The Tool Desk
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Start by defining what “performance” means for your application
“Slow” can mean several different things. A route might have high request latency while the process has low CPU use; a batch job might take too long because of CPU-heavy JavaScript; an application might start slowly or use too much memory. These symptoms call for different measurements. Do not begin by changing code based on a general list of tips.
- Latency: Identify the operation or request whose elapsed time matters, and keep the start and end boundaries consistent.
- Throughput: Define the representative work completed over a given interval. A change that improves one operation’s timing does not automatically improve total throughput.
- CPU: Use a CPU profile to investigate where execution time is spent.
- Memory or runtime context: Use diagnostic reports when the investigation needs heap, native stack, runtime, or system-limit information beyond a CPU profile.
- Startup or timeline behavior: Measure the relevant startup or application interval, and consider tracing when events over time are important.
Use a workload that resembles the work you need to improve. The Node.js documentation provides measurement and diagnostic facilities, but it does not establish universal latency targets or prove that any particular source-level optimization will help every workload.
Build a repeatable baseline with Node.js performance APIs
The stable node:perf_hooks APIs support high-resolution timing, the performance timeline, user timing, and resource timing. Use marks and measures around meaningful operations rather than timing arbitrary fragments. The example below uses the built-in performance timeline to measure a known asynchronous operation:
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import { performance } from 'node:perf_hooks';
performance.mark('work:start');
await performRepresentativeWork();
performance.mark('work:end');
performance.measure('representative-work', 'work:start', 'work:end');
const result = performance.getEntriesByName('representative-work').at(-1);
console.log({ durationMs: result.duration });
performance.clearMarks('work:start');
performance.clearMarks('work:end');
performance.clearMeasures('representative-work');
Replace performRepresentativeWork() with the operation you actually want to measure. In a long-running process, clear entries when appropriate so repeated measurements do not accumulate indefinitely. For CommonJS, the equivalent import is const { performance } = require('node:perf_hooks');.
This simple measurement describes the elapsed duration between the marks; it does not by itself tell you why the operation took that long. A baseline is useful only if you can repeat the same workload and understand what is included in the measured interval. Consult the Node.js performance measurement API documentation for the documented v26.8.1 release, and check documentation matching your deployed Node.js version before relying on a particular API or behavior.
Choose a diagnostic that matches the question
Use a CPU profile to find execution hotspots
If the concern is where CPU time goes, capture a CPU profile and inspect it for expensive JavaScript execution. Node’s Inspector API can start and stop profiling programmatically and save the resulting profile. The profile is evidence to interpret, not a fix: it may show which functions consume CPU, but you still need to determine whether those functions are on the important workload path and what change is appropriate.
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For command-line capture, the Node.js documentation describes --cpu-prof flags as stable as of Node.js v22.4.0 and v20.16.0. Exact flags and behavior depend on the runtime version; verify them against the documentation for the version you run. See the Node.js Inspector documentation for the programmatic profiling example and the Node.js all-APIs documentation for the CPU-profiler flag history.
Use a diagnostic report for wider process context
A diagnostic report is a JSON summary intended to help with problem determination. It can include JavaScript and native stack traces, V8 heap information, libuv handles, CPU and memory usage, and system limits. That broader context is useful when a CPU profile alone does not explain the symptom or you need runtime and platform details alongside stacks. See the Node.js diagnostic report documentation for report details and available mechanisms.
Use tracing when a timeline helps
Trace events can collect information from V8, Node.js core, and user code, including performance API measurements. This can help when you need to understand the ordering and timing of events rather than only a single duration or CPU profile. Trace output can be opened in Chrome’s tracing interface. However, the Node.js tracing module is documented as experimental; check compatibility and release-specific behavior before making it a routine production diagnostic. See Node.js Trace events.
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Treat node:bench as version-specific and early development
The Node.js v26.10.0 documentation describes the built-in node:bench runner as available with --experimental-bench and labels it “Stability 1.0, Early Development.” It does not force a particular optimization state or decide whether a benchmark measured its intended work. It is not a mature, universally available default: check the documentation for your target runtime before adopting it. See Node.js Benchmark runner.
Make one change and compare compatible runs
- Record the baseline. Save the workload definition, raw timing results, Node.js version, machine or container limits, and the measurement boundaries.
- Pick a change tied to the observed bottleneck. A CPU hotspot calls for a different investigation than a memory or startup symptom. Avoid applying a general-purpose optimization without evidence that it relates to the measured work.
- Change one relevant factor. Keeping other conditions stable makes it easier to attribute a difference to the change.
- Repeat the same workload and capture results again. Compare runs made with the same runtime version, comparable machine or container limits, and consistent measurement boundaries.
- Inspect raw samples and verify surprising results. Noise or skew can hide or exaggerate a change. Confirm an unexpected outcome with an independent benchmark shape before treating it as a real improvement.
Benchmark results are affected by JIT compilation, garbage collection, CPU frequency changes, and other system load. The Node.js benchmark documentation cautions: “A statistically consistent result does not prove that a benchmark measured the intended work.” Make sure the work is observable, allow enough operations to amortize timer and harness overhead, and account for warmup, optimization tiering, and garbage-collection effects. If unused work can be optimized away or the benchmark encourages an unrealistically narrow specialization, a consistent result can still misrepresent the application.
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Common mistakes and how to recover
The code change has no measurable effect
First confirm that the measured interval includes the operation you intended and that your test workload resembles the real one. If the change targets CPU but the evidence points elsewhere, capture the diagnostic that matches the actual symptom. A null result is not proof that the measurement is broken; it may mean the changed code was not the limiting factor in that workload.
Benchmark numbers jump between runs
Check raw samples and note environmental conditions. JIT behavior, garbage collection, CPU frequency changes, and system load can all affect results. Repeat under comparable conditions, make sure enough work is measured to reduce harness overhead, and investigate skew rather than accepting a single run or treating a confidence interval as an automatic pass/fail rule.
A benchmark improves, but the application does not
Reconsider whether the benchmark measured observable, representative work. An optimizing runtime can eliminate unused work or specialize for a narrower pattern than the application uses. Validate the result using an independent benchmark shape and a workload that reflects the task whose performance matters.
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A profiling option is unavailable or behaves differently
Confirm the exact Node.js release and use its matching documentation. CPU-profiler flag stability has version history, tracing is experimental, and the documented node:bench runner is early development in v26.10.0. Do not infer availability or identical behavior from documentation for a different release.
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const res = await fetch(`https://api.screenshotneo.com/v1/shot?${q}`);
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