-oriented services. I decided to use Azure Container Apps and, in hindsight, find it analogous to Google Cloud Run.

Google publishes interface definitions of Google APIs (services) that support REST and gRPC in a repo called Google APIs. Google’s SDKs uses gRPC to access these services but, how to do this using e.g. gRPCurl?

I wanted to debug Cloud Profiler and its agent makes UpdateProfile RPCs to cloudprofiler.googleapis.com. Cloud Profiler is more challenging service to debug because (a) it’s publicly “write-only”; and (b) it has complex messages. UpdateProfile sends UpdateProfileRequest messages that include Profile messages that include profile_bytes which are gzip compressed serialized protos of pprof’s Profile.

NOTE cert-manager is a better solution to what follows.

I wrote about deploying Secure (TLS) gRPC services with Vultr Kubernetes Engine (VKE). This week, I’ve reproduced this deployment using Linode Kubernetes Engine (LKE).

Thanks to the consistency provided by Kubernetes, the Kubernetes programming is almost identical. The main differences are between the CLI’s provided by these platforms. Both are good. They’re just different.

I’m going to include the linode-cli commands I’m using in this post as I found it slightly more quirky.

NOTE cert-manager is a better solution to what follows.

I’ve a need to deploy a Vultr Kubernetes Engine (VKE) cluster on a daily basis (create and delete within a few hours) and expose (securely|TLS) a gRPC service.

I have an existing solution Automatic Certs w/ Golang gRPC service on Compute Engine that combines a gRPC Healthchecking and an ACME service and decided to reuse this.

In order for it work, we need:

Last year, I wrote about using Automatic Certs w/ Golang gRPC service on Compute Engine. That solution uses ACME with (the wonderful) Let’s Encrypt. Google is offering a private preview of Automate Public Certificates Lifecycle Management via RFC 8555 (ACME) and, because I’m using Google Cloud Platform extensively to build a “thing” and I think it would be useful to have a backup to Let’s Encrypt, I thought I’d give the solution a try. You’ll need to sign-up for the private preview, for what follows to work.

I needed to deploy a healthcheck-enabled gRPC TLS-enabled service. Fortunately, most (all?) of the SDKs include an implementation, e.g. Golang has grpc-go/health.

I learned in my travels that:

• DigitalOcean [App] platform does not (link) work with TLS-based gRPC apps.
• Fly has a regression (link) that breaks gRPC

So, I resorted to Google Cloud Platform (GCP). Although Cloud Run would be well-suited to running the gRPC app, it uses a proxy|sidecar to provision a cert for the app and I wanted to be able to (easily use a custom domain) and give myself a somewhat general-purpose solution.

I’ve written before about a project in which I’m using Firebase Authentication in combination with Google Cloud Endpoints and a gRPC service running on Cloud Run:

• Firebase Authentication, Cloud Endpoints and gRPC (1of2)
• Firebase Authentication, Cloud Endpoints and gRPC (2of2)

This works well with one caveat, the ID tokens (JWTs) minted by Firebase Authentication have a 3600 second (one hour) lifetime.

The user flow in my app is that whenever the user invokes the app’s CLI:

For… reasons, I wanted to pre-filter gRPC requests to check for authorization. Authorization is implemented as a ‘micro-service’ and I wanted the authorization server to run in the same process as the gRPC client.

TL;DR:

• Shiju’s “Writing gRPC Interceptors in Go” is great
• This Stack overflow answer ostensibly for writing unit tests for gRPC got me an in-process server

What follows stands on these folks’ shoulders…

A key motivator for me to write blog posts is that helps me ensure that I understand things. Writing this post, I realized I’d not researched gRPC Interceptors and, as luck would have it, I found some interesting content, not on grpc.io but on the grpc-ecosystem repo, specifically Go gRPC middleware. But, I refer again to Shiju’s clear and helpful “Writing gRPC Interceptors in Go”

Earlier this week, I wrote about using Firebase Authentcation, Cloud Endpoints and gRPC (1of2). Since then, I learned some more and added a gRPC interceptor to implement basic authorization for the service.

ESPv2 --allow-unauthenticated

The Cloud Enpoints (ESPv2) proxy must be run as --allow-unauthenticated on Cloud Run to ensure that requests make it to the proxy where the request is authenticated and only authenticated requests make it on to the backend service. Thanks Google’s Teju Nareddy!

I’m building a service that requires user authentication. The primary endpoint is a gRPC-based service. I would like to consider using certificate-based auth but this feels… challenging. Instead, I have been aware of, but never used, Firebase Authentication and was interested to see that Cloud Endpoints includes Firebase Authentication as one of its supported auth mechanisms. Curiosity piqued, I confirmed that gRPC supports Google token-based authentication.

The following is a summary of what I did but I’ll leave the extensive documentation to Google, (Google’s) Firebase and gRPC, all of which, in this case, provide really good explanations.

See:

• Multiplexing gRPC and HTTP endpoints with Cloud Run
• gRPC, Cloud Run & Endpoints
• ESPv2: Configure Cloud Endpoints to proxy traffic to a Cloud Run multiplexed (gRPC|HTTP) service

Challenges:

• Cloud Run permits single port
• Cloud Run services publishing e.g. gRPC and Prometheus, must multiplex transports
• Cloud Run services publishing multiplexed transports are challenging to expose using Cloud Endpoints

Hypothesis #1: Multiplexed transports work with Cloud Run

See: Multiplexing gRPC and HTTP endpoints with Cloud Run

Google Cloud Run is useful but, each service is limited to exposing a single port. This caused me problems with a gRPC service that serves (non-gRPC) Prometheus metrics because customarily, you would serve gRPC on one port and the Prometheus metrics on another.

Fortunately, cmux provides a solution by providing a mechanism that multiplexes both services (gRPC and HTTP) on a single port!

TL;DR See the cmux Limitations and use:

grpcl := m.MatchWithWriters(
   cmux.HTTP2MatchHeaderFieldSendSettings("content-type", "application/grpc"))

Extending the example from the cmux repo:

I spent some time over the weekend understanding Fly.io. It’s always fascinating to me how many smart people are building really neat solutions. Fly.io is subtly different to other platforms that I use (Kubernetes, GCP, DO, Linode) and I’ve found the Fly.io team to be highly responsive and helpful to my noob questions.

