Full Width [alt+shift+f] Shortcuts [alt+shift+k]
Sign Up [alt+shift+s] Log In [alt+shift+l]
20
Recently, customers who have purchased a copy of ONCE/Campfire were invited to participate in a live walk through the app’s CSS code. Campfire was built with vanilla CSS, fully #nobuild without compiling or preprocessors, and uses the latest web platform features available in evergreen browsers—CSS nesting, :has(), :is(), and :where(); wide-gamut colors, View Transitions and more. In this post we’ll take a look at how we’re using some of these features and share some helpful patterns discovered along the way. Colors Campfire uses oklch() to define colors in CSS. oklch() offers access to wider color spaces (like Display-P3) and greatly improves developer ergonomics when working with colors. For example, let’s take a look at these greys used in Campfire’s UI. :root { --lch-gray: 96% 0.005 96; --lch-gray-dark: 92% 0.005 96; --lch-gray-darker: 75% 0.005 96; } At first glance they may seem unfamiliar but they’re actually more readable and quite easy to use once you get...
a year ago

Improve your reading experience

Logged in users get linked directly to articles resulting in a better reading experience. Please login for free, it takes less than 1 minute.

More from 37signals Dev

Announcing Hotwire Native 1.2

We’ve just launched Hotwire Native v1.2 and it’s the biggest update since the initial launch last year. The update has several key improvements, bug fixes, and more API consistency between platforms. And we’ve created all new iOS and Android demo apps to show it off! A web-first framework for building native mobile apps Improvements There are a few significant changes in v1.2 that are worth specifically highlighting. Route decision handlers Hotwire Native apps route internal urls to screens in your app, and route external urls to the device’s browser. Historically, though, it wasn’t straightforward to customize the default behavior for unique app needs. In v1.2, we’ve introduced the RouteDecisionHandler concept to iOS (formerly only on Android). Route decisions handlers offer a flexible way to decide how to route urls in your app. Out-of-the-box, Hotwire Native registers these route decision handlers to control how urls are routed: AppNavigationRouteDecisionHandler: Routes all internal urls on your app’s domain through your app. SafariViewControllerRouteDecisionHandler: (iOS Only) Routes all external http/https urls to a SFSafariViewController in your app. BrowserTabRouteDecisionHandler: (Android Only) Routes all external http/https urls to a Custom Tab in your app. SystemNavigationRouteDecisionHandler: Routes all remaining external urls (such as sms: or mailto:) through device’s system navigation. If you’d like to customize this behavior you can register your own RouteDecisionHandler implementations in your app. See the documentation for details. Server-driven historical location urls If you’re using Ruby on Rails, the turbo-rails gem provides the following historical location routes. You can use these to manipulate the navigation stack in Hotwire Native apps. recede_or_redirect_to(url, **options) — Pops the visible screen off of the navigation stack. refresh_or_redirect_to(url, **options) — Refreshes the visible screen on the navigation stack. resume_or_redirect_to(url, **options) — Resumes the visible screen on the navigation stack with no further action. In v1.2 there is now built-in support to handle these “command” urls with no additional path configuration setup necessary. We’ve also made improvements so they handle dismissing modal screens automatically. See the documentation for details. Bottom tabs When starting with Hotwire Native, one of the most common questions developers ask is how to support native bottom tab navigation in their apps. We finally have an official answer! We’ve introduced a HotwireTabBarController for iOS and a HotwireBottomNavigationController for Android. And we’ve updated the demo apps for both platforms to show you exactly how to set them up. New demo apps To better show off all the features in Hotwire Native, we’ve created new demo apps for iOS and Android. And there’s a brand new Rails web app for the native apps to leverage. Hotwire Native demo app Clone the GitHub repos to build and run the demo apps to try them out: iOS repo Android repo Rails app Huge thanks to Joe Masilotti for all the demo app improvements. If you’re looking for more resources, Joe even wrote a Hotwire Native for Rails Developers book! Release notes v1.2 contains dozens of other improvements and bug fixes across both platforms. See the full release notes to learn about all the additional changes: iOS release notes Android release notes Take a look If you’ve been curious about using Hotwire Native for your mobile apps, now is a great time to take a look. We have documentation and guides available on native.hotwired.dev and we’ve created really great demo apps for iOS and Android to help you get started.

