Rig Dev Product Features Analytics
You set up rig dev product features analytics by linking cluster logs to a metrics store. This setup tracks app health, rollouts, and resource use across your pods. Teams often grant broad cluster roles to get these dashboards working fast. Broad roles let any user run live commands or read secret keys in production. How rig dev product features analytics works in your cluster The platform groups your container workloads into simple units called capsules. Each capsule runs your app code and s
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You set up rig dev product features analytics by linking cluster logs to a metrics store. This setup tracks app health, rollouts, and resource use across your pods. Teams often grant broad cluster roles to get these dashboards working fast. Broad roles let any user run live commands or read secret keys in production.
How rig dev product features analytics works in your cluster
The platform groups your container workloads into simple units called capsules. Each capsule runs your app code and sends health data to the control plane. Admins link this control plane to a time-series database for deeper tracking. Then the system collects CPU load, memory use, and pod restart counts.
Your team reads these charts to check if a new build runs well. The platform also tracks every rollout and config change in an event log. Engineers use the dashboard or command tool to inspect live container states. Native role settings decide who can view charts or change capsule rules.
You map your user groups to built-in admin, viewer, or editor roles. Viewers check app metrics while editors push new container images and update settings. Admins manage cluster links, user roles, and project spaces across the whole team. This role setup keeps basic dashboard duties clear for daily work.
What native metrics solve and where role gaps remain
Native charts show you when a capsule runs out of memory or crashes. They help your team spot bad rollouts and turn back to a good build. Project roles also stop casual viewers from deleting a running app by mistake. Yet these project roles only check who logs in at the start.
They do not check what a user does inside an active pod shell. When a pod fails, an engineer often opens a shell or checks database rows. The native setup gives that engineer full rights to the target pod or store. Once inside, the user can run any command or read raw customer tables.
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Standard cluster logs record that a user opened a shell session. They do not record the exact queries or commands run during that session. They also cannot rewrite sensitive fields like emails or card numbers in query results. Teams need fine controls that check every single command and query in flight.
How to tighten role rules for metric and pod checks
Start by splitting your metric viewers from your production fix roles. Give most developers read-only rights to capsule charts and build logs. Remove direct pod shell rights and database keys from standard developer profiles. Store all app secrets in a dedicated secret manager instead of plain files.
Next, review the service accounts that pull data for your analytics boards. Give those accounts read rights only to metric endpoints, not to app data. Set short lifetimes on all user login tokens so old sessions close fast. Finally, add a control point that inspects live commands and masks sensitive output fields.
Add runtime guardrails and masking beside your databases and APIs
Login rules, role grants, and CI checks govern who connects to your stack. Yet any allowed script, engineer, or AI agent still runs commands with the full scope of its role. For workloads on Postgres, MySQL, SQL Server, MongoDB, ClickHouse, Kubernetes, SSH, or internal HTTP services, hoop.dev sits beside the service as an open-source proxy, a sidecar for your database or API.
Before a query runs, hoop.dev guardrails check the statement against your rules. An AI Analyzer you configure can score intent or hold risky calls for human review. Next, hoop.dev data masking rewrites sensitive fields in responses, and each verdict is logged per listener. Explore the MIT-licensed project on GitHub.
Frequently asked questions
What data does rig dev product features analytics track by default?
It tracks capsule CPU use, memory load, pod restarts, and rollout history. You can also link external metric tools to store long-term trend data.
Can native cluster roles block specific database queries during a check?
No, native roles only control who can log in to a project or pod. They cannot inspect or block individual database queries inside an active session.
How does data masking protect customer records during live checks?
Data masking finds patterns like phone numbers or national IDs in query results. It rewrites those matched fields before the results reach the user screen.