Metrics over OTLP

Mirador ingests OTLP metrics through the same gateway and authentication used for traces and logs. Metrics appear in the live catalog and can be composed into dashboards with stat, timeseries, histogram, bar, pie, and table widgets.

Supported metric types

OTLP metric type	Ingestion	Common use
Gauge	Supported	Current temperature, queue depth, active sessions
Sum	Supported with cumulative and delta temporality	Counters, request totals, bytes processed
Histogram	Supported	Latency and payload distributions
ExponentialHistogram	Supported	High-dynamic-range distributions
Summary	Accepted and stored	Precomputed count, sum, and quantiles

Mirador derives counter rate and increase at query time, including handling monotonic counter resets. Histogram and exponential-histogram widgets support count, average, min, max, and quantile views.

Export metrics

Configure a standard OTLP/HTTP metric exporter:

export OTEL_EXPORTER_OTLP_METRICS_PROTOCOL=http/protobuf
export OTEL_EXPORTER_OTLP_METRICS_ENDPOINT=https://otel.mirador.org/v1/metrics
export OTEL_EXPORTER_OTLP_METRICS_HEADERS=Authorization=mir_srv_xxx

Or configure the Node.js metrics SDK directly:

import { resourceFromAttributes } from '@opentelemetry/resources';
import {
  MeterProvider,
  PeriodicExportingMetricReader,
} from '@opentelemetry/sdk-metrics';
import { OTLPMetricExporter } from '@opentelemetry/exporter-metrics-otlp-http';

const exporter = new OTLPMetricExporter({
  url: 'https://otel.mirador.org/v1/metrics',
  headers: {
    Authorization: `Bearer ${process.env.MIRADOR_API_KEY}`,
  },
});

const meterProvider = new MeterProvider({
  resource: resourceFromAttributes({
    'service.name': 'checkout-api',
  }),
  readers: [
    new PeriodicExportingMetricReader({ exporter }),
  ],
});

const meter = meterProvider.getMeter('checkout');
const payments = meter.createCounter('payments.completed', {
  description: 'Completed payments',
  unit: '{payment}',
});

payments.add(1, {
  'payment.provider': 'stripe',
  'deployment.environment.name': 'production',
});

Attributes and series

Mirador preserves resource and datapoint attributes. Attribute values are converted to strings; arrays and key-value lists are JSON encoded. Use stable, bounded attributes for filtering and grouping:

Good: service.name, http.request.method, http.route, cloud.region, chain.name
Avoid: request IDs, transaction hashes, UUIDs, timestamps, and wallet addresses

Every distinct attribute set creates a separate time series. High-cardinality or constantly changing values increase storage and make dashboards harder to interpret.

Partial success

An export can accept valid datapoints while rejecting malformed or oversized ones. Mirador reports the rejected count in the standard OTLP ExportMetricsPartialSuccess response. Datapoints can be rejected when they have no metric name, carry the NO_RECORDED_VALUE flag, contain malformed histogram buckets, or exceed ingestion limits. Publish failures return a retryable OTLP error so normal SDK retry behavior applies.

Build a dashboard

After the first export:

Open your project in Mirador.
Go to Metrics → Dashboards.
Create a dashboard and select Add widget.
Choose a metric, aggregation, visualization, filters, and optional group-by attribute.

Dashboard ranges update live, and timeseries widgets can be zoomed into an absolute time window for comparison with related logs and traces.

Getting Started

Core Concepts

Web3 Integration

OpenTelemetry

Automations

AI & Agents

Advanced

Best Practices

Supported metric types

Export metrics

Attributes and series

Partial success

Build a dashboard

Next steps

OpenTelemetry overview

Logs over OTLP

​Supported metric types

​Export metrics

​Attributes and series

​Partial success

​Build a dashboard

​Next steps

OpenTelemetry overview

Logs over OTLP

Supported metric types

Export metrics

Attributes and series

Partial success

Build a dashboard

Next steps