2 months ago 32 votes
Monitoring 10 Petabytes of data in Pure Storage

As the final part of our move out of the cloud, we are working on moving 10 petabytes of data out of AWS Simple Storage Service (S3). After exploring different alternatives, we decided to go with Pure Storage FlashBlade solution. We store different kinds of information on S3, from the attachments customers upload to Basecamp to the Prometheus long-term metrics. On top of that, Pure’s system also provides filesystem-based capabilities, enabling other relevant usages, such as database backup storage. This makes the system a top priority for observability. Although the system has great reliability, out-of-the-box internal alerting, and autonomous ticket creation, it would also be good to have our metrics and alerts to facilitate problem-solving and ensure any disruptions are prioritized and handled. For more context on our current Prometheus setup, see how we use Prometheus at 37signals. Pure OpenMetrics exporter Pure maintains two OpenMetrics exporters, pure-fb-openmetrics-exporter and pure-fa-openmetrics-exporter. Since we use Pure Flashblade (fb), this post covers pure-fb-openmetrics-exporter, although overall usage should be similar. The setup is straightforward and requires only binary and basic authentication installation. Here is a snippet of our Chef recipe that installs it: pure_api_token = "token" # If you use Chef, your token should come from an ecrypted databag. Changed to hardcoded here to simplify PURE_EXPORTER_VERSION = "1.0.13".freeze # Generally, we use Chef node metadata for version management. Changed to hardcoded to simplify directory "/opt/pure_exporter/#{PURE_EXPORTER_VERSION}" do recursive true owner 'pure_exporter' group 'pure_exporter' end # Avoid recreating under /tmp after reboot if target_binary is already there target_binary = "/opt/pure_exporter/#{PURE_EXPORTER_VERSION}/pure-fb-openmetrics-exporter" remote_file "/tmp/pure-fb-openmetrics-exporter-v#{PURE_EXPORTER_VERSION}-linux-amd64.tar.gz" do source "https://github.com/PureStorage-OpenConnect/pure-fb-openmetrics-exporter/releases/download/v#{PURE_EXPORTER_VERSION}/pure-fb-openmetrics-exporter-v#{PURE_EXPORTER_VERSION}-linux-amd64.tar.gz" not_if { ::File.exist?(target_binary) } end archive_file "/tmp/pure-fb-openmetrics-exporter-v#{PURE_EXPORTER_VERSION}-linux-amd64.tar.gz" do destination "/tmp/pure-fb-openmetrics-exporter-v#{PURE_EXPORTER_VERSION}" action :extract not_if { ::File.exist?(target_binary) } end execute "copy binary" do command "sudo cp /tmp/pure-fb-openmetrics-exporter-v#{PURE_EXPORTER_VERSION}/pure-fb-openmetrics-exporter /opt/pure_exporter/#{PURE_EXPORTER_VERSION}/pure-exporter" creates "/opt/pure_exporter/#{PURE_EXPORTER_VERSION}/pure-exporter" not_if { ::File.exist?(target_binary) } end tokens = <<EOF main: address: purestorage-mgmt.mydomain.com api_token: #{pure_api_token['token']} EOF file "/opt/pure_exporter/tokens.yml" do content tokens owner 'pure_exporter' group 'pure_exporter' sensitive true end systemd_unit 'pure-exporter.service' do content <<-EOU # Caution: Chef managed content. This is a file resource from #{cookbook_name}::#{recipe_name} # [Unit] Description=Pure Exporter After=network.target [Service] Restart=on-failure PIDFile=/var/run/pure-exporter.pid User=pure_exporter Group=pure_exporter ExecStart=/opt/pure_exporter/#{PURE_EXPORTER_VERSION}/pure-exporter \ --tokens=/opt/pure_exporter/tokens.yml ExecReload=/bin/kill -HUP $MAINPID SyslogIdentifier=pure-exporter [Install] WantedBy=multi-user.target EOU action [ :create, :enable, :start ] notifies :reload, "service[pure-exporter]" end service 'pure-exporter' Prometheus Job Configuration The simplest way of ingesting the metrics is to configure a basic Job without any customization: - job_name: pure_exporter metrics_path: /metrics static_configs: - targets: ['<%= @hostname %>:9491'] labels: environment: 'production' job: pure_exporter params: endpoint: [main] # From the tokens configuration above For a production-ready setup, we are using a slightly different approach. The exporter supports the usage of specific metric paths to allow for split Prometheus jobs configuration that reduces the overhead of pulling the metrics all at once: - job_name: pure_exporter_array metrics_path: /metrics/array static_configs: - targets: ['<%= @hostname %>:9491'] labels: environment: 'production' job: pure_exporter metric_relabel_configs: - source_labels: [name] target_label: ch regex: "([^.]+).*" replacement: "$1" action: replace - source_labels: [name] target_label: fb regex: "[^.]+\\.([^.]+).*" replacement: "$1" action: replace - source_labels: [name] target_label: bay regex: "[^.]+\\.[^.]+\\.([^.]+)" replacement: "$1" action: replace params: endpoint: [main] # From the tokens configuration above - job_name: pure_exporter_clients metrics_path: /metrics/clients static_configs: - targets: ['<%= @hostname %>:9491'] labels: environment: 'production' job: pure_exporter params: endpoint: [main] # From the tokens configuration above - job_name: pure_exporter_usage metrics_path: /metrics/usage static_configs: - targets: ['<%= @hostname %>:9491'] labels: environment: 'production' job: pure_exporter params: endpoint: [main] - job_name: pure_exporter_policies metrics_path: /metrics/policies static_configs: - targets: ['<%= @hostname %>:9491'] labels: environment: 'production' job: pure_exporter params: endpoint: [main] # From the tokens configuration above We also configure some metric_relabel_configs to extract labels from name using regex. Those labels help reduce the complexity of queries that aggregate metrics by different components. Detailed documentation on the available metrics can be found here. Alerts Auto Generated Alerts As I shared earlier, the system has an internal Alerting module that automatically triggers alerts for critical