Node.js v26.9.0 documentation

Benchmark runner#

The node:bench module supports defining and running JavaScript benchmarks in the current process, and running one benchmark file in a fresh child process. The module is only available when Node.js is started with the --experimental-bench flag and can only be imported with the node: scheme:

import { bench, suite } from 'node:bench';
const { bench, suite } = require('node:bench');
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Example benchmark#

Save the following as benchmark.mjs:

import { bench, suite } from 'node:bench';

suite('URL', () => {
  const input = 'https://example.com/a?b=c';

  bench('construct', {
    samples: 30,
    params: { input: 'short' },
  }, (b) => {
    const operations = 10_000;
    let totalLength = 0;

    b.start();
    for (let i = 0; i < operations; i++) {
      totalLength += new URL(input).href.length;
    }
    b.end(operations);

    if (totalLength !== operations * input.length) {
      throw new Error('Unexpected URL result');
    }
  });
});
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Run the benchmark from the command line:

node --experimental-bench --bench benchmark.mjs
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Benchmarks are executed serially in declaration order. Declared benchmarks are scheduled automatically. Call run() during the same turn as the declarations to consume the event stream or configure filtering. If an automatically scheduled run fails and run() was not called, the process exit code is set to 1.

Measurement model#

Each warmup and measured sample invokes the benchmark function once with a fresh {BenchContext}. The function must either call context.start() and context.end(operations) exactly once, or call context.record(sample) exactly once to provide an externally measured sample. Setup before start() and cleanup after end() are outside the measured region. Promise-returning functions are awaited.

By default, an event loop turn occurs between sample invocations. An embedded runner can disable this using yieldBetweenSamples. The runner executes benchmarks serially, but it does not provide process isolation. Other work in the process, JIT compilation, garbage collection, CPU frequency changes, and system load can all affect results. Keep raw samples when comparing results and investigate noisy or skewed distributions rather than treating a confidence interval as a pass/fail threshold.

Measurement integrity#

A statistically consistent result does not prove that a benchmark measured the intended work. An optimizing runtime can remove work whose result is unused or specialize it more narrowly than the workload being modeled. Framework and loop overhead can also dominate operations that are too short. To reduce these risks:

• Make values produced by measured work observable outside the measured interval, for example by validating an aggregate derived from every result. Passing them only through unused local computations is insufficient.
• Perform enough operations in each sample to amortize fixed timer reads and calls to context.start() and context.end(). If loop bookkeeping is material relative to one operation, batch multiple operations per iteration and report the total operation count.
• Inspect raw samples for trends that indicate insufficient warmup or optimization tiering, pauses consistent with garbage collection, and multimodal distributions.
• Validate surprising results with an independent benchmark shape that performs the same intended work differently.

node:bench does not force a particular optimization state or infer whether an engine eliminated work. Such controls and diagnostics are runtime-specific and heuristic, and do not replace validating the benchmark workload.

Dynamic sampling and variable batches#

Calling context.done() during a measured sample completes the benchmark after that sample. This allows a higher-level tool to treat samples as a maximum and implement a dynamic sampling policy.

The number of operations can differ between samples. Summary statistics treat each sample's rate as one equally weighted observation. In particular, summary.mean is the arithmetic mean of the per-sample rates. It is not the pooled throughput calculated as:

1_000_000_000 * sum(sample.operations) / sum(sample.duration_ns)
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The two values can differ when sample durations vary because pooled throughput weights each per-sample rate by its duration. A higher-level tool that varies batch sizes should choose the aggregation that matches its analysis. It can calculate pooled throughput from the raw samples; operation counts should be summed as bigint values because their total can exceed Number.MAX_SAFE_INTEGER even though each count cannot.

Comparing benchmark results#

node:bench does not designate a benchmark as a baseline or produce a pass/fail comparison between runs. It exposes raw samples, stable benchmark identities, parameters, and tags so that comparison policy can remain in higher-level tools. A tool can use benchId to match the same declaration and parameters across compatible source layouts, and use a tag or its own metadata to identify a baseline.

Comparison tools should retain the raw sample rates and verify that execution plans and relevant environment details are comparable. The appropriate analysis depends on the experimental design and distribution. For example, independent samples might use Welch's t-test or a rank-based test, while observations that were deliberately paired require paired analysis. Tools should also consider effect sizes, uncertainty, and correction when testing multiple benchmarks. The general-purpose <Histogram> statistics in node:perf_hooks can support such analysis, but the runner does not select a method or significance threshold.

Reusable runners#

The module-level declaration functions use a shared runner and schedule it automatically. Higher-level tools can create isolated, explicitly started runners instead:

import { createRunner } from 'node:bench';

const runner = createRunner({ yieldBetweenSamples: false });

runner.bench('example', { samples: 100 }, (b) => {
  const operations = chooseOperationCount();
  b.start();
  runOperations(operations);
  const sample = b.end(operations);

  if (hasEnoughData(sample)) b.done();
});

for await (const record of runner.run()) {
  // Consume structured benchmark records.
}
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Each runner has independent declarations, hooks, filtering, and output. Unlike the module-level declarations, creating a benchmark on an explicit runner does not schedule execution. This allows packages to collect declarations and start them later. Calling the explicit runner's run() function prevents additional declarations and a second call to run() is an error.

