可 以 从 官 网 下 载 Flume 组 件 安 装 包 ， 下 载 地 址 如 下 URL 链 接 所 示 https://archive.apache.org/dist/flume/1.6.0/

[root@master ~]# lsanaconda-ks.cfg         jdk-8u152-linux-x64.tar.gzapache-flume-1.6.0-bin.tar.gz  mysqlapache-hive-2.0.0-bin.tar.gz  mysql-connector-java-5.1.46.jarderby.log            sqoop-1.4.7.bin__hadoop-2.6.0.tar.gzhadoop-2.7.1.tar.gz       zookeeper-3.4.8.tar.gzhbase-1.2.1-bin.tar.gz#使用 root 用户解压 Flume 安装包到“/usr/local/src”路径，并修改解压后文件夹名为 flume。[root@master ~]# tar xf apache-flume-1.6.0-bin.tar.gz -C /usr/local/src/[root@master ~]# cd /usr/local/src/[root@master src]# lsapache-flume-1.6.0-bin  hadoop  hbase  hive  jdk  sqoop  zookeeper[root@master src]# mv apache-flume-1.6.0-bin flume[root@master src]# lsflume  hadoop  hbase  hive  jdk  sqoop  zookeeper

步骤一：使用 root 用户设置 Flume 环境变量，并使环境变量对所有用户生效。

(资料图片仅供参考)

[root@master ~]# vim /etc/profile.d/flume.shexport FLUME_HOME=/usr/local/src/flumeexport PATH=${FLUME_HOME}/bin:$PATH

步骤二：修改 Flume 相应配置文件。

首先，切换到 hadoop 用户，并切换当前工作目录到 Flume 的配置文件夹。

[root@master ~]# chown -R hadoop.hadoop /usr/local/src/[root@master ~]# su - hadoopLast login: Fri Apr 14 16:31:48 CST 2023 on pts/1[hadoop@master ~]$ lsderby.log  input  student.java  zookeeper.out[hadoop@master ~]$ cd /usr/local/src/flume/conf/[hadoop@master conf]$ lsflume-conf.properties.template  flume-env.sh.templateflume-env.ps1.template      log4j.properties

拷贝 flume-env.sh.template 文件并重命名为 flume-env.sh。

[hadoop@master conf]$ cp flume-env.sh.template flume-env.sh[hadoop@master conf]$ lsflume-conf.properties.template  flume-env.sh      log4j.propertiesflume-env.ps1.template      flume-env.sh.template

步骤三：修改并配置 flume-env.sh 文件。

删除 JAVA_HOME 变量前的注释，修改为 JDK 的安装路径。

[hadoop@master conf]$ vi flume-env.shexport JAVA_HOME=/usr/loocal/src/jdk#export HBASE_CLASSPATH=/usr/local/src/hadoop/etc/hadoop

使用 flume-ng version 命令验证安装是否成功，若能够正常查询 Flume 组件版本为 1.6.0，则表示安装成功。

[hadoop@master conf]$ flume-ng versionError: Could not find or load main class org.apache.flume.tools.GetJavaPropertyFlume 1.6.0Source code repository: https://git-wip-us.apache.org/repos/asf/flume.gitRevision: 2561a23240a71ba20bf288c7c2cda88f443c2080Compiled by hshreedharan on Mon May 11 11:15:44 PDT 2015From source with checksum b29e416802ce9ece3269d34233baf43f

通过 Flume 将 Web 服务器中数据传输到 HDFS 中。

步骤一：在 Flume 安装目录中创建 xxx.conf 文件。

[hadoop@master flume]$ vi xxx.confa1.sources=r1a1.sinks=k1a1.channels=c1a1.sources.r1.type=spooldira1.sources.r1.spoolDir=/usr/local/src/hadoop/logsa1.sources.r1.fileHeader=truea1.sinks.k1.type=hdfsa1.sinks.k1.hdfs.path=hdfs://master:9000/tmp/flumea1.sinks.k1.hdfs.rollsize=1048760a1.sinks.k1.hdfs.rollCount=0a1.sinks.k1.hdfs.rollInterval=900a1.sinks.k1.hfds.useLocalTimeStamp=truea1.channels.c1.type=filea1.channels.c1.capacity=1000a1.channels.c1.transactionCapacity=100a1.sources.r1.channels = c1a1.sinks.k1.channel = c1