One of the team’s posts, Docker without Docker surfaced in my Feedly feed (hackernews) and it piqued my interest.

It’s a good name, I read it as “dapper” but I frequently type “darp” :-(

Was interested to read that Dapr is now v1.0 and decided to check it out. I was initially confused between Dapr and service mesh functionality. But, having used Dapr, it appears to be more focused in aiding the development of (cloud-native) (distributed) apps by providing developers with abstractions for e.g. service discovery, eventing, observability whereas service meshes feel (!) more oriented towards simplifying the deployment of existing apps. Both use the concept of proxies, deployed alongside app components (as sidecars on Kubernetes) to provide their functionality to apps.

Following on from waPC & Protobufs, I can now remotely invoke (arbitrary) WASM functions:

Client:

The logging isn’t perfectly clear but, the client gets (a previously added) WASM binary from the server (using the SHA-256 of the WASM binary as a unique identifier). The result includes metadata that includes a protobuf descriptor of the WASM binary’s functions. The descriptor defines gRPC services (that represent the WASM functions) with input (parameters) and output (results) messages.

I’ve been hacking on a Rust-based transparent application for Google Trillian. As appears to be my fixation, this personality is for another package manager. This time, Rust’s Crates often found in crates.io which is Rust’s Package Registry. I discussed this project earlier this month Rust Crate Transparency && Rust SDK for Google Trillian and and earlier approach for Python’s packages with pypi-transparency.

This time, of course, I’m using Rust. And, by way of a first for me, for the gRPC server implementation (aka “personality”). I’ve been lazy thanks to the excellent gRPCurl and have been using it way of a client. Because I’m more familiar with Golang and because I’ve written (most) other Trillian personalities in Golang, I resorted to quickly implementing Crate Transparency in Golang too in order to uncover bugs with the Rust implementation. I’ll write a follow-up post on the complexity I seem to struggle with when using protobufs and gRPC [in Golang].

I’m noodling the utility of a Transparency solution for Rust Crates. When developers push crates to Cargo, a bunch of metadata is associated with the crate. E.g. protobuf. As with Golang Modules, Python packages on PyPi etc., there appears to be utility in making tamperproof recordings of these publications. Then, other developers may confirm that a crate pulled from cates.io is highly unlikely to have been changed.

On Linux, Cargo stores downloaded crates under ${HOME}/.crates/registry. In the case of the latest version (2.12.0) of protobuf, on my machine, I have:

I’ve written previously (Google Trillian for Noobs) about Google’s interesting project Trillian and about some of the “personalities” (e.g. PyPi Transparency) that I’ve build using it.

Having gone slight cra-cra on Cloud Run and gRPC this week with Golang gRPC Cloud Run and gRPC, Cloud Run & Endpoints, I thought it’d be fun to deploy Trillian and a personality to Cloud Run.

It mostly (!) works :-)

At the end of the post, I’ve summarized creating a Cloud SQL instance to host the Trillian data(base).

<3 Google but there’s quite often an assumption that we’re all sitting around the engineering table and, of course, we’re not.

Cloud Endpoints is a powerful offering but – IMO – it’s super confusing to understand and complex to deploy.

If you’re familiar with the motivations behind service meshes (e.g. Istio), Cloud Endpoints fits in a similar niche (“neesh” or “nitch”?). The underlying ambition is that, developers can take existing code and by adding a proxy (or sidecar), general-purpose abstractions, security, logging etc. may be added.

Update: 2020-03-24: Since writing this post, I’ve contributed Golang and Rust samples to Google’s project. I recommend you start there.

Google explained how to run gRPC servers with Cloud Run. The examples are good but only Python and Node.JS:

• gRPC comes to Cloud Run
• gRPC in Google Cloud Run

Missing Golang…. until now ;-)

I had problems with 1.14 and so I’m using 1.13.

Project structure

I’ll tidy up my repo but the code may be found:

Tweaked yesterday’s solution so that it will randomly select one from several hosts with which it’s configured.

package proxy

import (
	"log"
	"math/rand"
	"net/http"
	"net/url"
	"os"
	"strings"
	"time"
)

func robin() {
	hostsList := os.Getenv("PROXY_HOST")
	if hostsList == "" {
		log.Fatal("'PROXY_HOST' environment variable should contain comma-separated list of hosts")
	}
    // Comma-separated lists of hosts
    hosts := strings.Split(hostsList, ",")
    
    urls := make([]*url.URL, len(hosts))
    for i, host := range hosts {
		var origin = Endpoint{
			Host: host,
			Port: os.Getenv("PROXY_PORT"),
		}
		url, err := origin.URL()
		if err != nil {
			log.Fatal(err)
		}
		urls[i] = url
	}

	s := rand.NewSource(time.Now().UnixNano())
	q := rand.New(s)

	Handler = func(w http.ResponseWriter, r *http.Request) {
		// Pick one of the URLs at random
		url := urls[q.Int31n(int32(len(urls)))]
		log.Printf("[Handler] Forwarding: %s", url.String())
        // Forward to it
        reverseproxy(url, w, r)
	}
}

This requires a minor tweak to the deployment to escape the commas within the PROXY_HOST string to disambiguate these for gcloud:

I’m investigating the use of LetsEncrypt for gRPC services. I found this straightforward post by Scott Devoid and am going to try this approach.

Before I can do that, I need to be able to publish services (make them Internet-accessible) and would like to try to continue to use GCP for free.

Some time ago, I wrote about using the excellent Microk8s on GCP. Using an f1-micro, I’m hoping (!) to stay within the Compute Engine free tier. I’ll also try to be diligent and delete the instance when it’s not needed. This gives me a runtime platform and I can expose services to the Instance’s (Node)Ports but, I’d prefer to not be billed for a simple proxy.

Cloud Run supports gRPC startup|liveness probes which I’d not used before.

I’m using Cloud Run v2 and specifically projects.locations.services.create and Service

PROJECT="..."
REGION="..."
REPO=".."

