Cmd-C 复制文件
Cmd-V 粘贴文件
Option-拖动 复制文件到新地址
Cmd-拖动 移动并自动对齐
Cmd-Delete 删除
Cmd-Option-拖动 做替身(快捷方式)
Cmd-Shift-Delete 清空垃圾桶
Cmd-Shift-Option-Delete 强制清空垃圾桶
Tab 选定下一个项目
Shift-Tab 选定上一个项目
Return 执行默认动作
Escape 关闭对话框
Page Up 向上翻页
向上 箭头 选取上一个文件
Page Down 向下翻页
向下 箭头 选取下一个文件
Cmd-Shift-G 打开’前往文件夹’对话框
Cmd-句号 [.] 关闭对话框
Exposé 和系统的快捷
F8 切换Space
Shift-F8 慢速切换Space
F9（默认设置） 使用 Exposé 显示所有打开的窗口
F10（默认设置） 使用 Exposé 在某个应用程序中显示所有打开的窗口
F11（默认设置） 使用 Exposé 隐藏所有打开的窗口并显示桌面
Cmd-H 隐藏程序
Cmd-Option-H 隐藏其他程序
Cmd-Q 退出程序
Cmd-Shift-Q 退出所有程序并且注销用户
Cmd-Option-Shift-Q 强制注销用户
Cmd-Tab 切换到下一个应用程序
Cmd-Shift-Tab 切换到上一个应用程序
Cmd-拖动 整理菜单栏
按下 Option 键并点按一个窗口 切换窗口并隐藏当前窗口
按住 Option 并点按 Dock 图标 切换到另一个应用程序并隐藏当前应用程序
按下 Control 键并点按该项 查看某个项的快捷（上下文）菜单
将光标移到该词上面，然后按 Cmd-Control-D 使用 Dictionary 查看对字词在应用程序中的定义
停止响应
Cmd-句号 [.] 停止进程
Cmd-Option-Escape 打开’强制退出’
电源键 关机
Cmd-Option-Shift-电源键 强制关机或重新启动（在某些电脑上）
Cmd-Control-电源键 强制重启
Finder
Cmd-点击 标题 查看当前窗口的路径
Cmd-双击 (文件夹上) 新窗口中打开文件夹
Option-双击 (文件夹上) 新窗口中打开文件夹并关闭当前窗口
Cmd-1 用图标浏览
Cmd-2 用列表浏览
Cmd-Option-向右 箭头 列表模式下显示包含的目录
向左 箭头 列表模式下关闭选定目录
Cmd-向下 箭头 在图标或列表模式下打开选定目录
Cmd-Option-向下 箭头 在图标或列表模式下在新窗口打开选定目录并关闭当前窗口
Cmd-Shift-Option-向下 箭头 (慢速)在图标或列表模式下在新窗口打开选定目录并关闭当前窗口
Cmd-向上 箭头 打开上一级目录
Cmd-Option-向上 箭头 打开上一级目录并关闭当前目录
Cmd-3 用分栏浏览
Cmd-4 用cover flow浏览
Cmd-Y 打开快速查看
Cmd-Option-Y 用幻灯片显示
Cmd-Shift-H 打开用户文件夹
Cmd-Option-Shift-向上 箭头 聚焦桌面
Cmd-Shift-I 打开iDisk
Cmd-Shift-D 打开桌面
Cmd-Shift-C 打开’电脑’
Cmd-Shift-K 打开网络
Cmd-Shift-A 打开应用程序
双击 标题 最小化窗口
Cmd-M 最小化窗口
Option-点击 按钮 应用到所有激活的窗口
按下并按住滚动条 快速浏览长文稿
按住 Option 键并点按滚动条 迅速在“滚动到当前位置”和“滚动到页面”之间切换
Cmd-波浪符号 (~) 激活当前应用程序中的上一个或下一个窗口
Dock
拖动 分割线 自定义Dock大小
Option-拖动 分割线 调整Dock到合适大小
Control-点击 显示Dock快捷菜单
Control-点击图标 显示项目的快捷菜单
Cmd-点击 打开图标所在文件夹
Option-点击 切换并隐藏当前程序
Cmd-Option-点击 切换并隐藏所有程序
Cmd-Option-拖动 强制程序打开文件
Cmd-Option-D 显示/隐藏Dock
启动
*快捷键只能在启动时使用
当您看到进程指示器（看起来像旋转的齿轮）时，请按住左边的 Shift 键。 防止自动登录
听到启动音之后立即按住 Shift 键，然后当您看到进程指示器（看起来像旋转的齿轮）时释放该键。 以安全模式启动（只
有必要的 Mac OS X 项被启动， 一些功能和应用程序可能无法正常工作。）
在登录屏幕上点按“登录”按钮之后，请按住 Shift 键。 防止登录时打开“登录项”和 Finder 窗口