[hadoop@master flume]$ ls /usr/local/src/hadoop/bin  etc    lib    LICENSE.txt  NOTICE.txt  sbin  tmpdfs  include  libexec  logs     README.txt  share[hadoop@master flume]$ ls /usr/local/src/hadoop/logs/hadoop-hadoop-namenode-master.example.com.loghadoop-hadoop-namenode-master.example.com.outhadoop-hadoop-namenode-master.example.com.out.1hadoop-hadoop-namenode-master.example.com.out.2hadoop-hadoop-namenode-master.example.com.out.3hadoop-hadoop-namenode-master.example.com.out.4hadoop-hadoop-namenode-master.example.com.out.5hadoop-hadoop-secondarynamenode-master.example.com.loghadoop-hadoop-secondarynamenode-master.example.com.outhadoop-hadoop-secondarynamenode-master.example.com.out.1hadoop-hadoop-secondarynamenode-master.example.com.out.2hadoop-hadoop-secondarynamenode-master.example.com.out.3hadoop-hadoop-secondarynamenode-master.example.com.out.4hadoop-hadoop-secondarynamenode-master.example.com.out.5SecurityAuth-hadoop.audityarn-hadoop-resourcemanager-master.example.com.logyarn-hadoop-resourcemanager-master.example.com.outyarn-hadoop-resourcemanager-master.example.com.out.1yarn-hadoop-resourcemanager-master.example.com.out.2yarn-hadoop-resourcemanager-master.example.com.out.3yarn-hadoop-resourcemanager-master.example.com.out.4yarn-hadoop-resourcemanager-master.example.com.out.5

步骤二：使用 flume-ng agent 命令加载 simple-hdfs-flume.conf 配置信息，启 动 flume 传输数据。

[[hadoop@master flume]$ flume-ng agent --conf-file xxx.conf --name a1Warning: No configuration directory set! Use --conf  to override.Info: Including Hadoop libraries found via (/usr/local/src/hadoop/bin/hadoop) for HDFS accessInfo: Excluding /usr/local/src/hadoop/share/hadoop/common/lib/slf4j-api-1.7.10.jar from classpathInfo: Excluding /usr/local/src/hadoop/share/hadoop/common/lib/slf4j-log4j12-1.7.10.jar from classpathInfo: Including HBASE libraries found via (/usr/local/src/hbase/bin/hbase) for HBASE accessInfo: Excluding /usr/local/src/hbase/lib/slf4j-api-1.7.7.jar from classpathInfo: Excluding /usr/local/src/hbase/lib/slf4j-log4j12-1.7.5.jar from classpathInfo: Excluding /usr/local/src/hadoop/share/hadoop/common/lib/slf4j-api-1.7.10.jar from classpathInfo: Excluding /usr/local/src/hadoop/share/hadoop/common/lib/slf4j-log4j12-1.7.10.jar from classpathInfo: Including Hive libraries found via (/usr/local/src/hive) for Hive access...23/04/21 16:02:35 INFO instrumentation.MonitoredCounterGroup: Shutdown Metric for type: SOURCE, name: r1. src.append.accepted == 023/04/21 16:02:35 INFO instrumentation.MonitoredCounterGroup: Shutdown Metric for type: SOURCE, name: r1. src.append.received == 023/04/21 16:02:35 INFO instrumentation.MonitoredCounterGroup: Shutdown Metric for type: SOURCE, name: r1. src.events.accepted == 1723/04/21 16:02:35 INFO instrumentation.MonitoredCounterGroup: Shutdown Metric for type: SOURCE, name: r1. src.events.received == 1723/04/21 16:02:35 INFO instrumentation.MonitoredCounterGroup: Shutdown Metric for type: SOURCE, name: r1. src.open-connection.count == 023/04/21 16:02:35 INFO source.SpoolDirectorySource: SpoolDir source r1 stopped. Metrics: SOURCE:r1{src.events.accepted=17, src.open-connection.count=0, src.append.received=0, src.append-batch.received=1, src.append-batch.accepted=1, src.append.accepted=0, src.events.received=17}