# Must be in an Artifact Registry repo
IMAGE="${REGION}-docker.pkg.dev/${PROJECT}/${REPO}/..."

# Run v2
ENDPOINT="https://run.googleapis.com/v2"

PARENT="projects/${PROJECT}/locations/${REGION}"

SERVICE="..."

I like to use Jsonnet (specifically go-jsonnet) to help templating Kubernetes(-like) deployments.

cloudrun.jsonnet:

local project = std.extVar("project");
local region = std.extVar("region");
local service = std.extVar("service");
local image = std.extVar("image");

local port = 8080;
local health_checking_service = "foo";

{
    "labels":{
        "type": "test"
    },
    "annotations": {
        "type": "test"
    },
    "template":{
        "containers": {
            "name": service,
            "image": image,
            "args": [],
            "resources": {
                "limits": {
                    "cpu": "1000m",
                    "memory": "512Mi"
                }
            },
            "ports": [
                {
                    "name": "http1",
                    "containerPort": port
                }
            ],
            "startupProbe": {
                "grpc": {
                    "port": port,
                    "service": health_checking_service
                }
            }
        },
        "scaling": {
            "maxInstanceCount": 1
        }
    }
}

And deploy it using:

I build (and therefore) manage many container images. It’s easy (common?) to overlook that these images contain vulnerabilities, hopefully vulns that are fixed and that the images must be rebuilt to accommodate these changes.

I have used Google’s very expensive container vulnerability scanning tool but wanted something cheaper. I found this list of open source solutions on Reddit and decided to look into Trivy.

It’s possible to install Trivy via a package manager, a binary or to build the Go binary locally but I prefer to use containers whenever possible:

I build (and therefore) manage many container images. It’s easy (common?) to overlook that these images contain vulnerabilities, hopefully vulns that are fixed and that the images must be rebuilt to accommodate these changes.

I have used Google’s very expensive container vulnerability scanning tool but wanted something cheaper. I found this list of open source solutions on Reddit and decided to look into Trivy.

It’s possible to install Trivy via a package manager, a binary or to build the Go binary locally but I prefer to use containers whenever possible:

A lazy Sunday afternoon and my interest was piqued by XML-RPC

Client

A very basic XML-RPC client wrapped in a Cloud Functions function:

main.py:

import functions_framework
import os
import xmlrpc.client


endpoint = os.get_env("ENDPOINT")

proxy = xmlrpc.client.ServerProxy(endpoint)

@functions_framework.http
def add(request):
    print(request)
    rqst = request.get_json(silent=True)

    resp = proxy.add(
        {"x":{
            "real":rqst["x"]["real"],
            "imag":rqst["x"]["imag"]
        },
        "y":{
            "real":rqst["y"]["real"],
            "imag":rqst["y"]["imag"]
        }
    })

    return resp

requirements.txt:

functions-framework==3.*

Run it:

python3 -m venv venv
source venv/bin/activate
python3 -m pip install --requirement requirements.txt

export ENDPOINT="..."

python3 main.py

Server

Forcing myself to go Rust first and there’s an (old) xml-rpc crate.

A lazy Sunday afternoon and my interest was piqued by XML-RPC

Client

A very basic XML-RPC client wrapped in a Cloud Functions function:

main.py:

import functions_framework
import os
import xmlrpc.client


endpoint = os.get_env("ENDPOINT")

proxy = xmlrpc.client.ServerProxy(endpoint)

@functions_framework.http
def add(request):
    print(request)
    rqst = request.get_json(silent=True)

    resp = proxy.add(
        {"x":{
            "real":rqst["x"]["real"],
            "imag":rqst["x"]["imag"]
        },
        "y":{
            "real":rqst["y"]["real"],
            "imag":rqst["y"]["imag"]
        }
    })

    return resp

requirements.txt:

functions-framework==3.*

Run it:

python3 -m venv venv
source venv/bin/activate
python3 -m pip install --requirement requirements.txt

export ENDPOINT="..."

python3 main.py

Server

Forcing myself to go Rust first and there’s an (old) xml-rpc crate.

I naively (!) began exploring JSON marshaling of Protobufs in rust. Other protobuf language SDKs include JSON marshaling making the process straightforward. I was to learn that, in rust, it’s not so simple. Unfortunately, for me, this continues to discourage my further use of rust (rust is just hard).

My goal was to marshal an arbitrary protocol buffer message that included a oneof feature. I was unable to JSON marshal the rust generated by tonic for such a message.

Responded to Get Custom K8s Resource using Python and found the CustomObjectsApi documentation unclear.

If you have a cluster and a kubeconfig file with a correctly configured current-context, so that you can successfully:

PLURAL="checks"

kubectl get ${PLURAL} \
--all-namespaces

NOTE I’m using Ackal’s CRDs in these examples.

Then you can use the following code to access the cluster’s REST API server to enumerate its CRDs:

main.py:

from __future__ import print_function
from kubernetes import client, config
from kubernetes.client.rest import ApiException

config.load_kube_config()
api = client.CustomObjectsApi()

# Ackal's Group|Version and some Kinds
group = 'ack.al'
version = 'v1'
plurals = ['checks','customers']

for plural in plurals:
    try:
        resp = api.list_cluster_custom_object(
            group,
            version,
            plural,
        )

        for item in resp["items"]:
            spec = item["spec"]
            print(spec)
    except ApiException as e:
        print(e)

python3 -m venv venv
source venv/bin/activate
python3 -m pip install kubernetes==26.1.0
python3 main.py

That’s all!

Python’s Protocol Buffers code-generation using protoc has had significant changes that can cause developers… “challenges”. This post summarizes my experience of these mostly to save me from repreatedly recreating this history for myself when I forget it.

• Version change
• Generated code change
• Implementation Backends

I’ll use this summarized table of proto and the Pypi library’s history in this post. protoc refers to the compiler that supports code-generation in multiple languages. protobuf refers to the corresponding Python (runtime) library on Pypi:

Phew! Programmitcally deploying Cloud Run services should be easy but it didn’t find it so.

My issues were that the Cloud Run Admin (!) API is poorly documented and it uses non-standard endpoints (thanks Sal!). Here, for others who may struggle with this, is how I got this to work.

