无密码登录
[hadoop@master hadoop]$ rpm -qa | grep openssh
openssh-server-7.4p1-16.el7.x86_64
openssh-clients-7.4p1-16.el7.x86_64
openssh-7.4p1-16.el7.x86_64
[hadoop@master hadoop]$ ssh-keygen -t rsa -P ''
Generating public/private rsa key pair.
Enter file in which to save the key (/home/hadoop/.ssh/id_rsa):
/home/hadoop/.ssh/id_rsa already exists.
Overwrite (y/n)? y
Your identification has been saved in /home/hadoop/.ssh/id_rsa.
Your public key has been saved in /home/hadoop/.ssh/id_rsa.pub.
The key fingerprint is:
SHA256:hHo70xwWmDm6dePHlYAdtXsaYP02NpguZCwAi8XPi58 hadoop@master
The key's randomart image is:
+---[RSA 2048]----+
| .o ... |
| o.o = o o . |
| . .oB + = o |
| oo+ + o * |
| o.o.S + B B |
| .+.B B o * o |
| ..+.+ + o |
| Eo . . |
| |
+----[SHA256]-----+
[hadoop@master /]$ cd ~/.ssh
[hadoop@master .ssh]$ ls
id_rsa id_rsa.pub
[hadoop@master .ssh]$ cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys
[hadoop@master .ssh]$ ssh-copy-id [email protected]
/bin/ssh-copy-id: INFO: Source of key(s) to be installed: "/home/hadoop/.ssh/id_rsa.pub"
The authenticity of host '192.168.100.130 (192.168.100.130)' can't be established.
ECDSA key fingerprint is SHA256:PBtGVMglru206eEDbi9G1WgfQEtCgE78HO8doBP7hl4.
ECDSA key fingerprint is MD5:0e:4f:4f:70:7f:5f:1f:a2:a2:78:4f:37:a4:b3:fa:86.
Are you sure you want to continue connecting (yes/no)? yes
/bin/ssh-copy-id: INFO: attempting to log in with the new key(s), to filter out any that are already installed
/bin/ssh-copy-id: INFO: 1 key(s) remain to be installed -- if you are prompted now it is to install the new keys
[email protected]'s password:
Number of key(s) added: 1
Now try logging into the machine, with: "ssh '[email protected]'"
and check to make sure that only the key(s) you wanted were added.
[hadoop@master .ssh]$ ssh-copy-id [email protected]
/bin/ssh-copy-id: INFO: Source of key(s) to be installed: "/home/hadoop/.ssh/id_rsa.pub"
The authenticity of host '192.168.100.131 (192.168.100.131)' can't be established.
ECDSA key fingerprint is SHA256:PBtGVMglru206eEDbi9G1WgfQEtCgE78HO8doBP7hl4.
ECDSA key fingerprint is MD5:0e:4f:4f:70:7f:5f:1f:a2:a2:78:4f:37:a4:b3:fa:86.
Are you sure you want to continue connecting (yes/no)? yes
/bin/ssh-copy-id: INFO: attempting to log in with the new key(s), to filter out any that are already installed
/bin/ssh-copy-id: INFO: 1 key(s) remain to be installed -- if you are prompted now it is to install the new keys
[email protected]'s password:
Number of key(s) added: 1
Now try logging into the machine, with: "ssh '[email protected]'"
and check to make sure that only the key(s) you wanted were added.
[hadoop@master .ssh]$ ssh [email protected]
Last login: Wed Mar 15 10:13:25 2023 from 192.168.100.1
[root@slave1 ~]# exit
logout
Connection to 192.168.100.130 closed.
[hadoop@master .ssh]$ ssh [email protected]
Last login: Wed Mar 15 10:13:19 2023 from 192.168.100.1
[root@slave2 ~]# exit
logout
Connection to 192.168.100.131 closed.
