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Deployment Status Monitoring Block for Radix UI
Six deploys with a chosen signal before and after as two numbers, an arrow, a signed change and a word (Worse, Same, Better), a select for error rate, p95 latency or CPU, and an hour plot with the deploy as a pin.
Installation
Overview
Show what each deploy did to a signal, not just that it shipped, with this deployment status panel for React with Radix UI. It is Shipharbor, a deployment tracker. A Native Select in the header picks the signal (error rate, p95 latency or CPU), and a table of six deploys (version in mono, environment, time, who shipped it) shows the signal's average over the 30 minutes before and the 30 minutes after as two numbers, an arrow, a signed change and a word: Worse, Same or Better, decided by a change of 5% or more. The header sentence compares the two newest deploys ("v2.4.12 raised p95 latency by 38 ms; v2.4.11 changed nothing."). Choosing a deploy draws the hour around it in a hairline frame with a plus mark on each corner: the signal as a line, the deploy as a pin in the middle, the two windows bracketed and each window's average as a dashed line. The figures are typed averages and the hour is a steady wobble around them. Guides draw the frame and its bands. For release reviews, rollback decisions and anyone asking whether a deploy hurt.
FAQ
How do I load my own deploys?
Edit DEPLOY_LINES: version, environment, time, the author's initials, then the before and after averages for the error rate, p95 latency and CPU. The verdict, the arrow, the change and the header are worked out from those. In your app compute the two 30-minute averages from your metrics store and keep the same shape.
How are Worse, Same and Better decided?
A rise of 5% or more over the before average is Worse, a fall of 5% or more is Better, anything else is Same. NOTICE holds the threshold and the status line states it. A small absolute change in a tiny rate can still be Worse, which is why the share is used, not the size.
Is the hour real?
No. The line is generated from the two averages with a steady wobble and a short ramp at the deploy, so it is the same on every load. In your app plot the real samples with the same path helper.
Related
- Deployment Status DashboardReleases and testing
- Canary Deployments DashboardReleases and testing
- Deployment Log Tabs ChangelogDeploys and rollouts
- Pipeline Visualizer MonitoringDeploys and pipelines
- Vercel Deployment Log MonitoringDeploys and pipelines
- Error Rate Against Traffic MonitoringTraffic and errors