Goal

Programmatically (have Golang, Python, want Rust) deploy services to Cloud Run.

i.e. achieve this:

gcloud run deploy ${NAME} \
--image=${IMAGE} \
--platform=managed \
--no-allow-unauthenticated \
--region=${REGION} \
--project=${PROJECT}

TRICK --log-http is your friend

A friend mentioned OriginStamp to me.

NB There are 2 sites: originstamp.com and originstamp.org.

It’s an interesting project.

It’s a solution for providing auditable proof that you had a(ccess to) some digital thing before a certain date. OriginStamp provides user-|developer-friendly means to submit files|hashes (of your content) and have these bundled into transactions that are submitted to e.g. bitcoin.

I won’t attempt to duplicate the narrative here, review OriginStamp’s site and some of its content.

I’ve been noodling around with another Trillian personality.

Another in a theme that interests me in providing tamperproof logs for the packages in the popular package management registries.

The Golang team recently announced Go Module Mirror which is built atop Trillian. It seems to me that all the package registries (Go Modules, npm, Maven, NuGet etc.) would benefit from tamperproof logs hosted by a trusted 3rd-party.

As you may have guessed, PyPi Transparency is a log for PyPi packages. PyPi is comprehensive, definitive and trusted but, as with Go Module Mirror, it doesn’t hurt to provide a backup of some of its data. In the case of this solution, Trillian hosts a log of self-calculated SHA-256 hashes for Python packages that are added to it.

A lazy Sunday afternoon and my interest was piqued by XML-RPC

Client

A very basic XML-RPC client wrapped in a Cloud Functions function:

main.py:

import functions_framework
import os
import xmlrpc.client


endpoint = os.get_env("ENDPOINT")

proxy = xmlrpc.client.ServerProxy(endpoint)

@functions_framework.http
def add(request):
    print(request)
    rqst = request.get_json(silent=True)

    resp = proxy.add(
        {"x":{
            "real":rqst["x"]["real"],
            "imag":rqst["x"]["imag"]
        },
        "y":{
            "real":rqst["y"]["real"],
            "imag":rqst["y"]["imag"]
        }
    })

    return resp

requirements.txt:

functions-framework==3.*

Run it:

python3 -m venv venv
source venv/bin/activate
python3 -m pip install --requirement requirements.txt

export ENDPOINT="..."

python3 main.py

Server

Forcing myself to go Rust first and there’s an (old) xml-rpc crate.

This is so useful that it’s worth its own post.

I write many gRPC services. As these generally run securely, it’s best to test them that way too but, even with e.g. Let’s Encrypt, it can be challenging to generate appropriate TLS certs.

Tailscale makes this trivial.

Assuming there’s a gRPC service running on localhost:50051, we want to avoid -plaintext:

PORT="50051"

grpcurl \
-plaintext 0.0.0.0:${PORT} \
list

NOTE I’m using list and assuming your service has reflection enabled but you can, of course, use relevant methods.

[Tailscale] is really good. I’ve been using it as a virtual private network to span 2 home networks and to securely (!) access my hosts when I’m remote.

Recently Tailscale added Webhook functionality to permit processing subscribed-to (Tailscale) events. I’m always a sucker for a webhook ;-)

Here’s a curl command to send a test event to a Tailscale Webhook:

URL=""

# From Tailscale's docs
# https://tailscale.com/kb/1213/webhooks/#events-payload
BODY='
[
  {
    "timestamp": "2022-09-21T13:37:51.658918-04:00",
    "version": 1,
    "type": "test",
    "tailnet": "example.com",
    "message": "This is a test event",
    "data": null
  }
]
'

T=$(date +%s)
V=$(\
  printf "${T}.${BODY}" \
  | openssl dgst -sha256 -hmac "${SECRET}" -hex -r \
  | head --bytes=64)

curl \
--request POST \
--header "Tailscale-Webhook-Signature:t=${T},v1=${V}" \
--header "Content-Type: application/json" \
--data "${BODY}" \
https://${URL}

There must be a better way of extracting the hashed value from the openssl output.

A lazy Sunday afternoon and my interest was piqued by XML-RPC

Client

A very basic XML-RPC client wrapped in a Cloud Functions function:

main.py:

import functions_framework
import os
import xmlrpc.client


endpoint = os.get_env("ENDPOINT")

proxy = xmlrpc.client.ServerProxy(endpoint)

@functions_framework.http
def add(request):
    print(request)
    rqst = request.get_json(silent=True)

    resp = proxy.add(
        {"x":{
            "real":rqst["x"]["real"],
            "imag":rqst["x"]["imag"]
        },
        "y":{
            "real":rqst["y"]["real"],
            "imag":rqst["y"]["imag"]
        }
    })

    return resp

requirements.txt:

functions-framework==3.*

Run it:

python3 -m venv venv
source venv/bin/activate
python3 -m pip install --requirement requirements.txt

export ENDPOINT="..."

python3 main.py

Server

Forcing myself to go Rust first and there’s an (old) xml-rpc crate.

For the first time, I chose Rust to solve a problem. Until this, I’ve been trying to use Rust to learn the language and to rewrite existing code. But, this problem led me to Rust because my other tools wouldn’t cut it.

The question was how to represent oneof fields in Kubernetes Custom Resource Definitions (CRDs).

CRDs use OpenAPI schema and the YAML that results can be challenging to grok.

apiVersion: apiextensions.k8s.io/v1
kind: CustomResourceDefinition
metadata:
  name: deploymentconfigs.example.com
spec:
  group: example.com
  names:
    categories: []
    kind: DeploymentConfig
    plural: deploymentconfigs
    shortNames: []
    singular: deploymentconfig
  scope: Namespaced
  versions:
  - additionalPrinterColumns: []
    name: v1alpha1
    schema:
      openAPIV3Schema:
        description: An example schema
        properties:
          spec:
            properties:
              deployment_strategy:
                oneOf:
                - required:
                  - rolling_update
                - required:
                  - recreate
                properties:
                  recreate:
                    properties:
                      something:
                        format: uint16
                        minimum: 0.0
                        type: integer
                    required:
                    - something
                    type: object
                  rolling_update:
                    properties:
                      max_surge:
                        format: uint16
                        minimum: 0.0
                        type: integer
                      max_unavailable:
                        format: uint16
                        minimum: 0.0
                        type: integer
                    required:
                    - max_surge
                    - max_unavailable
                    type: object
                type: object
            required:
            - deployment_strategy
            type: object
        required:
        - spec
        title: DeploymentConfig
        type: object
    served: true
    storage: true
    subresources: {}

I’ve developed several Kubernetes Operators using the Operator SDK in Go (which builds upon Kubebuilder).