C 从光盘启动
N 从默认的 NetBoot 磁盘映像启动
T 以目标磁盘模式启动
Option 选择启动磁盘（在某些电脑上）
Cmd-X 使用 Mac OS X 而不是 Mac OS 9 来进行启动（如果两者均位于同一宗卷上）
按住鼠标键 推出可去掉的光盘
Cmd-Option-P-R 还原参数 RAM
Cmd-V 显示详细的状态信息（详细模式）
Cmd-S 以单一用户模式启动
Safari
Cmd-Option-F google搜索栏
Option-向上 箭头 向上翻页
Option-向下 箭头 向下翻页
Cmd-点击 链接 在后台用新标签打开
Cmd-Shift-点击 链接 打开并激活新标签
Cmd-Option-点击 链接 打开新窗口
Option-点击 Close 按钮 关闭其他标签
Cmd-Shift-] 选取下一个标签
Cmd-Shift-[ 选取上一个标签
Cmd-Shift-H 打开主页
Cmd-Shift-K 切换’禁止弹出窗口’
Cmd-Option-E 清空缓存
Cmd-Option-R 不用缓存并刷新页面
Cmd-F 查找
Cmd-M 最小化窗口
Shift-点击 按钮 慢动作动画效果
Cmd-加号[+] 增大字体
Cmd-减号[-] 减小字体
Cmd-0 默认字体
Dashboard
使用这些快捷来处理 Dashboard 和 Dashboard widget。
F12（默认设置） 显示或隐藏 Dashboard
Cmd-R 重新载入当前 widget
Cmd-等号 (=) 显示或隐藏 widget 栏
Cmd-向左箭头键，Cmd-向右箭头键 滚动 widget 栏
注:要更改 Dashboard 的快捷，请选取“文件”>“系统偏好设置”，点按“Exposé & Spaces”，然后点按“Exposé”。
Front Row
您可以使用键盘来控制 Front Row 而无需使用 Apple Remote 遥控器。
Cmd-Esc (Escape) 打开 Front Row
Cmd-Esc 或 Esc 从打开的菜单中关闭 Front Row
向上箭头键，向下箭头键 浏览菜单和列表
Cmd-Esc 或 Esc 返回上一级菜单
空格键或 Return 选择菜单或列表中的项
空格键或 Return 播放和暂停音频或视频
向上箭头键，向下箭头键 更改音量
向右箭头键，向左箭头键 前往下一个或上一个歌曲或照片
向右箭头键，向左箭头键 前往所播放 DVD 的下一章或上一章
右箭头键，左箭头键（按住按钮） 快进或倒回歌曲、视频或 DVD
在某些 Apple 键盘和便携式电脑上，您或许也可以使用特定按键来更改音量和控制回放。
键盘导航
Control-F1 打开/关闭全键盘控制
Control-F2 聚焦菜单栏
Control-F3 聚焦Dock
Control-F4 聚焦活跃窗口或下一个窗口
Control-F5 聚焦窗口工具栏
Control-F6 聚焦浮动窗口
Control-F7 在控制或文本框与列表之间移动
Control-F8 聚焦菜单栏中的状态菜单
Cmd-Accent [`] 聚焦活跃应用程序的下一个窗口
Cmd-Shift-Accent [`] 聚焦活跃应用程序的上一个窗口
Cmd-Option-Accent [`] 聚焦窗口抽屉
Cmd-Option-T 显示或隐藏字符调板
While messing around with MapReduce code, I’ve found it to be a bit tedious having to generate the jarfile, copy it to the machine running the JobTracker, and then run the job every time the job has been altered. I should be able to run my jobs directly from my development environment, as illustrated in the figure below. This post explains how I’ve “solved” this problem. This may also help when integrating Hadoop with other applications. I do by no means claim that this is the proper way to do it, but it does the trick for me.
