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<li><em>KusonStack一站式可编程配置技术栈: <a href="https://github.com/KusionStack/kusion">https://github.com/KusionStack/kusion</a></em></li>
<li><em>凹语言™: <a href="https://github.com/wa-lang/wa">https://github.com/wa-lang/wa</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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