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diff --git a/perf_opt/con_perf.png b/perf_opt/con_perf.png new file mode 100644 index 0000000..4073e76 Binary files /dev/null and b/perf_opt/con_perf.png differ diff --git a/perf_opt/index.adoc b/perf_opt/index.adoc index 6bb06e5..203c920 100644 --- a/perf_opt/index.adoc +++ b/perf_opt/index.adoc @@ -23,7 +23,7 @@ The builtin Go CPU profiler uses the setitimer(2) system call to ask the operati Since Go uses non-blocking I/O, Goroutines that wait on I/O are parked and not running on any threads. Therefore they end up being largely invisible to Go's builtin CPU profiler. ____ -每秒被唤醒 100 次,记录每个线程上的栈。 +每秒被唤醒 100 次,记录每个线程上的栈,那些等待 IO 被 gopark 之类挂起的 goroutine 不会被采集到,因为不在线程上运行,gopark 挂起 goroutine 后,当前线程一般会进 schedule -> findrunnable 的调度循环。 === fgprof @@ -36,15 +36,41 @@ ____ 可以用来诊断 CPU、IO 混合的执行时间占比。 +这个成本还是比普通的 pprof 高一些的。 + === trace 一般用来诊断一些诡异的抖动问题,或 runtime 的 bug(或者用来学习 runtime 的执行流),用来做问题诊断效果一般。 基本原理是在 runtime 中埋了大量点,记录一堆 event 来追踪 runtime 执行流程。 +如果对一些调度问题有疑问,可以在 trace 里做观察,不过拿来定位问题还是比较费劲的。 + +https://xargin.com/a-rlock-story/ + === perf -perf 也是可以用的。 +perf 也是可以用的,比如线上没开 pprof 的时候,发现 CPU 炸了,perf 可以看看到底在干啥,因为 Go 默认会把 DWARF 调试信息带进二进制文件中,通过 perf 的 zoom 功能也可以一直看到哪行代码(或者是汇编)占用了比较高的 CPU。 + +[source, text] +---- +$ perf stat -e task-clock,cycles,instructions,cache-references,cache-misses ./hello +yyyy + + Performance counter stats for './hello': + + 1.464376 task-clock (msec) # 0.979 CPUs utilized + 3,681,288 cycles # 2.514 GHz + 1,722,170 instructions # 0.47 insn per cycle + 46,475 cache-references # 31.737 M/sec + 21,479 cache-misses # 46.216 % of all cache refs + + 0.001495925 seconds time elapsed +---- + +perf top + +image::perf.png[] == 局部优化 @@ -131,6 +157,46 @@ TODO 显而易见,O(logn) 和 O(n),O(logn) 最多就 64 次,而 O(n) 可能耗尽计算资源。 +runtime 里的算法优化: + +``` + ┌──────────────────────┐ + │ │ + │ │ + │ npagesKey: 130 │ + │ spanKey: 0x1234567 │ + │ priority: 1 │ + │ │ + │ │ + └──────────────────────┘ + │ + ┌────────────────────┴──────────────────┐ + │ │ + ▼ ▼ +┌──────────────────────┐ ┌──────────────────────┐ +│ │ │ │ +│ │ │ │ +│ npagesKey: 129 │ │ npagesKey: 132 │ +│ spanKey: 0x4231560 │ │ spanKey: 0x2234521 │ +│ priority: 10 │ │ priority: 12 │ +│ │ │ │ +│ │ │ │ +└──────────────────────┘ └──────────────────────┘ + │ + ┌───────────┴───────────────────┐ + │ │ + ▼ ▼ + ┌──────────────────────┐ ┌──────────────────────┐ + │ │ │ │ + │ │ │ │ + │ npagesKey: 132 │ │ npagesKey: 136 │ + │ spanKey: 0x2234000 │ │ spanKey: 0x2314213 │ + │ priority: 14 │ │ priority: 131 │ + │ │ │ │ + │ │ │ │ + └──────────────────────┘ └──────────────────────┘ +``` + === 过多的系统调用 合并调用,writev? @@ -153,6 +219,14 @@ struct 可以复用,slice 可以复用,但 map 不太好复用。 如果数据不可变,只作查询,也可以考虑 offheap,但局限性较大。 +下面三个库可以看看。 + +https://github.com/glycerine/offheap + +https://github.com/coocood/freecache + +https://github.com/allegro/bigcache + ==== 减少变量逃逸 使用 go build -gcflags="-m -m" 来分析逃逸。 @@ -181,14 +255,16 @@ https://github.com/valyala/fasthttp/blob/master/workerpool.go#L19 有些开源库设计是一个 struct 对应一个 sync.Pool,这种时候,如果你不对该 struct 进行复用,就会触发 runtime 中的锁冲突: -TODO +参考本文中的第一个案例: + +https://xargin.com/lock-contention-in-go/ ==== 解决方案 -* map -> sync.Map。 -* 换用无锁结构。 +* map -> sync.Map +* 换用无锁结构,如 lock free queue、stack 等 * 分段锁 -* copy on write +* copy on write,业务逻辑允许的前提下,在修改时拷贝一份,再修改 === 程序局部性 @@ -197,15 +273,15 @@ TODO 时间局部性、空间局部性 [source, go] -==== +---- var semtable [semTabSize]struct { root semaRoot pad [cpu.CacheLinePadSize - unsafe.Sizeof(semaRoot{})]byte } -==== +---- [source, go] -==== +---- var timers [timersLen]struct { timersBucket @@ -213,19 +289,19 @@ var timers [timersLen]struct { // between timersBucket values. pad [cpu.CacheLinePadSize - unsafe.Sizeof(timersBucket{})%cpu.CacheLinePadSize]byte } -==== +---- 类似下面的二维数组,怎么遍历更快? [source, go] -==== +---- var a = [10000][10000]int{} -==== +---- 在标准库中,考虑到局部性而实现的 sort 的例子: [source, go] -==== +---- func quickSort_func(data lessSwap, a, b, maxDepth int) { for b-a > 12 { if maxDepth == 0 { @@ -251,7 +327,7 @@ func quickSort_func(data lessSwap, a, b, maxDepth int) { insertionSort_func(data, a, b) } } -==== +---- ==== true sharing @@ -264,9 +340,9 @@ ____ === timer 性能问题 -* 老版本的 timer 会有大量的 syscall -> https://github.com/golang/go/issues/25471 +* 老版本的 timer 会有高压力下触发不准时问题,且触发大量的 syscall -> https://github.com/golang/go/issues/25471 [source, text] -==== +---- // xiaorui.cc go1.13 @@ -286,12 +362,16 @@ go1.14 11.55 0.950730 44 21569 epoll_pwait 9.75 0.802715 36 22181 sched_yield:w -==== +---- + +优化后,CPU 占用降低,到时不触发的问题也有所改善。 * 用时间轮实现粗粒度的时间库 可以搜搜大量的 timewheel 库。 +ticker 使用时要尤其注意泄露问题,否则程序 CPU 使用会逐渐上涨。 + === 汇编优化 SIMD 优化,如 math 库。gonum 中也有一些例子。 @@ -315,7 +395,7 @@ GC 需要抢占所有 goroutine,老版本的抢占需要用户协程在 morest === 物理机负载高时,延迟非线性增长 -TODO +压力高会导致响应慢,响应慢会导致并发执行的 goroutine 数变多,响应结束后的垃圾变多,同时会导致更高的调度成本和 GC 扫描成本,级联恶化。 === 调度导致 CPU 密集型业务超时 @@ -329,6 +409,10 @@ TODO,bcrypt 的例子 导致在每一轮 GC 后都有延迟抖动,升级 1.13 后长尾请求有改善。 +sync.Pool 的设计思路:尽量从本地拿到 cache 对象,拿不到通过无锁 shared 队列去找,还是找不到,全局 lock 找或者生成新的。 + +这种思路比较类似 L1 -> L2 -> L3 的多级缓存设计,runtime 的内存分配演进也是类似的思路。 + === 当前问题定位工具的局限性 难以定位抖动问题。 @@ -341,4 +425,10 @@ TODO,bcrypt 的例子 ==== continuous profiling -早发现,早治疗。 +在生产环境对更细粒度的程序性能做实时监控,方便及时发现、定位、分析问题。 + +早发现,早治疗,晚发现,成本高。 + +https://storage.googleapis.com/pub-tools-public-publication-data/pdf/36575.pdf + +image::con_perf.png[] diff --git a/perf_opt/index.html b/perf_opt/index.html index b95389e..be2a45d 100644 --- a/perf_opt/index.html +++ b/perf_opt/index.html @@ -1,979 +1,1078 @@ -<!DOCTYPE html> -<html lang="en"> -<head> -<meta charset="UTF-8"> -<meta http-equiv="X-UA-Compatible" content="IE=edge"> -<meta name="viewport" content="width=device-width, initial-scale=1.0"> -<meta name="generator" content="Asciidoctor 2.0.8.dev"> -<title>Go 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#toc,body.book #preamble,body.book h1.sect0,body.book .sect1>h2{page-break-before:always} -.listingblock code[data-lang]::before{display:block} -#footer{padding:0 .9375em} -.hide-on-print{display:none!important} -.print-only{display:block!important} -.hide-for-print{display:none!important} -.show-for-print{display:inherit!important}} -@media