situations and creates tickets. To cover those alerts on the Prometheus side, we added an alerting configuration of our own that relies on the incoming severities: - alert: PureAlert annotations: summary: '{{ $labels.summary }}' description: '{{ $labels.component_type }} - {{ $labels.component_name }} - {{ $labels.action }} - {{ $labels.kburl }}' dashboard: 'https://grafana/your-dashboard' expr: purefb_alerts_open{environment="production"} == 1 for: 1m We still need to evaluate how the pure-generated alerts will interact with the custom alerts I will cover below, and we might decide to stick to one or the other depending on what we find out. Hardware Before I continue, the image below helps visualize how some of the Pure FlashBlade components are physically organized: Because of Pure’s reliability, most isolated hardware failures do not require the immediate attention of an Ops team member. To cover the most basic hardware failures, we configure an alert that sends a message to the Ops Basecamp 4 project chat: - alert: PureHardwareFailed annotations: summary: Hardware {{ $labels.name }} in chassis {{ $labels.ch }} is failed description: 'The Pure Storage hardware {{ $labels.name }} in chassis {{ $labels.ch }} is failed' dashboard: 'https://grafana/your-dashboard' expr: purefb_hardware_health == 0 for: 1m labels: severity: chat-notification We also configure alerts that check for multiple hardware failures of the same type. This doesn’t mean two simultaneous failures will result in a critical state, but it is a fair guardrail for unexpected scenarios. We also expect those situations to be rare, keeping the risk of causing unnecessary noise low. - alert: PureMultipleHardwareFailed annotations: summary: Pure chassis {{ $labels.ch }} has {{ $value }} failed {{ $labels.type }} description: 'The Pure Storage chassis {{ $labels.ch }} has {{ $value }} failed {{ $labels.type }}, close to the healthy limit of two simultaneous failures. Ensure that the hardware failures are being worked on' dashboard: 'https://grafana/your-dashboard' expr: count(purefb_hardware_health{type!~"eth|mgmt_port|bay"} == 0) by (ch,type,environment) > 1 for: 1m labels: severity: page # We are looking for multiple failed bays in the same blade - alert: PureMultipleBaysFailed annotations: summary: Pure chassis {{ $labels.ch }} has fb {{ $labels.fb }} with {{ $value }} failed bays description: 'The Pure Storage chassis {{ $labels.ch }} has fb {{ $labels.fb }} with {{ $value }} failed bays, close to the healthy limit of two simultaneous failures. Ensure that the hardware failures are being worked on' dashboard: 'https://grafana/your-dashboard' expr: count(purefb_hardware_health{type="bay"} == 0) by (ch,type,fb,environment) > 1 for: 1m labels: severity: page Finally, we configure high-level alerts for chassis and XFM failures: - alert: PureChassisFailed annotations: summary: Chassis {{ $labels.name }} is failed description: 'The Pure Storage hardware chassis {{ $labels.name }} is failed' dashboard: 'https://grafana/your-dashboard' expr: purefb_hardware_health{type="ch"} == 0 for: 1m labels: severity: page - alert: PureXFMFailed annotations: summary: Xternal Fabric Module {{ $labels.name }} is failed description: 'The Pure Storage hardware Xternal fabric module {{ $labels.name }} is failed' dashboard: 'https://grafana/your-dashboard' expr: purefb_hardware_health{type="xfm"} == 0 for: 1m labels: severity: page Latency Using the metric purefb_array_performance_latency_usec we can set a threshold for all the different protocols and dimensions (read, write, etc), so we are alerted if any problem causes the latency to go above an expected level. - alert: PureLatencyHigh annotations: summary: Pure {{ $labels.dimension }} - {{ $labels.protocol }} latency high description: 'Pure {{ $labels.protocol }} latency for dimension {{ $labels.dimension }} is above 100ms' dashboard: 'https://grafana/your-dashboard' expr: (avg_over_time(purefb_array_performance_latency_usec{protocol="all"}[30m]) * 0.001) for: 1m labels: severity: chat-notification Saturation For saturation, we are primarily worried about something unexpected causing excessive use of array space, increasing the risk of hitting the cluster capacity. With that in mind, it’s good to have a simple alert in place, even if we don’t expect it to fire anytime soon: - alert: PureArraySpace annotations: summary: Pure Cluster {{ $labels.instance }} available space is expected to be below 10% description: 'The array space for pure cluster {{ $labels.instance }} is expected to be below 10% in a month, please investigate and ensure there is no risk of running out of capacity' dashboard: 'https://grafana/your-dashboard' expr: (predict_linear(purefb_array_space_bytes{space="empty",type="array"}[30d], 730 * 3600)) < (purefb_array_space_bytes{space="capacity",type="array"} * 0.10) for: 1m labels: severity: chat-notification HTTP We use BigIp load balancers to front-end the cluster, which means that all the alerts we already had in place for the BigIp HTTP profiles, virtual servers, and pools also cover access to Pure. The solution for each organization on this topic will be different, but it is a good practice to keep an eye on HTTP status codes and throughput. Grafana Dashboards The project’s GitHub repository includes JSON files for Grafana dashboards that are based on the metrics generated by the exporter. With simple adjustments to fit each setup, it’s possible to import them quickly. Wrapping up On top of the system’s built-in capabilities, Pure also provides options to integrate their system into well-known tools like Prometheus and Grafana, facilitating the process of managing the cluster the same way we manage everything else. I hope this post helps any other team interested in working with them better understand the effort involved. Thanks for reading!

