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<li><em>Go语言圣经读书笔记(挖坑中...): <a href="https://github.com/chai2010/gopl-notes-zh">https://github.com/chai2010/gopl-notes-zh</a></em></li>
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<hr>
<h2 id="115-剖析"><a class="header" href="#115-剖析">11.5. 剖析</a></h2>
<p>基准测试（Benchmark）对于衡量特定操作的性能是有帮助的，但是当我们试图让程序跑的更快的时候，我们通常并不知道从哪里开始优化。每个码农都应该知道Donald Knuth在1974年的“Structured Programming with go to Statements”上所说的格言。虽然经常被解读为不重视性能的意思，但是从原文我们可以看到不同的含义：</p>
<blockquote>
<p>毫无疑问，对效率的片面追求会导致各种滥用。程序员会浪费大量的时间在非关键程序的速度上，实际上这些尝试提升效率的行为反倒可能产生很大的负面影响，特别是当调试和维护的时候。我们不应该过度纠结于细节的优化，应该说约97%的场景：过早的优化是万恶之源。</p>
<p>当然我们也不应该放弃对那关键3%的优化。一个好的程序员不会因为这个比例小就裹足不前，他们会明智地观察和识别哪些是关键的代码；但是仅当关键代码已经被确认的前提下才会进行优化。对于很多程序员来说，判断哪部分是关键的性能瓶颈，是很容易犯经验上的错误的，因此一般应该借助测量工具来证明。</p>
</blockquote>
<p>当我们想仔细观察我们程序的运行速度的时候，最好的方法是性能剖析。剖析技术是基于程序执行期间一些自动抽样，然后在收尾时进行推断；最后产生的统计结果就称为剖析数据。</p>
<p>Go语言支持多种类型的剖析性能分析，每一种关注不同的方面，但它们都涉及到每个采样记录的感兴趣的一系列事件消息，每个事件都包含函数调用时函数调用堆栈的信息。内建的<code>go test</code>工具对几种分析方式都提供了支持。</p>
<p>CPU剖析数据标识了最耗CPU时间的函数。在每个CPU上运行的线程在每隔几毫秒都会遇到操作系统的中断事件，每次中断时都会记录一个剖析数据然后恢复正常的运行。</p>
<p>堆剖析则标识了最耗内存的语句。剖析库会记录调用内部内存分配的操作，平均每512KB的内存申请会触发一个剖析数据。</p>
<p>阻塞剖析则记录阻塞goroutine最久的操作，例如系统调用、管道发送和接收，还有获取锁等。每当goroutine被这些操作阻塞时，剖析库都会记录相应的事件。</p>
<p>只需要开启下面其中一个标志参数就可以生成各种分析文件。当同时使用多个标志参数时需要当心，因为一项分析操作可能会影响其他项的分析结果。</p>
<pre><code>$ go test -cpuprofile=cpu.out
$ go test -blockprofile=block.out
$ go test -memprofile=mem.out
</code></pre>
<p>对于一些非测试程序也很容易进行剖析，具体的实现方式，与程序是短时间运行的小工具还是长时间运行的服务会有很大不同。剖析对于长期运行的程序尤其有用，因此可以通过调用Go的runtime API来启用运行时剖析。</p>
<p>一旦我们已经收集到了用于分析的采样数据，我们就可以使用pprof来分析这些数据。这是Go工具箱自带的一个工具，但并不是一个日常工具，它对应<code>go tool pprof</code>命令。该命令有许多特性和选项，但是最基本的是两个参数：生成这个概要文件的可执行程序和对应的剖析数据。</p>
<p>为了提高分析效率和减少空间，分析日志本身并不包含函数的名字；它只包含函数对应的地址。也就是说pprof需要对应的可执行程序来解读剖析数据。虽然<code>go test</code>通常在测试完成后就丢弃临时用的测试程序，但是在启用分析的时候会将测试程序保存为foo.test文件，其中foo部分对应待测包的名字。</p>
<p>下面的命令演示了如何收集并展示一个CPU分析文件。我们选择<code>net/http</code>包的一个基准测试为例。通常最好是对业务关键代码的部分设计专门的基准测试。因为简单的基准测试几乎没法代表业务场景，因此我们用-run=NONE参数禁止那些简单测试。</p>
<pre><code>$ go test -run=NONE -bench=ClientServerParallelTLS64 \
-cpuprofile=cpu.log net/http
PASS
BenchmarkClientServerParallelTLS64-8 1000
3141325 ns/op 143010 B/op 1747 allocs/op
ok net/http 3.395s
$ go tool pprof -text -nodecount=10 ./http.test cpu.log
2570ms of 3590ms total (71.59%)
Dropped 129 nodes (cum &lt;= 17.95ms)
Showing top 10 nodes out of 166 (cum &gt;= 60ms)
flat flat% sum% cum cum%
1730ms 48.19% 48.19% 1750ms 48.75% crypto/elliptic.p256ReduceDegree
230ms 6.41% 54.60% 250ms 6.96% crypto/elliptic.p256Diff
120ms 3.34% 57.94% 120ms 3.34% math/big.addMulVVW
110ms 3.06% 61.00% 110ms 3.06% syscall.Syscall
90ms 2.51% 63.51% 1130ms 31.48% crypto/elliptic.p256Square
70ms 1.95% 65.46% 120ms 3.34% runtime.scanobject
60ms 1.67% 67.13% 830ms 23.12% crypto/elliptic.p256Mul
60ms 1.67% 68.80% 190ms 5.29% math/big.nat.montgomery
50ms 1.39% 70.19% 50ms 1.39% crypto/elliptic.p256ReduceCarry
50ms 1.39% 71.59% 60ms 1.67% crypto/elliptic.p256Sum
</code></pre>
<p>参数<code>-text</code>用于指定输出格式，在这里每行是一个函数，根据使用CPU的时间长短来排序。其中<code>-nodecount=10</code>参数限制了只输出前10行的结果。对于严重的性能问题，这个文本格式基本可以帮助查明原因了。</p>
<p>这个概要文件告诉我们，HTTPS基准测试中<code>crypto/elliptic.p256ReduceDegree</code>函数占用了将近一半的CPU资源，对性能占很大比重。相比之下，如果一个概要文件中主要是runtime包的内存分配的函数，那么减少内存消耗可能是一个值得尝试的优化策略。</p>
<p>对于一些更微妙的问题，你可能需要使用pprof的图形显示功能。这个需要安装GraphViz工具，可以从 http://www.graphviz.org 下载。参数<code>-web</code>用于生成函数的有向图，标注有CPU的使用和最热点的函数等信息。</p>
<p>这一节我们只是简单看了下Go语言的数据分析工具。如果想了解更多，可以阅读Go官方博客的“Profiling Go Programs”一文。</p>
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