Metric Explorer

Metric Explorer is a no-code interface for navigating Prometheus-compatible metrics, designed to help you quickly move from raw telemetry to actionable insights—without writing PromQL.

With just a few clicks, it constructs basic queries behind the scenes, allowing you to intuitively explore, visualize, and analyze metrics through a structured, query-free workflow. Acting as a drawer-style interface, Metric Explorer allows you to explore new insights without leaving or disrupting your current view.

Use Metric Explorer to:

Search and explore unknown metrics: Start with a simple string search to discover relevant metric names, associated labels, and their values—even when the structure or naming conventions are unfamiliar.
Identify common patterns: Uncover shared labels and values across multiple metrics to inform join queries and enrich your analysis. Surface metrics related to the one in focus to expand your investigation.
Break down metrics: Slice data by metric name, label, or value to detect anomalies and investigate root causes.

Get started

Begin your metrics exploration in two ways, depending on your investigation path:

Begin with a metric name if you're looking into a broad system behavior (e.g., high CPU or memory usage) and want to drill into what’s happening across services or infrastructure.
Begin with a label/value if you're already focused on a specific entity (e.g., a service, container, or cluster) based on an incident, alert, or known context.

Begin with a metric name

Use the Metric search panel to find and select metrics by name or keyword. Selecting a metric name focuses the investigation on the nature of the data being collected— such as CPU usage, memory consumption, network errors, or disk IO.

Selecting a metric name surfaces only the relevant labels associated with it, making subsequent filtering more precise.

Example:

You search for container and select container_memory_usage_By, container_cpu_utilization_1, and container_fs_usage_bytes.

Why it matters:

Starting with a metric name is ideal for system-wide investigations where the symptom (e.g., CPU spikes) is known but the specific source is not. It enables broad visibility into how the selected resource behaves across different services, nodes, and environments, leading to faster problem isolation.

Begin with a label/value

Use the Label search panel to find and select metrics by label or label/value pair. Selecting a label or value focuses the investigation around a specific entity, such as an application, container, service, or cluster.

Example:

You search for the label k8s_node_name and select the value ip-172-31-14-14.us-east-2.

This action filters the view to display only metrics associated this Kubernetes node, making it easy to analyze CPU usage, memory consumption, error rates, and response times for that specific service.

Why it matters:

Starting with a label or value is ideal when the problem space is already narrowed based on external triggers (e.g., user-reported incidents, monitoring alerts). It accelerates time-to-insight by immediately focusing the exploration on the affected service or environment.

Filter to refine your analysis

Filter your metrics by label or label/value pair to dig deeper. Once a label or label/value pair is selected, click Filter metric to filter the metric pool to surface only the associated metrics.

Once a filter is in place, you may search for specific metrics within the filtered pool.

Generate a PromQL query

As you select metrics, labels, and values, Metric Explorer dynamically generates a PromQL query that reflects your current state.

The generated query appears in the Generated PromQL Query section at the bottom of the screen. You can copy the query with a single click to reuse it in alerts, dashboards, or manual Prometheus queries.

The automatic query builder bridges the gap between visual exploration and repeatable configuration, allowing users to move seamlessly from investigation to alerting or dashboard creation without needing to construct queries manually.

Example:

After selecting container_cpu_utilization_1 and filtering by k8s_node_name = 'ip-172-31-14-14.us-east-2.compute.internal', the system generates:

container_cpu_utilization_1{k8s_node_name = 'ip-172-31-14-14.us-east-2.compute.internal'}

Click Copy Query to save it for reuse.

Visualize metrics

Automatically generate visualizations for the filtered data, selecting the most appropriate chart type based on the metric type (e.g., counter, gauge). Click Show chart.

Metrics are displayed individually.

Example:

View line graphs for CPU usage, memory consumption, and response time for selected containers or services.

Why it matters:

Time-series visualizations help users quickly detect anomalies, spot performance regressions, and correlate events across different dimensions, significantly speeding up root cause analysis.

Investigate metric permutations

Visualize all permutations of a metric, grouped by selected labels such as service, team, environment, or node.

Example:

You explore CPU usage across Coralogix environments, teams, services, and pods to identify which combinations show anomalies or outliers.

Why it matters:

Comparing metric permutations surfaces hidden patterns, outliers, or systemic problems that may not be obvious when viewing aggregate or isolated metrics.

Visual Explorer experience

Using Visual Explorer, access Metric Explorer.

Using the Query Builder, select metrics as your source.
From the metrics search dropdown, click Open Metric Explorer.
Once you have generated a PromQL query in Metric Explorer, click Apply query to view it in Custom Dashboards.

Custom Dashboards experience

Using the Custom Dashboards Query Builder, access Metric Explorer.

During your widget setup, a query panel will appear in the lower part of the dashboard panel. Toggle between Builder and Query modes.
In Builder mode, select metrics as your source.
Click Open Metric Explorer.
Once you have generated a PromQL query in Metric Explorer, click Apply query to view it in Custom Dashboards.

Reserved Fields

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