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+![](https://raw.githubusercontent.com/RifeWang/images/master/go/gc1.png) 我们需要等待所有 goroutine 暂停,而暂停操作是需要出现一次函数调用才能完成,如果某个 goroutine 始终没有发生函数调用(比如一直在执行某个非常长的循环操作)而其它 goroutine 却完成了会怎样,就会如下图: -![](/images/go/gc2.png) +![](https://raw.githubusercontent.com/RifeWang/images/master/go/gc2.png) 然而,必须所有的 goroutine 全部都暂停,垃圾回收才能继续进行,不然就会卡在这里一直等待,结果就是延迟越来越高。这个问题官方团队计划将在 1.14 版本通过优先策略进行优化。 @@ -54,14 +54,14 @@ STW 是 Stop The World 的缩写,意思是 GC 的时候会暂停其它所有 进行标记,Concurrent 表示这个过程是并发进行的,不会 STW ,GC 会先征用 25% 的 CPU 资源,如下图: -![](/images/go/gc3.png) +![](https://raw.githubusercontent.com/RifeWang/images/master/go/gc3.png) GC 占用了 P1 逻辑处理器,而其它 goroutine 正常的并发运行。 但是,有些时候 GC 的任务特别繁重,需要更多的资源,这个时候怎么办?开启 Mark Assit 协助工作,如下图中的 MA : -![](/images/go/gc4.png) +![](https://raw.githubusercontent.com/RifeWang/images/master/go/gc4.png) 标记完成,进行下一个阶段。 @@ -71,12 +71,12 @@ GC 占用了 P1 逻辑处理器,而其它 goroutine 正常的并发运行。 标记终止。关闭 Write Barrier(写屏障),执行各种清理任务,然后计算下一次 GC 的目标,这个阶段也是需要 STW 的,平均 60 - 90 微秒: -![](/images/go/gc5.png) +![](https://raw.githubusercontent.com/RifeWang/images/master/go/gc5.png) 一旦 GC 完成,goroutine 继续执行: -![](/images/go/gc6.png) +![](https://raw.githubusercontent.com/RifeWang/images/master/go/gc6.png) ### Sweeping - Concurrent @@ -99,7 +99,7 @@ GODEBUG=gctrace=1 通过指标数据可以看到各个过程及耗时情况,比如: -![](/images/go/gc7.jpeg) +![](https://raw.githubusercontent.com/RifeWang/images/master/go/gc7.jpeg) 2、使用 pprof diff --git a/content/posts/influxdb/1.md b/content/posts/influxdb/influxdb-1.md similarity index 93% rename from content/posts/influxdb/1.md rename to content/posts/influxdb/influxdb-1.md index b08dcfb5..7fe07ad1 100644 --- a/content/posts/influxdb/1.md +++ b/content/posts/influxdb/influxdb-1.md @@ -92,12 +92,12 @@ field key = field value 键值对也是存储具体的数据,但不会被索 示例: -![image](/images/influxdb/line-protocol.png) +![image](https://raw.githubusercontent.com/RifeWang/images/master/influxdb/line-protocol.png) 怎么去理解 series 和 point ?先看下图: -![image](/images/influxdb/series-point.webp) +![image](https://raw.githubusercontent.com/RifeWang/images/master/influxdb/series-point.webp) 这张图选取了三种时序数据库的历年排名得分情况。首先,整个图表可以看成是一个 measurement ,它包含了许多数据;然后我们根据 db 名称构建 tag ,把 score 排名得分作为 field ,那么所有数据行就类似于: @@ -187,10 +187,10 @@ InfluxDB 为了更高的性能做了一些设计与权衡之道: --- 相关文章: -- [时序数据库 InfluxDB(一)](/posts/influxdb/1/) -- [时序数据库 InfluxDB(二)](/posts/influxdb/2/) -- [时序数据库 InfluxDB(三)](/posts/influxdb/3/) -- [时序数据库 InfluxDB(四)](/posts/influxdb/4/) -- [时序数据库 InfluxDB(五)](/posts/influxdb/5/) -- [时序数据库 InfluxDB(六)](/posts/influxdb/6/) -- [时序数据库 InfluxDB(七)](/posts/influxdb/7/) +- [时序数据库 InfluxDB(一)](/influxdb-1/) +- [时序数据库 InfluxDB(二)](/influxdb-2/) +- [时序数据库 InfluxDB(三)](/influxdb-3/) +- [时序数据库 InfluxDB(四)](/influxdb-4/) +- [时序数据库 InfluxDB(五)](/influxdb-5/) +- [时序数据库 InfluxDB(六)](/influxdb-6/) +- [时序数据库 InfluxDB(七)](/influxdb-7/) diff --git a/content/posts/influxdb/2.md b/content/posts/influxdb/influxdb-2.md similarity index 84% rename from content/posts/influxdb/2.md rename to content/posts/influxdb/influxdb-2.md index f8b890f1..31ebe02e 100644 --- a/content/posts/influxdb/2.md +++ b/content/posts/influxdb/influxdb-2.md @@ -32,7 +32,7 @@ shard 是什么? 先来看数据的层次结构: -![image](/images/influxdb/shard.webp) +![image](https://raw.githubusercontent.com/RifeWang/images/master/influxdb/shard.webp) 如果所示,一个 database 对应一个实际的磁盘上的文件夹,该数据库下不同的 RP 策略对应不同的文件夹。 @@ -65,7 +65,7 @@ shard 从属于唯一一个 shard group ,shard duration 和 shard group durati 默认情况下,shard group duration 根据 RP duration 的值来确定,对应关系如下图: -![image](/images/influxdb/rp-duration.png) +![image](https://raw.githubusercontent.com/RifeWang/images/master/influxdb/rp-duration.png) @@ -80,7 +80,7 @@ shard group duration 设置为多久才最好? 