6 months ago 67 votes
Announcing Hotwire Spark: live reloading for Rails applications

Today, we are releasing Hotwire Spark, a live-reloading system for Rails Applications. Reloading the browser automatically on source changes is a problem that has been well-solved for a long time. Here, we wanted to put an accent on smoothness. If the reload operation is very noticeable, the feedback loop is similar to just reloading the page yourself. But if it’s smooth enough—if you only perceive the intended change—the feedback loop becomes terrific. To use, just install the gem in development: group :development do gem "hotwire-spark" end It will update the current page on three types of change: HTML content, CSS, and Stimulus controllers. How do we achieve that desired smoothness with each? For HTML content, it morphs the <body> of the page into the new <body>. Also, it disconnects and reconnects all the Stimulus controllers on the page. For CSS, it reloads the changed stylesheet. For Stimulus controllers, it fetches the changed controller, replaces its module in Stimulus, and reconnects all the controllers. We designed Hotwire Spark to shine with the #nobuildapproach we use and recommend. Serving CSS and JS assets as standalone files is ideal when you want to fetch and update only what has changed. There is no need to use bundling or any tooling. Hot Module Replacement for Stimulus controllers without any frontend building tool is pretty cool! 2024 has been a very special year for Rails. We’re thrilled to share Hotwire Spark before the year wraps up. Wishing you all a joyful holiday season and a fantastic start to 2025.