Command-line runner#

The --bench flag runs one or more explicit benchmark files or glob patterns:

node --experimental-bench --bench benchmark.mjs
node --experimental-bench --bench --bench-reporter=json 'benchmarks/**/*.js'
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Files are sorted and executed serially. The default --bench-isolation=process mode runs each file in a separate child process and emits one aggregate summary. Structured events are transferred to the parent without JSON conversion, preserving BigInt durations, errors, and parameter values. Child writes to stdout and stderr are emitted as diagnostic records so they do not corrupt reporter output.

--bench-isolation=none imports all files into the runner process. This mode has lower startup overhead, but module, heap, and process state carry between files, and user writes share stdout and stderr with reporters.

Worker-thread isolation is not a CLI mode. Each newly constructed <Worker> has a separate V8 isolate, JavaScript heap, and event loop, typically with lower startup cost than a child process. Reusing a worker preserves its module and heap state. Workers also share libuv's process-wide thread pool and can share process-global native or addon state, so they do not provide the same boundary as process isolation.

Higher-level tools can experiment with worker isolation by loading benchmark code inside a worker, measuring there, transferring structured sample data, and passing it to context.record(). The reported duration_ns can exclude message transport when the worker captures both timestamps. Tools should identify worker modules and workloads explicitly. They should not stringify arbitrary functions or closures to move them between isolates, because closures cannot be reconstructed with their original lexical environment.

Benchmark files passed to --bench should declare benchmarks but must not call run(). The CLI supports --bench-name-pattern, --bench-samples, --bench-warmup, --bench-reporter, and --bench-reporter-destination. See the command-line options documentation for details.

Preload modules passed through --require or --import should not declare benchmarks. Such declarations are not associated with an entry file and have an entryFile value of null. Their fileRunId identifies the runner or child execution in which they occurred. With process isolation, a preload is evaluated and its declarations run once for every benchmark child process.

Benchmark reporters#

The built-in reporters are available from the scheme-only node:bench/reporters module:

import { json, spec } from 'node:bench/reporters';
const { json, spec } = require('node:bench/reporters');
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Reporter values can be passed directly to stream.compose():

import { bench, run } from 'node:bench';
import { spec } from 'node:bench/reporters';
import process from 'node:process';

bench('example', (b) => {
  b.start();
  doWork();
  b.end(1);
});

run().compose(spec).pipe(process.stdout);
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The spec reporter buffers results and outputs a concise table containing the sample count, mean rate, 95% confidence interval for the mean, median rate, and warnings. A coefficient of variation above 5% is reported as noisy, and an absolute skewness above 1 is reported as skewed. The exact human-readable format is subject to change.

The json reporter emits every lifecycle record as newline-delimited JSON. BigInt values, including duration_ns, are encoded as decimal strings. Errors are represented using their name, message, stack, code, cause, and errors properties. As required by JSON, non-finite numbers are encoded as null.

Custom reporters use the same composition contract. They can be transforms or functions accepted by stream.compose(). The composed readable can be piped to any writable destination:

import { run } from 'node:bench';
import process from 'node:process';

async function* names(source) {
  for await (const { type, data } of source) {
    if (type === 'bench:complete') {
      yield `${data.name}\n`;
    }
  }
}

run().compose(names).pipe(process.stdout);
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createRunner([options])#

• options <Object>
  • yieldBetweenSamples <boolean> Schedule an event loop turn between sample callbacks. Disabling this also prevents timer-based abort signals from firing between synchronous callbacks. Benchmark timeouts continue to be checked against a monotonic deadline. Default: true.
• Returns: <Object> An isolated benchmark runner with bound after, afterEach, before, beforeEach, bench, describe, run, and suite functions.

Creates an explicitly started benchmark runner. Declarations made through one runner do not interact with declarations made through another runner or through the module-level functions. Call the returned run() function to start the runner and obtain its {BenchmarksStream}.

Each runner can be started once. Its run() function accepts the same options as the module-level run(). run({ yieldBetweenSamples }) overrides the value passed to createRunner().

bench([name][, options], fn)#

• name <string> The benchmark name. Default: The name property of fn, or '<anonymous>' when fn has no name.
• options <Object>
  • diagnosticChannels <Array> String diagnostics channel names, deduplicated and inherited from containing suites by union. Symbol values in the array are silently ignored. Default: [].
  • only <boolean> When any benchmark or containing suite has only set, benchmarks without only in their hierarchy are skipped. Default: false.
  • params <Object> String, finite number, or boolean metadata identifying this benchmark configuration. Parameter keys are sorted when constructing the stable benchmark identity. Default: An empty object.
  • samples <number> The maximum number of measured callback invocations. Must be a positive 32-bit unsigned integer. The benchmark may finish earlier by calling context.done(). Default: 30.
  • signal <AbortSignal> Allows aborting this benchmark.
  • skip <boolean> | <string> If truthy, the benchmark is skipped. A string is included in the result as the skip reason. Default: false.
  • tags <string>[] Labels associated with the benchmark. Tags are lowercased, deduplicated, and inherited from containing suites by union. Default: [].
  • timeout <number> The number of milliseconds after which the benchmark fails. Default: Infinity.
  • warmup <number> The number of unreported callback invocations before measured samples. Must be a 32-bit unsigned integer. Default: 0.
• fn <Function> | <AsyncFunction> The benchmark function. It receives a {BenchContext}.
• Returns: <Promise> Fulfilled with the benchmark result after a top-level benchmark finishes, or with undefined immediately when declared in a suite.