ctrl+c 退出 flume 传输

#首先得开启所有节点[hadoop@master flume]$ start-all.shThis script is Deprecated. Instead use start-dfs.sh and start-yarn.shStarting namenodes on [master]master: starting namenode, logging to /usr/local/src/hadoop/logs/hadoop-hadoop-namenode-master.example.com.out192.168.88.201: starting datanode, logging to /usr/local/src/hadoop/logs/hadoop-hadoop-datanode-slave2.example.com.out192.168.88.200: starting datanode, logging to /usr/local/src/hadoop/logs/hadoop-hadoop-datanode-slave1.example.com.outStarting secondary namenodes [0.0.0.0]0.0.0.0: starting secondarynamenode, logging to /usr/local/src/hadoop/logs/hadoop-hadoop-secondarynamenode-master.example.com.outstarting yarn daemonsstarting resourcemanager, logging to /usr/local/src/hadoop/logs/yarn-hadoop-resourcemanager-master.example.com.out192.168.88.200: starting nodemanager, logging to /usr/local/src/hadoop/logs/yarn-hadoop-nodemanager-slave1.example.com.out192.168.88.201: starting nodemanager, logging to /usr/local/src/hadoop/logs/yarn-hadoop-nodemanager-slave2.example.com.out[hadoop@master flume]$ ss -antlState      Recv-Q Send-Q Local Address:Port               Peer Address:Port              LISTEN     0      128    192.168.88.101:9000                      *:*                  LISTEN     0      128           *:50090                     *:*                  LISTEN     0      128           *:50070                     *:*                  LISTEN     0      128           *:22                        *:*                  LISTEN     0      128       ::ffff:192.168.88.101:8030                     :::*                  LISTEN     0      128       ::ffff:192.168.88.101:8031                     :::*                  LISTEN     0      128       ::ffff:192.168.88.101:8032                     :::*                  LISTEN     0      128       ::ffff:192.168.88.101:8033                     :::*                  LISTEN     0      80           :::3306                     :::*                  LISTEN     0      128          :::22                       :::*                  LISTEN     0      128       ::ffff:192.168.88.101:8088                     :::*                  

步骤三：查看 Flume 传输到 HDFS 的文件，若能查看到 HDFS 上/tmp/flume 目 录有传输的数据文件，则表示数据传输成功。

[hadoop@master flume]$ hdfs dfs -ls /Found 5 itemsdrwxr-xr-x   - hadoop supergroup          0 2023-04-07 15:20 /hbasedrwxr-xr-x   - hadoop supergroup          0 2023-03-17 17:33 /inputdrwxr-xr-x   - hadoop supergroup          0 2023-03-17 18:45 /outputdrwx------   - hadoop supergroup          0 2023-04-21 16:02 /tmpdrwxr-xr-x   - hadoop supergroup          0 2023-04-14 20:48 /user[hadoop@master flume]$ hdfs dfs -ls /tmp/flumeFound 72 items-rw-r--r--   2 hadoop supergroup       1560 2023-04-21 16:02 /tmp/flume/FlumeData.1682064143693-rw-r--r--   2 hadoop supergroup       1398 2023-04-21 16:02 /tmp/flume/FlumeData.1682064143694-rw-r--r--   2 hadoop supergroup       1456 2023-04-21 16:02 /tmp/flume/FlumeData.1682064143695-rw-r--r--   2 hadoop supergroup       1398 2023-04-21 16:02 /tmp/flume/FlumeData.1682064143696-rw-r--r--   2 hadoop supergroup       1403 2023-04-21 16:02 /tmp/flume/FlumeData.1682064143697-rw-r--r--   2 hadoop supergroup       1434 2023-04-21 16:02 /tmp/flume/FlumeData.1682064143698-rw-r--r--   2 hadoop supergroup       1383 2023-04-21 16:02 /tmp/flume/FlumeData.1682064143699...-rw-r--r--   2 hadoop supergroup       1508 2023-04-21 16:02 /tmp/flume/FlumeData.1682064143760-rw-r--r--   2 hadoop supergroup       1361 2023-04-21 16:02 /tmp/flume/FlumeData.1682064143761-rw-r--r--   2 hadoop supergroup       1359 2023-04-21 16:02 /tmp/flume/FlumeData.1682064143762-rw-r--r--   2 hadoop supergroup       1502 2023-04-21 16:02 /tmp/flume/FlumeData.1682064143763-rw-r--r--   2 hadoop supergroup       1399 2023-04-21 16:02 /tmp/flume/FlumeData.1682064143764

