perf: Use @@toStringTag and Map for better perf

[ShuiRuTian]

There are 2 macro optimizations:

1. Use instanceof rather than toString, this is about 100% faster, and makes things more accurate.
2. Use Map rather than Object, this is about 30% faster.

However, I am not sure about isPromise. According to the name, it only want to judge whether it's a Promise or not. But current logic is to judge whether it's thennable. This might be a breaking change.

[Justineo]

instanceof cannot work across <iframe> boundaries. Using Object.prototype.toString is a common alternative to determine the type of an object. This covers more use cases.

[ShuiRuTian]

@Justineo Thanks. I hardly use iframe and did not think about them...

Use Symbol.toStringTag instead if possible, and it's even faster. However, I found the profiling time vary a lot on my local machine, is there some robot I could call out to profile?

[Justineo]

You could consider setting up some benchmarks on platforms like jsbench.me.

[ShuiRuTian]

Here is my benchmark example: https://jsbench.me/krm45pp7nd/1

And here is the result:

• Current: 2266万 ops/s ± 3.28%
• instanceof: 2.3亿 ops/s ± 1.22%
• @@toStringTag: 2.2亿 ops/s ± 2.1%

Looks good.

And here is benchmark comparation result, I only picked the result that is changed larger than 5% and rme is less than 1%:

I do not know which part is important, but it looks good to me, especially there are 3 cases that improves 20%+

   · write ref, read computed (with effect)                         7,683,786.00  0.0001  0.3473  0.0001  0.0002  0.0002  0.0003  0.0004  ±0.24%   3841893  [0.93x] ⇓
     write ref, read computed (with effect)                         8,265,654.35  0.0001  0.7701  0.0001  0.0001  0.0002  0.0003  0.0004  ±0.80%   4132828  (baseline)
   · write ref, don't read 1000 computeds (without effect)         22,545,853.49  0.0000  0.4810  0.0000  0.0001  0.0001  0.0001  0.0003  ±0.30%  11272929  [1.06x] ⇑
     write ref, don't read 1000 computeds (without effect)         21,235,565.75  0.0000  0.8568  0.0000  0.0001  0.0001  0.0001  0.0003  ±0.61%  10617785  (baseline)
   · write ref, read 1000 computeds (with multiple effects)            13,229.05  0.0680  0.9690  0.0756  0.0739  0.1381  0.1837  0.3030  ±0.64%      6615  [0.91x] ⇓
     write ref, read 1000 computeds (with multiple effects)            14,535.18  0.0650  0.2618  0.0688  0.0684  0.0814  0.0944  0.1181  ±0.15%      7268  (baseline)
   · 1000 refs, read 1 computed (with effect)                          57,845.38  0.0153  0.3155  0.0173  0.0177  0.0258  0.0286  0.0484  ±0.25%     28923  [0.94x] ⇓
     1000 refs, read 1 computed (with effect)                          61,730.02  0.0148  0.1841  0.0162  0.0166  0.0229  0.0245  0.0271  ±0.11%     30866  (baseline)
   · write reactive map, don't read computed (invoked)              6,280,659.49  0.0001   0.0844  0.0002  0.0002  0.0003  0.0003  0.0005   ±0.10%  3140331  [1.36x] ⇑
     write reactive map, don't read computed (invoked)              4,616,937.08  0.0001   0.3780  0.0002  0.0002  0.0004  0.0005  0.0007   ±0.46%  2308469  (baseline)
   · write reactive map, read computed                              3,317,786.00  0.0002   0.1023  0.0003  0.0003  0.0005  0.0005  0.0008   ±0.08%  1658893  [1.21x] ⇑
     write reactive map, read computed                              2,751,338.35  0.0003   0.2377  0.0004  0.0004  0.0007  0.0008  0.0010   ±0.35%  1375670  (baseline)
   · write reactive map, don't read 1000 computeds (invoked)        5,989,028.00  0.0001   0.0963  0.0002  0.0002  0.0003  0.0004  0.0005   ±0.10%  2994514  [1.41x] ⇑
     write reactive map, don't read 1000 computeds (invoked)        4,245,770.30  0.0001   1.2880  0.0002  0.0003  0.0004  0.0005  0.0007   ±0.84%  2122886  (baseline)
   · 1 ref invoking 10 effects                         3,628,439.27   0.0002   0.3025   0.0003   0.0003   0.0005   0.0005   0.0007  ±0.38%  1814220  [1.10x] ⇑
     1 ref invoking 10 effects                         3,289,414.00   0.0002   0.1333   0.0003   0.0003   0.0004   0.0005   0.0007  ±0.14%  1644707  (baseline)
   · 1 ref invoking 100 effects                          410,433.67   0.0023   0.2580   0.0024   0.0024   0.0028   0.0036   0.0048  ±0.12%   205217  [1.10x] ⇑
     1 ref invoking 100 effects                          374,439.98   0.0025   1.0689   0.0027   0.0027   0.0035   0.0042   0.0051  ±0.43%   187221  (baseline)
   · update ref to trigger watchEffect (scheduled but not executed)  2,781,636.33  0.0002  0.1086  0.0004  0.0004  0.0007  0.0008  0.0010  ±0.10%  1390819  [1.06x] ⇑
     update ref to trigger watchEffect (scheduled but not executed)  2,628,404.42  0.0003  0.1979  0.0004  0.0004  0.0007  0.0008  0.0009  ±0.13%  1314203  (baseline)
   · read reactive obj property   14,950,800.00  0.0000  0.2837  0.0001  0.0001  0.0001  0.0002  0.0003   ±0.30%  7475400  [1.07x] ⇑
     read reactive obj property   13,940,586.00  0.0000  0.2386  0.0001  0.0001  0.0001  0.0001  0.0003   ±0.24%  6970293  (baseline)

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Size Report

Bundles

File	Size	Gzip	Brotli
runtime-dom.global.prod.js	100 kB (+144 B)	38 kB (+27 B)	34.3 kB (+55 B)
vue.global.prod.js	158 kB (+142 B)	57.8 kB (+33 B)	51.5 kB (+54 B)

Usages

Name	Size	Gzip	Brotli
createApp (CAPI only)	47.2 kB (+76 B)	18.4 kB (+9 B)	16.8 kB (+17 B)
createApp	55.3 kB (+89 B)	21.4 kB (+14 B)	19.5 kB (+24 B)
createSSRApp	59.4 kB (+91 B)	23.1 kB (+17 B)	21 kB (+13 B)
defineCustomElement	60.2 kB (+91 B)	22.9 kB (+11 B)	20.9 kB (+31 B)
overall	69.2 kB (+87 B)	26.5 kB (+8 B)	24.1 kB (+25 B)

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commit: c46cd24

[edison1105]

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[edison1105]

Use Map rather than Object, this is about 30% faster.

I got different performance data, see https://measurethat.net/Benchmarks/Show/32894/1/test-map-vs-object

[ShuiRuTian]

@edison1105

here is my 5 cents about benchmark: https://measurethat.net/Benchmarks/Show/32902/0/test-map-vs-object-with-string-which-use-string-as-key

For set-value, the perf is similar, for get-value, Map is 200% faster(wow...) as shown in this case. If for the cache in Vue, the key is more frequently to be different with the key last time visited, then this test case might reflect real world better.

[edison1105]

LGTM