默认配置对于大多数场景都运行的很好,然而,高吞吐量或长时间运行的实例将受益于更长的 shard group duration ,官方建议的配置如下: -![image](/images/influxdb/rp-duration-config.webp) +![image](https://raw.githubusercontent.com/RifeWang/images/master/influxdb/rp-duration-config.webp) @@ -96,10 +96,10 @@ RP 策略可以动态调整,删除一个 RP 将会删除其下的所有数据 --- 相关文章: -- [时序数据库 InfluxDB(一)](/posts/influxdb/1/) -- [时序数据库 InfluxDB(二)](/posts/influxdb/2/) -- [时序数据库 InfluxDB(三)](/posts/influxdb/3/) -- [时序数据库 InfluxDB(四)](/posts/influxdb/4/) -- [时序数据库 InfluxDB(五)](/posts/influxdb/5/) -- [时序数据库 InfluxDB(六)](/posts/influxdb/6/) -- [时序数据库 InfluxDB(七)](/posts/influxdb/7/) +- [时序数据库 InfluxDB(一)](/influxdb-1/) +- [时序数据库 InfluxDB(二)](/influxdb-2/) +- [时序数据库 InfluxDB(三)](/influxdb-3/) +- [时序数据库 InfluxDB(四)](/influxdb-4/) +- [时序数据库 InfluxDB(五)](/influxdb-5/) +- [时序数据库 InfluxDB(六)](/influxdb-6/) +- [时序数据库 InfluxDB(七)](/influxdb-7/) diff --git a/content/posts/influxdb/3.md b/content/posts/influxdb/influxdb-3.md similarity index 91% rename from content/posts/influxdb/3.md rename to content/posts/influxdb/influxdb-3.md index 974c9a7c..5840a160 100644 --- a/content/posts/influxdb/3.md +++ b/content/posts/influxdb/influxdb-3.md @@ -109,10 +109,10 @@ series-id-set-cache-size :使用内存缓存的 series 集的大小,由于 T --- 相关文章: -- [时序数据库 InfluxDB(一)](/posts/influxdb/1/) -- [时序数据库 InfluxDB(二)](/posts/influxdb/2/) -- [时序数据库 InfluxDB(三)](/posts/influxdb/3/) -- [时序数据库 InfluxDB(四)](/posts/influxdb/4/) -- [时序数据库 InfluxDB(五)](/posts/influxdb/5/) -- [时序数据库 InfluxDB(六)](/posts/influxdb/6/) -- [时序数据库 InfluxDB(七)](/posts/influxdb/7/) +- [时序数据库 InfluxDB(一)](/influxdb-1/) +- [时序数据库 InfluxDB(二)](/influxdb-2/) +- [时序数据库 InfluxDB(三)](/influxdb-3/) +- [时序数据库 InfluxDB(四)](/influxdb-4/) +- [时序数据库 InfluxDB(五)](/influxdb-5/) +- [时序数据库 InfluxDB(六)](/influxdb-6/) +- [时序数据库 InfluxDB(七)](/influxdb-7/) diff --git a/content/posts/influxdb/4.md b/content/posts/influxdb/influxdb-4.md similarity index 85% rename from content/posts/influxdb/4.md rename to content/posts/influxdb/influxdb-4.md index 3d52eba7..02d7afc9 100644 --- a/content/posts/influxdb/4.md +++ b/content/posts/influxdb/influxdb-4.md @@ -20,7 +20,7 @@ disableComments = true InfluxDB 数据的写入如下图所示: -![write data](/images/influxdb/write-data.png) +![write data](https://raw.githubusercontent.com/RifeWang/images/master/influxdb/write-data.png) 所有数据先写入到 WAL( Write Ahead Log )预写日志文件,并同步到 Cache 缓存中,当 Cache 缓存的数据达到了一定的大小,或者达到一定的时间间隔之后,数据会被写入到 TSM 文件中。 @@ -36,7 +36,7 @@ Compactor 压缩器则负责具体的 Compression 压缩工作。 为了处理文件,存储引擎通过 Writers/Readers 处理数据的写和读。另外存储引擎还会使用 In-Memory Index 内存索引快速访问 measurements、tags、series 等数据。 -![in-memory index](/images/influxdb/in-memory-index.png) +![in-memory index](https://raw.githubusercontent.com/RifeWang/images/master/influxdb/in-memory-index.png) 存储引擎的组成部分: @@ -92,7 +92,7 @@ IOPS( Input/Output Operations Per Second ):每秒读写数,衡量存储 不同负载情况下的硬件配置参考如下: -![hard config](/images/influxdb/hard-config.png) +![hard config](https://raw.githubusercontent.com/RifeWang/images/master/influxdb/hard-config.png) 由于 SSD 固态硬盘的性能更高,官方也建议使用 SSD ,上图也是使用 SSD 的情况。 @@ -110,10 +110,10 @@ IOPS( Input/Output Operations Per Second ):每秒读写数,衡量存储 --- 相关文章: -- [时序数据库 InfluxDB(一)](/posts/influxdb/1/) -- [时序数据库 InfluxDB(二)](/posts/influxdb/2/) -- [时序数据库 InfluxDB(三)](/posts/influxdb/3/) -- [时序数据库 InfluxDB(四)](/posts/influxdb/4/) -- [时序数据库 InfluxDB(五)](/posts/influxdb/5/) -- [时序数据库 InfluxDB(六)](/posts/influxdb/6/) -- [时序数据库 InfluxDB(七)](/posts/influxdb/7/) +- [时序数据库 InfluxDB(一)](/influxdb-1/) +- [时序数据库 InfluxDB(二)](/influxdb-2/) +- [时序数据库 InfluxDB(三)](/influxdb-3/) +- [时序数据库 InfluxDB(四)](/influxdb-4/) +- [时序数据库 InfluxDB(五)](/influxdb-5/) +- [时序数据库 InfluxDB(六)](/influxdb-6/) +- [时序数据库 InfluxDB(七)](/influxdb-7/) diff --git a/content/posts/influxdb/5.md b/content/posts/influxdb/influxdb-5.md similarity index 88% rename from content/posts/influxdb/5.md rename to content/posts/influxdb/influxdb-5.md index fef18ea7..edefbc54 100644 --- a/content/posts/influxdb/5.md +++ b/content/posts/influxdb/influxdb-5.md @@ -116,13 +116,13 @@ bind-address = "127.0.0.1:8088" 备份命令: -![backup](/images/influxdb/backup.png) +![backup](https://raw.githubusercontent.com/RifeWang/images/master/influxdb/backup.png) 恢复命令: -![restore](/images/influxdb/restore.png) +![restore](https://raw.githubusercontent.com/RifeWang/images/master/influxdb/restore.png) @@ -143,10 +143,10 @@ bind-address = "127.0.0.1:8088" --- 相关文章: -- [时序数据库 InfluxDB(一)](/posts/influxdb/1/) -- [时序数据库 InfluxDB(二)](/posts/influxdb/2/) -- [时序数据库 InfluxDB(三)](/posts/influxdb/3/) -- [时序数据库 InfluxDB(四)](/posts/influxdb/4/) -- [时序数据库 InfluxDB(五)](/posts/influxdb/5/) -- [时序数据库 InfluxDB(六)](/posts/influxdb/6/) -- [时序数据库 InfluxDB(七)](/posts/influxdb/7/) +- [时序数据库 InfluxDB(一)](/influxdb-1/) +- [时序数据库 InfluxDB(二)](/influxdb-2/) +- [时序数据库 InfluxDB(三)](/influxdb-3/) +- [时序数据库 InfluxDB(四)](/influxdb-4/) +- [时序数据库 InfluxDB(五)](/influxdb-5/) +- [时序数据库 InfluxDB(六)](/influxdb-6/) +- [时序数据库 InfluxDB(七)](/influxdb-7/) diff --git a/content/posts/influxdb/6.md b/content/posts/influxdb/influxdb-6.md similarity index 95% rename from content/posts/influxdb/6.md rename to content/posts/influxdb/influxdb-6.md index 2e5a488d..87e86de1 100644 --- a/content/posts/influxdb/6.md +++ b/content/posts/influxdb/influxdb-6.md @@ -325,10 +325,10 @@ EVERY 1h 大于 GROUP BY time(30m),因此 CQ 每隔 1h 执行一次;FOR 90m --- 相关文章: -- [时序数据库 InfluxDB(一)](/posts/influxdb/1/) -- [时序数据库 InfluxDB(二)](/posts/influxdb/2/) -- [时序数据库 InfluxDB(三)](/posts/influxdb/3/) -- [时序数据库 InfluxDB(四)](/posts/influxdb/4/) -- [时序数据库 InfluxDB(五)](/posts/influxdb/5/) -- [时序数据库 InfluxDB(六)](/posts/influxdb/6/) -- [时序数据库 InfluxDB(七)](/posts/influxdb/7/) +- [时序数据库 InfluxDB(一)](/influxdb-1/) +- [时序数据库 InfluxDB(二)](/influxdb-2/) +- [时序数据库 InfluxDB(三)](/influxdb-3/) +- [时序数据库 InfluxDB(四)](/influxdb-4/) +- [时序数据库 InfluxDB(五)](/influxdb-5/) +- [时序数据库 InfluxDB(六)](/influxdb-6/) +- [时序数据库 InfluxDB(七)](/influxdb-7/) diff --git a/content/posts/influxdb/7.md b/content/posts/influxdb/influxdb-7.md similarity index 81% rename from content/posts/influxdb/7.md rename to content/posts/influxdb/influxdb-7.md index 73104f09..eabc7e17 100644 --- a/content/posts/influxdb/7.md +++ b/content/posts/influxdb/influxdb-7.md @@ -21,7 +21,7 @@ InfluxDB 开源的社区版本面临的最大的问题就是单点故障和容 既然有单点故障的可能,那么索性写入多个节点,同时也解决了容灾备份的问题: -![double write](/images/influxdb/double-write.png) +![double write](https://raw.githubusercontent.com/RifeWang/images/master/influxdb/double-write.png) 1、在不同的机器上配置多个 InfluxDB 实例,写入数据时,直接由客户端并发写入多个实例。(为什么不用代理,因为代理自身就是个单点)。 @@ -46,10 +46,10 @@ InfluxDB 开源的社区版本面临的最大的问题就是单点故障和容 --- 相关文章: -- [时序数据库 InfluxDB(一)](/posts/influxdb/1/) -- [时序数据库 InfluxDB(二)](/posts/influxdb/2/) -- [时序数据库 InfluxDB(三)](/posts/influxdb/3/) -- [时序数据库 InfluxDB(四)](/posts/influxdb/4/) -- [时序数据库 InfluxDB(五)](/posts/influxdb/5/) -- [时序数据库 InfluxDB(六)](/posts/influxdb/6/) -- [时序数据库 InfluxDB(七)](/posts/influxdb/7/) +- [时序数据库 InfluxDB(一)](/influxdb-1/) +- [时序数据库 InfluxDB(二)](/influxdb-2/) +- [时序数据库 InfluxDB(三)](/influxdb-3/) +- [时序数据库 InfluxDB(四)](/influxdb-4/) +- [时序数据库 InfluxDB(五)](/influxdb-5/) +- [时序数据库 InfluxDB(六)](/influxdb-6/) +- [时序数据库 InfluxDB(七)](/influxdb-7/) diff --git a/content/posts/kubernetes/docker-guide.md b/content/posts/kubernetes/docker-guide.md index 77f000bc..d3633ada 100644 --- a/content/posts/kubernetes/docker-guide.md +++ b/content/posts/kubernetes/docker-guide.md @@ -22,7 +22,7 @@ disableComments = true 一张图慢慢讲: -![](/images/docker/docker1-1.jpeg) +![](https://raw.githubusercontent.com/RifeWang/images/master/docker/docker1-1.jpeg) 1、本地开发写好了 code ,首先我们需要通过 build 命令构建 image 镜像,而构建的规则呢,就需要写在这个 dockerfile 文件里。 @@ -50,7 +50,7 @@ disableComments = true Docker 中的镜像到底是什么?它是一个可供执行的文件系统包,里面包含了运行一个应用程序所需要的代码、库、环境变量和配置文件等等所有内容。 -![](/images/docker/docker2-1.jpeg) +![](https://raw.githubusercontent.com/RifeWang/images/master/docker/docker2-1.jpeg) 镜像是分层的。它是由一个或多个文件系统叠加而成,最底层是 bootfs 即引导文件系统,我们几乎永远不会与这个东西有什么交互,而且当容器启动时 bootfs 会被卸载掉。第二层是 rootfs ,通常是一个操作系统,其包含了程序运行所需的最基本环境,也称之为基础镜像。第三、第四、第N层,是由我们自己指定的其它资源文件。 @@ -168,7 +168,7 @@ Volume 翻译为卷,因为基本上用于挂载数据,所以也常常直接 所谓的挂载数据卷,实际上就是把宿主机本地的目录文件映射到 docker 容器内部的目录下。也就是说实际的目录文件是存放在本地磁盘上的,docker 容器通过挂载的方式可以直接使用本地磁盘上的文件。 -![](/images/docker/docker3-1.jpeg) +![](https://raw.githubusercontent.com/RifeWang/images/master/docker/docker3-1.jpeg) 如上图所示: @@ -182,7 +182,7 @@ Volume 翻译为卷,因为基本上用于挂载数据,所以也常常直接 如果有多个容器都需要挂载数据卷,难道需要每一个容器都挂载一遍到本地?当然不是。 -![](/images/docker/docker3-2.jpeg) +![](https://raw.githubusercontent.com/RifeWang/images/master/docker/docker3-2.jpeg) 如上图所示,这里引入了数据卷容器(图中的 Data Container ),其实就是一个普通的容器,我们只需要通过数据卷容器挂载( -v )一次数据卷,其他需要挂载的容器直接连接( --volumes-from )这个数据卷容器就行了,而再不需要知道实际的宿主机本地目录。 @@ -238,7 +238,7 @@ app.listen(PORT); 这样 web 容器便可以连接上 redis 容器了,如下图所示: -![](/images/docker/docker4-1.jpeg) +![](https://raw.githubusercontent.com/RifeWang/images/master/docker/docker4-1.jpeg) 使用 link 方法,其会在容器启动时(容器每次启动都会默认配置不同的虚拟网络)找到连接的目标容器并在本容器内部设置环境变量并修改 /etc/hosts 文件,这也是我们可以直接使用连接别名而不用指定具体 IP 地址的原因。 @@ -248,7 +248,7 @@ app.listen(PORT); 1、net 方式一,如下图所示: -![](/images/docker/docker4-2.jpeg) +![](https://raw.githubusercontent.com/RifeWang/images/master/docker/docker4-2.jpeg) 我们先将 redis 容器的端口暴露到本地宿主机,然后在 web 中指定本地宿主机具体的 IP 地址,这样也可以实现连接,但是需要注意的是,在 web 中不能直接使用 localhost ,因为前面已经提到了,每个容器都有自己独立的虚拟网络,使用 localhost 将会指向的是这个容器内部,而不是宿主机。 @@ -257,7 +257,7 @@ app.listen(PORT); 2、net 方式二,如下图所示: -![](/images/docker/docker4-3.jpeg) +![](https://raw.githubusercontent.com/RifeWang/images/master/docker/docker4-3.jpeg) 这里与前一种方式不同的是,我们直接通过 --net host 指定容器直接使用宿主机网络,这样在 web 中就可以直接通过 localhost 连接到 redis 了,不用知道宿主机具体的 IP 地址,对比上一种方式看似有一点小的改进。 @@ -265,7 +265,7 @@ app.listen(PORT); 3、net 方式三,如下图所示: -![](/images/docker/docker4-4.jpeg) +![](https://raw.githubusercontent.com/RifeWang/images/master/docker/docker4-4.jpeg) 这里通过 --net container 的方式直接指定 web 使用与 redis 相同的网络,这样既避免了无谓的端口暴露,同时又能保持容器与宿主机之间的隔离,这种方式是建议使用的。 @@ -284,7 +284,7 @@ docker network create -d bridge my-network 将 web 和 redis 容器连接到同一个自定义的网络中,并直接在 web 中的 redis host 指向 redis 容器的别名,即可完成连接,如下图所示: -![](/images/docker/docker4-5.jpeg) +![](https://raw.githubusercontent.com/RifeWang/images/master/docker/docker4-5.jpeg) 对于自定义网络,我们不仅能够在容器启动时通过 --net 直接指定,还能够在容器已经启动完成后通过: diff --git a/content/posts/kubernetes/k8s-crd-operator.md b/content/posts/kubernetes/k8s-crd-operator.md index 4ef345e2..127699a1 100644 --- a/content/posts/kubernetes/k8s-crd-operator.md +++ b/content/posts/kubernetes/k8s-crd-operator.md @@ -75,7 +75,7 @@ spec: 执行 `kubectl apply -f my-crontab.yaml` 就可以创建我们自定义的 CronTab,执行 `kubectl get crontab` 就可以查询到我们自定义的 CronTab 列表。 -![](/images/k8s/crd-operator/crd.png) +![](https://raw.githubusercontent.com/RifeWang/images/master/k8s/crd-operator/crd.png) 通过 `CRD` 自定义资源的优点是,我们无需操心自定义资源的数据存储,也无需再额外实现一个 http server 去对外暴露操作这些自定义资源的 API 接口,因为这些 k8s 都帮我们做好了,我们只需要像其它内置资源一样使用自定义资源即可。 @@ -85,7 +85,7 @@ spec: `Operator` 其实就是 custom resource controller(自定义资源的控制器),它干的事情就是监听自定义资源的变更,然后针对性地做一些操作。例如,监听到某个自定义资源被创建后,`Operator` 可以读取这个自定义资源的属性然后创建一个 pod 去运行具体的程序,并将这个 pod 绑定到自定义资源对象上。 -![](/images/k8s/crd-operator/k8s-operator-1800x1013.webp) +![](https://raw.githubusercontent.com/RifeWang/images/master/k8s/crd-operator/k8s-operator.png) 那 `Operator` 以何种方式存在呢?其实它跟普通的服务一样,可以是 `deployment`,也可以是 `statefuleSet`。 diff --git a/content/posts/kubernetes/k8s-secret-management.md b/content/posts/kubernetes/k8s-secret-management.md index a2677617..f20f8c47 100644 --- a/content/posts/kubernetes/k8s-secret-management.md +++ b/content/posts/kubernetes/k8s-secret-management.md @@ -40,7 +40,7 @@ Kubernetes 已经成为现代软件基础设施中不可或缺的一部分。因 `Sealed Secrets` 是一个开源的 Kubernetes 控制器和来自 Bitnami 的客户端 CLI 工具,旨在使用非对称密码加密解决“在 Git 中存储 secret ”问题的一部分。具有 RBAC 配置的 Sealed Secrets 防止非管理员读取 secret 是解决整个问题的绝佳解决方案。 -![Sealed Secrets](/images/k8s/sealed-secrets-architecture.jpeg) +![](https://raw.githubusercontent.com/RifeWang/images/master/k8s/sealed-secrets-architecture.jpeg) 它的工作原理如下: @@ -147,7 +147,7 @@ kubectl describe secret mysecret Sealed Secrets 是保护 secret 的方式之一,但除此之外还有更好的方法。使用 [External Secrets Operator (ESO)](https://external-secrets.io/v0.8.1/) 和外部 secret 管理系统,如 [HashiCorp Vault](https://www.vaultproject.io/)、[AWS Secrets Manager](https://aws.amazon.com/cn/secrets-manager/)、[Google Secrets Manager](https://cloud.google.com/secret-manager?hl=zh-cn) 或 [Azure Key Vault](https://azure.microsoft.com/en-us/products/key-vault/)。虽然设置起来有点复杂,但如果您使用云提供商来托管您的 Kubernetes 集群,这是一种更好的方法。ESO 支持许多这样的 secret 管理器并监视外部 secret 存储的变化,并使 Kubernetes secret 保持同步。 -![ESO](/images/k8s/external-secrets-operator-architecture.jpeg) +![](https://raw.githubusercontent.com/RifeWang/images/master/k8s/external-secrets-operator-architecture.jpeg) ESO 提供了四个 CRD 来管理 secret。`ExternalSecret` 和 `ClusterExternalSecret` CRD 定义需要获取哪些数据以及如何转换这些数据。`SecretStore` 和 `ClusterSecretStore` CRD 定义了与外部 secret 存储的连接细节。`Cluster` 前缀的 CRD 表示作用范围是集群。 @@ -300,7 +300,7 @@ kubectl describe externalsecret vault-example [Secrets Store CSI Driver](https://secrets-store-csi-driver.sigs.k8s.io/) 是一个原生的上游 Kubernetes 驱动程序,可用于从工作负载中抽象出 secret 的存储位置。如果您想使用云提供商的 secret 管理器而不将 secret 公开为 Kubernetes secret 对象,您可以使用 CSI 驱动程序将 secret 作为卷安装在您的 pod 中。如果您使用云提供商来托管您的 Kubernetes 集群,这是一个很好的选择。该驱动程序支持许多云提供商,并且可以与不同的 secret 管理器一起使用。 -![Secrets Store CSI Driver](/images/k8s/secret-csi-driver.jpeg) +![](https://raw.githubusercontent.com/RifeWang/images/master/k8s/secret-csi-driver.jpeg) Secrets Store CSI Driver 是一个 daemonset 守护进程,它与 secret 提供者通信以检索 `SecretProviderClass` 自定义资源中指定的 secret 。 