6 months ago 100 votes
A vanilla Rails stack is plenty

If you have the luxury of starting a new Rails app today, here’s our recommendation: go vanilla. Fight hard before adding Ruby dependencies. Keep that Gemfile that Rails generates as close to the original one as possible. Fight even harder before adding Javascript dependencies. You don’t need React or any other front-end frameworks, nor a JSON API to feed those. Hotwire is a fantastic, pragmatic, and ridiculously productive technology for the front end. Use it. The same goes for mobile apps: use Hotwire Native. With a hybrid approach you can combine the very same web app you have built with a wonderful native experience right where you want it. The productivity compared to a purely native approach is night and day. Embrace and celebrate rendering things on the server. It has become cool again. ERB templates and view helpers will take you as long as you need, and they are a fantastic common ground for designers to collaborate hands-on with the code. #nobuild is the simplest way to go; don’t close this door with your choices. Instead of bundling Javascript, use import maps. Don’t bundle CSS, just use modern standard CSS goodies and serve them all with Propshaft. If you have 100 Javascript files and 100 stylesheets, serve 200 standalone requests multiplexed over HTTP2. You will be delighted. Don’t add Redis to the mix. Use solid_cache for caching, solid_queue for jobs, and solid_cable for Action Cable. They will all work on your beloved relational database and are battle-tested. Test your apps with Minitest. Use fixtures and build a realistic set of those as you cook your app. Make your app a PWA, which is fully supported by Rails 8. This may be more than enough before caring about mobile apps at all. Deploy your app with Kamal. If you want heuristics, your importmap.rb should import Turbo, Stimulus, your app controllers, and little else. Your Gemfile should be almost identical to the one that Rails generates. I know it sounds radical, but going vanilla is a radical stance in this convoluted world of endless choices. This is the Rails 8 stack we have chosen for our new apps at 37signals. We are a tiny crew, so we care a lot about productivity. And we sell products, not stacks, so we care a lot about delighting our users. This is our Omakase stack because it offers the optimal balance for achieving both. Vanilla means your app stays nimble. Fewer dependencies mean fewer future headaches. You get a tight integration out of the box, so you can focus on building things. It also maximizes the odds of having smoother future upgrades. Vanilla requires determination, though, because new dependencies always look shiny and shinier. It’s always clear what you get when you add them, but never what you lose in the long term. It is certainly up to you. Rails is a wonderful big tent. These are our opinions. If it resonates, choose vanilla! Guess what our advice is for architecting your app internals?

6 months ago 53 votes
Mission Control — Jobs 1.0 released

We’ve just released Mission Control — Jobs v1.0.0, the dashboard and set of extensions to operate background jobs that we introduced earlier this year. This new version is the result of 92 pull requests, 67 issues and the help of 35 different contributors. It includes many bugfixes and improvements, such as: Support for Solid Queue’s recurring tasks, including running them on-demand. Support for API-only apps. Allowing immediate dispatching of scheduled and blocked jobs. Backtrace cleaning for failed jobs’ backtraces. A safer default for authentication, with Basic HTTP authentication enabled and initially closed unless configured or explicitly disabled. Recurring tasks in Mission Control — Jobs, with a subset of the tasks we run in production We use Mission Control — Jobs daily to manage jobs HEY and Basecamp 4, with both Solid Queue and Resque, and it’s the dashboard we recommend if you’re using Solid Queue for your jobs. Our plan is to upstream some of the extensions we’ve made to Active Job and continue improving it until it’s ready to be included by default in Rails together with Solid Queue. If you want to help us with that, are interested in learning more or have any issues or questions, head over to the repo in GitHub. We hope you like it!

