var arrRandom = [];
for(var intCtr=0; intCtr<1000; intCtr++) {
arrRandom.push(Math.floor(Math.random() * Math.floor(10000)));
}
function doRedeuce(pArray) {
return pArray.reduce(function(accum, curr) {return accum+curr});
}
function doLoop(pArray) {
var accum = 0;
for(var intCtr=0; intCtr<pArray.length; intCtr++) {
accum += pArray[intCtr];
}
return accum;
}
var redeuceResult=0;
redeuceResult = doRedeuce(arrRandom);
var loopResult=0;
loopResult = doLoop(arrRandom);
--enable-precise-memory-info
flag.
Test case name | Result |
---|---|
reduce | |
for loop |
Test name | Executions per second |
---|---|
reduce | 73620.5 Ops/sec |
for loop | 672650.7 Ops/sec |
I'd be happy to help you understand the provided JSON benchmark.
What is tested?
The provided JSON represents two JavaScript microbenchmarks: reduce
and for loop
. The benchmarks test which approach (reduction using Array.prototype.reduce()
or manual iteration with a for
loop) is faster for summing 1000 random numbers generated by the arrRandom
array.
Options compared
The options being tested are:
Array.prototype.reduce()
: This method uses a callback function to iterate over the elements of the array and accumulate the result.for
loop: This approach involves manually iterating over each element of the array using a for
loop, adding up the values as needed.Pros and Cons
Here are some pros and cons of each approach:
Array.prototype.reduce()
:for
loop:Library
In this benchmark, the Array.prototype.reduce()
method is used. The reduce()
function is a built-in JavaScript method that applies a callback function to each element of an array, accumulating a result.
Special JS feature/syntax
None are mentioned in the provided JSON.
Other considerations
When writing performance-critical code, it's essential to consider factors such as:
Alternatives
Other alternatives to these approaches include:
Array.prototype.forEach()
: Instead of reduce()
, you can use forEach()
with a callback function to iterate over the array.for...of
loop: Modern JavaScript introduces for...of
loops, which provide a more concise way to iterate over arrays compared to traditional for
loops.Keep in mind that the performance difference between these approaches may be negligible for small datasets like this one. However, as the dataset grows or becomes more complex, the performance differences become more significant.