Jobs

Monocle tracks your background job activity with a dedicated Jobs dashboard. You get visibility into job throughput, error rates, processing times, and queue delays across all your queues.

What Monocle tracks

Throughput over time with success/error breakdown
Error rate trends to spot problematic jobs
Processing time (average and P95) per job
Queue time between dispatch and processing start
Retry attempts and failure reasons
Per-job detail pages with recent executions and error breakdown

How it works

Monocle creates two types of spans for each job:

A producer span when a job is dispatched (enqueued)
A consumer span when a job is processed by a worker

These spans carry standardized OpenTelemetry Messaging Semantic Conventions attributes, so the Jobs dashboard can aggregate metrics regardless of which queue library you use.

Span attributes

All job instrumentations must emit these attributes for the Jobs dashboard to work correctly.

Standard semconv attributes

These follow the official OpenTelemetry Messaging Semantic Conventions:

Attribute	Description
`messaging.system`	Queue system identifier (e.g., `"bullmq"`)
`messaging.destination.name`	Queue name
`messaging.operation.type`	`"send"` for producers, `"process"` for consumers
`messaging.operation.name`	Operation name (e.g., `Queue.add`, `Worker.run`)
`messaging.message.id`	Job ID
`messaging.client.id`	Worker name (consumer spans)
`messaging.batch.message_count`	Number of jobs in bulk operations
`messaging.message.retry.count`	Number of retry attempts

Monocle attributes

These custom attributes power specific dashboard features:

Attribute	Dashboard Feature
`messaging.job.name`	Job name grouping, per-job detail page
`messaging.operation.type`	Distinguishing published vs processed jobs in throughput charts
`messaging.job.queue_time_ms`	Queue time metrics and charts
`entry_point.type` = `"job"`	Identifies the span as a job entry point, used for dashboard routing

Supported libraries

BullMQ

Redis-backed job queue with priorities, retries, rate limiting, and flows

boringnode/queue

Lightweight job queue for Node.js. Instrumentation is built into the Monocle agent when @boringnode/queue is installed.

Getting started

See the individual instrumentation pages above for setup instructions, or the AdonisJS guide if you are using AdonisJS.

Sampling high-volume jobs

If one job runs very often or creates very large traces, use job sampling to reduce trace volume for successful executions while keeping failed executions.

config/monocle.ts

import { defineConfig, Sample } from "@monocle.sh/adonisjs-agent";

export default defineConfig({
  // ...
  sampling(ctx) {
    if (ctx.kind === "job" && ctx.name === "SendNewsletterEmail") {
      return Sample.rate(0.01).keepErrors();
    }

    return Sample.default();
  },
});

Getting Started

Monocle Studio

Frameworks

Features

Integrations

Other

What Monocle tracks

How it works

Span attributes

Standard semconv attributes

Monocle attributes

Supported libraries

BullMQ

boringnode/queue

Getting started

Sampling high-volume jobs

​What Monocle tracks

​How it works

​Span attributes

​Standard semconv attributes

​Monocle attributes

​Supported libraries

BullMQ

boringnode/queue

​Getting started

​Sampling high-volume jobs

What Monocle tracks

How it works

Span attributes

Standard semconv attributes

Monocle attributes

Supported libraries

Getting started

Sampling high-volume jobs