6 months ago 47 votes

More in programming

An Analysis of Links From The White House’s “Wire” Website

A little while back I heard about the White House launching their version of a Drudge Report style website called White House Wire. According to Axios, a White House official said the site’s purpose was to serve as “a place for supporters of the president’s agenda to get the real news all in one place”. So a link blog, if you will. As a self-professed connoisseur of websites and link blogs, this got me thinking: “I wonder what kind of links they’re considering as ‘real news’ and what they’re linking to?” So I decided to do quick analysis using Quadratic, a programmable spreadsheet where you can write code and return values to a 2d interface of rows and columns. I wrote some JavaScript to: Fetch the HTML page at whitehouse.gov/wire Parse it with cheerio Select all the external links on the page Return a list of links and their headline text In a few minutes I had a quick analysis of what kind of links were on the page: This immediately sparked my curiosity to know more about the meta information around the links, like: If you grouped all the links together, which sites get linked to the most? What kind of interesting data could you pull from the headlines they’re writing, like the most frequently used words? What if you did this analysis, but with snapshots of the website over time (rather than just the current moment)? So I got to building. Quadratic today doesn’t yet have the ability for your spreadsheet to run in the background on a schedule and append data. So I had to look elsewhere for a little extra functionality. My mind went to val.town which lets you write little scripts that can 1) run on a schedule (cron), 2) store information (blobs), and 3) retrieve stored information via their API. After a quick read of their docs, I figured out how to write a little script that’ll run once a day, scrape the site, and save the resulting HTML page in their key/value storage. From there, I was back to Quadratic writing code to talk to val.town’s API and retrieve my HTML, parse it, and turn it into good, structured data. There were some things I had to do, like: Fine-tune how I select all the editorial links on the page from the source HTML (I didn’t want, for example, to include external links to the White House’s social pages which appear on every page). This required a little finessing, but I eventually got a collection of links that corresponded to what I was seeing on the page. Parse the links and pull out the top-level domains so I could group links by domain occurrence. Create charts and graphs to visualize the structured data I had created. Selfish plug: Quadratic made this all super easy, as I could program in JavaScript and use third-party tools like tldts to do the analysis, all while visualizing my output on a 2d grid in real-time which made for a super fast feedback loop! Once I got all that done, I just had to sit back and wait for the HTML snapshots to begin accumulating! It’s been about a month and a half since I started this and I have about fifty days worth of data. The results? Here’s the top 10 domains that the White House Wire links to (by occurrence), from May 8 to June 24, 2025: youtube.com (133) foxnews.com (72) thepostmillennial.com (67) foxbusiness.com (66) breitbart.com (64) x.com (63) reuters.com (51) truthsocial.com (48) nypost.com (47) dailywire.com (36) From the links, here’s a word cloud of the most commonly recurring words in the link headlines: “trump” (343) “president” (145) “us” (134) “big” (131) “bill” (127) “beautiful” (113) “trumps” (92) “one” (72) “million” (57) “house” (56) The data and these graphs are all in my spreadsheet, so I can open it up whenever I want to see the latest data and re-run my script to pull the latest from val.town. In response to the new data that comes in, the spreadsheet automatically parses it, turn it into links, and updates the graphs. Cool! If you want to check out the spreadsheet — sorry! My API key for val.town is in it (“secrets management” is on the roadmap). But I created a duplicate where I inlined the data from the API (rather than the code which dynamically pulls it) which you can check out here at your convenience. Email · Mastodon · Bluesky

22 hours ago 2 votes
Building a container orchestrator

Kubernetes is not exactly the most fun piece of technology around. Learning it isn’t easy, and learning the surrounding ecosystem is even harder. Even those who have managed to tame it are still afraid of getting paged by an ETCD cluster corruption, a Kubelet certificate expiration, or the DNS breaking down (and somehow, it’s always the DNS). Samuel Sianipar If you’re like me, the thought of making your own orchestrator has crossed your mind a few times. The result would, of course, be a magical piece of technology that is both simple to learn and wouldn’t break down every weekend. Sadly, the task seems daunting. Kubernetes is a multi-million lines of code project which has been worked on for more than a decade. The good thing is someone wrote a book that can serve as a good starting point to explore the idea of building our own container orchestrator. This book is named “Build an Orchestrator in Go”, written by Tim Boring, published by Manning. The tasks The basic unit of our container orchestrator is called a “task”. A task represents a single container. It contains configuration data, like the container’s name, image and exposed ports. Most importantly, it indicates the container state, and so acts as a state machine. The state of a task can be Pending, Scheduled, Running, Completed or Failed. Each task will need to interact with a container runtime, through a client. In the book, we use Docker (aka Moby). The client will get its configuration from the task and then proceed to pull the image, create the container and start it. When it is time to finish the task, it will stop the container and remove it. The workers Above the task, we have workers. Each machine in the cluster runs a worker. Workers expose an API through which they receive commands. Those commands are added to a queue to be processed asynchronously. When the queue gets processed, the worker will start or stop tasks using the container client. In addition to exposing the ability to start and stop tasks, the worker must be able to list all the tasks running on it. This demands keeping a task database in the worker’s memory and updating it every time a task change’s state. The worker also needs to be able to provide information about its resources, like the available CPU and memory. The book suggests reading the /proc Linux file system using goprocinfo, but since I use a Mac, I used gopsutil. The manager On top of our cluster of workers, we have the manager. The manager also exposes an API, which allows us to start, stop, and list tasks on the cluster. Every time we want to create a new task, the manager will call a scheduler component. The scheduler has to list the workers that can accept more tasks, assign them a score by suitability and return the best one. When this is done, the manager will send the work to be done using the worker’s API. In the book, the author also suggests that the manager component should keep track of every tasks state by performing regular health checks. Health checks typically consist of querying an HTTP endpoint (i.e. /ready) and checking if it returns 200. In case a health check fails, the manager asks the worker to restart the task. I’m not sure if I agree with this idea. This could lead to the manager and worker having differing opinions about a task state. It will also cause scaling issues: the manager workload will have to grow linearly as we add tasks, and not just when we add workers. As far as I know, in Kubernetes, Kubelet (the equivalent of the worker here) is responsible for performing health checks. The CLI The last part of the project is to create a CLI to make sure our new orchestrator can be used without having to resort to firing up curl. The CLI needs to implement the following features: start a worker start a manager run a task in the cluster stop a task get the task status get the worker node status Using cobra makes this part fairly straightforward. It lets you create very modern feeling command-line apps, with properly formatted help commands and easy argument parsing. Once this is done, we almost have a fully functional orchestrator. We just need to add authentication. And maybe some kind of DaemonSet implementation would be nice. And a way to handle mounting volumes…

