[Hackathon] Render Incident Command Center with SuperPlane#5093
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metonym wants to merge 7 commits into
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[Hackathon] Render Incident Command Center with SuperPlane#5093metonym wants to merge 7 commits into
metonym wants to merge 7 commits into
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List recent deploys for a Render service to support rollback and incident workflows. Adds the shared render operation mapper and action configuration helpers used by the new incident-response actions.
Fetch Render CPU, memory, request, and connection metrics and emit normalized summaries (latest, avg, max, count, unit) alongside the raw series for monitoring and incident-response workflows.
Query recent Render logs across one or more resources in the configured workspace, with level, type, text, and path filters. Emits log entries plus a count and error count for incident triage.
Update Render service settings, currently autoDeploy, so workflows can freeze deploys during an incident and thaw them after recovery. Emits serviceId, autoDeploy, and status.
Update Render autoscaling settings (enabled, min/max instances, and CPU and memory targets) for a web service. Emits the requested bounds, targets, status, and Render response.
Create a Render one-off job from an existing service and retrieve its status, enabling health checks, queue drains, repair tasks, and operational runbooks.
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This is a Hackathon submission for https://luma.com/3yx41qr9?tk=19uZD7
Background
This project demonstrates an incident response workflow for a multi-service application running on Render. The demo uses SuperPlane as an operational command center that can inspect Render services, collect runtime metrics, check application health, evaluate incident conditions, and run remediation actions from a single canvas.
The Render demo stack includes a web API, background worker, Key Value cache/queue, and Postgres database.
Changes
This adds new functionality to the existing Render integration to achieve this demo.
Flow
The SuperPlane canvas starts with an Open Incident trigger and fans out into service inspection, deploy lookup, CPU and memory metric collection, Render log inspection, API health checks, DB health checks, and queue health checks. After merging those signals, it branches into two practical paths:
The canvas also writes incident snapshots and decisions to
render-incidentsmemory so a Console can show incident status, service state, threshold decisions, failed redeploys, and manual operator actions like freeze deploys, thaw deploys, purge cache, redeploy, rollback, scale up, and scale down.Triage flow (no remediation)
no scale required.Triage flow (remediation triggered)
pendingJobs: 250.TRUE.In this flow, the remediation triggered, and services were scaled.
To reliably simulate this, the Trigger modal has a
forceScaletoggle (demo purposes).Final canvas.yaml (service IDs are not sensitive)