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Get usage and health data about your Node.js process.

doc is a small module that helps you collect health metrics about your Node.js process. It does that by using only the API available on Node itself (no native dependencies). It doesn't have any ties with an APM platform, so you are free to use anything you want for that purpose. Its API lets you access both computed and raw values, where possible.

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Installation

latest stable version
$ npm i @dnlup/doc
latest development version
$ npm i @dnlup/doc@next

Usage

You can import the module by using either CommonJS or ESM.

By default doc returns a Sampler instance that collects metrics about cpu, memory usage, event loop delay and event loop utilization.

Importing with CommonJS
const doc = require('@dnlup/doc')

const sampler = doc() // Use the default options

sampler.on('sample', () => {
  doStuffWithCpuUsage(sampler.cpu.usage)
  doStuffWithMemoryUsage(sampler.memory)
  doStuffWithEventLoopDelay(sampler.eventLoopDelay.computed)
  doStuffWithEventLoopUtilization(sampler.eventLoopUtilization.utilization) // Available only on Node versions that support it
})
Importing with ESM
import doc from '@dnlup/doc'

const sampler = doc()

sampler.on('sample', () => {
  doStuffWithCpuUsage(sampler.cpu.usage)
  doStuffWithMemoryUsage(sampler.memory)
  doStuffWithEventLoopDelay(sampler.eventLoopDelay.computed)
  doStuffWithEventLoopUtilization(sampler.eventLoopUtilization.utilization) // Available only on Node versions that support it
})
Note

A Sampler holds a snapshot of the metrics taken at the specified sample interval. This behavior makes the instance stateful. On every tick, a new snapshot will overwrite the previous one.

Enable/disable metrics collection

You can disable the metrics that you don't need.

const doc = require('@dnlup/doc')

// Collect only the event loop delay
const sampler = doc({ collect: { cpu: false, memory: false } })

sampler.on('sample', () => {
  // `sampler.cpu` will be `undefined`
  // `sampler.memory` will be `undefined`
  doStuffWithEventLoopDelay(sampler.eventLoopDelay.computed)
  doStuffWithEventLoopUtilization(sampler.eventLoopUtilization.utilization) // Available only on Node versions that support it
})

You can enable more metrics if you need them.

Garbage collection
const doc = require('@dnlup/doc')

const sampler = doc({ collect: { gc: true } })
sampler.on('sample', () => {
  doStuffWithCpuUsage(sampler.cpu.usage)
  doStuffWithMemoryUsage(sampler.memory)
  doStuffWithEventLoopDelay(sampler.eventLoopDelay.computed)
  doStuffWithEventLoopUtilization(sampler.eventLoopUtilization.utilization) // Available only on Node versions that support it
  doStuffWithGarbageCollectionDuration(sampler.gc.pause)
})
Active handles
const doc = require('@dnlup/doc')

const sampler = doc({ collect: { activeHandles: true } })

sampler.on('sample', () => {
  doStuffWithCpuUsage(sampler.cpu.usage)
  doStuffWithMemoryUsage(sampler.memory)
  doStuffWithEventLoopDelay(sampler.eventLoopDelay.computed)
  doStuffWithEventLoopUtilization(sampler.eventLoopUtilization.utilization) // Available only on Node versions that support it
  doStuffWithActiveHandles(sampler.activeHandles)
})

Examples

You can find more examples in the examples folder.

API

doc([options])

It creates a metrics Sampler instance with the given options.

Class: doc.Sampler

Metrics sampler.

It collects the selected metrics at a regular interval. A Sampler instance is stateful so, on each tick, only the values of the last sample are available. Each time the sampler emits the sample event, it will overwrite the previous one.

new doc.Sampler([options])

If options.collect.resourceUsage is set to true, options.collect.cpu will be set to false because the cpu metric is already available in the resource usage metric.

Event: 'sample'

Emitted every sampleInterval, it signals that new data the sampler has collected new data.

sampler.start()

Start collecting metrics.

sampler.stop()

Stop collecting metrics.

sampler.cpu

Resource usage metric instance.

sampler.resourceUsage

Resource usage metric instance.

sampler.eventLoopDelay

Event loop delay metric instance.

sampler.eventLoopUtilization

Event loop utilization metric instance.

sampler.gc

Garbage collector metric instance.

sampler.activeHandles

Number of active handles returned by process._getActiveHandles().

sampler.memory

Object returned by process.memoryUsage().