print,amzn-kf8{#header>h1:first-child{margin-top:1.25rem} -.sect1{padding:0!important} -.sect1+.sect1{border:0} -#footer{background:none} -#footer-text{color:rgba(0,0,0,.6);font-size:.9em}} -@media amzn-kf8{#header,#content,#footnotes,#footer{padding:0}} -</style> -<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/4.7.0/css/font-awesome.min.css"> -</head> -<body class="article"> -<div id="header"> -<h1>Go 语言性能优化</h1> -</div> -<div id="content"> -<div id="preamble"> -<div class="sectionbody"> -<div class="paragraph"> -<p>Latency numbers every programmer should know:</p> -</div> -<div class="paragraph"> -<p><a href="https://colin-scott.github.io/personal_website/research/interactive_latency.html" class="bare">https://colin-scott.github.io/personal_website/research/interactive_latency.html</a></p> -</div> -</div> -</div> -<div class="sect1"> -<h2 id="true优化工作流"><a class="anchor" href="#true优化工作流"></a>优化工作流</h2> -<div class="sectionbody"> -<div class="paragraph"> -<p>建立评估指标(eg. Latency) → 提出解决方案 → 尝试方案</p> -</div> -<div class="paragraph"> -<p>不断重复</p> -</div> -</div> -</div> -<div class="sect1"> -<h2 id="true问题定位工具"><a class="anchor" href="#true问题定位工具"></a>问题定位工具</h2> -<div class="sectionbody"> -<div class="sect2"> -<h3 id="truepprof"><a class="anchor" href="#truepprof"></a>pprof</h3> -<div class="paragraph"> -<p>基本原理:</p> -</div> -<div class="quoteblock"> -<blockquote> -<div class="paragraph"> -<p>The builtin Go CPU profiler uses the setitimer(2) system call to ask the operating system to be sent a SIGPROF signal 100 times a second. Each signal stops the Go process and gets delivered to a random thread’s sigtrampgo() function. This function then proceeds to call sigprof() or sigprofNonGo() to record the thread’s current stack.</p> -</div> -<div class="paragraph"> -<p>Since Go uses non-blocking I/O, Goroutines that wait on I/O are parked and not running on any threads. Therefore they end up being largely invisible to Go’s builtin CPU profiler.</p> -</div> -</blockquote> -</div> -<div class="paragraph"> -<p>每秒被唤醒 100 次,记录每个线程上的栈。</p> -</div> -</div> -<div class="sect2"> -<h3 id="truefgprof"><a class="anchor" href="#truefgprof"></a>fgprof</h3> -<div class="quoteblock"> -<blockquote> -<div class="paragraph"> -<p>fgprof is implemented as a background goroutine that wakes up 99 times per second and calls runtime.GoroutineProfile. This returns a list of all goroutines regardless of their current On/Off CPU scheduling status and their call stacks.</p> -</div> -</blockquote> -</div> -<div class="paragraph"> -<p>比较类似,但是会包含那些 Off CPU 的 goroutine。</p> -</div> -<div class="paragraph"> -<p>可以用来诊断 CPU、IO 混合的执行时间占比。</p> -</div> -</div> -<div class="sect2"> -<h3 id="truetrace"><a class="anchor" href="#truetrace"></a>trace</h3> -<div class="paragraph"> -<p>一般用来诊断一些诡异的抖动问题,或 runtime 的 bug(或者用来学习 runtime 的执行流),用来做问题诊断效果一般。</p> -</div> -<div class="paragraph"> -<p>基本原理是在 runtime 中埋了大量点,记录一堆 event 来追踪 runtime 执行流程。