步骤一：查看 Linux 系统的信息（uname -a）

[root@master ~]# uname -aLinux master.example.com 3.10.0-862.el7.x86_64 #1 SMP Fri Apr 20 16:44:24 UTC 2018 x86_64 x86_64 x86_64 GNU/Linux

结果显示，该 Linux 节点名称为 master，内核发行号为 3.10.0-693.el7.x86_64。

步骤二：查看硬盘信息

（1）查看所有分区（fdisk -l）

[root@master ~]# fdisk -lDisk /dev/sda: 107.4 GB, 107374182400 bytes, 209715200 sectorsUnits = sectors of 1 * 512 = 512 bytesSector size (logical/physical): 512 bytes / 512 bytesI/O size (minimum/optimal): 512 bytes / 512 bytesDisk label type: dosDisk identifier: 0x000af885   Device Boot      Start         End      Blocks   Id  System/dev/sda1   *        2048     2099199     1048576   83  Linux/dev/sda2         2099200   209715199   103808000   8e  Linux LVMDisk /dev/mapper/centos-root: 53.7 GB, 53687091200 bytes, 104857600 sectorsUnits = sectors of 1 * 512 = 512 bytesSector size (logical/physical): 512 bytes / 512 bytesI/O size (minimum/optimal): 512 bytes / 512 bytesDisk /dev/mapper/centos-swap: 8455 MB, 8455716864 bytes, 16515072 sectorsUnits = sectors of 1 * 512 = 512 bytesSector size (logical/physical): 512 bytes / 512 bytesI/O size (minimum/optimal): 512 bytes / 512 bytesDisk /dev/mapper/centos-home: 44.1 GB, 44149243904 bytes, 86228992 sectorsUnits = sectors of 1 * 512 = 512 bytesSector size (logical/physical): 512 bytes / 512 bytesI/O size (minimum/optimal): 512 bytes / 512 bytes

结果显示，硬盘空间为 107.4GB。

（2）查看所有交换分区（swapon -s）

[root@master ~]# swapon -sFilenameTypeSizeUsedPriority/dev/dm-1                              partition82575320-1

结果显示，交换分区为 8257532。

（3）查看文件系统占比（df -h）

[root@master ~]# df -hFilesystem               Size  Used Avail Use% Mounted on/dev/mapper/centos-root   50G  4.6G   46G  10% /devtmpfs                 1.9G     0  1.9G   0% /devtmpfs                    1.9G     0  1.9G   0% /dev/shmtmpfs                    1.9G   12M  1.9G   1% /runtmpfs                    1.9G     0  1.9G   0% /sys/fs/cgroup/dev/mapper/centos-home   42G   36M   42G   1% /home/dev/sda1               1014M  142M  873M  14% /boottmpfs                    378M     0  378M   0% /run/user/0

结果显示，挂载点“/”的容量为 50G，已使用 4.6G。

步骤三：查看网络 IP 地址（ip a）

[root@master ~]# ip a1: lo:  mtu 65536 qdisc noqueue state UNKNOWN group default qlen 1000    link/loopback 00:00:00:00:00:00 brd 00:00:00:00:00:00    inet 127.0.0.1/8 scope host lo       valid_lft forever preferred_lft forever    inet6 ::1/128 scope host        valid_lft forever preferred_lft forever2: ens33:  mtu 1500 qdisc pfifo_fast state UP group default qlen 1000    link/ether 00:0c:29:20:cd:03 brd ff:ff:ff:ff:ff:ff    inet 192.168.88.101/24 brd 192.168.88.255 scope global noprefixroute ens33       valid_lft forever preferred_lft forever    inet6 fe80::75a3:9da2:ede2:5be7/64 scope link noprefixroute        valid_lft forever preferred_lft forever    inet6 fe80::1c1b:d0e3:f01a:7c11/64 scope link tentative noprefixroute dadfailed        valid_lft forever preferred_lft forever

结果显示 ens33 的 IP 地址为 192.168.88.101，子网掩码为 255.255.255.0；回环地址 为 127.0.0.1，子网掩码为 255.0.0.0。

步骤四：查看所有监听端口（netstat -lntp）

[root@master ~]# netstat -lntpActive Internet connections (only servers)Proto Recv-Q Send-Q Local Address           Foreign Address         State       PID/Program name    tcp        0      0 0.0.0.0:22              0.0.0.0:*               LISTEN      908/sshd            tcp6       0      0 :::3306                 :::*                    LISTEN      1053/mysqld         tcp6       0      0 :::22                   :::*                    LISTEN      908/sshd    

