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!

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.

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?

Quality Assurance (QA) is a team of two at 37signals: Michael, who created the department 12 years ago, and Gabriel, who joined the team in 2022. Together, we have a hand in projects across all of our products, from kickoff to release. Our goal is to help designers and programmers ship their best work. Our process revolves around manual testing and has been tuned to match the rhythm of Shape Up. Here, we’ll share the ins and outs of our methods and touch on a few of the tools we use along the way. Kicking things off At 37signals we run projects in six-week cycles informed by Shape Up. At the beginning of each cycle, Brian, our Head of Product, posts a kick-off message detailing what we plan to ship. This usually consists of new features and improvements for Basecamp, HEY, or a ONCE product. Each gets its own Basecamp project, and each project includes a pitch. The pitch lays out the problem or need, a proposed solution, and the “appetite” or time budget. The kick-off is also QA’s cue to dive in! We offer early feedback, ask questions or illuminate things that aren’t covered, and give extra consideration to flows and interactions that may require extra work on the accessibility front. We then step back and let the teams focus, design, and build things for a while. The right time to test We wait until the feature or product reaches a usable state to start testing in earnest. This helps us keep a fresh perspective, unencumbered by the knowledge of compromises made along the way. We use a Card Table within our QA Team project to track what’s ready for testing or in progress. Teams add a card to the Ready for QA (Triage) section when the time is right. The table is kept simple with just two columns, In Progress and Pending Input, for when we’ve completed our test run and the team is addressing the feedback. Depending on the breadth and complexity of the work being tested, this flow can take anywhere from a few hours to a few days. A holistic approach to QA Once we take on a request, we explore and scrutinize the feature much like an (extremely zealous!) customer would. We want to help teams ship the most polished features they can. We look out for bugs of all kinds: performance issues, visual glitches, unexpected changes, and so on, but perhaps most importantly, we offer feedback on the usability of the feature. We guide our feedback with questions like: Is this feature easy to discover and access? Is it in the right spot? Does it interact in an unexpected way with another part of the app? How does the change play with our mobile apps? Does this solve the problem in a way that customers will find obvious? Critically, what we raise with this type of QA testing are suggestions, not must-haves. The designer and programmer working on the feature make the call on what to address and what to shelve. We document this feedback in a dedicated Card Table within the feature’s Basecamp project. The designer and programmer will then review the cards we’ve added to Triage and direct them to the In Progress and Not Now columns as appropriate. From In Progress, cards are moved to a column called QA to confirm fixed, then finally to Done. More focus, less bloat Our overall approach to testing is guided exploration. We don’t maintain an exhaustive collection of test cases to dogmatically review each time we test a feature. We’ve tried using dedicated test plan tools and comprehensive spreadsheets of test cases upon test cases; the time spent certifying every little thing was considerable, yet it didn’t translate into finding more issues. Worse, it left us with less time to spend sitting with the feature in a more subjective way. We’ve landed on a more pragmatic approach. We’ve boiled down the test plan to a concise list of considerations that live in Basecamp as to-do list templates, one for each product. Instead of a multitude of test cases, each template contains around 100 items. These act as pointers, touching on overall concepts (like commenting, dark mode, email notifications), specific areas of the app, and platform-specific considerations. We reflect on the work presented and how it ties into these areas. Some examples from recent projects have been: Did we update exporting to consider this new addition of time tracking entries? Are email notifications properly reflecting the new Steps feature we added to Card Table? How about print styles, do they look good? QA Considerations for Basecamp 4 We create a to-do list via the template directly in the project we are working on, and use that as our reference for reviewing the work. We also ask the feature team if there are areas that deserve extra attention. Being flexible and discerning about how much time and coverage we use in our testing allows us to cover anywhere from 4 to 12+ projects in a very short span of time. We love working as a team of two and being able to riff on how to approach testing a feature. Sometimes, we divide and conquer; other times, both of us review the work. Fresh eyes provide a good chance of catching something new. Gabriel has a better knack for Android conventions and Michael for iOS, but we actively avoid over-specializing. Keeping up with multiple platforms requires extra effort, but it’s worth it when considering the consistency of the experience across all of them. Accessibility As part of our review, we test the accessibility of the changes. We use a combination of keyboard navigation and at least one screen reader on each platform to vet how well the feature will work for someone who relies on accessible technology. We also use browser extensions like axe and Accessibility Insights for Web to validate semantics of the code and Headings Map to make sure heading levels are sequential. At times, we bring in customers who use a screen reader full-time to help us validate whether everything makes sense and learn where things can improve. Our new colleague, Bruno, is a full-time user of the NVDA screen reader and can offer this sort of direct feedback on how a feature or flow works for him. Explorations in tooling A recent addition to our toolkit is a visual regression tool built on BackstopJS with the help of our colleague Lewis. Whenever we review work, we can run the suite of tests — mostly a list of URLs for various pages around the app — first pointed to production, then against a beta environment where the new feature is staged. Any visual differences will be flagged in a report we review, then write up bug report cards for the team if needed. Walking the walk Part of what enables us to keep our process minimal is that we use our products daily, both on the job and in our everyday lives. This affords us an intimate understanding of how they work and how they can be improved. We’re passionate about what we do. We find ourselves fortunate to work with each other and with so many talented colleagues. We hope this post has given you some helpful insight into the way we do things! If you have questions or if there are topics you’d like us to cover in future posts, drop us an email at qa@37signals.com.