[hadoop@master .ssh]$
1. 实验一：hadoop 集群运行
1.1. 实验目的
完成本实验，您应该能够：
掌握 hadoop 的运行状态
掌握 hadoop 文件系统格式化配置
掌握 hadoop java 运行状态查看
掌握 hadoop hdfs 报告查看
掌握 hadoop 节点状态查看
掌握停止 hadoop 进程操作
1.2. 实验要求
熟悉如何查看 hadoop 的运行状态
熟悉停止 hadoop 进程的操作
1.3. 实验环境
本实验所需之主要资源环境如表 1-1 所示。
表 1-1 资源环境
| 服务器集群 | 单节点，机器最低配置：双核 CPU、8GB 内存、100G 硬盘 |
|---|---|
| 运行环境 | CentOS.7.3 |
| 服务和组件 | 服务和组件根据实验需求安装 |
1.4. 实验过程
1.4.1. 实验任务一：配置 Hadoop 格式化
1.4.1.1.步骤一：NameNode 格式化
将 NameNode 上的数据清零，第一次启动 HDFS 时要进行格式化，以后启动无需再格式 化，否则会缺失 DataNode 进程。另外，只要运行过 HDFS，Hadoop 的工作目录（本书设置为 /usr/local/src/hadoop/tmp）就会有数据，如果需要重新格式化，则在格式化之前一定要先删 除工作目录下的数据，否则格式化时会出问题。
执行如下命令，格式化 NameNode
[root@master ~]# su - hadoop
[hadoop@master ~]$ cd /usr/local/src/hadoop/
[hadoop@master hadoop]$ ls
bin dfs etc include lib libexec LICENSE.txt NOTICE.txt README.txt sbin share tmp
[hadoop@master hadoop]$ ls dfs
data name
[hadoop@master hadoop]$ ./bin/hdfs namenode -format
23/03/15 11:16:15 INFO namenode.FSImage: Allocated new BlockPoolId: BP-678784440-192.168.100.129-1678850175834
23/03/15 11:16:16 INFO common.Storage: Storage directory /usr/local/src/hadoop/dfs/name has been successfully formatted.
23/03/15 11:16:16 INFO namenode.NNStorageRetentionManager: Going to retain 1 images with txid >= 0
23/03/15 11:16:16 INFO util.ExitUtil: Exiting with status 0
23/03/15 11:16:16 INFO namenode.NameNode: SHUTDOWN_MSG:
/************************************************************
SHUTDOWN_MSG: Shutting down NameNode at master/192.168.100.129
************************************************************/
1.4.1.2. 步骤二：启动 NameNode
执行如下命令，启动 NameNode：
[hadoop@master hadoop]$ jps
1684 Jps
[hadoop@master hadoop]$ hadoop-daemon.sh start namenode
starting namenode, logging to /usr/local/src/hadoop/logs/hadoop-hadoop-namenode-master.out
1.4.2. 实验任务二：查看 Java 进程
启动完成后，可以使用 JPS 命令查看是否成功。JPS 命令是 Java 提供的一个显示当前 所有 Java 进程 pid 的命令
[hadoop@master hadoop]$ jps
1717 NameNode
1784 Jps
1.4.2.1. 步骤一：slave 启动 DataNode
[hadoop@slave1 ~]$ cd /usr/local/src/hadoop/
[hadoop@slave1 hadoop]$ jps
1233 Jps
[hadoop@slave1 hadoop]$ hadoop-daemon.sh start datanode
starting datanode, logging to /usr/local/src/hadoop/logs/hadoop-hadoop-datanode-slave1.out
[hadoop@slave1 hadoop]$ jps
1257 DataNode
1322 Jps
[hadoop@slave2 ~]$ cd /usr/local/src/hadoop
[hadoop@slave2 hadoop]$ hadoop-daemon.sh start datanode
starting datanode, logging to /usr/local/src/hadoop/logs/hadoop-hadoop-datanode-slave2.out
[hadoop@slave2 hadoop]$ jps
1285 DataNode
1324 Jps
1.4.2.2. 步骤二：启动 SecondaryNameNode
执行如下命令，启动 SecondaryNameNode：