For the first time, I chose Rust to solve a problem. Until this, I’ve been trying to use Rust to learn the language and to rewrite existing code. But, this problem led me to Rust because my other tools wouldn’t cut it.

The question was how to represent oneof fields in Kubernetes Custom Resource Definitions (CRDs).

CRDs use OpenAPI schema and the YAML that results can be challenging to grok.

apiVersion: apiextensions.k8s.io/v1
kind: CustomResourceDefinition
metadata:
  name: deploymentconfigs.example.com
spec:
  group: example.com
  names:
    categories: []
    kind: DeploymentConfig
    plural: deploymentconfigs
    shortNames: []
    singular: deploymentconfig
  scope: Namespaced
  versions:
  - additionalPrinterColumns: []
    name: v1alpha1
    schema:
      openAPIV3Schema:
        description: An example schema
        properties:
          spec:
            properties:
              deployment_strategy:
                oneOf:
                - required:
                  - rolling_update
                - required:
                  - recreate
                properties:
                  recreate:
                    properties:
                      something:
                        format: uint16
                        minimum: 0.0
                        type: integer
                    required:
                    - something
                    type: object
                  rolling_update:
                    properties:
                      max_surge:
                        format: uint16
                        minimum: 0.0
                        type: integer
                      max_unavailable:
                        format: uint16
                        minimum: 0.0
                        type: integer
                    required:
                    - max_surge
                    - max_unavailable
                    type: object
                type: object
            required:
            - deployment_strategy
            type: object
        required:
        - spec
        title: DeploymentConfig
        type: object
    served: true
    storage: true
    subresources: {}

I’ve developed several Kubernetes Operators using the Operator SDK in Go (which builds upon Kubebuilder).

An interesting question on Stack overflow prompted me to understand how to use Visual Studio Code and Delve to remotely debug a Golang app running on Kubernetes (MicroK8s).

The OP is using Gin which was also new to me so the question gave me an opportunity to try out several things.

Sources

A simple healthz handler:

package main

import (
	"flag"
	"log/slog"
	"net/http"

	"github.com/gin-gonic/gin"
)

var (
	addr = flag.String("addr", "0.0.0.0:8080", "HTTP server endpoint")
)

func healthz(c *gin.Context) {
	c.String(http.StatusOK, "ok")
}
func main() {
	flag.Parse()

	router := gin.Default()
	router.GET("/fib", handler())
	router.GET("healthz", healthz)

	slog.Info("Server starting")
	slog.Info("Server error",
		"err", router.Run(*addr),
	)
}

Containerfile:

Interested to explore Fly Kubernetes after being accepted into the closed beta.

The folks at Fly are innovative in their technology uses and, having been a long-time Kubernetes user, I was intrigued to learn that Fly.io has implemented Kubernetes atop Fly.

My first Deployment failed:

Authentication required to access image "ghcr.io/{image}"

It was confirmed to me that FKS does not support pulling from private registries. The solution is pull-tag-push images to registry.fly.io but, Fly’s repository is app-specific and so, you need to do some querying to grab the Fly app created by FKS (for your namespace):

Spent time today yak-shaving which resulted in an unplanned migration from MicroK8s ‘prometheus’ add-on to the new and not fully-documented ‘observability’ add-on:

sudo microk8s.enable prometheus

Infer repository core for addon prometheus
DEPRECATION WARNING: 'prometheus' is deprecated and will soon be removed. Please use 'observability' instead.
...

The reason for the name change is unclear.

It’s unclear whether there’s a difference in the primary components that are installed too (I’d thought Grafana wasn’t included in ‘prometheus’), (Grafana) Loki and (Grafana) Tempo definitely weren’t included and I don’t want them either.

Responded to Get Custom K8s Resource using Python and found the CustomObjectsApi documentation unclear.

If you have a cluster and a kubeconfig file with a correctly configured current-context, so that you can successfully:

PLURAL="checks"

kubectl get ${PLURAL} \
--all-namespaces

NOTE I’m using Ackal’s CRDs in these examples.

Then you can use the following code to access the cluster’s REST API server to enumerate its CRDs:

main.py:

from __future__ import print_function
from kubernetes import client, config
from kubernetes.client.rest import ApiException

config.load_kube_config()
api = client.CustomObjectsApi()

# Ackal's Group|Version and some Kinds
group = 'ack.al'
version = 'v1'
plurals = ['checks','customers']

for plural in plurals:
    try:
        resp = api.list_cluster_custom_object(
            group,
            version,
            plural,
        )

        for item in resp["items"]:
            spec = item["spec"]
            print(spec)
    except ApiException as e:
        print(e)

python3 -m venv venv
source venv/bin/activate
python3 -m pip install kubernetes==26.1.0
python3 main.py

That’s all!

Google announced Firestore … integration with Eventarc. Ackal uses Firestore to persist Customer and Check information and it uses Google Cloud Firestore Triggers to handle events on these document types.

Eventarc feels like the strategic future of eventing in Google Cloud and I’ve been concerned since adopting the technology that Google would abandon Google Cloud Firestore Triggers.

For this reason, when I saw last week’s announcement, I thought I should evaluate the mechanism and this blog post is a summary of that work.

I’ve been spending time recently optimizing Ackal’s use of Google Cloud Logging and Cloud Monitoring in posts:

• Filtering metrics w/ Google Managed Prometheus
• Kubernetes metrics, metrics everywhere
• Google Metric Diagnostics and Metric Data Ingested

Yesterday, I read that Robusta has a new open source project Kubernetes Resource Recommendations (KRR) so I took some time to evaluate it.

This post describes the changes I had to make to get KRR working with Google Managed Prometheus (GMP):

I’ve been on an efficiency drive with Cloud Logging and Cloud Monitoring.