10 hours ago 2 votes
Digital hygiene: Emails

Email is your most important online account, so keep it clean.

6 hours ago 1 votes
AmigaGuide Reference Library

As I slowly but surely work towards the next release of my setcmd project for the Amiga (see the 68k branch for the gory details and my total noob-like C flailing around), I’ve made heavy use of documentation in the AmigaGuide format. Despite it’s age, it’s a great Amiga-native format and there’s a wealth of great information out there for things like the C API, as well as language guides and tutorials for tools like the Installer utility - and the AmigaGuide markup syntax itself. The only snag is, I had to have access to an Amiga (real or emulated), or install one of the various viewer programs on my laptops. Because like many, I spend a lot of time in a web browser and occasionally want to check something on my mobile phone, this is less than convenient. Fortunately, there’s a great AmigaGuideJS online viewer which renders AmigaGuide format documents using Javascript. I’ve started building up a collection of useful developer guides and other files in my own reference library so that I can access this documentation whenever I’m not at my Amiga or am coding in my “modern” dev environment. It’s really just for my own personal use, but I’ll be adding to it whenever I come across a useful piece of documentation so I hope it’s of some use to others as well! And on a related note, I now have a “unified” code-base so that SetCmd now builds and runs on 68k-based OS 3.x systems as well as OS 4.x PPC systems like my X5000. I need to: Tidy up my code and fix all the “TODO” stuff Update the Installer to run on OS 3.x systems Update the documentation Build a new package and upload to Aminet/OS4Depot Hopefully I’ll get that done in the next month or so. With the pressures of work and family life (and my other hobbies), progress has been a lot slower these last few years but I’m still really enjoying working on Amiga code and it’s great to have a fun personal project that’s there for me whenever I want to hack away at something for the sheer hell of it. I’ve learned a lot along the way and the AmigaOS is still an absolute joy to develop for. I even brought my X5000 to the most recent Kickstart Amiga User Group BBQ/meetup and had a fun day working on the code with fellow Amigans and enjoying some classic gaming & demos - there was also a MorphOS machine there, which I think will be my next target as the codebase is slowly becoming more portable. Just got to find some room in the “retro cave” now… This stuff is addictive :)