结果显示，在监听的端口分别为 22、3306。

步骤五：查看所有已经建立的连接（netstat -antp）

[root@master ~]# netstat -antpActive Internet connections (servers and established)Proto Recv-Q Send-Q Local Address           Foreign Address         State       PID/Program name    tcp        0      0 0.0.0.0:22              0.0.0.0:*               LISTEN      908/sshd            tcp        0     36 192.168.88.101:22       192.168.88.1:61450      ESTABLISHED 1407/sshd: root@pts tcp6       0      0 :::3306                 :::*                    LISTEN      1053/mysqld         tcp6       0      0 :::22                   :::*                    LISTEN      908/sshd

结果显示，已经连接上的本地端口分别为 50608、22、9000、8031 等。

步骤六：实时显示进程状态（top），该命令可以查看进程对 CPU、内存的占比 等。

步骤七：查看 CPU 信息（ cat /proc/cpuinfo）

[root@master ~]# cat /proc/cpuinfo processor: 0vendor_id: AuthenticAMDcpu family: 25model: 80model name: AMD Ryzen 7 5800U with Radeon Graphicsstepping: 0cpu MHz: 1896.438cache size: 512 KBphysical id: 0siblings: 2core id: 0cpu cores: 2apicid: 0initial apicid: 0fpu: yesfpu_exception: yescpuid level: 16wp: yesflags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc art rep_good nopl tsc_reliable nonstop_tsc extd_apicid eagerfpu pni pclmulqdq ssse3 fma cx16 sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw topoext retpoline_amd vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero ibpb ibrs arat pku ospke overflow_recov succorbogomips: 3792.87TLB size: 2560 4K pagesclflush size: 64cache_alignment: 64address sizes: 45 bits physical, 48 bits virtualpower management:processor: 1vendor_id: AuthenticAMDcpu family: 25model: 80model name: AMD Ryzen 7 5800U with Radeon Graphicsstepping: 0cpu MHz: 1896.438cache size: 512 KBphysical id: 0siblings: 2core id: 1cpu cores: 2apicid: 1initial apicid: 1fpu: yesfpu_exception: yescpuid level: 16wp: yesflags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc art rep_good nopl tsc_reliable nonstop_tsc extd_apicid eagerfpu pni pclmulqdq ssse3 fma cx16 sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw topoext retpoline_amd vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero ibpb ibrs arat pku ospke overflow_recov succorbogomips: 3792.87TLB size: 2560 4K pagesclflush size: 64cache_alignment: 64address sizes: 45 bits physical, 48 bits virtual

步骤八：查看内存信息（ cat /proc/meminfo），该命令可以查看总内存、空 闲内存等信息。

[root@master ~]# cat /proc/meminfo MemTotal:        3863564 kBMemFree:         2971196 kBMemAvailable:    3301828 kBBuffers:            2120 kBCached:           529608 kBSwapCached:            0 kBActive:           626584 kBInactive:         130828 kBActive(anon):     226348 kBInactive(anon):    11152 kBActive(file):     400236 kBInactive(file):   119676 kBUnevictable:           0 kBMlocked:               0 kBSwapTotal:       8257532 kBSwapFree:        8257532 kBDirty:                 0 kBWriteback:             0 kBAnonPages:        225716 kBMapped:            28960 kBShmem:             11816 kBSlab:              59428 kBSReclaimable:      30680 kBSUnreclaim:        28748 kBKernelStack:        4432 kBPageTables:         3664 kBNFS_Unstable:          0 kBBounce:                0 kBWritebackTmp:          0 kBCommitLimit:    10189312 kBCommitted_AS:     773684 kBVmallocTotal:   34359738367 kBVmallocUsed:      185376 kBVmallocChunk:   34359310332 kBHardwareCorrupted:     0 kBAnonHugePages:    180224 kBCmaTotal:              0 kBCmaFree:               0 kBHugePages_Total:       0HugePages_Free:        0HugePages_Rsvd:        0HugePages_Surp:        0Hugepagesize:       2048 kBDirectMap4k:       73536 kBDirectMap2M:     3072000 kBDirectMap1G:     3145728 kB

步骤一：切换到 hadoop 用户

[root@master ~]# su - hadoopLast login: Fri Apr 21 15:26:05 CST 2023 on pts/0Last failed login: Fri Apr 21 16:02:08 CST 2023 from slave1 on ssh:nottyThere were 4 failed login attempts since the last successful login.