Class: CpuMetric

It exposes both computed and raw values of the cpu usage.

cpuMetric.usage

Cpu usage in percentage.

cpuMetric.raw

Raw value returned by process.cpuUsage().

Class: ResourceUsageMetric

It exposes both computed and raw values of the process resource usage.

resourceUsage.cpu

Cpu usage in percentage.

resourceUsage.raw

Raw value returned by process.resourceUsage().

Class: EventLoopDelayMetric

It exposes both computed and raw values about the event loop delay.

eventLoopDelay.computed

Event loop delay in milliseconds. It computes this value using the mean of the Histogram instance.

eventLoopDelay.raw

Exposes the Histogram instance.

eventLoopDelay.compute(raw)

Class: EventLoopUtilizationMetric

It exposes statistics about the event loop utilization.

eventLoopUtilization.idle

The idle value in the object returned by performance.eventLoopUtilization() during the sampleInterval window.

eventLoopUtilization.active

The active value in the object returned by performance.eventLoopUtilization() during the sampleInterval window.

eventLoopUtilization.utilization

The utilization value in the object returned by performance.eventLoopUtilization() during the sampleInterval window.

eventLoopUtilization.raw

Raw value returned by performance.eventLoopUtilization() during the sampleInterval window.

Class: GCMetric

It exposes the garbage collector activity statistics in the specified sampleInterval using hdr histograms.

new GCMetric(options)

gcMetric.pause

It tracks the global activity of the garbage collector.

gcMetric.major

The activity of the operation of type major. It's present only if GCMetric has been created with the option aggregate equal to true.

See performanceEntry.kind.

gcMetric.minor

The activity of the operation of type minor. It's present only if GCMetric has been created with the option aggregate equal to true.

See performanceEntry.kind.

gcMetric.incremental

The activity of the operation of type incremental. It's present only if GCMetric has been created with the option aggregate equal to true.

See performanceEntry.kind.

gcMetric.weakCb

The activity of the operation of type weakCb. It's present only if GCMetric has been created with the option aggregate equal to true.

See performanceEntry.kind.

Class: GCEntry

It contains garbage collection data, represented with an histogram. All timing values are expressed in nanoseconds.

new GCEntry()

The initialization doesn't require options. It is created internally by a GCMetric.

gcEntry.totalDuration

It is the total time of the entry in nanoseconds.

gcEntry.totalCount

It is the total number of operations counted.

gcEntry.mean

It is the mean value of the entry in nanoseconds.

gcEntry.max

It is the maximum value of the entry in nanoseconds.

gcEntry.min

It is the minimum value of the entry in nanoseconds.

gcEntry.stdDeviation

It is the standard deviation of the entry in nanoseconds.

gcEntry.getPercentile(percentile)

Class: GCAggregatedEntry

It extends GCEntry and contains garbage collection data plus the flags associated with it (see https://nodejs.org/dist/latest-v18.x/docs/api/perf_hooks.html#performanceentryflags).

new GCAggregatedEntry()

The initialization doesn't require options. It is created internally by a GCMetric.

gcAggregatedEntry.flags

This object contains the various histograms of each flag.

gcAggregatedEntry.flags.no

gcAggregatedEntry.flags.constructRetained

gcAggregatedEntry.flags.forced

gcAggregatedEntry.flags.synchronousPhantomProcessing

gcAggregatedEntry.flags.allAvailableGarbage

gcAggregatedEntry.flags.allExternalMemory

gcAggregatedEntry.flags.scheduleIdle

doc.errors

In the errors object are exported all the custom errors used by the module.

ErrorError CodeDescription
InvalidArgumentErrorDOC_ERR_INVALID_ARGAn invalid option or argument was used
NotSupportedErrorDOC_ERR_NOT_SUPPORTEDA metric is not supported on the Node.js version used

Diagnostics Channel support

Node diagnostics channel are supported.

const diagnosticsChannel = require('diagnostics_channel')
const doc = require('@dnlup/doc)

diagnosticsChannel.subscribe(doc.constants.DOC_CHANNEL, s => {
  console.log('A new instance', s)
})

diagnosticsChannel.subscribe(doc.constants.DOC_SAMPLES_CHANNEL, s => {
  console.log('A new sample', s)
})

doc()

License

ISC