I looked at five ways to parse what I’ll call a static Array. This is an array that once it is written you aren’t going to add anything new too it, you simply access its data as needed and when you are done you delete it.
- Seek. Create an index Array based exactly on the primary Array. It only contains names or unique ids in the same exact order as the primary. Then search for indexArray.indexOf(â€śsome unique idâ€ť) and apply that integer against the primary Array, for example primaryArray to get your result. If this doesnâ€™t make sense take a look at code in my JSFiddle.
- Loop. Loop thru every element until I find the matching item, then break out of the loop. This pattern should be the most familiar to everyone.
- Filter. Use Array.prototype.filter.
- Some. Use Array.prototype.Some.
- Object. Create an Object and access it’s key/value pairs directly using an Object pattern such as parsedImage.image1 or parseImage[“image1″]. It’s not an Array, per se, but it works with the static access pattern that I need.
I used the Performance Interface to get high precision, sub-millisecond numbers needed for this test. Note, this Interface only works on Chrome 20 and 24+, Firefox 15+ and IE 10. It wonâ€™t run on Safari or Chrome on iOS. I bolted in a shim so you can also run these tests on your iPad or iPhone.
MyÂ JSFiddleÂ app creates an Array containing many base64 images and then loops thru runs hundreds of tests against it using the five approaches. It performs a random seek on the Array, or Object during each iteration. The offers a better reflection of how the array parse algorithm would work under production conditions.Â After the loops are finished, it then spits out an average completion time for each approaches.
The results are very interesting in terms of which approach is more efficient. Now, I understand in a typical application you might only loop an Array a few times. In those cases a tenth or even hundredth of a millisecond may not really matter. However if you are doing hundreds or even thousands of manipulations repetitively, then having the most efficient algorithm will start to pay off for your app performance.
Here are some of the test results based on 300 random array seeks against a decent size array that contained 300 elements. It’s actually the same base64 image copied into all 300 elements. You can tweak the JSFiddle and experiment with different size arrays and number of test loops. I saw similar performance between Firefox 29 and Chrome 34 on my MacBook Pro as well as on Windows. Approach #1 SEEK seems to be consistently the fastest on Arrays and Object is by far the fastest of any of the approaches:
OBJECT Average 0.0005933333522989415* (Fastest.~191% less time than LOOP)
SEEK Average 0.0012766665895469487 (181% less time than LOOP)
SOME Average 0.010226666696932321
FILTER Average 0.019943333354603965
LOOP Average 0.02598666658741422 (Slowest)
OBJECT Average 0.0006066666883028423* (Fastest.~191% less time than slowest)
SEEK Average 0.0012900000368244945 (181% less time than LOOP)
SOME Average 0.012076666820018242
FILTER Average 0.020773333349303962
LOOP Average 0.026383333122745777 (Slowest)
As for testing on Android, I used my Android Nexus 4 running 4.4.2. Itâ€™s interesting to note that the OBJECT approach was still the fastest, however the LOOP approach (Approach #2) was consistently dead last.
On my iPad 3 Retina using both Safari and Chrome, the OBJECT approach was also the fastest, however the FILTER (Approach #3) seemed to come in dead last.
I wasnâ€™t able to test this on IE 10 at the time I wrote this post and ran out of time.
Some folks have blogged that you should never use Arrays for associative search. I think this depends on exactly what you need to do with the array, for example if you need to do things like slice(), shift() or pop() then sticking to an Array structure will make your life easier. For my requirements where I’m using a static Array pattern, it looks like using the Object pattern has a significant performance advantage. If you do need an actual Array then the SEEK pattern was a close second in terms of speed.
[Updated: May 18, 16:06, fixed incorrect info]