Konduo Manual

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Prometheus Plugin ​

The Prometheus plugin is a metric source plugin that lets Konduo query Prometheus-compatible HTTP APIs and execute resource-owned logical metrics as PromQL queries.

Highlights ​

  • Executes range and instant queries for resource dashboards and alert evaluation.
  • Supports label exploration, query inspection, and mapping-pack review.
  • Can register a Prometheus server itself as a resource to inspect targets, TSDB, rule/evaluation, and runtime signals.
  • Protects management actions such as reload and snapshot behind configuration, confirmation, and cooldown controls.

Before Registration ​

  • The Konduo backend must be able to reach the Prometheus HTTP API.
  • Prepare endpoint URL, authentication mode, timeout, and query-limit policy.
  • If this Prometheus instance will be used as a managed metric collection write target, run it with --web.enable-remote-write-receiver.
  • Apply resource plugin mapping packs so dashboards and alert rules can resolve expected logical metrics.

Operations Tips ​

  • If dashboards are empty, check scrape state, label filters, and mapping-pack installation together.
  • Bound query ranges and steps to avoid excessive sample volume.
  • Treat Prometheus target health as metric evidence, not as the direct availability source for the scraped resource.

Operator Views ​

  • The metric source view is the entry point for HTTP API connectivity, label exploration, query inspection, and mapping-pack status.
  • The Prometheus resource view exposes Prometheus' own targets, rule groups, alert state, TSDB, and runtime status.
  • The default dashboard adds time series for accepted samples per scrape, query evaluation duration, TSDB block/WAL storage, and rule evaluation duration alongside availability and throughput.
  • Query and rule evaluation duration are five-minute averages. Compare them with query rate and rule intervals, and review TSDB storage together with retention and compaction health.
  • If a Prometheus-compatible backend does not expose Prometheus's own metric names, these optional panels can show no data without failing mapping-pack validation.
  • Mapping-pack views show how a resource plugin's logical metric keys resolve to PromQL.
  • Query inspection helps diagnose empty dashboards or partial alert evaluation by showing the actual query and label matchers.

Diagnostics And Alerts ​

  • Because this plugin provides metric evidence for other resources, target health, scrape delay, and rule evaluation state are operationally important.
  • Diagnostics separate HTTP API connectivity, scrape state, rule evaluation, active alerts, TSDB/runtime pressure, and mapping coverage.
  • Prometheus alerting covers metric-platform risk; resource outage decisions for Redis, Kafka, PostgreSQL, and others remain owned by the relevant plugin and Core availability.
  • When queries are slow or sample volume is high, tune range, step, label filters, and dashboard panel count together.

Management Boundary ​

  • Reload and snapshot are operationally meaningful management actions and stay behind confirmation and cooldown controls.
  • Arbitrary PromQL query support is for operational inspection, not a general long-running analytics workbench.
  • When Prometheus is used as a managed collection write target, it stores samples; resource plugins and Core own collection cadence and target access.
  • Mapping-pack overrides adapt exporter metric names and labels; they must not be used to mask real resource health.

Example Workflows ​

  • For an empty resource dashboard, check Prometheus target health, label filters, mapping-pack application, and query-inspect output in order.
  • For partial alert evaluation, compare the logical metric's PromQL with actual series labels.
  • For Prometheus pressure, review rule evaluation delay, query concurrency, TSDB head block behavior, and scrape intervals.
  • If managed collection samples are missing, confirm remote-write receiver mode, Core write status, and the resource identity label.

Prometheus Enterprise Overlay ​

The Prometheus Enterprise overlay adds MCP descriptors and Prometheus self-telemetry anomaly rules to metric source resources. It detects metric platform risk through scrape, ingestion, cardinality, and query pressure signals.

Highlights ​

  • Exposes Prometheus resource metadata, diagnostics, metrics catalog, and anomaly rules through MCP.
  • Declarative metric rules use logical metrics such as scrape duration, ingestion rate, TSDB head series, and query rate.
  • Simulator series validate cardinality and ingestion pressure scenarios.
  • Prometheus remains a metric provider and does not run plugin-mode detectors.

Before Registration ​

  • Prometheus API endpoint and query permissions must be healthy.
  • Connect a mapping pack for Prometheus self-metrics.
  • MCP users need metric-source read permission and the required scopes.

Operations Tips ​

  • Track cardinality growth together with exporter, label, and instrumentation changes.
  • When scrape latency and ingestion pressure rise together, inspect target growth and remote write state.
  • The default dashboard adds time series for accepted samples per scrape, query evaluation duration, TSDB block/WAL storage, and rule evaluation duration alongside availability and throughput.
  • Query and rule evaluation duration are five-minute averages. Compare them with query rate and rule intervals, and review TSDB storage together with retention and compaction health.
  • If a Prometheus-compatible backend does not expose Prometheus's own metric names, these optional panels can show no data without failing mapping-pack validation.

Operational Scenarios ​

  • When Prometheus supplies metrics for many resources, inspect scrape health, target churn, TSDB cardinality, and query pressure before trusting downstream signals.
  • If a plugin's anomaly signals suddenly disappear or spike, check the Prometheus mapping pack and source telemetry together.
  • MCP descriptors give agents a read-only catalog for metric-platform health.

Boundaries ​

  • The Prometheus plugin is both a metric provider and a monitored target; it does not execute plugin-mode anomaly detectors.
  • Recording rules, scrape config, and remote write settings belong to Prometheus operations and are not directly mutated by this overlay.