yesterday 4 votes
That boolean should probably be something else

One of the first types we learn about is the boolean. It's pretty natural to use, because boolean logic underpins much of modern computing. And yet, it's one of the types we should probably be using a lot less of. In almost every single instance when you use a boolean, it should be something else. The trick is figuring out what "something else" is. Doing this is worth the effort. It tells you a lot about your system, and it will improve your design (even if you end up using a boolean). There are a few possible types that come up often, hiding as booleans. Let's take a look at each of these, as well as the case where using a boolean does make sense. This isn't exhaustive—[1]there are surely other types that can make sense, too. Datetimes A lot of boolean data is representing a temporal event having happened. For example, websites often have you confirm your email. This may be stored as a boolean column, is_confirmed, in the database. It makes a lot of sense. But, you're throwing away data: when the confirmation happened. You can instead store when the user confirmed their email in a nullable column. You can still get the same information by checking whether the column is null. But you also get richer data for other purposes. Maybe you find out down the road that there was a bug in your confirmation process. You can use these timestamps to check which users would be affected by that, based on when their confirmation was stored. This is the one I've seen discussed the most of all these. We run into it with almost every database we design, after all. You can detect it by asking if an action has to occur for the boolean to change values, and if values can only change one time. If you have both of these, then it really looks like it is a datetime being transformed into a boolean. Store the datetime! Enums Much of the remaining boolean data indicates either what type something is, or its status. Is a user an admin or not? Check the is_admin column! Did that job fail? Check the failed column! Is the user allowed to take this action? Return a boolean for that, yes or no! These usually make more sense as an enum. Consider the admin case: this is really a user role, and you should have an enum for it. If it's a boolean, you're going to eventually need more columns, and you'll keep adding on other statuses. Oh, we had users and admins, but now we also need guest users and we need super-admins. With an enum, you can add those easily. enum UserRole { User, Admin, Guest, SuperAdmin, } And then you can usually use your tooling to make sure that all the new cases are covered in your code. With a boolean, you have to add more booleans, and then you have to make sure you find all the places where the old booleans were used and make sure they handle these new cases, too. Enums help you avoid these bugs. Job status is one that's pretty clearly an enum as well. If you use booleans, you'll have is_failed, is_started, is_queued, and on and on. Or you could just have one single field, status, which is an enum with the various statuses. (Note, though, that you probably do want timestamp fields for each of these events—but you're still best having the status stored explicitly as well.) This begins to resemble a state machine once you store the status, and it means that you can make much cleaner code and analyze things along state transition lines. And it's not just for storing in a database, either. If you're checking a user's permissions, you often return a boolean for that. fn check_permissions(user: User) -> bool { false // no one is allowed to do anything i guess } In this case, true means the user can do it and false means they can't. Usually. I think. But you can really start to have doubts here, and with any boolean, because the application logic meaning of the value cannot be inferred from the type. Instead, this can be represented as an enum, even when there are just two choices. enum PermissionCheck { Allowed, NotPermitted(reason: String), } As a bonus, though, if you use an enum? You can end up with richer information, like returning a reason for a permission check failing. And you are safe for future expansions of the enum, just like with roles. You can detect when something should be an enum a proliferation of booleans which are mutually exclusive or depend on one another. You'll see multiple columns which are all changed at the same time. Or you'll see a boolean which is returned and used for a long time. It's important to use enums here to keep your program maintainable and understandable. Conditionals But when should we use a boolean? I've mainly run into one case where it makes sense: when you're (temporarily) storing the result of a conditional expression for evaluation. This is in some ways an optimization, either for the computer (reuse a variable[2]) or for the programmer (make it more comprehensible by giving a name to a big conditional) by storing an intermediate value. Here's a contrived example where using a boolean as an intermediate value. fn calculate_user_data(user: User, records: RecordStore) { // this would be some nice long conditional, // but I don't have one. So variables it is! let user_can_do_this: bool = (a && b) && (c || !d); if user_can_do_this && records.ready() { // do the thing } else if user_can_do_this && records.in_progress() { // do another thing } else { // and something else! } } But even here in this contrived example, some enums would make more sense. I'd keep the boolean, probably, simply to give a name to what we're calculating. But the rest of it should be a match on an enum! * * * Sure, not every boolean should go away. There's probably no single rule in software design that is always true. But, we should be paying a lot more attention to booleans. They're sneaky. They feel like they make sense for our data, but they make sense for our logic. The data is usually something different underneath. By storing a boolean as our data, we're coupling that data tightly to our application logic. Instead, we should remain critical and ask what data the boolean depends on, and should we maybe store that instead? It comes easier with practice. Really, all good design does. A little thinking up front saves you a lot of time in the long run. I know that using an em-dash is treated as a sign of using LLMs. LLMs are never used for my writing. I just really like em-dashes and have a dedicated key for them on one of my keyboard layers. ↩ This one is probably best left to the compiler. ↩

yesterday 4 votes