With regards Cloud Logging, I’m contemplating (!) eliminating almost all log storage. As it is I’ve buzz cut log storage with a _Default sink that has comprehensive sets of NOT LOG_ID(X) inclusion and exclusion filters. As I was doing so, I began to wonder why I need to pay for the storage of much logging. There’s the comfort from knowing that everything you may ever need is being logged (at least for 30 days) but there’s also the costs that that entails. I use logs exclusively for debugging which got me thinking, couldn’t I just capture logs when I’m debugging (rather thna all the time?). I’ve not taken that leap yet but I’m noodling on it.

I’ve been tinkering with ways to “unit-test” my assumptions when using cloud platforms. I recently wrote about good posts by Google describing achieving cost savings with Cloud Monitoring and Cloud Logging:

• How to identify and reduce costs of your Google Cloud observability in Cloud Monitoring
• Cloud Logging pricing for Cloud Admins: How to approach it & save cost

With Cloud Monitoring, I’ve restricted the prometheus.googleapis.com metrics that are being ingested but realized I wanted to track the number of Pods (and Containers) deployed to a GKE cluster.

Ackal uses a Kubernetes Operator to orchestrate the lifecycle of its health checks. Ackal’s Operator is written in Go using kubebuilder.

Yesterday, my interest was piqued by a MetalBear blog post Writing a Kubernetes Operator [in Rust]. I spent some time reimplementing one of Ackal’s CRDs (Check) using kube-rs and not only refreshed my Rust knowledge but learned a bunch more about Kubernetes and Operators.

While rummaging around the Kubernetes documentation, I discovered flant’s Shell-operator and spent some time today exploring its potential.

NOTE cert-manager is a better solution to what follows.

I wrote about deploying Secure (TLS) gRPC services with Vultr Kubernetes Engine (VKE). This week, I’ve reproduced this deployment using Linode Kubernetes Engine (LKE).

Thanks to the consistency provided by Kubernetes, the Kubernetes programming is almost identical. The main differences are between the CLI’s provided by these platforms. Both are good. They’re just different.

I’m going to include the linode-cli commands I’m using in this post as I found it slightly more quirky.

NOTE cert-manager is a better solution to what follows.

I’ve a need to deploy a Vultr Kubernetes Engine (VKE) cluster on a daily basis (create and delete within a few hours) and expose (securely|TLS) a gRPC service.

I have an existing solution Automatic Certs w/ Golang gRPC service on Compute Engine that combines a gRPC Healthchecking and an ACME service and decided to reuse this.

In order for it work, we need:

I’ve begun exploring Vultr after the company announced a managed Kubernetes offering Vultr Kubernetes Engine (VKE).

In my brief experience, it’s a decent platform and its CLI vultr-cli is mostly (!) good. The CLI has a limitation in that command output is text formatted and this makes it challenging to parse the output when scripting.

NOTE The Vultr developers have a branch rewrite that includes a solution to this problem.

Example

ID              12345678-90ab-cdef-1234-567890abcdef
LABEL           test
DATE CREATED    2022-01-01T00:00:00+00:00
CLUSTER SUBNET  255.255.255.255/16
SERVICE SUBNET  255.255.255.255/12
IP              255.255.255.255
ENDPOINT        12345678-90ab-cdef-1234-567890abcdef.vultr-k8s.com
VERSION         v1.23.5+3
REGION          mars
STATUS          pending
 
NODE POOLS
ID              12345678-90ab-cdef-1234-567890abcdef
DATE CREATED    2022-01-01T00:00:00+00:00
DATE UPDATED    2022-01-01T00:00:00+00:00
LABEL           nodepool
TAG             foo
PLAN            vc2-1c-2gb
STATUS          pending
NODE QUANTITY   1
AUTO SCALER     false
MIN NODES       1
MAX NODES       1
 
NODES
ID                                      DATE CREATED                    LABEL                   STATUS
12345678-

Until that’s available, I’m lazy writing very simple bash scripts to parse vultr-cli command output as JSON. The repo is vultr-cli-format.

Phew!

For Want of a Nail

I was interested in learning how to Manage Resources for Containers. On the way, I learned and discovered:

• kubectl top
• Vertical Pod Autoscaler
• A (valuable) digression through PodMonitor
• kube-state-metrics
• `kubectl-patch
• Created a Graph
• References

Kubernetes Resources

Visual Studio Code has begun to bug me (reasonably) to add resources to Kubernetes manifests.

E.g.:

resources:
  limits:
    cpu: "1"
    memory: "512Mi"

I’ve been spending time with Deislab’s Akri and decided to determine whether Akri’s primary resources (Agent, Controller) and some of my creations HTTP Device and Discovery, were being suitably constrained.

I’ve spent time this week returning to the interesting Deislabs project Krustlet. Since the last time, the bootstrapping process has been simplified using Kubernetes Bootstrap Tokens. I know this updated process works with MicroK8s. Unfortunately, I’m struggling with it on GKE and thought I’d try DigitalOcean Managed Kubernetes.

It worked first time!

In the following, we run both the Kubernetes cluster and the Krustlet Droplet on DigitalOcean but, as long as the cluster and the VM are able to communicate with one another, you should be able to run these anywhere.

I developed an admission webhook for Akri, twice (Golang, Rust). I naively followed other examples for the generation of the certificates, created a 1.20 cluster and broke that process.

I’d briefly considered using cert-manager recently but quickly abandoned the idea thinking it would be onerous and unnecessary complexity for little-old-me. I was wrong. It’s excellent and I recommend it highly.

I won’t reproduce the v1beta1 and v1 examples from the Stackoverflow question as they should be self-explanatory. I suspect (!?) that I should not have used Kubernete’s (API Server’s) CA for the Webhook but it could well be that I just don’t understand the correct approach.

I spent some time last week writing my first admission webhook for Kubernetes. I wrote the handler in Golang because I’m most familiar with Golang and because, as Kubernetes’ native language, I was more confident that the necessary SDKs would exist and that the documentation would likely use Golang by default. I struggled to find useful documentation and so this post is to help you (and me!) remember how to do this next time!