[hadoop@master hadoop]$ hadoop-daemon.sh start secondarynamenode
starting secondarynamenode, logging to /usr/local/src/hadoop/logs/hadoop-hadoop-secondarynamenode-master.out
[hadoop@master hadoop]$ jps
1330 SecondaryNameNode
1365 Jps
1240 NameNode
查看到有 NameNode 和 SecondaryNameNode 两个进程，就表明 HDFS 启动成功。
1.4.2.3. 步骤三：查看 HDFS 数据存放位置：
执行如下命令，查看 Hadoop 工作目录：
[hadoop@master hadoop]$ ls dfs
data name
[hadoop@master hadoop]$ ls tmp
dfs
[hadoop@master hadoop]$ ls tmp/dfs
namesecondary
[hadoop@master hadoop]$ ls tmp/dfs/namesecondary/
in_use.lock
可以看出 HDFS 的数据保存在/usr/local/src/hadoop/dfs 目录下，NameNode、DataNode 和/usr/local/src/hadoop/tmp/目录下,SecondaryNameNode 各有一个目录存放数据。
1.4.3. 实验任务三：查看 HDFS 的报告
[hadoop@master hadoop]$ hdfs dfsadmin -report
Configured Capacity: 57936977920 (53.96 GB)
Present Capacity: 53951262720 (50.25 GB)
DFS Remaining: 53951254528 (50.25 GB)
DFS Used: 8192 (8 KB)
DFS Used%: 0.00%
Under replicated blocks: 0
Blocks with corrupt replicas: 0
Missing blocks: 0
Missing blocks (with replication factor 1): 0
-------------------------------------------------
Live datanodes (2):
Name: 192.168.100.130:50010 (slave1)
Hostname: slave1
Decommission Status : Normal
Configured Capacity: 28968488960 (26.98 GB)
DFS Used: 4096 (4 KB)
Non DFS Used: 1994018816 (1.86 GB)
DFS Remaining: 26974466048 (25.12 GB)
DFS Used%: 0.00%
DFS Remaining%: 93.12%
Configured Cache Capacity: 0 (0 B)
Cache Used: 0 (0 B)
Cache Remaining: 0 (0 B)
Cache Used%: 100.00%
Cache Remaining%: 0.00%
Xceivers: 1
Last contact: Wed Mar 15 18:02:19 CST 2023
Name: 192.168.100.131:50010 (slave2)
Hostname: slave2
Decommission Status : Normal
Configured Capacity: 28968488960 (26.98 GB)
DFS Used: 4096 (4 KB)
Non DFS Used: 1991696384 (1.85 GB)
DFS Remaining: 26976788480 (25.12 GB)
DFS Used%: 0.00%
DFS Remaining%: 93.12%
Configured Cache Capacity: 0 (0 B)
Cache Used: 0 (0 B)
Cache Remaining: 0 (0 B)
Cache Used%: 100.00%
Cache Remaining%: 0.00%
Xceivers: 1
Last contact: Wed Mar 15 18:02:20 CST 2023
1.4.4. 实验任务四：在windowns中配置master，salve1，salve2的域名解析
1.4.4. 实验任务四：使用浏览器查看节点状态
在浏览器的地址栏输入http://master:50070，进入页面可以查看NameNode和DataNode 信息，如图 5-2 所示。
在浏览器的地址栏输入 http://master:50090，进入页面可以查看 SecondaryNameNode信息，如图 5-3 所示
可以使用 start-dfs.sh 命令启动 HDFS。这时需要配置 SSH 免密码登录，否则在启动过
程中系统将多次要求确认连接和输入 Hadoop 用户密码。
[hadoop@master hadoop]$ stop-dfs.sh
Stopping namenodes on [master]
master: stopping namenode
192.168.100.131: stopping datanode
192.168.100.130: stopping datanode
Stopping secondary namenodes [0.0.0.0]
The authenticity of host '0.0.0.0 (0.0.0.0)' can't be established.