My Hadoop infrastructure
I assume that you have a (single-node) Hadoop 1.0.3 cluster properly installed on a dedicated or virtual machine. In this example, the JobTracker and HDFS resides on IP address 192.168.102.131.Let’s start out with a simple job that does nothing except to start up and terminate:
package com.pcbje.hadoopjobs;
import java.io.IOException;
import java.util.Date;
import java.util.Iterator;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.FileInputFormat;
import org.apache.hadoop.mapred.FileOutputFormat;
import org.apache.hadoop.mapred.JobClient;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.Mapper;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reporter;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapred.Reducer;
public class MyFirstJob 
{
public static void main(String[] args) throws Exception {
Configuration config = new Configuration();
JobConf job = new JobConf(config);
job.setJarByClass(MyFirstJob.class);
job.setJobName("My first job");
FileInputFormat.setInputPaths(job, new Path(args[0));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
job.setMapperClass(MyFirstJob.MyFirstMapper.class);
job.setReducerClass(MyFirstJob.MyFirstReducer.class);
JobClient.runJob(job);
}
private static class MyFirstMapper extends MapReduceBase implements Mapper {
public void map(LongWritable key, Text value, OutputCollector output, Reporter reporter) throws IOException {
}
}
private static class MyFirstReducer extends MapReduceBase implements Reducer {
public void reduce(Text key, Iterator values, OutputCollector output, Reporter reporter) throws IOException {
}
}
}
Now, most of the examples you find online typically shows you a local mode setup where all the components of Hadoop (HDFS, JobTracker, etc) run on the same machine. A typical mapred-site.xml configuration might look like:
<configuration>
<property>
<name>mapred.job.tracker</name>
<value>localhost:9001</value>
</property>
</configuration> As far as I can tell, such a configuration requires that jobs are submitted from the same node as the JobTracker. This is what I want to avoid. The first thing to do is to change the fs.default.name attribute to the IP address of my NameNode.
Configuration conf = new Configuration();
conf.set("fs.default.name", "192.168.102.131:9000");And in core-site.xml:
<configuration>
<property>
<name>fs.default.name</name>
<value>192.168.102.131:9000</value>
</property>
</configuration> This tells the job to connect to the HDFS residing on a different machine. Running the job with this configuration will read from and write to the remote HDFS correctly, but the JobTracker at 192.168.102.131:9001 will not notice it. This means that the admin panel at 192.168.102.131:50030 wont list the job either. So the next thing to do is to tell the job configuration to submit the job to the appropriate JobTracker like this:
config.set("mapred.job.tracker", "192.168.102.131:9001"); You also need to change mapred-site.xml to allow external connections, this can be done by replacing “localhost” with the JobTracker’s IP address:
<configuration>
<property>
<name>mapred.job.tracker</name>
<value>192.168.102.131:9001</value>
</property>
</configuration> Restart hadoop.Upon trying to run your job, you may get an exception like this:
SEVERE: PriviledgedActionException as:[user] cause:org.apache.hadoop.security.AccessControlException:
org.apache.hadoop.security.AccessControlException: Permission denied: user=[user], access=WRITE, inode="mapred":root:supergroup:rwxr-xr-x
If you do, this may be solved by adding the following mapred-site.xml:
<configuration>
<property>
<name>mapreduce.jobtracker.staging.root.dir</name>
<value>/user</value>
</property>
</configuration> And then execute the following commands:
stop-mapred.sh
start-mapred.sh
When you now submit your job, it should be picked up by the admin page over at :50030. However, it will most probably fail and the log will be telling you something like:
java.lang.ClassNotFoundException: com.pcbje.hadoopjobs.MyFirstJob$MyFirstMapper
In order to fix this, you have to ensure that all dependencies of the submitted job are available to the JobTracker. This can be achieved by exporting the project in as a runnable jar, and then execute something like:
java -jar myfirstjob-jar-with-dependencies.jar /input/path /output/path
If your user has the appropriate permissions to the input and out directory on HDFS, the job should now run successfully. This can be verified in the console and on the administration panel.
