作者發現是因為 find() 找出所有的連結後 (a 元素)，跑去每一個連結上面綁定事件造成的效能問題：
The .on("click") call attached a click event listener to nearly every link in the content so that the corresponding section would open if the clicked link contained a hash fragment. For short articles with few links, the performance impact was negligible. But long articles like ”United States” included over 4,000 links, leading to over 200ms of execution time on low-end devices.
Worse yet, this behavior was unnecessary. The downstream code that listened to the hashchange event already called the same method that the click event listener called. Unless the window’s location already pointed at the link’s destination, clicking a link called the checkHash method twice — once for the link click event handler and once more for the hashchange handler.
首先是在 -O3 的情況下 (也就是作者使用的參數)，可以看到類似的結果：(我桌機的 CPU 是定速，沒有跑動態調整)
$ repeat 10 ./a
[-] Took: 248830 ns.
[-] Took: 249150 ns.
[-] Took: 248760 ns.
[-] Took: 248730 ns.
[-] Took: 248770 ns.
[-] Took: 248861 ns.
[-] Took: 248760 ns.
[-] Took: 253050 ns.
[-] Took: 248640 ns.
[-] Took: 249211 ns.
$ repeat 10 ./b
[-] Took: 686660 ns.
[-] Took: 696090 ns.
[-] Took: 696310 ns.
[-] Took: 694431 ns.
[-] Took: 691971 ns.
[-] Took: 697690 ns.
[-] Took: 693241 ns.
[-] Took: 692900 ns.
[-] Took: 654751 ns.
[-] Took: 679101 ns.
從版本 A 的 objdump -d -S -M intel a 可以看到作者 screenshot 內也有看的 unroll 與 SSE2 指令集：
$ repeat 10 ./a
[-] Took: 571140 ns.
[-] Took: 570280 ns.
[-] Took: 571271 ns.
[-] Took: 573971 ns.
[-] Took: 571981 ns.
[-] Took: 569650 ns.
[-] Took: 566361 ns.
[-] Took: 571600 ns.
[-] Took: 571330 ns.
[-] Took: 571030 ns.
$ repeat 10 ./b
[-] Took: 697521 ns.
[-] Took: 696961 ns.
[-] Took: 696201 ns.
[-] Took: 694921 ns.
[-] Took: 696930 ns.
[-] Took: 695001 ns.
[-] Took: 701661 ns.
[-] Took: 698100 ns.
[-] Took: 702430 ns.
[-] Took: 702641 ns.
從 objdump 可以看到版本 A 的變化，退化成一次只處理一個，但把所有的數字都用 xmmN 存放計算：
$ repeat 10 ./a
[-] Took: 1097091 ns.
[-] Took: 1092941 ns.
[-] Took: 1092501 ns.
[-] Took: 1091991 ns.
[-] Took: 1092441 ns.
[-] Took: 1093970 ns.
[-] Took: 1091341 ns.
[-] Took: 1093931 ns.
[-] Took: 1094111 ns.
[-] Took: 1092231 ns.
$ repeat 10 ./b
[-] Took: 2703282 ns.
[-] Took: 2705933 ns.
[-] Took: 2703582 ns.
[-] Took: 2702622 ns.
[-] Took: 2703043 ns.
[-] Took: 2702262 ns.
[-] Took: 2703352 ns.
[-] Took: 2703532 ns.
[-] Took: 2703112 ns.
[-] Took: 2702533 ns.
Memray is a memory profiler for Python. It can track memory allocations in Python code, in native extension modules, and in the Python interpreter itself.
套件有多種輸出，其中一種是可以產生出記憶體使用情況的 flamegraph，轉成圖檔後像是這樣：
官方支援 Python 3.7+：
Memray requires Python 3.7+ and can be easily installed using most common Python packaging tools.
While there are many other python profiling projects, almost all of them require modifying the profiled program in some way. Usually, the profiling code runs inside of the target python process, which will slow down and change how the program operates. This means it's not generally safe to use these profilers for debugging issues in production services since they will usually have a noticeable impact on performance. The only other Python profiler that runs totally in a separate process is pyflame, which profiles remote python processes by using the ptrace system call. While pyflame is a great project, it doesn't support Python 3.7 yet and doesn't work on OSX or Windows.
Xdebug's built-in garbage collection statistics profiler allows you to find out when the PHP internal garbage collector triggers, how many variables it was able to clean up, how long it took, and how how much memory was actually freed.
Provides an extension to the native PDO along with a profiler and connection locator. Because ExtendedPdo is an extension of the native PDO, code already using the native PDO or typehinted to the native PDO can use ExtendedPdo without any changes.
Lazy connection. ExtendedPdo connects to the database only on method calls that require a connection. This means you can create an instance and not incur the cost of a connection if you never make a query.