ECDSA key fingerprint is SHA256:PBtGVMglru206eEDbi9G1WgfQEtCgE78HO8doBP7hl4.
ECDSA key fingerprint is MD5:0e:4f:4f:70:7f:5f:1f:a2:a2:78:4f:37:a4:b3:fa:86.
Are you sure you want to continue connecting (yes/no)? yes
0.0.0.0: Warning: Permanently added '0.0.0.0' (ECDSA) to the list of known hosts.
0.0.0.0: stopping secondarynamenode
[hadoop@master hadoop]$ start-dfs.sh
Starting namenodes on [master]
master: starting namenode, logging to /usr/local/src/hadoop/logs/hadoop-hadoop-namenode-master.out
192.168.100.131: starting datanode, logging to /usr/local/src/hadoop/logs/hadoop-hadoop-datanode-slave2.out
192.168.100.130: starting datanode, logging to /usr/local/src/hadoop/logs/hadoop-hadoop-datanode-slave1.out
Starting secondary namenodes [0.0.0.0]
0.0.0.0: starting secondarynamenode, logging to /usr/local/src/hadoop/logs/hadoop-hadoop-secondarynamenode-master.out
运行测试： 下面运行 WordCount 官方案例，统计 data.txt 文件中单词的出现频度。这个案例可以 用来统计年度十大热销产品、年度风云人物、年度最热名词等。
1.4.4.1. 步骤一：在 HDFS 文件系统中创建数据输入目录
确保 dfs 和 yarn 都启动成功
[hadoop@master hadoop]$ start-yarn.sh
starting yarn daemons
starting resourcemanager, logging to /usr/local/src/hadoop/logs/yarn-hadoop-resourcemanager-master.out
192.168.100.131: starting nodemanager, logging to /usr/local/src/hadoop/logs/yarn-hadoop-nodemanager-slave2.out
192.168.100.130: starting nodemanager, logging to /usr/local/src/hadoop/logs/yarn-hadoop-nodemanager-slave1.out
[hadoop@master hadoop]$ jps
2213 SecondaryNameNode
2026 NameNode
2397 ResourceManager
2654 Jps
如果是第一次运行 MapReduce 程序，需要先在 HDFS 文件系统中创建数据输入目录，存 放输入数据。这里指定/input 目录为输入数据的存放目录。 执行如下命令，在 HDFS 文件系统中创建/input 目录：
[hadoop@master hadoop]$ hdfs dfs -mkdir /input
[hadoop@master hadoop]$ hdfs dfs -ls /
Found 1 items
drwxr-xr-x - hadoop supergroup 0 2023-03-16 20:53 /input
此处创建的/input 目录是在 HDFS 文件系统中，只能用 HDFS 命令查看和操作。
1.4.4.2. 步骤二：将输入数据文件复制到 HDFS 的/input 目录中
测试用数据文件仍然是上一节所用的测试数据文件~/input/data.txt，内容如下所示。
[hadoop@master ~]$ mkdir input
[hadoop@master ~]$ ls
input
[hadoop@master ~]$ vi input/data.txt
[hadoop@master input]$ cat data.txt
hello wangjialin
hello xiaoying
hello hadoop
执行如下命令，将输入数据文件复制到 HDFS 的/input 目录中：
[hadoop@master input]$ hdfs dfs -put ~/input/data.txt /input
确认文件已复制到 HDFS 的/input 目录：
[hadoop@master hadoop]$ hdfs dfs -ls /input
Found 1 items
-rw-r--r-- 3 hadoop supergroup 45 2023-03-16 21:07 /input/data.txt
1.4.4.3. 步骤三：运行 WordCount 案例，计算数据文件中各单词的频度。
运行 MapReduce 命令需要指定数据输出目录，该目录为 HDFS 文件系统中的目录，会自 动生成。如果在执行 MapReduce 命令前，该目录已经存在，则执行 MapReduce 命令会出错。 例如 MapReduce 命令指定数据输出目录为/output，/output 目录在 HDFS 文件系统中已经存 在，则执行相应的 MapReduce 命令就会出错。所以如果不是第一次运行 MapReduce，就要先 查看HDFS中的文件，是否存在/output目录。如果已经存在/output目录，就要先删除/output 目录，再执行上述命令。
自动创建的/output 目录在 HDFS 文件系统中，使用 HDFS 命令查看和操作。
[hadoop@master hadoop]$ hdfs dfs -mkdir /output
先执行如下命令查看 HDFS 中的文件：
[hadoop@master hadoop]$ hdfs dfs -ls /
Found 3 items
drwxr-xr-x - hadoop supergroup 0 2020-05-02 22:32 /input
drwxr-xr-x - hadoop supergroup 0 2020-05-02 22:49 /output