Manually exporting runnable jars requires a lot of clicks in IDEs such as Eclipse. If you are using Maven, you can tell it to build the jar with its dependencies (See
this answer for details). This would make the process a whole lot easier.Finally, to make it even easier, place a tiny bash-script in the same folder as pom.xml for building the maven project and executing the jar:
#!/bin/sh
mvn assembly:assembly
java -jar $1 $2 $3
After making the script executable, you can build and submit the job with the following command:
./build-and-run-job target/myfirstjob-jar-with-dependencies.jar /input/path
如果是在WINDOWS的ECLIPSE中，运行HBASE的MAPREDUCE，会出现异常，这是由于默认运行MAPREDUCE任务是在本地运行，而由于会建立文件赋权限是按照UNIX的方式进行，因此会报错：
java.lang.RuntimeException: Error while running command to get file permissions : java.io.IOException: Cannot run program "ls": CreateProcess error=2, 解决办法是将任务发到运程主机，通常是LINUX上运行，在hbase-site.xml中加入：
<property>
<name>mapred.job.tracker</name>
<value>master:9001</value>
</property> 同时需把HDFS的权限机制关掉：
<property>
<name>dfs.permissions</name>
<value>false</value>
</property> 另外由于是在远程上执行任务，自定义的类文件，如Maper/Reducer等需打包成jar文件上传，具体见方案：
Hadoop作业提交分析（五）
http://www.cnblogs.com/spork/archive/2010/04/21/1717592.html
研究了好几天，终于搞清楚，CONFIGUARATION就是JOB的配置信息，远程JOBTRACKER就是以此为参数构建JOB去执行，由于远程主机并没有自定义的MAPREDUCE类，需打成JAR包后，上传到主机处，但无需每次都手动传，可以代码设置：
conf.set("tmpjars", "d:/aaa.jar"); 另注意，如果在WINDOWS系统中，文件分隔号是“；”，生成的JAR包信息是以“；”间隔的，在远程主机的LINUX上是无法辨别，需改为：
System.setProperty("path.separator", ":"); 参考文章：
http://www.cnblogs.com/xia520pi/archive/2012/05/20/2510723.html使用hadoop eclipse plugin提交Job并添加多个第三方jar（完美版）
http://heipark.iteye.com/blog/1171923
1.在host中加入master 127.0.0.1
2.实现无需密码登录ssh
3.hadoop配置文件
core-site.xml
<?xml version="1.0"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
<!-- Put site-specific property overrides in this file. -->
<configuration>
<property>
<name>hadoop.tmp.dir</name>
<value>/Users/paul/Documents/PAUL/DOWNLOAD/SOFTWARE/DEVELOP/HADOOP/hadoop-tmp-data</value>
<description>A base for other temporary directories.</description>
</property>
<property>
<name>fs.default.name</name>
<value>hdfs://master:9000</value>
<description>The name of the default file system. A URI whose
scheme and authority determine the FileSystem implementation. The
uri's scheme determines the config property (fs.SCHEME.impl) naming
the FileSystem implementation class. The uri's authority is used to
determine the host, port, etc. for a filesystem.</description>
</property>
</configuration>
hdfs-site.xml
<?xml version="1.0"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
<!-- Put site-specific property overrides in this file. -->
<configuration>
<property>
<name>dfs.replication</name>
<value>1</value>
<description>Default block replication.
The actual number of replications can be specified when the file is created.
The default is used if replication is not specified in create time.
</description>
</property>
<!--
<property>
<name>dfs.name.dir</name>
<value>/Users/paul/Documents/PAUL/DOWNLOAD/SOFTWARE/DEVELOP/HADOOP/hadoop-tmp-data/hdfs-data-name</value>
</property>
<property>
<name>dfs.data.dir</name>
<value>/Users/paul/Documents/PAUL/DOWNLOAD/SOFTWARE/DEVELOP/HADOOP/hadoop-tmp-data/hdfs-data</value>
</property>
-->
</configuration>
<?xml version="1.0"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
<!-- Put site-specific property overrides in this file. -->
<configuration>
<property>
<name>mapred.job.tracker</name>
<value>master:9001</value>
<description>The host and port that the MapReduce job tracker runs
at. If "local", then jobs are run in-process as a single map
and reduce task.