diff --git a/content/posts/kubernetes/why-cilium-for-k8s.md b/content/posts/kubernetes/why-cilium-for-k8s.md index 9af43dd0..9f716c5b 100644 --- a/content/posts/kubernetes/why-cilium-for-k8s.md +++ b/content/posts/kubernetes/why-cilium-for-k8s.md @@ -62,7 +62,7 @@ disableComments = true 使用 bpfilter(而不是 iptables)进行路由[意味着](https://www.admin-magazine.com/Archive/2019/50/Bpfilter-offers-a-new-approach-to-packet-filtering-in-Linux)将过滤任务转移到内核空间,这会产生令人印象深刻的性能提升。这正是项目设计、大量文章和第三方基准测试所承诺的。我们自己的测试证实,与我们之前使用的 Flannel + kube-proxy 相比,处理流量速度有显着提升。 -![](https://blog.palark.com/wp-content/uploads/2023/03/ebpf-host-routing-diagram.png) +![](https://raw.githubusercontent.com/RifeWang/images/master/k8s/ebpf-host-routing-diagram.png) *eBPF host-routing compared to using iptables. source: [“CNI Benchmark: Understanding Cilium Network Performance”](https://cilium.io/blog/2021/05/11/cni-benchmark/)* @@ -92,11 +92,11 @@ disableComments = true 现在举几个例子,说明如何在我的 Kubernetes 沙箱中使用 Hubble。首先,这里我们有带有 Ingress-NGINX 控制器的命名空间。我们可以看到一个外部用户通过 Dex 授权后进入了 Hubble UI。会是谁呢?... -![](https://blog.palark.com/wp-content/uploads/2023/03/cilium-hubble-dex-ingress.png) +![](https://raw.githubusercontent.com/RifeWang/images/master/k8s/cilium-hubble-dex-ingress.png) 现在,这里有一个更有趣的例子:Hubble 花了大约一分钟的时间可视化 Prometheus 命名空间如何与集群的其余部分通信。您可以看到 Prometheus 从众多服务中抓取了指标。多么棒的功能!在您花费数小时为您的项目绘制所有这些基础架构图之前,您应该已经知道了! -![](https://blog.palark.com/wp-content/uploads/2023/03/cilium-hubble-prometheus-services.png) +![](https://raw.githubusercontent.com/RifeWang/images/master/k8s/cilium-hubble-prometheus-services.png) ### 6. 可视化策略编辑器 diff --git a/content/posts/middleware/grpc.md b/content/posts/middleware/grpc.md index 444e3fda..fa3c591a 100644 --- a/content/posts/middleware/grpc.md +++ b/content/posts/middleware/grpc.md @@ -30,7 +30,7 @@ gRPC 是谷歌开源的一套 RPC 实现机制,低延迟、高性能,其基 示例: -![](/images/middleware/grpc1.png) +![](https://raw.githubusercontent.com/RifeWang/images/master/middleware/grpc1.png) 1、`syntax` 声明使用的 protocol buffers 协议版本,现行的是第三版。 2、`package` 声明自定义的包名,这里的 package 可以理解为 go 中的包,或者 node.js 中的 module 。 @@ -50,7 +50,7 @@ gRPC 是谷歌开源的一套 RPC 实现机制,低延迟、高性能,其基 1、构建 gRPC 服务端: -![](/images/middleware/grpc2.jpeg) +![](https://raw.githubusercontent.com/RifeWang/images/master/middleware/grpc2.jpeg) 如图所示,我们需要导入前面定义好的 .proto 文件,同时由于语言本身数据类型的不同,可以设置类型转换,比如将 .proto 中定义的枚举类型转换为 node.js 中的 string 类型。 @@ -75,18 +75,18 @@ https://github.com/protocolbuffers/protobuf/releases 1、执行 protoc 指令: -![](/images/middleware/grpc3.png) +![](https://raw.githubusercontent.com/RifeWang/images/master/middleware/grpc3.png) 编译 .proto 文件生成 .pb.go 代码包,在后续的使用中需要导入这个代码包。 2、构造 gRPC 服务端: -![](/images/middleware/grpc4.jpeg) +![](https://raw.githubusercontent.com/RifeWang/images/master/middleware/grpc4.jpeg) 3、构建 gRPC 客户端发起 RPC 调用: -![](/images/middleware/grpc5.png) +![](https://raw.githubusercontent.com/RifeWang/images/master/middleware/grpc5.png) `protoc` 编译 `.proto` 文件生成的 `.pb.go` 代码包里面包含了所有的服务、方法、数据结构等等,在我们的 go 代码中引用它们即可。 diff --git a/content/posts/middleware/kafka.md b/content/posts/middleware/kafka.md index 8a9b889c..05442b10 100644 --- a/content/posts/middleware/kafka.md +++ b/content/posts/middleware/kafka.md @@ -42,7 +42,7 @@ kafka 的流处理,可以持续获取输入流的数据,然后进行加工 kafka 中的相关概念如下图所示: -![](/images/middleware/kafka1.jpeg) +![](https://raw.githubusercontent.com/RifeWang/images/master/middleware/kafka1.jpeg) 1、Producer :生产者,投递消息。 @@ -90,7 +90,7 @@ Leader 和 follower 指的都是 broker 对象。 Topic 只是逻辑上的概念,partition 才是实际存储消息的地方,每个 topic 拥有多个 partition 分区。 -![](/images/middleware/kafka2.png) +![](https://raw.githubusercontent.com/RifeWang/images/master/middleware/kafka2.png) 每个 partition 分区都是一个有序的不可变的记录序列,消息一定是以顺序化的方式追加写入的,也正是这种方式保证了 kafka 的高吞吐量。而每个 partition 分区中的消息都有一个 offset 偏移量作为其唯一标识。 @@ -103,7 +103,7 @@ Topic 只是逻辑上的概念,partition 才是实际存储消息的地方, Consumer 消费消息。 -![](/images/middleware/kafka3.jpeg) +![](https://raw.githubusercontent.com/RifeWang/images/master/middleware/kafka3.jpeg) 每个 consumer 一定从属于一个 consumer group 消费组。 diff --git a/content/posts/middleware/nsq.md b/content/posts/middleware/nsq.md index 2006f39d..264d104e 100644 --- a/content/posts/middleware/nsq.md +++ b/content/posts/middleware/nsq.md @@ -30,7 +30,7 @@ NSQ 组件: 如下图所示: -![](/images/middleware/nsq1-1.jpeg) +![](https://raw.githubusercontent.com/RifeWang/images/master/middleware/nsq1-1.jpeg) 1、生产者 producer 将消息投递到指定的 nsqd 中指定的 topic 主题。 @@ -42,7 +42,7 @@ NSQ 组件: 再看下图: -![](/images/middleware/nsq1-2.jpeg) +![](https://raw.githubusercontent.com/RifeWang/images/master/middleware/nsq1-2.jpeg) 通过 nsqadmin 可以看到整个集群的统计信息并进行管理,多个 nsqd 节点组成集群并将其基本信息注册到 nsqlookupd 中,通过 nsqlookupd 可以寻址到具体的 nsqd 节点,而不论是消息的生产者还是消费者,其本质上都是与 nsqd 进行通信(如第一张图所示)。 @@ -79,7 +79,7 @@ NSQ 组件: NSQ 也不例外,如下图所示: -![](/images/middleware/nsq2-1.jpeg) +![](https://raw.githubusercontent.com/RifeWang/images/master/middleware/nsq2-1.jpeg) nsqd 是接受、排队、传递消息的守护进程,消息队列中的核心。 @@ -89,7 +89,7 @@ nsqd 是接受、排队、传递消息的守护进程,消息队列中的核心 生产者包装消息,将消息传递到 nsqd 中指定的 topic 。在 NSQ 中这一个步骤相当简单,通过 HTTP 接口就能完成: -![](/images/middleware/nsq2-2.jpeg) +![](https://raw.githubusercontent.com/RifeWang/images/master/middleware/nsq2-2.jpeg) 发送消息必须指定 topic ,而 topic 的作用其实就是对消息进行逻辑上的分区。 @@ -102,7 +102,7 @@ nsqd 是接受、排队、传递消息的守护进程,消息队列中的核心 上面已经说过,topic 只是用来将消息进行逻辑划分,channel 才是真正存放消息的地方,而 nsqd 在接受到消息后,会将消息复制给所有与这个 topic 相连的 channel 并存放。 -![](/images/middleware/nsq2-3.gif) +![](https://raw.githubusercontent.com/RifeWang/images/master/middleware/nsq2-3.gif) ### nsqd >> consumer @@ -119,7 +119,7 @@ nsqd 是接受、排队、传递消息的守护进程,消息队列中的核心 先来看看详细的过程: -![](/images/middleware/nsq2-4.jpeg) +![](https://raw.githubusercontent.com/RifeWang/images/master/middleware/nsq2-4.jpeg) 如上图所示,consumer 需要先连接到 nsqd,并且订阅指定的 topic 和 channel ,在一切准备就绪之后发送 RDY 状态表示可以接受消息,并指明一次可以处理的最大消息数量 max-in-flight 为 2 ,随后 nsqd 向 consumer 投递消息,consumer 消费者在接受到消息后进行业务处理,并且需要向 nsqd 响应 FIN(消息处理成功)或者 REQ( re-queue 重新排队),投递完成但未响应的这段时间内的消息状态为 in-flight 。 @@ -147,7 +147,7 @@ nsqd 是接受、排队、传递消息的守护进程,消息队列中的核心 有时候 consumer 处理消息面临很大的压力,随时有崩溃的风险,这种情况下可以主动向 nsq 发送 RDY 0 实现 backoff ,换句话说就是消费端暂停接受等多消息,以减轻自身压力避免崩溃,等到有更多处理能力时再取消暂停状态慢慢接收更多消息。当然进入 backoff 然后慢慢恢复是一个需要动态调节的过程。 -![](/images/middleware/nsq2-5.png) +![](https://raw.githubusercontent.com/RifeWang/images/master/middleware/nsq2-5.png) 事实上加快消息的处理才是我们需要关注的重中之重。 @@ -174,7 +174,7 @@ nsqlookupd 提供服务发现的功能,用来寻址特定主题的 nsqd。如 NSQ 是一个分布式的设计,可以有效的避免 SPOF 单点故障。 -![](/images/middleware/nsq2-6.jpeg) +![](https://raw.githubusercontent.com/RifeWang/images/master/middleware/nsq2-6.jpeg) 如图所示,我们可以轻松的部署足够多的 nsqd 到多台机器上,并让消费者与之连接(这个图简化处理了,我们仍应该使用 nsqlookupd )。每一个 nsqd 之间是相互独立的,没有任何关联。这就是说如果三个 nsqd 具有相同的 topic 和 channel ,我们向它们发送同一条消息,本质上就是分别发送了三条消息,结果就是连接这三个 nsqd 的 consumer 将会收到三条消息。这样做显然有效的提高了可靠性,但是在消费端一定要做好重复消息的处理问题。 diff --git a/content/posts/middleware/rabbitmq.md b/content/posts/middleware/rabbitmq.md index af86fb35..5f2db16b 100644 --- a/content/posts/middleware/rabbitmq.md +++ b/content/posts/middleware/rabbitmq.md @@ -22,7 +22,7 @@ disableComments = true 先来简单的了解一下 RabbitMQ 相关的基本概念: -![](/images/middleware/rabbitmq1-1.jpeg) +![](https://raw.githubusercontent.com/RifeWang/images/master/middleware/rabbitmq1-1.jpeg) Producer :生产者,生成消息并把消息发送给 RabbitMQ 。 @@ -54,7 +54,7 @@ Binding :绑定交换器和队列,只有绑定后消息才能被交换器分 - Channel :信道,建立完 TCP 连接后还必须建立一个信道,消息都是在信道中传递和操作的。 -![](/images/middleware/rabbitmq1-2.jpeg) +![](https://raw.githubusercontent.com/RifeWang/images/master/middleware/rabbitmq1-2.jpeg) 上图形象的展示了连接和信道之间的关系,一个连接中可以建立多个信道,而且每个信道之间都是完全隔离的,同时我们需要记住的是创建和销毁 TCP 连接是很消耗资源的,而信道则不是,所以能够通过创建多个信道来隔离环境的不要通过创建多个连接。 @@ -444,7 +444,7 @@ RPC 是什么?Remote Procedure Call,远程过程调用,比如某个服务 使用 RabbitMQ 如何实现 RPC 的功能: -![](/images/middleware/rabbitmq4-1.png) +![](https://raw.githubusercontent.com/RifeWang/images/master/middleware/rabbitmq4-1.png) 如上图所示,客户端发起请求到一个 rpc 队列,并指定一个 correlationId 作为该请求的唯一标识,且通过 reply_to 指定一个 callback 队列接收请求处理结果(这里的 callback 并不是指 node 中的回掉函数,注意区别)。服务端通过订阅指定的 rpc 队列接收到请求然后进行处理,处理完之后将结果发送到 reply_to 指定的 callback 队列中,客户端通过订阅 callback 队列获取请求结果,并通过 correlationId 对应不同的请求。 @@ -544,7 +544,7 @@ function fibonacci(n) { //时间复杂度比较高 RabbitMQ 本身并没有直接支持延时队列这个功能,我们需要简单的拐个弯间接实现: -![](/images/middleware/rabbitmq4-4.jpeg) +![](https://raw.githubusercontent.com/RifeWang/images/master/middleware/rabbitmq4-4.jpeg) 具体流程如上图所示,生产者先将消息投递到一个死信队列中,消息在死信队列中延时,并指定 deadLetterExchange 也就是消息延时结束后重新分发到的交换器,以及 deadLetterRoutingKey,重新分发后的交换器据此将消息分发到另一个队列,消费者订阅此队列以接受消息。 diff --git a/content/posts/uncate/image-processing.md b/content/posts/uncate/image-processing.md index 97df89af..2c16ba8b 100644 --- a/content/posts/uncate/image-processing.md +++ b/content/posts/uncate/image-processing.md @@ -150,7 +150,7 @@ resize = cv.resize(img, None, fx = 2, fy = 2, interpolation = cv.INTER_LINEAR) OpenCV 提供的 resize 缩放算法包括: -![](/images/uncate/opencv-resize.jpeg) +![](https://raw.githubusercontent.com/RifeWang/images/master/uncate/opencv-resize.jpeg) 根据官方的文档,缩小图像时建议使用 INTER_AREA 算法,放大图像时建议使用 INTER_CUBIC(较慢)算法或者 INTER_LINEAR(更快效果也不错)算法。 