</p> -</div> -</div> -<div class="sect2"> -<h3 id="trueperf"><a class="anchor" href="#trueperf"></a>perf</h3> -<div class="paragraph"> -<p>perf 也是可以用的。</p> -</div> -</div> -</div> -</div> -<div class="sect1"> -<h2 id="true局部优化"><a class="anchor" href="#true局部优化"></a>局部优化</h2> -<div class="sectionbody"> -<div class="admonitionblock warning"> -<table> -<tr> -<td class="icon"> -<i class="fa icon-warning" title="Warning"></i> -</td> -<td class="content"> -<div class="paragraph"> -<p>调用外部命令</p> -</div> -</td> -</tr> -</table> -</div> -<div class="listingblock"> -<div class="content"> -<pre class="highlightjs highlight"><code data-lang="go" class="language-go hljs">package main - -import ( - "os/exec" - "testing" - - uuid "github.com/satori/go.uuid" -) - -var uu []byte -var u1 uuid.UUID - -func BenchmarkUUIDExec(b *testing.B) { - for i := 0; i < b.N; i++ { - uu, _ = exec.Command("uuidgen").Output() - } -} - -func BenchmarkUUIDLib(b *testing.B) { - for i := 0; i < b.N; i++ { - u1 = uuid.NewV4() - } -}</code></pre> -</div> -</div> -<div class="admonitionblock warning"> -<table> -<tr> -<td class="icon"> -<i class="fa icon-warning" title="Warning"></i> -</td> -<td class="content"> -<div class="paragraph"> -<p>字符串操作</p> -</div> -</td> -</tr> -</table> -</div> -<div class="paragraph"> -<p>用加号连接,和 Sprintf 差别还是比较大的:</p> -</div> -<div class="listingblock"> -<div class="content"> -<pre class="highlightjs highlight"><code data-lang="go" class="language-go hljs">func BenchmarkBytesBufferAppend(b *testing.B) { - for i := 0; i < b.N; i++ { - var msg bytes.Buffer - msg.WriteString("userid : " + "1") - msg.WriteString("location : " + "ab") - } -} - -func BenchmarkBytesBufferAppendSprintf(b *testing.B) { - for i := 0; i < b.N; i++ { - var msg bytes.Buffer - msg.WriteString(fmt.Sprintf("userid : %d", 1)) - msg.WriteString(fmt.Sprintf("location : %s", "ab")) - } -}</code></pre> -</div> -</div> -<div class="imageblock"> -<div class="content"> -<img src="string_bench.png" alt="string bench"> -</div> -</div> -<div class="paragraph"> -<p>fmt.打印系列大部分会造成变量逃逸(interface 参数)。</p> -</div> -</div> -</div> -<div class="sect1"> -<h2 id="true全局优化"><a class="anchor" href="#true全局优化"></a>全局优化</h2> -<div class="sectionbody"> -<div class="paragraph"> -<p>寻找程序的整体瓶颈。</p> -</div> -<div class="paragraph"> -<p>wrk、pprof、压测平台</p> -</div> -</div> -</div> -<div class="sect1"> -<h2 id="true性能瓶颈举例"><a class="anchor" href="#true性能瓶颈举例"></a>性能瓶颈举例</h2> -<div class="sectionbody"> -<div class="sect2"> -<h3 id="true业务逻辑"><a class="anchor" href="#true业务逻辑"></a>业务逻辑</h3> -<div class="ulist"> -<ul> -<li> -<p>查数据库本来 in 查询就可以了,但是却 for 循环查。 -TODO</p> -</li> -</ul> -</div> -</div> -<div class="sect2"> -<h3 id="true序列化-cpu-占用过高"><a class="anchor" href="#true序列化-cpu-占用过高"></a>序列化 CPU 占用过高</h3> -<div class="paragraph"> -<p>寻找一些针对性进行过优化的库,或者从文本协议更换为二进制协议。</p> -</div> -</div> -<div class="sect2"> -<h3 id="true算法时间复杂度"><a class="anchor" href="#true算法时间复杂度"></a>算法时间复杂度</h3> -<div class="paragraph"> -<p>显而易见,O(logn) 和 O(n),O(logn) 最多就 64 次,而 O(n) 可能耗尽计算资源。