</description>
</property>
<property>
<name>mapred.tasktracker.tasks.maximum</name>
<value>8</value>
<description>The maximum number of tasks that will be run simultaneously by a
a task tracker
</description>
</property>
</configuration>
master
4. 格式化namenode
5. 启动hadoop
6. hbase配置文件
hbase-site.xml
<?xml version="1.0"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
<!--
/**
* Copyright 2010 The Apache Software Foundation
*
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you under the Apache License, Version 2.0 (the
* "License"); you may not use this file except in compliance
* with the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/Users/paul/Documents/PAUL/DOWNLOAD/SOFTWARE/DEVELOP/HADOOP/hadoop-tmp-data
*/
-->
<configuration>
<property>
<name>hbase.rootdir</name>
<value>hdfs://master:9000/hbase</value>
</property>
<property>
<name>hbase.cluster.distributed</name>
<value>true</value>
</property>
<property>
<name>hbase.zookeeper.quorum</name>
<value>localhost</value><!--单机配这个-->
</property>
</configuration>
7. 启动hbase
1、JVM的启动参数
我是这样设置的：
java -Xmx1024m -Xms1024m -Xss128k -XX:NewRatio=4 -XX:SurvivorRatio=4 -XX:MaxPermSize=16m
启动tomcat之后，使用 jmap -heap `pgrep -u root java`，得到如下信息：
Heap Configuration:
MinHeapFreeRatio = 40
MaxHeapFreeRatio = 70
MaxHeapSize = 1073741824 (1024.0MB)
NewSize = 1048576 (1.0MB)
MaxNewSize = 4294901760 (4095.9375MB)
OldSize = 4194304 (4.0MB)
NewRatio = 4
SurvivorRatio = 4
PermSize = 12582912 (12.0MB)
MaxPermSize = 16777216 (16.0MB)
Heap Usage:
New Generation (Eden + 1 Survivor Space):
capacity = 178913280 (170.625MB)
used = 51533904 (49.14656066894531MB)
free = 127379376 (121.47843933105469MB)
28.80384508070055% used
Eden Space:
capacity = 143130624 (136.5MB)
used = 51533904 (49.14656066894531MB)
free = 91596720 (87.35343933105469MB)
36.00480635087569% used
From Space:
capacity = 35782656 (34.125MB)
used = 0 (0.0MB)
free = 35782656 (34.125MB)
0.0% used
To Space:
capacity = 35782656 (34.125MB)
used = 0 (0.0MB)
free = 35782656 (34.125MB)
0.0% used
tenured generation:
capacity = 859045888 (819.25MB)
used = 1952984 (1.8625106811523438MB)
free = 857092904 (817.3874893188477MB)
0.22734338494383202% used
Perm Generation:
capacity = 12582912 (12.0MB)
used = 6656024 (6.347679138183594MB)
free = 5926888 (5.652320861816406MB)
52.897326151529946% used
------------------------------------------华丽的分割线---------------------------------------
-Xmx1024m -Xms1024m -Xss128k -XX:NewRatio=4 -XX:SurvivorRatio=4 -XX:MaxPermSize=16m
-Xmx1024m 最大堆内存为 1024M
-Xms1024m 初始堆内存为 1024M
-XX:NewRatio=4
则 年轻代:年老代=1:4 1024M/5=204.8M
故 年轻代=204.8M 年老代=819.2M
-XX:SurvivorRatio=4
则年轻代中 2Survivor:1Eden=2:4 204.8M/6=34.13333333333333M
故 Eden=136.5333333333333M 1Suivivor=34.13333333333333M
用 jmap -heap <pid>
查看的结果 与我们计算的结果一致
-----------------------------------华丽的分割线-------------------------------------------