上述目录中/input 目录是输入数据存放的目录，/output 目录是输出数据存放的目录。 执行如下命令，删除/output 目录。
[hadoop@master hadoop]$ hdfs dfs -rm -r -f /output
执行如下命令运行 WordCount 案例：
[hadoop@master hadoop]$ hadoop jar /usr/local/src/hadoop/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.1.jar wordcount /input/data.txt /output
23/03/16 21:21:57 INFO client.RMProxy: Connecting to ResourceManager at master/192.168.100.129:8032
23/03/16 21:21:58 INFO input.FileInputFormat: Total input paths to process : 1
23/03/16 21:21:58 INFO mapreduce.JobSubmitter: number of splits:1
23/03/16 21:21:58 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1678970903012_0001
23/03/16 21:21:59 INFO impl.YarnClientImpl: Submitted application application_1678970903012_0001
23/03/16 21:21:59 INFO mapreduce.Job: The url to track the job: http://master:8088/proxy/application_1678970903012_0001/
23/03/16 21:21:59 INFO mapreduce.Job: Running job: job_1678970903012_0001
23/03/16 21:22:08 INFO mapreduce.Job: Job job_1678970903012_0001 running in uber mode : false
23/03/16 21:22:08 INFO mapreduce.Job: map 0% reduce 0%
23/03/16 21:22:09 INFO mapreduce.Job: Task Id : attempt_1678970903012_0001_m_000000_0, Status : FAILED
Container launch failed for container_1678970903012_0001_01_000002 : org.apache.hadoop.yarn.exceptions.InvalidAuxServiceException: The auxService:mapreduce_shuffle does not exist
at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)
at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
at java.lang.reflect.Constructor.newInstance(Constructor.java:423)
at org.apache.hadoop.yarn.api.records.impl.pb.SerializedExceptionPBImpl.instantiateException(SerializedExceptionPBImpl.java:168)
at org.apache.hadoop.yarn.api.records.impl.pb.SerializedExceptionPBImpl.deSerialize(SerializedExceptionPBImpl.java:106)
at org.apache.hadoop.mapreduce.v2.app.launcher.ContainerLauncherImpl$Container.launch(ContainerLauncherImpl.java:155)
at org.apache.hadoop.mapreduce.v2.app.launcher.ContainerLauncherImpl$EventProcessor.run(ContainerLauncherImpl.java:375)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
23/03/16 21:22:11 INFO mapreduce.Job: Task Id : attempt_1678970903012_0001_m_000000_1, Status : FAILED
Container launch failed for container_1678970903012_0001_01_000003 : org.apache.hadoop.yarn.exceptions.InvalidAuxServiceException: The auxService:mapreduce_shuffle does not exist
at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)
at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
at java.lang.reflect.Constructor.newInstance(Constructor.java:423)
at org.apache.hadoop.yarn.api.records.impl.pb.SerializedExceptionPBImpl.instantiateException(SerializedExceptionPBImpl.java:168)
at org.apache.hadoop.yarn.api.records.impl.pb.SerializedExceptionPBImpl.deSerialize(SerializedExceptionPBImpl.java:106)
at org.apache.hadoop.mapreduce.v2.app.launcher.ContainerLauncherImpl$Container.launch(ContainerLauncherImpl.java:155)
