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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
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-<h1>Go 语言性能优化</h1>
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-<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) &#8594; 提出解决方案 &#8594; 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&#8217;s sigtrampgo() function. This function then proceeds to call sigprof() or sigprofNonGo() to record the thread&#8217;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&#8217;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 &lt; b.N; i++ {
-		uu, _ = exec.Command("uuidgen").Output()
-	}
-}
-
-func BenchmarkUUIDLib(b *testing.B) {
-	for i := 0; i &lt; 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 &lt; b.N; i++ {
-		var msg bytes.Buffer
-		msg.WriteString("userid : " + "1")
-		msg.WriteString("location : " + "ab")
-	}
-}
-
-func BenchmarkBytesBufferAppendSprintf(b *testing.B) {
-	for i := 0; i &lt; 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 &lt; 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 &#8594; 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 &gt; 12 {
-		if maxDepth == 0 {
-			heapSort_func(data, a, b)
-			return
-		}
-		maxDepth--
-		mlo, mhi := doPivot_func(data, a, b)
-		if mlo-a &lt; b-mhi {
-			quickSort_func(data, a, mlo, maxDepth)
-			a = mhi
-		} else {
-			quickSort_func(data, mhi, b, maxDepth)
-			b = mlo
-		}
-	}
-	if b-a &gt; 1 {
-		for i := a + 6; i &lt; 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 &#8594; <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>
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-<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/9.15.6/highlight.min.js"></script>
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128 changes: 109 additions & 19 deletions perf_opt/index.adoc
Original file line number Diff line number Diff line change
Expand Up @@ -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

Expand All @@ -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

Expand Down Expand Up @@ -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?
Expand All @@ -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。
Expand Down Expand Up @@ -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

Expand All @@ -197,35 +273,35 @@ 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
// The padding should eliminate false sharing
// 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 {
Expand All @@ -251,7 +327,7 @@ func quickSort_func(data lessSwap, a, b, maxDepth int) {
insertionSort_func(data, a, b)
}
}
====
----

==== true sharing

Expand All @@ -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
Expand All @@ -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。
Expand All @@ -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

Expand All @@ -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。
Expand All @@ -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[]
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