若当前的用户为 root，请切换到 hadoop 用户进行操作。

步骤二：切换到 Hadoop 的安装目录

[hadoop@master ~]$ cd /usr/local/src/hadoop/

步骤三：启动 Hadoop

[hadoop@master ~]$ start-all.sh This script is Deprecated. Instead use start-dfs.sh and start-yarn.shStarting namenodes on [master]master: starting namenode, logging to /usr/local/src/hadoop/logs/hadoop-hadoop-namenode-master.example.com.out192.168.88.201: starting datanode, logging to /usr/local/src/hadoop/logs/hadoop-hadoop-datanode-slave2.example.com.out192.168.88.200: starting datanode, logging to /usr/local/src/hadoop/logs/hadoop-hadoop-datanode-slave1.example.com.outStarting secondary namenodes [0.0.0.0]0.0.0.0: starting secondarynamenode, logging to /usr/local/src/hadoop/logs/hadoop-hadoop-secondarynamenode-master.example.com.outstarting yarn daemonsstarting resourcemanager, logging to /usr/local/src/hadoop/logs/yarn-hadoop-resourcemanager-master.example.com.out192.168.88.200: starting nodemanager, logging to /usr/local/src/hadoop/logs/yarn-hadoop-nodemanager-slave1.example.com.out192.168.88.201: starting nodemanager, logging to /usr/local/src/hadoop/logs/yarn-hadoop-nodemanager-slave2.example.com.out

步骤四：关闭 Hadoop

[hadoop@master hadoop]$ stop-all.sh This script is Deprecated. Instead use stop-dfs.sh and stop-yarn.shStopping namenodes on [master]master: stopping namenode192.168.88.201: stopping datanode192.168.88.200: stopping datanodeStopping secondary namenodes [0.0.0.0]0.0.0.0: stopping secondarynamenodestopping yarn daemonsstopping resourcemanager192.168.88.200: stopping nodemanager192.168.88.201: stopping nodemanagerno proxyserver to stop

步骤一：确认切换到目录 /usr/local/src/hadoop

[hadoop@master hadoop]$ cd /usr/local/src/hadoop/

步骤二：返回主机界面在 Master 主机上执行 start-all.sh

#master 节点启动 zookeeper[hadoop@master hadoop]$ zkServer.sh startZooKeeper JMX enabled by defaultUsing config: /usr/local/src/zookeeper/bin/../conf/zoo.cfgStarting zookeeper ... STARTED#slave1 节点启动 zookeeper[root@slave1 ~]# zkServer.sh startZooKeeper JMX enabled by defaultUsing config: /usr/local/src/zookeeper/bin/../conf/zoo.cfgStarting zookeeper ... STARTED#slave2 节点启动 zookeeper[root@slave2 ~]# zkServer.sh startZooKeeper JMX enabled by defaultUsing config: /usr/local/src/zookeeper/bin/../conf/zoo.cfgStarting zookeeper ... STARTEDmaster节点[hadoop@master hadoop]$ start-all.sh 

步 骤 三 ： 执 行 JPS 命 令 ， 发 现 Master 上 有 NodeManager 进程和 ResourceManager 进程，则 YARN 启动完成。

[hadoop@master hadoop]$ jps

执行结果如下，说明 YARN 已启动。

[hadoop@master hadoop]$ jps2642 QuorumPeerMain2994 SecondaryNameNode3154 ResourceManager3413 Jps2795 NameNode

步骤一：目录操作

切换到 hadoop 目录，执行 cd /usr/local/src/hadoop 命令

[hadoop@master hadoop]$ cd /usr/local/src/hadoop/

查看 HDFS 目录

[hadoop@master hadoop]$  ./bin/hdfs dfs -ls /Found 5 itemsdrwxr-xr-x   - hadoop supergroup          0 2023-04-07 15:20 /hbasedrwxr-xr-x   - hadoop supergroup          0 2023-03-17 17:33 /inputdrwxr-xr-x   - hadoop supergroup          0 2023-03-17 18:45 /outputdrwx------   - hadoop supergroup          0 2023-04-21 16:02 /tmpdrwxr-xr-x   - hadoop supergroup          0 2023-04-14 20:48 /user