Warmup invocations use the same callback and timing contract as measured samples, but their samples are discarded. An exception, rejection, timeout, abort, missing timing call, or duplicate timing call stops the current benchmark. Later benchmarks continue to run.

After a timeout or abort, the runner briefly waits for asynchronous benchmark work to settle before continuing. If it remains pending, all later benchmarks that were selected to run fail without running so that their measurements cannot overlap with that work.

For each warmup and measured callback, the runner subscribes to the configured diagnostics channels. Each publication queues a context diagnostic whose message is { name, message }, containing the string channel name and the published message. Subscriptions are removed when the callback settles or is aborted.

A timeout or abort cannot interrupt synchronous JavaScript and does not forcibly cancel asynchronous work that ignores context.signal.

The benchId is based on the declaration source file, hierarchical suite and benchmark names, and canonicalized parameters. It is stable for repeated runs from the same source location, but the embedded source value is not normalized across checkout roots, module formats, operating systems, or path casing.

Execution scope is represented separately. A runId identifies one logical run, while fileRunId identifies a file runner or child execution within that run. The entryFile field records which entry-file import caused a declaration and is null for declarations made by preload modules. The same benchId can therefore occur under multiple fileRunId values when entry files use a shared declaration helper. Declaring the same benchId more than once within one file execution scope reports an error rather than merging the samples.

bench.skip([name][, options], fn)#

Shorthand for bench(name, { ...options, skip: true }, fn).

bench.only([name][, options], fn)#

Shorthand for bench(name, { ...options, only: true }, fn).

suite([name][, options], fn)#

• name <string> The suite name. Default: The name property of fn, or '<anonymous>' when fn has no name.
• options <Object>
  • diagnosticChannels <Array> String diagnostics channel names inherited by nested suites and benchmarks. Symbol values in the array are silently ignored. Default: [].
  • only <boolean> Selects all benchmarks nested in this suite. Default: false.
  • skip <boolean> | <string> Skips all benchmarks nested in this suite. Default: false.
  • tags <string>[] Labels inherited by nested suites and benchmarks. Default: [].
• fn <Function> | <AsyncFunction> A function that declares nested suites, benchmarks, and hooks.
• Returns: <Promise> Fulfilled when a top-level suite finishes, or with undefined immediately when declared in another suite.

Suite functions run while declarations are collected. Promise-returning suite functions are awaited before benchmark execution begins.

describe([name][, options], fn)#

Alias for suite().

before(fn)#

• fn <Function> | <AsyncFunction> The hook function.

Registers a hook that runs once before the benchmarks in the current suite.

after(fn)#

• fn <Function> | <AsyncFunction> The hook function.

Registers a hook that runs once after the benchmarks in the current suite.

beforeEach(fn)#

• fn <Function> | <AsyncFunction> The hook function. It receives an object with the benchmark's name, params, and signal.

Registers a hook that runs once before each complete logical benchmark in the current suite. It does not run before every sample. Per-sample setup belongs in the benchmark function before context.start() or context.record().

afterEach(fn)#

• fn <Function> | <AsyncFunction> The hook function. It receives an object with the benchmark's name, params, and signal.

Registers a hook that runs once after each complete logical benchmark in the current suite. It does not run after every sample. Per-sample cleanup belongs in the benchmark function after context.end() or context.record().

run([options])#

• options <Object>
  • namePattern <string> | <RegExp> Only runs benchmarks whose full hierarchical name matches the pattern. String values are interpreted as JavaScript regular expressions.
  • samples <number> Overrides the maximum number of measured callback invocations for every benchmark. Must be a positive 32-bit unsigned integer.
  • signal <AbortSignal> Allows aborting in-progress benchmark execution.
  • warmup <number> Overrides the number of unreported warmup callback invocations for every benchmark. Must be a 32-bit unsigned integer.
  • yieldBetweenSamples <boolean> Schedule an event loop turn between sample callbacks. Default: true, or the value passed to createRunner() for an explicit runner.
• Returns: {BenchmarksStream}

Returns the object-mode event stream for the in-process benchmark run. Call run() during the same turn in which benchmarks are declared, before automatic execution begins. Calling run() is optional when the returned stream is not needed. An explicit runner created by createRunner() does not run automatically, so its run() function may be called later.

import { bench, run } from 'node:bench';

bench('example', { samples: 3 }, (b) => {
  b.start();
  doWork();
  b.end(1)