diff --git a/content/posts/uncate/image-similarity.md b/content/posts/uncate/image-similarity.md index 79fbf678..d6a7811d 100644 --- a/content/posts/uncate/image-similarity.md +++ b/content/posts/uncate/image-similarity.md @@ -26,7 +26,7 @@ disableComments = true 下面以 Average Hash 算法为例描述这一基本过程: -![](/images/uncate/image-ahash.jpeg) +![](https://raw.githubusercontent.com/RifeWang/images/master/uncate/image-ahash.jpeg) 1、Reduce size : 将原图压缩到 8 x 8 即 64 像素大小,忽略细节。 @@ -56,7 +56,7 @@ disableComments = true 这些哈希算法的具体实现过程不在本文的讲述范围内,我们重点关注的是他们的实际表现。 -![](/images/uncate/image-hash/jpeg) +![](https://raw.githubusercontent.com/RifeWang/images/master/uncate/image-hash/jpeg) 如上图所示,左下角标明了如水印、椒盐噪声、旋转、缩放、jpeg压缩、高斯噪声、高斯模糊、对比度等对抗影响,右下角则是各种哈希算法,圆锥体的高度则代表哈希算法对各种影响的抗性,高度越高说明抗性越高、越能成功匹配。 @@ -64,7 +64,7 @@ disableComments = true 下面运用这些哈希算法对某张图分别计算其哈希值,观察他们的输出结果: -![](/images/uncate/image-hash-result.jpeg) +![](https://raw.githubusercontent.com/RifeWang/images/master/uncate/image-hash-result.jpeg) 从上图中可以看到,ColorMomentHash 比较特别,输出的是浮点数,它也是唯一一个能够对抗旋转的哈希算法,但是也局限于 -90 ~ 90 度。 @@ -99,19 +99,19 @@ Hamming distance 汉明距离,指的是两个等长字符串对应位置不同 「一双丹凤双角眼,两弯柳叶吊梢眉」,人脸可以有特征,那么图像呢?当然也有,只要图像具有类似的特征,那么就可以认为他们是相似的,这也就是局部相似性: -![](/images/uncate/image3-1.jpeg) +![](https://raw.githubusercontent.com/RifeWang/images/master/uncate/image3-1.jpeg) 例如上面左右两张图,特征匹配,局部相似。 什么是特征?特征一定是图片的低频部分。 -![](/images/uncate/image3-2.png) +![](https://raw.githubusercontent.com/RifeWang/images/master/uncate/image3-2.png) 上图三个部分,显然蓝色圈能匹配更多,黑色圈次之,红色圈最不易匹配,如果要选择一个作为特征,当然就是红色圈。 Corner Detection : 图像特征提取的基础算法,目的在于提取图像中的 corner ,这里的 corner 可并不是四个边框角,而是图像中的具有突变特征的点,例如: -![](/images/uncate/image3-3.jpeg) +![](https://raw.githubusercontent.com/RifeWang/images/master/uncate/image3-3.jpeg) Corner detectors 最大的缺点在于无法应对伸缩情况,为了解决这个问题 SIFT 特征提取算法问世,SIFT 的全称即 Scale Invariant Feature Transform 。 @@ -119,7 +119,7 @@ Keypoint 和 Descriptor :keypoint 也就是图像的特征点,descriptor 则 SIFT 提取特征点示例: -![](/images/uncate/image-sift.jpeg) +![](https://raw.githubusercontent.com/RifeWang/images/master/uncate/image-sift.jpeg) 需要注意的是一张图像的特征点是有多个的。 diff --git a/content/posts/uncate/lets-encrypt.md b/content/posts/uncate/lets-encrypt.md index 07480e15..fd87c5d7 100644 --- a/content/posts/uncate/lets-encrypt.md +++ b/content/posts/uncate/lets-encrypt.md @@ -43,11 +43,11 @@ acme.sh --issue --force --dns -d <二级域名> -d <泛域名> \ issue 之后的结果如图所示: -![](/images/uncate/lets-encrypt1.jpeg) +![](https://raw.githubusercontent.com/RifeWang/images/master/uncate/lets-encrypt1.jpeg) 按照说明你需要分别添加 _acme-challenge.<二级域名> 和 _acme-challenge.<泛域名> 这两个域名的 TXT 类型的域名解析: -![](/images/uncate/lets-encrypt2.jpeg) +![](https://raw.githubusercontent.com/RifeWang/images/master/uncate/lets-encrypt2.jpeg) 之所以要添加域名解析是为了验证你对此域名的所有权。 @@ -61,7 +61,7 @@ acme.sh --renew --force --dns -d <二级域名> -d <泛域名> \ --yes-I-know- 示例结果如图所示: -![](/images/uncate/lets-encrypt3.jpeg) +![](https://raw.githubusercontent.com/RifeWang/images/master/uncate/lets-encrypt3.jpeg) 输出结果除了会告诉你证书签发成功之外,还会在最后说明证书的存放位置,默认是 `~/.acme.sh/<二级域名>/` 这个目录。 diff --git a/content/posts/uncate/nodejs-event-loop-architecture.md b/content/posts/uncate/nodejs-event-loop-architecture.md index 162ac0a1..16d5bef4 100644 --- a/content/posts/uncate/nodejs-event-loop-architecture.md +++ b/content/posts/uncate/nodejs-event-loop-architecture.md @@ -17,7 +17,7 @@ disableComments = true https://medium.com/preezma/node-js-event-loop-architecture-go-deeper-node-core-c96b4cec7aa4* -![](/images/uncate/node-event-loop1.jpeg) +![](https://raw.githubusercontent.com/RifeWang/images/master/uncate/node-event-loop1.jpeg) 关于 Node.js ,相信你已经了解过不少内容,诸如 Node.js 内核、事件循环、单线程、setTimeout 或 setImmediate 函数的执行机制等等。 @@ -51,7 +51,7 @@ Node.js 是单线程? Unix 系统中的工作基于 sockets 套接字。Socket 就是一个整数,返回一个 Socket() 系统调用,它被称为 socket descriptor(套接字描述符)或者 file descriptor(文件描述符)。 -![](/images/uncate/node-event-loop2.jpeg) +![](https://raw.githubusercontent.com/RifeWang/images/master/uncate/node-event-loop2.jpeg) Sockets 通过虚拟的接口( read / write / pool / close 等)指向系统内核中的对象。 @@ -81,7 +81,7 @@ C++ 在后台执行 JavaScript 代码并且拥有访问线程的权限。如果 但是如果在同一个 CPU(2核)中执行这个方法的异步版本,总耗时则为 2 * 2 ms = 4 ms ,因为处理器将使用默认 4 个线程(下文将会说明),将它托管到两个进程中并执行。 -![](/images/uncate/node-event-loop3.jpeg) +![](https://raw.githubusercontent.com/RifeWang/images/master/uncate/node-event-loop3.jpeg) 这也就是:Node.js 并发地执行异步方法。 @@ -100,15 +100,15 @@ Node.js 使用一组预先分配的线程,称之为线程池,如果我们没 Thread per connection : -![](/images/uncate/node-event-loop4.png) +![](https://raw.githubusercontent.com/RifeWang/images/master/uncate/node-event-loop4.png) 创建一个 TCP server 最简单的方式就是创建一个 socket ,绑定这个 socket 到某个端口上然后 listen 监听。 -![](/images/uncate/node-event-loop5.png) +![](https://raw.githubusercontent.com/RifeWang/images/master/uncate/node-event-loop5.png) 在我们调用 listen 之前,该 socket 可用于建立连接或接受连接。当我们调用 listen 时,我们准备接受连接。 -![](/images/uncate/node-event-loop6.png) +![](https://raw.githubusercontent.com/RifeWang/images/master/uncate/node-event-loop6.png) 当连接到达并且我们需要写入它时,直到我们完成写入之前,我们都无法接受另一个连接,这就是我们将它推入另一个线程的原因。所以我们将 socket descriptor 和 function pointer 传递给线程。 @@ -121,7 +121,7 @@ Epoll 是 unix 系统相关底层知识。 Epoll 循环: -![](/images/uncate/node-event-loop7.png) +![](https://raw.githubusercontent.com/RifeWang/images/master/uncate/node-event-loop7.png) Epoll 能为我们带来什么,为什么要使用它。使用 Epoll 允许我们告诉 Kernel(系统内核)我们关注的事件,并且 Kernel 将会告诉我们这些事件何时发生。在上面的例子中,我们关注的是传入的 TCP 连接,因此,我们创建一个 Epoll 描述符并将其添加到 Epoll 循环中,并调用 wait 。每当有 TCP 连接传入时便会唤醒,然后将它添加到 Epoll 循环中并等待来自它的数据。这就是事件循环为我们做的事情。 @@ -174,7 +174,7 @@ Event loop 事件循环的一次迭代称之为 Tick,它有自己的 phases(阶段)。 -![](/images/uncate/node-event-loop8.jpeg) +![](https://raw.githubusercontent.com/RifeWang/images/master/uncate/node-event-loop8.jpeg) 更多关于 event loop 的 phases、Timers、process.nextTick() 等请查阅官方文档。 diff --git a/content/posts/uncate/nodejs-thread-block.md b/content/posts/uncate/nodejs-thread-block.md index 412b9b40..ca5b4035 100644 --- a/content/posts/uncate/nodejs-thread-block.md +++ b/content/posts/uncate/nodejs-thread-block.md @@ -21,7 +21,7 @@ CPU密集型任务会阻塞 Node.js 吗? 让我们使用加密任务做个简单测试: -![](/images/uncate/nodejs-block1.png) +![](https://raw.githubusercontent.com/RifeWang/images/master/uncate/nodejs-block1.png) 如图所示,连续执行四次加密任务,打印耗时,结果会发生什么? @@ -37,24 +37,24 @@ Hash: 1297 这四次加密任务计时的起始时间都是相同的,然后最终的结束时间却几乎一致,这个结果说明了什么?说明它们是并发执行的。 -![](/images/uncate/nodejs-block2.png) +![](https://raw.githubusercontent.com/RifeWang/images/master/uncate/nodejs-block2.png) 如果不是并发执行,那么结果就会如下图所示: -![](/images/uncate/nodejs-block3.jpeg) +![](https://raw.githubusercontent.com/RifeWang/images/master/uncate/nodejs-block3.jpeg) 那么为什么这里没有发生阻塞? -![](/images/uncate/nodejs-block4.jpeg) +![](https://raw.githubusercontent.com/RifeWang/images/master/uncate/nodejs-block4.jpeg) Node.js 的执行过程如上图所示,我们要注意的是 libuv 默认使用了四个线程!上述示例中的四个加密任务分别推送到了四个不同的线程中去并发执行,所以才没有发生阻塞。 那么问题来了?如果连续执行五个加密任务呢? -![](/images/uncate/nodejs-block5.png) +![](https://raw.githubusercontent.com/RifeWang/images/master/uncate/nodejs-block5.png) 输出结果: @@ -68,7 +68,7 @@ Hash: 2104 可以看到前四个任务仍然是并发执行的,但是第五个任务发生了阻塞。 -![](/images/uncate/nodejs-block6.png) +![](https://raw.githubusercontent.com/RifeWang/images/master/uncate/nodejs-block6.png) 为什么?因此 libuv 的四个线程都在忙碌,第五个任务只有等待线程的任务执行完毕才能推送到线程中去执行。 @@ -77,21 +77,21 @@ Hash: 2104 1、四个线程都在忙碌,其它任务必须等待: -![](/images/uncate/nodejs-block7.jpeg) +![](https://raw.githubusercontent.com/RifeWang/images/master/uncate/nodejs-block7.jpeg) 2、某个线程任务完成,继续执行其它任务: -![](/images/uncate/nodejs-block8.jpeg) +![](https://raw.githubusercontent.com/RifeWang/images/master/uncate/nodejs-block8.jpeg) libuv 线程池中的线程数量是否可以设置? 通过环境变量 UV_THREADPOOL_SIZE 即可设置。 比如: -![](/images/uncate/nodejs-block9.png) +![](https://raw.githubusercontent.com/RifeWang/images/master/uncate/nodejs-block9.png) 我把线程数设置为 5 ,执行的结果就会是下图所示: -![](/images/uncate/nodejs-block10.png) +![](https://raw.githubusercontent.com/RifeWang/images/master/uncate/nodejs-block10.png) 请注意测试环境的 CPU 核心数是四个,需要说明的有两点:第一,五个任务被推送到了五个线程中去并发执行,这一点上文已经说明;第二,每个任务的耗时有了明显的增加,为什么?因为我们只有四核,但是却有五个线程,操作系统需要进行平衡调度、通过上下文切换以保证每个线程分配到相同的时间去执行任务。 \ No newline at end of file diff --git a/static/images/docker/docker1-1.jpeg b/static/images/docker/docker1-1.jpeg deleted file mode 100644 index 14903accc0c6ebf71d0eca1a308f9539debc46fc..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 65776 zcmeFZ2UwHa)-V_g7C7iZ1f+@52}S7w0!MlaNC}ZHO(68pq=`ob1q7rdNY~I45~KtO zp&U?plP;l23BC8u#B*)u-uunm@0)w)pJ(QOc^>!5-fOq@zI*lU@u%a@fL}FK)Kvf{ zP5=NW$bW$2L4YFQ%xQ`<6sOOep*VB)?3r_vzfw}3KTmo2!o^>HWw?Ch3d3c3dL~vb zHYR2c7JB+?0@paWdH8^QjBJ881$l3B@$&J08RW#-vu7#KQPNUU((*FVGx7enpW{CO 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