步骤二：查看 HDSF 的报告，执行命令： bin/hdfs dfsadmin -report

[hadoop@master hadoop]$ bin/hdfs dfsadmin -reportConfigured Capacity: 107321753600 (99.95 GB)Present Capacity: 102855995392 (95.79 GB)DFS Remaining: 102852079616 (95.79 GB)DFS Used: 3915776 (3.73 MB)DFS Used%: 0.00%Under replicated blocks: 0Blocks with corrupt replicas: 0Missing blocks: 0Missing blocks (with replication factor 1): 0-------------------------------------------------Live datanodes (2):Name: 192.168.88.201:50010 (slave2)Hostname: slave2Decommission Status : NormalConfigured Capacity: 53660876800 (49.98 GB)DFS Used: 1957888 (1.87 MB)Non DFS Used: 2232373248 (2.08 GB)DFS Remaining: 51426545664 (47.89 GB)DFS Used%: 0.00%DFS Remaining%: 95.84%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: 1Last contact: Sun Apr 23 21:56:46 CST 2023Name: 192.168.88.200:50010 (slave1)Hostname: slave1Decommission Status : NormalConfigured Capacity: 53660876800 (49.98 GB)DFS Used: 1957888 (1.87 MB)Non DFS Used: 2233384960 (2.08 GB)DFS Remaining: 51425533952 (47.89 GB)DFS Used%: 0.00%DFS Remaining%: 95.83%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: 1Last contact: Sun Apr 23 21:56:46 CST 2023

步骤三：查看 HDFS 空间情况，执行命令：hdfs dfs -df

[hadoop@master hadoop]$ hdfs dfs -df /Filesystem                  Size     Used     Available  Use%hdfs://master:9000  107321753600  3915776  102852079616    0%

步骤一：启动运行 HBase

切换到 HBase 安装目录/usr/local/src/hbase，命令如下：

[hadoop@master hadoop]$ cd /usr/local/src/hbase/[hadoop@master hbase]$ hbase versionHBase 1.2.1Source code repository git://asf-dev/home/busbey/projects/hbase revision=8d8a7107dc4ccbf36a92f64675dc60392f85c015Compiled by busbey on Wed Mar 30 11:19:21 CDT 2016From source with checksum f4bb4a14bb4e0b72b46f729dae98a772

结果显示 HBase1.2.1，说明 HBase 正在运行，版本号为 1.2.1。

如果没有启动，则执行命令 start-hbase.sh 启动 HBase。[hadoop@master hbase]$ start-hbase.sh master: starting zookeeper, logging to /usr/local/src/hbase/logs/hbase-hadoop-zookeeper-master.example.com.outslave1: starting zookeeper, logging to /usr/local/src/hbase/logs/hbase-hadoop-zookeeper-slave1.example.com.outslave2: starting zookeeper, logging to /usr/local/src/hbase/logs/hbase-hadoop-zookeeper-slave2.example.com.outstarting master, logging to /usr/local/src/hbase/logs/hbase-hadoop-master-master.example.com.outslave2: starting regionserver, logging to /usr/local/src/hbase/logs/hbase-hadoop-regionserver-slave2.example.com.outslave1: starting regionserver, logging to /usr/local/src/hbase/logs/hbase-hadoop-regionserver-slave1.example.com.out

步骤二：查看 HBase 版本信息

执行命令hbase shell，进入HBase命令交互界面

[hadoop@master hbase]$ hbase shellSLF4J: Class path contains multiple SLF4J bindings.SLF4J: Found binding in [jar:file:/usr/local/src/hbase/lib/slf4j-log4j12-1.7.5.jar!/org/slf4j/impl/StaticLoggerBinder.class]SLF4J: Found binding in [jar:file:/usr/local/src/hadoop/share/hadoop/common/lib/slf4j-log4j12-1.7.10.jar!/org/slf4j/impl/StaticLoggerBinder.class]SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.SLF4J: Actual binding is of type [org.slf4j.impl.Log4jLoggerFactory]HBase Shell; enter "help" for list of supported commands.Type "exit" to leave the HBase ShellVersion 1.2.1, r8d8a7107dc4ccbf36a92f64675dc60392f85c015, Wed Mar 30 11:19:21 CDT 2016hbase(main):001:0> 