at org.apache.hadoop.mapreduce.v2.app.launcher.ContainerLauncherImpl$EventProcessor.run(ContainerLauncherImpl.java:375)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
23/03/16 21:22:13 INFO mapreduce.Job: Task Id : attempt_1678970903012_0001_m_000000_2, Status : FAILED
Container launch failed for container_1678970903012_0001_01_000004 : org.apache.hadoop.yarn.exceptions.InvalidAuxServiceException: The auxService:mapreduce_shuffle does not exist
at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)
at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
at java.lang.reflect.Constructor.newInstance(Constructor.java:423)
at org.apache.hadoop.yarn.api.records.impl.pb.SerializedExceptionPBImpl.instantiateException(SerializedExceptionPBImpl.java:168)
at org.apache.hadoop.yarn.api.records.impl.pb.SerializedExceptionPBImpl.deSerialize(SerializedExceptionPBImpl.java:106)
at org.apache.hadoop.mapreduce.v2.app.launcher.ContainerLauncherImpl$Container.launch(ContainerLauncherImpl.java:155)
at org.apache.hadoop.mapreduce.v2.app.launcher.ContainerLauncherImpl$EventProcessor.run(ContainerLauncherImpl.java:375)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
23/03/16 21:22:16 INFO mapreduce.Job: map 100% reduce 100%
23/03/16 21:22:16 INFO mapreduce.Job: Job job_1678970903012_0001 failed with state FAILED due to: Task failed task_1678970903012_0001_m_000000
Job failed as tasks failed. failedMaps:1 failedReduces:0
23/03/16 21:22:16 INFO mapreduce.Job: Counters: 4
Job Counters
Other local map tasks=3
Data-local map tasks=1
Total time spent by all maps in occupied slots (ms)=0
Total time spent by all reduces in occupied slots (ms)=0
在浏览器的地址栏输入 http://master:50070，进入页面，在 Utilities 菜单中选择 Browse the file system，可以查看 HDFS 文件系统内容。如图 5-5 所示，查看 HDFS 的根目录，可 以看到 HDFS 根目录中有三个目录，input、output 和 tmp。
查看 output 目录，如图 5-6 所示，发现有两个文件。文件_SUCCESS 表示处理成功，处 理的结果存放在 part-r-00000 文件中。在页面上不能直接查看文件内容，需要下载到本地 系统才行。
可以使用 HDFS 命令直接查看 part-r-00000 文件内容，结果如下所示：
[hadoop@master output]$ hdfs dfs -cat /output/part-r-00000
Hello 3
Huasan 1
World 1
Hadoop 1
可以看出统计结果正确，说明 Hadoop 运行正常。
1.4.5. 实验任务五：停止 Hadoop
1.4.5.1. 步骤一：停止 yarn
[hadoop@master output]$ stop-yarn.sh
stopping yarn daemons
stopping resourcemanager
192.168.100.131: stopping nodemanager
192.168.100.130: stopping nodemanager
192.168.100.130: nodemanager did not stop gracefully after 5 seconds: killing with kill -9
no proxyserver to stop
1.4.5.2. 步骤二：停止 DataNode
[hadoop@slave1 data]$ hadoop-daemon.sh stop datanode
stopping datanode
[hadoop@slave1 data]$
[hadoop@slave2 data]$ hadoop-daemon.sh stop datanode
stopping datanode
[hadoop@slave2 data]$
1.4.5.3. 步骤二：停止 NameNode
[hadoop@master input]$ hadoop-daemon.sh stop namenode
stopping namenode
[hadoop@master input]$
1.4.5.4. 步骤三：停止 SecondaryNameNode
[hadoop@master ~]$ hadoop-daemon.sh stop secondarynamenode
stopping secondarynamenode
1.4.5.5. 步骤四：查看 JAVA 进程，确认 HDFS 进程已全部关闭
[hadoop@master ~]$ jps
3403 Jps
30838 RunJar