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Array.prototype.sort() has no single, portable runtime for a million rows. The time depends on the JavaScript engine and version, the data and its existing order, the comparator’s work, and what you include in the measurement. To find the cost in your application, measure sorting separately from preparation and rendering, then profile the representative workload.

What determines the time to sort a million rows?

Think of the elapsed time as several costs, not a fixed percentage breakdown. The sorting implementation compares and rearranges elements; the comparator may do substantial work each time it runs; and an application may also spend time creating, copying, displaying, or transferring the data. A sort-only benchmark and a user-visible end-to-end measurement answer different questions.

The engine’s sorting implementation

ECMAScript requires stable sorting: rows with equal sort keys retain their relative order. It does not require a particular algorithm. V8 documents that it uses Timsort, but that is an implementation detail of V8, not a guarantee for every JavaScript engine. See V8’s explanation of stable Array.prototype.sort.

Input arrangement can affect the work an algorithm performs. In a 2018 account of V8’s Timsort work, the V8 project reported up to a 17× speedup over its older JavaScript Quicksort baseline for a particular constructed pattern of two reverse-sorted sequences. That result was not a million-row benchmark, was not a comparison of all current engines, and should not be read as a present-day speedup promise. The same article discusses differences between random, ordered, and partially ordered inputs: Getting things sorted in V8.

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The comparator

A comparator runs as part of the ordering process and may call user JavaScript repeatedly. Reading properties, converting values, parsing strings or dates, doing locale-aware comparisons, allocating objects, or deriving keys inside it can add meaningful work. The V8 project has noted that JavaScript comparisons can cost much more than memory access. In one specific Chai workload described in its 2018 article, a string-distance comparison function accounted for a third of runtime. Neither observation establishes the comparator’s share in another application.

Preparation, copying, and what happens afterward

Generating rows, deriving keys, cloning the array, and copying data are separate costs unless the measured region includes them. Likewise, state updates, serialization, worker communication, and rendering may affect when a user sees the result without being time spent inside sort(). Measure these phases separately when you need to know whether the bottleneck is sorting or the surrounding application work.

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How to benchmark your own million-row workload

There is no current, reproducible million-row timing established here for a named machine, runtime, dataset, and comparator. Rather than rely on a number from a different workload, make the benchmark represent the question you need to answer.

  1. Choose the measurement. For isolated sort latency, exclude data generation and validate the input before timing. For user-visible completion time, deliberately include the relevant preparation, communication, and rendering phases and report that wider scope.
  2. Fix and record the workload. Note the JavaScript runtime and version, machine, row count, data representation, comparator, and input distribution or order. If you are measuring a browser application, identify the browser and version as well.
  3. Use a fresh input for each run. Since sorting mutates the array, reusing the same array can turn later measurements into tests of already-sorted data. Prepare or copy a fresh array outside the timed region for a sort-only test.
  4. Test representative arrangements. Include random, already sorted, reverse-sorted, and realistic partially ordered inputs when they resemble your application. Do not infer a current timing from V8’s historical input-pattern results.
  5. Warm up and repeat. Avoid judging performance from one best run. Report a clear summary or distribution and state how many samples you used. In Node.js v26.10.0 documentation, the node:bench runner is available behind --experimental-bench; it supports configurable warmup and samples and process isolation. The feature is marked early development and was added in v26.9.0, so confirm availability and behavior in your exact runtime: Node.js v26.10.0 benchmark-runner documentation.
  6. Check correctness. Validate the result and test the comparator’s behavior on equal keys and relevant edge cases before comparing timings.

Make the comparator correct before making it fast

A comparator should define a consistent ordering and avoid changing data or producing different results for the same pair. MDN documents that malformed comparators can produce different results across engines. Check that the comparator returns a negative value when the first item should precede the second, a positive value when it should follow, and zero when the items compare equal; ensure ties have the intended behavior. See MDN’s Array.prototype.sort() reference.

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If comparison repeatedly performs expensive parsing or key derivation, measure whether preparing sort keys once improves the total task. Include the key-preparation cost in an end-to-end comparison, and keep it outside the timed region only when the question is specifically sort-only latency. Do not assume an optimization helps until you measure the same representative input and verify the output remains correct.

Use profiling to locate work, not to guess

When a benchmark is slow, profile the representative workload to find where execution time is going. V8 documents an opt-in sample-based profiler: the --prof workflow records JavaScript and C/C++ stacks in a v8.log file. Sampling can point to likely hot work, but it is not an exact per-function wall-clock accounting. Compare profiler output with unprofiled timings, then validate any proposed change with repeatable benchmark runs. See V8’s profiler documentation.

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What to conclude from published performance figures

Historical engine results can explain why workload shape and comparator cost matter, but they cannot substitute for a test of your code on your target runtime. V8’s 2017-era Web Tooling Benchmark article reported that its suite score had improved by around 60% since V8 v5.8; that was a suite-level historical result, not a million-row sorting measurement: V8’s Web Tooling Benchmark article. None of these figures supplies a universal answer for how long your million rows will take.

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