</p> -</div> -</div> -<div class="sect2"> -<h3 id="true过多的系统调用"><a class="anchor" href="#true过多的系统调用"></a>过多的系统调用</h3> -<div class="paragraph"> -<p>合并调用,writev?</p> -</div> -</div> -<div class="sect2"> -<h3 id="true过多的对象"><a class="anchor" href="#true过多的对象"></a>过多的对象</h3> -<div class="sect3"> -<h4 id="truesync-pool"><a class="anchor" href="#truesync-pool"></a>sync.Pool</h4> -<div class="paragraph"> -<p>sync.Pool 才能实现 zero garbage。benchmark 中的 0 alloc,其实是因为对象有复用,alloc 平均 < 1。</p> -</div> -<div class="paragraph"> -<p>struct 可以复用,slice 可以复用,但 map 不太好复用。</p> -</div> -<div class="paragraph"> -<p>复用本身可能导致 bug,例如:</p> -</div> -<div class="ulist"> -<ul> -<li> -<p>拿出时不 Reset</p> -</li> -<li> -<p>slice 缩容时,被缩掉对象如果不置 nil,是不会释放的</p> -</li> -<li> -<p>在 Put 回 Pool 时,不判断大小,导致了进程占内存越来越大</p> -</li> -</ul> -</div> -</div> -<div class="sect3"> -<h4 id="trueoffheap"><a class="anchor" href="#trueoffheap"></a>offheap</h4> -<div class="paragraph"> -<p>如果数据不可变,只作查询,也可以考虑 offheap,但局限性较大。</p> -</div> -</div> -<div class="sect3"> -<h4 id="true减少变量逃逸"><a class="anchor" href="#true减少变量逃逸"></a>减少变量逃逸</h4> -<div class="paragraph"> -<p>使用 go build -gcflags="-m -m" 来分析逃逸。</p> -</div> -<div class="paragraph"> -<p>如果要分析某个 package 内的逃逸情况,可以打全 package 名,例如 go build -gcflags="-m -m" github.com/cch123/elasticsql</p> -</div> -</div> -</div> -<div class="sect2"> -<h3 id="true过多的调度-cpu-占用例如火焰图中schedule-有一大条"><a class="anchor" href="#true过多的调度-cpu-占用例如火焰图中schedule-有一大条"></a>过多的调度 CPU 占用(例如火焰图中,schedule 有一大条)</h3> -<div class="paragraph"> -<p>类似 fasthttp 的 workerpool。</p> -</div> -<div class="paragraph"> -<p><a href="https://github.com/valyala/fasthttp/blob/master/workerpool.go#L19" class="bare">https://github.com/valyala/fasthttp/blob/master/workerpool.go#L19</a></p> -</div> -<div class="paragraph"> -<p>创建好的 goroutine 可以反复使用,并且自己实现可以控制最大的并发 worker 数。</p> -</div> -</div> -<div class="sect2"> -<h3 id="true锁冲突"><a class="anchor" href="#true锁冲突"></a>锁冲突</h3> -<div class="paragraph"> -<p>通过阶梯加压,观察 goroutine 的变化趋势。当触发锁瓶颈时,会出现大量等锁的 goroutine。</p> -</div> -<div class="sect3"> -<h4 id="true原因"><a class="anchor" href="#true原因"></a>原因</h4> -<div class="paragraph"> -<p>临界区太大,其中包含系统调用。</p> -</div> -<div class="paragraph"> -<p>有些锁是避免不了的,例如 fs.Write,一定有锁,且该锁在 runtime 内部。</p> -</div> -<div class="paragraph"> -<p>性能敏感场合,全局锁,比如 rand 的全局锁。单机 10w+ QPS 即可能触发该瓶颈(和环境以及程序行为有关)</p> -</div> -<div class="paragraph"> -<p>有些开源库设计是一个 struct 对应一个 sync.Pool,这种时候,如果你不对该 struct 进行复用,就会触发 runtime 中的锁冲突:</p> -</div> -<div class="paragraph"> -<p>TODO</p> -</div> -</div> -<div class="sect3"> -<h4 id="true解决方案"><a class="anchor" href="#true解决方案"></a>解决方案</h4> -<div class="ulist"> -<ul> -<li> -<p>map → sync.Map。</p> -</li> -<li> -<p>换用无锁结构。