const a = [1,2,3,4,5,6,7,8,9,10];
const b = [a];
console.log(b);
const a = [1,2,3,4,5,6,7,8,9,10];
const b = [1,2,3,4,5,6,7,8,9,10];
console.log(b);
--enable-precise-memory-info
flag.
Test case name | Result |
---|---|
Assign | |
Destructure |
Test name | Executions per second |
---|---|
Assign | 261111.9 Ops/sec |
Destructure | 262752.1 Ops/sec |
Let's dive into the world of JavaScript microbenchmarks on MeasureThat.net!
Benchmark Definition
The benchmark definition is essentially the script that defines the test case. In this case, there are two test cases:
const b = [1,2,3,4,5,6,7,8,9,10];
) and destructuring an existing array using the spread operator (const b = [...a];
).const b = [1,2,3,4,5,6,7,8,9,10];
) and destructuring an existing array using the spread operator.Options Compared
In both test cases, two options are compared:
Pros and Cons
Here's a brief summary of the pros and cons of each approach:
const b = [1,2,3,4,5,6,7,8,9,10];
.Library and Purpose
In this benchmark, there is no explicit library mentioned. However, it's likely that the spread operator (...
) is a part of the ECMAScript standard, which is implemented by modern JavaScript engines.
Special JS Feature or Syntax
There are no special features or syntaxes used in these test cases. The code is straightforward and uses only basic JavaScript operators.
Other Alternatives
If you're interested in exploring alternative approaches to this benchmark, here are a few options:
Array.prototype.slice()
: Instead of using the spread operator, you could use Array.prototype.slice()
to create a new array: const b = a.slice();
. This approach is similar to assignment but uses a method call instead._.cloneDeep()
function: const b = _.cloneDeep(a);
. This approach would provide more control over the cloning process but might be slower due to additional overhead.Keep in mind that these alternative approaches may not be as efficient or readable as the original test cases. The spread operator is often a good choice for creating new arrays, especially when working with large datasets.