Array.from(Array(100)).map(u => u = 10);
[Array(100)].map(u => u = 10);
--enable-precise-memory-info
flag.
Test case name | Result |
---|---|
Array.from | |
Spread |
Test name | Executions per second |
---|---|
Array.from | 88220.7 Ops/sec |
Spread | 110126.6 Ops/sec |
Let's break down the benchmark and explain what's being tested.
The provided JSON represents a JavaScript microbenchmark that compares two approaches to create an array with a fixed length and then map over it:
Array.from()
: Creates a new array from an iterable or an array-like object.Spread syntax
(or "spread operator"): Expands the elements of an array into individual elements.The purpose of this comparison is to evaluate which method is faster and more efficient in different browsers.
Now, let's examine the pros and cons of each approach:
Array.from()
Pros:
Cons:
Spread syntax
Pros:
Array.from()
.Cons:
In general, Array.from()
is a more explicit and robust method, but the spread syntax can be faster due to its concise nature. However, this trade-off comes at the cost of readability and maintainability.
The benchmark results show that:
Spread
approach outperforms Array.from()
by about 25%.Now, let's talk about the libraries used in this benchmark. None are explicitly mentioned, but we can infer that some internal JavaScript engines or browser-specific optimizations might be at play. However, since there are no external library dependencies, we'll focus on the language-specific aspects.
As for special JS features or syntax, none are explicitly mentioned. The benchmark uses standard JavaScript features and syntax.
Other alternatives to consider:
Array.prototype.slice()
instead of Array.from()
or spread syntax could be another approach, but it might not provide a significant performance boost.new Int32Array()
or new Uint8Array()
, is unlikely to outperform the benchmarked approaches.To optimize this benchmark for different browsers and devices, consider:
uglifyjs
or esbuild
) to minimize size and improve execution speed.Keep in mind that this is a microbenchmarking exercise, aiming to measure relative performance differences between different approaches. The results might not be representative of real-world scenarios, where other factors like data complexity, cache locality, and branch prediction can influence performance.