</p> -</li> -<li> -<p>分段锁</p> -</li> -<li> -<p>copy on write</p> -</li> -</ul> -</div> -</div> -</div> -<div class="sect2"> -<h3 id="true程序局部性"><a class="anchor" href="#true程序局部性"></a>程序局部性</h3> -<div class="sect3"> -<h4 id="truefalse-sharing"><a class="anchor" href="#truefalse-sharing"></a>false sharing</h4> -<div class="paragraph"> -<p>时间局部性、空间局部性</p> -</div> -<div class="exampleblock"> -<div class="content"> -<div class="paragraph"> -<p>var semtable [semTabSize]struct { - root semaRoot - pad [cpu.CacheLinePadSize - unsafe.Sizeof(semaRoot{})]byte -}</p> -</div> -</div> -</div> -<div class="exampleblock"> -<div class="content"> -<div class="paragraph"> -<p>var timers [timersLen]struct { - timersBucket</p> -</div> -<div class="literalblock"> -<div class="content"> -<pre> // The padding should eliminate false sharing - // between timersBucket values. - pad [cpu.CacheLinePadSize - unsafe.Sizeof(timersBucket{})%cpu.CacheLinePadSize]byte -}</pre> -</div> -</div> -</div> -</div> -<div class="paragraph"> -<p>类似下面的二维数组,怎么遍历更快?</p> -</div> -<div class="exampleblock"> -<div class="content"> -<div class="paragraph"> -<p>var a = [10000][10000]int{}</p> -</div> -</div> -</div> -<div class="paragraph"> -<p>在标准库中,考虑到局部性而实现的 sort 的例子:</p> -</div> -<div class="exampleblock"> -<div class="content"> -<div class="paragraph"> -<p>func quickSort_func(data lessSwap, a, b, maxDepth int) { - for b-a > 12 { - if maxDepth == 0 { - heapSort_func(data, a, b) - return - } - maxDepth-- - mlo, mhi := doPivot_func(data, a, b) - if mlo-a < b-mhi { - quickSort_func(data, a, mlo, maxDepth) - a = mhi - } else { - quickSort_func(data, mhi, b, maxDepth) - b = mlo - } - } - if b-a > 1 { - for i := a + 6; i < b; i++ { - if data.Less(i, i-6) { - data.Swap(i, i-6) - } - } - insertionSort_func(data, a, b) - } -}</p> -</div> -</div> -</div> -</div> -<div class="sect3"> -<h4 id="truetrue-sharing"><a class="anchor" href="#truetrue-sharing"></a>true sharing</h4> -<div class="paragraph"> -<p>这时候一般都有锁,所以本质上还是怎么降低锁的粒度。</p> -</div> -<div class="quoteblock"> -<blockquote> -<div class="paragraph"> -<p>sync: RWMutex scales poorly with CPU count</p> -</div> -</blockquote> -</div> -</div> -</div> -<div class="sect2"> -<h3 id="truetimer-性能问题"><a class="anchor" href="#truetimer-性能问题"></a>timer 性能问题</h3> -<div class="ulist"> -<ul> -<li> -<p>老版本的 timer 会有大量的 syscall → <a href="https://github.com/golang/go/issues/25471" class="bare">https://github.com/golang/go/issues/25471</a></p> -</li> -</ul> -</div> -<div class="exampleblock"> -<div class="content"> -<div class="paragraph"> -<p>go1.13</p> -</div> -<div class="paragraph"> -<p>% time seconds usecs/call calls errors syscall ------- ----------- ----------- --------- --------- ---------------- - 84.00 12.007993 459 26148 3874 futex - 11.43 1.634512 146 11180 nanosleep - 4.45 0.635987 32 20185 sched_yield</p> -</div> -<div class="paragraph"> -<p>go1.14</p> -</div> -<div class="paragraph"> -<p>% time seconds usecs/call calls errors syscall ------- ----------- ----------- --------- --------- ---------------- - 58.78 4.837332 174 27770 4662 futex - 19.50 1.605189 440 3646 nanosleep - 11.55 0.950730 44 21569 epoll_pwait - 9.75 0.802715 36 22181 sched_yield:w</p> -</div> -</div> -</div> -<div class="ulist"> -<ul> -<li> -<p>用时间轮实现粗粒度的时间库</p> -</li> -</ul> -</div> -<div class="paragraph"> -<p>可以搜搜大量的 timewheel 库。