I’m debugging an issue with Akri Zeroconf protocol in which Instance environment variables are no longer (!) being surfaced within the Broker pods. In my adventures, it seemed useful to better understand how Akri works and specifically, how Akri uses Kubernetes Device Plugins.

IIUC plugins register with the Kubelet (!) via a gRPC service (Registration) that the Kubelet exposes on a UNIX socket at /var/lib/kubelet/device-plugins/kubelet.sock

Then (!) if successful, devices should be reported by the Node’s metadata (spec) and available to be bound to Pods.

For the past couple of weeks, I’ve been playing around with Akri, a Microsoft (DeisLabs) project for building a connected edge with Kubernetes. Kubernetes, IoT, Rust (and Golang) make this all compelling to me.

Initially, I deployed an Akri End-to-End to MicroK8s on Google Compute Engine (link) and Digital Ocean (link). But I was interested to create me own example and so have proposed a very (!) simple HTTP-based protocol.

This blog summarizes my thoughts about Akri and an explanation of the HTTP protocol implementation in the hope that this helps others.

I was very interested to read about Microsoft’s DeisLab’s latest (rust-based) Kubernetes project: akri. If I understand it correctly, it provides a mechanism to make any (IoT) device accessible to containers running within a cluster. I need to spend more time playing around with it so that I can fully understand it. I had some problems getting the End-to-End demo running on Google Compute Engine (and then I tried DigitalOcean droplet) instances. So, here’s a two-ways solution to get you going.

Until today, I’d not accessed a Google Container Registry repo from a non-GKE Kubernetes deployment.

It turns out that it’s pretty well-documented (link) but, here’s an end-end example.

Assuming:

BILLING=[[YOUR-BILLING]]
PROJECT=[[YOUR-PROJECT]]
SERVER="us.gcr.io"

If not already:

gcloud projects create {$PROJECT}

gcloud beta billing projects link ${PROJECT} \
--billing-account=${BILLING}

gcloud services enable containerregistry.googleapis.com \
--project=${PROJECT}

Container Registry

IMAGE="busybox" # Or ...

docker pull ${IMAGE}

docker tag \
  ${IMAGE} \
  ${SERVER}/${PROJECT}/${IMAGE}

docker push ${SERVER}/${PROJECT}/${IMAGE}

gcloud container images list-tags ${SERVER}/${PROJECT}/${IMAGE}

Service Account

Create a service account that’s permitted to download (read-only) images from this project’s registry

I’ve written a couple of deployment options (Google Compute Engine; Kubernetes) for an open-source project. The Kubernetes deployment provides NodePort and (TCP) LoadBalancer options and I’ve been trying (unsuccessfully) to add HTTPS Load-balancing.

I should (!) try to deploy to Google Kubernetes Engine (GKE) but I’ve been using microk8s, Digital Ocean Managed Kubernetes and the Linode LKE Beta. Each of these requires an implementation of Ingress controller. For GKE, GCP’s HTTP/S Load-balancer (GCLB) is used. But, for the other services, NGINX Ingress is a good option and so I’ve been exploring NGINX Ingress (Controller) link. This Ingress appears to work except that I’ve been unable to get it to work with mutual TLS between the (NGINX) proxy and my backend services.

Google Cloud Platform Free Tier appears (please verify this for yourself) to provide the ability to run a(n admittedly miniscule) Kubernetes cluster for free. So, why do this? It provides a definitive Kubernetes (Engine) experience on Google Cloud Platform that you may use for learning and testing.

Kubernetes Engine the master node(s) and the control plane are free.

Kubernetes (i.e. Compute Engine) nodes potentially incur charges including for the VM runtime and any attached storage, snapshots etc. However, charges for these resources can be partially covered by the Free Tier.

An interesting question on Stack overflow prompted me to understand how to use Visual Studio Code and Delve to remotely debug a Golang app running on Kubernetes (MicroK8s).

The OP is using Gin which was also new to me so the question gave me an opportunity to try out several things.

Sources

A simple healthz handler:

package main

import (
	"flag"
	"log/slog"
	"net/http"

	"github.com/gin-gonic/gin"
)

var (
	addr = flag.String("addr", "0.0.0.0:8080", "HTTP server endpoint")
)

func healthz(c *gin.Context) {
	c.String(http.StatusOK, "ok")
}
func main() {
	flag.Parse()

	router := gin.Default()
	router.GET("/fib", handler())
	router.GET("healthz", healthz)

	slog.Info("Server starting")
	slog.Info("Server error",
		"err", router.Run(*addr),
	)
}

Containerfile:

An interesting question on Stack overflow prompted me to understand how to use Visual Studio Code and Delve to remotely debug a Golang app running on Kubernetes (MicroK8s).

The OP is using Gin which was also new to me so the question gave me an opportunity to try out several things.

Sources

A simple healthz handler:

package main

import (
	"flag"
	"log/slog"
	"net/http"

	"github.com/gin-gonic/gin"
)

var (
	addr = flag.String("addr", "0.0.0.0:8080", "HTTP server endpoint")
)

func healthz(c *gin.Context) {
	c.String(http.StatusOK, "ok")
}
func main() {
	flag.Parse()

	router := gin.Default()
	router.GET("/fib", handler())
	router.GET("healthz", healthz)

	slog.Info("Server starting")
	slog.Info("Server error",
		"err", router.Run(*addr),
	)
}

Containerfile:

An interesting question on Stack overflow prompted me to understand how to use Visual Studio Code and Delve to remotely debug a Golang app running on Kubernetes (MicroK8s).

The OP is using Gin which was also new to me so the question gave me an opportunity to try out several things.

Sources

A simple healthz handler:

package main

import (
	"flag"
	"log/slog"
	"net/http"

	"github.com/gin-gonic/gin"
)

var (
	addr = flag.String("addr", "0.0.0.0:8080", "HTTP server endpoint")
)

func healthz(c *gin.Context) {
	c.String(http.StatusOK, "ok")
}
func main() {
	flag.Parse()

	router := gin.Default()
	router.GET("/fib", handler())
	router.GET("healthz", healthz)

	slog.Info("Server starting")
	slog.Info("Server error",
		"err", router.Run(*addr),
	)
}

Containerfile:

An interesting question on Stack overflow prompted me to understand how to use Visual Studio Code and Delve to remotely debug a Golang app running on Kubernetes (MicroK8s).