3、编写测试页面
在网站根目录里新建页面perf.jsp，内容如下：
<%intsize = (int)(1024 * 1024 * m);byte[] buffer = new byte[size];Thread.sleep(s);%>
注：m值用来设置每次申请内存的大小，s 表示睡眠多少ms
4、使用jstat来监控内存变化
这里使用 jstat -gcutil `pgrep -u root java` 1500 10
再解释一下，这里有三个参数：
·pgrep -u root java --> 得到java的进程ID号
·1500 --> 表示每隔1500ms取一次数据
·10 --> 表示一共取10次数据
5、用ab来进行压测
压测的命令：[root@CentOS ~]# ab -c150 -n50000 "http://localhost/perf.jsp?m=1&s=10"
注：这里使用150个线程并发访问，一共访问50000次。
默认情况下你可以使用 http://localhost:8080/perf.jsp?m=1&s=10 来访问。
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下面开始进行实验：
·先启动Java内存的监听：
[root@CentOS ~]# jstat -gcutil 8570 1500 10
·在开启一个终端，开始压测：
[root@CentOS ~]# ab -c150 -n50000 "http://localhost/perf.jsp?m=1&s=10"
两个命令结束之后的结果如下：
jstat：
[root@CentOS ~]# jstat -gcutil 8570 1500 10
S0 S1 E O P YGC YGCT FGC FGCT GCT
0.06 0.00 53.15 2.03 67.18 52 0.830 1 0.218 1.048
0.00 0.04 18.46 2.03 67.18 55 0.833 1 0.218 1.052
0.03 0.00 28.94 2.03 67.18 56 0.835 1 0.218 1.053
0.00 0.04 34.02 2.03 67.18 57 0.836 1 0.218 1.054
0.04 0.00 34.13 2.03 67.18 58 0.837 1 0.218 1.055
0.00 0.04 38.62 2.03 67.18 59 0.838 1 0.218 1.056
0.04 0.00 8.39 2.03 67.18 60 0.839 1 0.218 1.058
0.04 0.00 8.39 2.03 67.18 60 0.839 1 0.218 1.058
0.04 0.00 8.39 2.03 67.18 60 0.839 1 0.218 1.058
0.04 0.00 8.39 2.03 67.18 60 0.839 1 0.218 1.058
结果简单解析：
可以看到JVM里S0和S1始终有一个是空的，Eden区达到一定比例之后就会产生Minor GC，由于我这里的Old Generation 区设置的比较大，所以没有产生Full GC。
ab
[root@CentOS ~]# ab -c150 -n50000 "http://localhost/perf.jsp?m=1&s=10"
This is ApacheBench, Version 2.0.40-dev <$Revision: 1.146 $> apache-2.0
Copyright 1996 Adam Twiss, Zeus Technology Ltd, http://www.zeustech.net/
Copyright 2006 The Apache Software Foundation, http://www.apache.org/
Benchmarking localhost (be patient)
Completed 5000 requests
Completed 10000 requests
Completed 15000 requests
Completed 20000 requests
Completed 25000 requests
Completed 30000 requests
Completed 35000 requests
Completed 40000 requests
Completed 45000 requests
Finished 50000 requests
Server Software: Apache/2.2.3
Server Hostname: localhost
Server Port: 80
Document Path: /perf.jsp?m=1&s=10
Document Length: 979 bytes
Concurrency Level: 150
Time taken for tests: 13.467648 seconds
Complete requests: 50000
Failed requests: 0
Write errors: 0
Non-2xx responses: 50005
Total transferred: 57605760 bytes
HTML transferred: 48954895 bytes
Requests per second: 3712.60 [#/sec] (mean)
Time per request: 40.403 [ms] (mean) #平均请求时间
Time per request: 0.269 [ms] (mean, across all concurrent requests)
Transfer rate: 4177.05 [Kbytes/sec] received
Connection Times (ms)
min mean[+/-sd] median max
Connect: 0 1 46.5 0 3701
Processing: 10 38 70.3 36 6885
Waiting: 3 35 70.3 33 6883
Total: 10 39 84.4 37 6901
Percentage of the requests served within a certain time (ms)
50% 37
66% 38
75% 39
80% 39
90% 41
95% 43
98% 50
99% 58
100% 6901 (longest request)