</p> -</div> -</div> -<div class="sect2"> -<h3 id="true汇编优化"><a class="anchor" href="#true汇编优化"></a>汇编优化</h3> -<div class="paragraph"> -<p>SIMD 优化,如 math 库。gonum 中也有一些例子。 -无法跨平台,如未来碰到国产化需求要上 ARM、龙芯(MIPS) 就尴尬了。</p> -</div> -<div class="paragraph"> -<p><a href="https://github.com/gonum/gonum/tree/master/internal/asm/f64" class="bare">https://github.com/gonum/gonum/tree/master/internal/asm/f64</a></p> -</div> -</div> -</div> -</div> -<div class="sect1"> -<h2 id="true语言本身的一些缺陷"><a class="anchor" href="#true语言本身的一些缺陷"></a>语言本身的一些缺陷</h2> -<div class="sectionbody"> -<div class="sect2"> -<h3 id="true越压越差"><a class="anchor" href="#true越压越差"></a>越压越差</h3> - -</div> -<div class="sect2"> -<h3 id="true调度和锁"><a class="anchor" href="#true调度和锁"></a>调度和锁</h3> -<div class="paragraph"> -<p>调度 + 锁出问题,难复现,难定位</p> -</div> -</div> -<div class="sect2"> -<h3 id="true不注意造成死循环会让整个进程-hang-住"><a class="anchor" href="#true不注意造成死循环会让整个进程-hang-住"></a>不注意造成死循环会让整个进程 hang 住</h3> -<div class="paragraph"> -<p>GC 需要抢占所有 goroutine,老版本的抢占需要用户协程在 morestack 时主动退出。</p> -</div> -<div class="paragraph"> -<p>卡 gcwaiting。</p> -</div> -</div> -<div class="sect2"> -<h3 id="true物理机负载高时延迟非线性增长"><a class="anchor" href="#true物理机负载高时延迟非线性增长"></a>物理机负载高时,延迟非线性增长</h3> -<div class="paragraph"> -<p>TODO</p> -</div> -</div> -<div class="sect2"> -<h3 id="true调度导致-cpu-密集型业务超时"><a class="anchor" href="#true调度导致-cpu-密集型业务超时"></a>调度导致 CPU 密集型业务超时</h3> -<div class="paragraph"> -<p>TODO,bcrypt 的例子</p> -</div> -<div class="paragraph"> -<p>因为调度导致的全部超时</p> -</div> -</div> -<div class="sect2"> -<h3 id="true老版本的问题"><a class="anchor" href="#true老版本的问题"></a>老版本的问题</h3> -<div class="sect3"> -<h4 id="truesync-pool-在-gc-时全清空"><a class="anchor" href="#truesync-pool-在-gc-时全清空"></a>sync.Pool 在 GC 时全清空</h4> -<div class="paragraph"> -<p>导致在每一轮 GC 后都有延迟抖动,升级 1.13 后长尾请求有改善。</p> -</div> -</div> -</div> -<div class="sect2"> -<h3 id="true当前问题定位工具的局限性"><a class="anchor" href="#true当前问题定位工具的局限性"></a>当前问题定位工具的局限性</h3> -<div class="paragraph"> -<p>难以定位抖动问题。</p> -</div> -<div class="paragraph"> -<p>无论 pprof、perf、fgprof、trace 都是人肉触发,抖动时人又不在系统旁边。</p> -</div> -<div class="paragraph"> -<p>这种情况需要 self-aware 的 profile dump 方式来解决问题。</p> -</div> -<div class="paragraph"> -<p>或者向 Google 看齐:</p> -</div> -<div class="sect3"> -<h4 id="truecontinuous-profiling"><a class="anchor" href="#truecontinuous-profiling"></a>continuous profiling</h4> -<div class="paragraph"> -<p>早发现,早治疗。</p> -</div> -</div> -</div> -</div> -</div> -</div> -<div id="footer"> -<div id="footer-text"> -Last updated 2020-09-10 01:34:10 +0800 -</div> -</div> -<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/9.15.6/styles/github.min.css"> -<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/9.15.6/highlight.min.js"></script> -<script>hljs.initHighlighting()</script> -</body> -</html> +<!DOCTYPE html> +<html lang="en"> +<head> +<meta charset="UTF-8"> +<meta http-equiv="X-UA-Compatible" content="IE=edge"> +<meta name="viewport" content="width=device-width, initial-scale=1.0"> +<meta 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