The OP is using Gin which was also new to me so the question gave me an opportunity to try out several things.

Sources

A simple healthz handler:

package main

import (
	"flag"
	"log/slog"
	"net/http"

	"github.com/gin-gonic/gin"
)

var (
	addr = flag.String("addr", "0.0.0.0:8080", "HTTP server endpoint")
)

func healthz(c *gin.Context) {
	c.String(http.StatusOK, "ok")
}
func main() {
	flag.Parse()

	router := gin.Default()
	router.GET("/fib", handler())
	router.GET("healthz", healthz)

	slog.Info("Server starting")
	slog.Info("Server error",
		"err", router.Run(*addr),
	)
}

Containerfile:

An interesting question on Stack overflow prompted me to understand how to use Visual Studio Code and Delve to remotely debug a Golang app running on Kubernetes (MicroK8s).

The OP is using Gin which was also new to me so the question gave me an opportunity to try out several things.

Sources

A simple healthz handler:

package main

import (
	"flag"
	"log/slog"
	"net/http"

	"github.com/gin-gonic/gin"
)

var (
	addr = flag.String("addr", "0.0.0.0:8080", "HTTP server endpoint")
)

func healthz(c *gin.Context) {
	c.String(http.StatusOK, "ok")
}
func main() {
	flag.Parse()

	router := gin.Default()
	router.GET("/fib", handler())
	router.GET("healthz", healthz)

	slog.Info("Server starting")
	slog.Info("Server error",
		"err", router.Run(*addr),
	)
}

Containerfile:

Spent time today yak-shaving which resulted in an unplanned migration from MicroK8s ‘prometheus’ add-on to the new and not fully-documented ‘observability’ add-on:

sudo microk8s.enable prometheus

Infer repository core for addon prometheus
DEPRECATION WARNING: 'prometheus' is deprecated and will soon be removed. Please use 'observability' instead.
...

The reason for the name change is unclear.

It’s unclear whether there’s a difference in the primary components that are installed too (I’d thought Grafana wasn’t included in ‘prometheus’), (Grafana) Loki and (Grafana) Tempo definitely weren’t included and I don’t want them either.

Phew!

For Want of a Nail

I was interested in learning how to Manage Resources for Containers. On the way, I learned and discovered:

• kubectl top
• Vertical Pod Autoscaler
• A (valuable) digression through PodMonitor
• kube-state-metrics
• `kubectl-patch
• Created a Graph
• References

Kubernetes Resources

Visual Studio Code has begun to bug me (reasonably) to add resources to Kubernetes manifests.

E.g.:

resources:
  limits:
    cpu: "1"
    memory: "512Mi"

I’ve been spending time with Deislab’s Akri and decided to determine whether Akri’s primary resources (Agent, Controller) and some of my creations HTTP Device and Discovery, were being suitably constrained.

It’s a good name, I read it as “dapper” but I frequently type “darp” :-(

Was interested to read that Dapr is now v1.0 and decided to check it out. I was initially confused between Dapr and service mesh functionality. But, having used Dapr, it appears to be more focused in aiding the development of (cloud-native) (distributed) apps by providing developers with abstractions for e.g. service discovery, eventing, observability whereas service meshes feel (!) more oriented towards simplifying the deployment of existing apps. Both use the concept of proxies, deployed alongside app components (as sidecars on Kubernetes) to provide their functionality to apps.

I’m debugging an issue with Akri Zeroconf protocol in which Instance environment variables are no longer (!) being surfaced within the Broker pods. In my adventures, it seemed useful to better understand how Akri works and specifically, how Akri uses Kubernetes Device Plugins.

IIUC plugins register with the Kubelet (!) via a gRPC service (Registration) that the Kubelet exposes on a UNIX socket at /var/lib/kubelet/device-plugins/kubelet.sock

Then (!) if successful, devices should be reported by the Node’s metadata (spec) and available to be bound to Pods.

For the past couple of weeks, I’ve been playing around with Akri, a Microsoft (DeisLabs) project for building a connected edge with Kubernetes. Kubernetes, IoT, Rust (and Golang) make this all compelling to me.

Initially, I deployed an Akri End-to-End to MicroK8s on Google Compute Engine (link) and Digital Ocean (link). But I was interested to create me own example and so have proposed a very (!) simple HTTP-based protocol.

This blog summarizes my thoughts about Akri and an explanation of the HTTP protocol implementation in the hope that this helps others.

I was very interested to read about Microsoft’s DeisLab’s latest (rust-based) Kubernetes project: akri. If I understand it correctly, it provides a mechanism to make any (IoT) device accessible to containers running within a cluster. I need to spend more time playing around with it so that I can fully understand it. I had some problems getting the End-to-End demo running on Google Compute Engine (and then I tried DigitalOcean droplet) instances. So, here’s a two-ways solution to get you going.

General

You’ll need a Google Cloud project with Cloud Translation (translate.googleapis.com) enabled and a Service Account (and key) with suitable permissions in order to run the following.

BILLING="..." # Your Billing ID (gcloud billing accounts list)
PROJECT="..." # Your Project ID
ACCOUNT="tester"

EMAIL="${ACCOUNT}@${PROJECT}.iam.gserviceaccount.com"

ROLES=(
    "roles/cloudtranslate.user"
    "roles/serviceusage.serviceUsageConsumer"
)

# Create Project
gcloud projects create ${PROJECT}

# Associate Project with your Billing Account
gcloud billing accounts link ${PROJECT} \
--billing-account=${BILLING}

# Enable Cloud Translation
gcloud services enable translate.googleapis.com \
--project=${PROJECT}

# Create Service Account
gcloud iam service-accounts create ${ACCOUNT} \
--project=${PROJECT}

# Create Service Account Key
gcloud iam service-accounts keys create ${PWD}/${ACCOUNT}.json \
--iam-account=${EMAIL} \
--project=${PROJECT}

# Update Project IAM permissions
for ROLE in "${ROLES[@]}"
do
  gcloud projects add-iam-policy-binding ${PROJECT} \
  --member=serviceAccount:${EMAIL} \
  --role=${ROLE}
done