输入 version，查询 HBase 版本

hbase(main):001:0> version1.2.1, r8d8a7107dc4ccbf36a92f64675dc60392f85c015, Wed Mar 30 11:19:21 CDT 2016

结果显示 HBase 版本为 1.2.1。

步骤三：查询 HBase 状态，在 HBase 命令交互界面，执行 status 命令

hbase(main):002:0> status1 active master, 0 backup masters, 2 servers, 0 dead, 1.0000 average load

查询结果显示，1 台活动 master，0 台备份 masters，共 3 台服务主机，平均加载 时间为 0.6667 秒。

我们还可以“简单”查询 HBase 的状态，执行命令 status "simple"

hbase(main):003:0> status "simple"active master:  master:16000 16822583214330 backup masters2 live servers    slave1:16020 1682258322908        requestsPerSecond=0.0, numberOfOnlineRegions=2, usedHeapMB=19, maxHeapMB=440, numberOfStores=2, numberOfStorefiles=0, storefileUncompressedSizeMB=0, storefileSizeMB=0, memstoreSizeMB=0, storefileIndexSizeMB=0, readRequestsCount=7, writeRequestsCount=4, rootIndexSizeKB=0 totalStaticIndexSizeKB=0, totalStaticBloomSizeKB=0, totalCompactingKVs=0, currentCompactedKVs=0, compactionProgressPct=NaN, coprocessors=[MultiRowMutationEndpoint]    slave2:16020 1682258322896        requestsPerSecond=0.0, numberOfOnlineRegions=0, usedHeapMB=11, maxHeapMB=440, numberOfStores=0, numberOfStorefiles=0, storefileUncompressedSizeMB=0, storefileSizeMB=0, memstoreSizeMB=0, storefileIndexSizeMB=0, readRequestsCount=0, writeRequestsCount=0, rootIndexSizeKB=0, totalStaticIndexSizeKB=0, totalStaticBloomSizeKB=0, totalCompactingKVs=0, currentCompactedKVs=0, compactionProgressPct=NaN, coprocessors=[]0 dead serversAggregate load: 0, regions: 2

显示更多的关于 Master、Slave1 和 Slave2 主机的服务端口、请求时间等详细信息。

如果需要查询更多关于 HBase 状态，执行命令 help "status"

hbase(main):004:0> help "status"Show cluster status. Can be "summary", "simple", "detailed", or "replication". Thedefault is "summary". Examples:  hbase> status  hbase> status "simple"  hbase> status "summary"  hbase> status "detailed"  hbase> status "replication"  hbase> status "replication", "source"  hbase> status "replication", "sink"  hbase(main):005:0> quit[hadoop@master hbase]$ 

结果显示出所有关于 status 的命令。

步骤四 停止 HBase 服务

停止 HBase 服务，则执行命令 stop-hbase.sh。

[hadoop@master hbase]$ stop-hbase.sh stopping hbase.................slave1: no zookeeper to stop because no pid file /tmp/hbase-hadoop-zookeeper.pidslave2: no zookeeper to stop because no pid file /tmp/hbase-hadoop-zookeeper.pidmaster: no zookeeper to stop because no pid file /tmp/hbase-hadoop-zookeeper.pid

没有错误提示，显示$提示符时，即停止了 HBase 服务。

步骤一：启动 Hive

切换到/usr/local/src/hive 目录，输入 hive，回车。

[hadoop@master hbase]$ cd /usr/local/src/hive/[hadoop@master hive]$ hiveSLF4J: Class path contains multiple SLF4J bindings.SLF4J: Found binding in [jar:file:/usr/local/src/hive/lib/hive-jdbc-2.0.0-standalone.jar!/org/slf4j/impl/StaticLoggerBinder.class]SLF4J: Found binding in [jar:file:/usr/local/src/hive/lib/log4j-slf4j-impl-2.4.1.jar!/org/slf4j/impl/StaticLoggerBinder.class]SLF4J: Found binding in [jar:file:/usr/local/src/hadoop/share/hadoop/common/lib/slf4j-log4j12-1.7.10.jar!/org/slf4j/impl/StaticLoggerBinder.class]SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.SLF4J: Actual binding is of type [org.apache.logging.slf4j.Log4jLoggerFactory]Logging initialized using configuration in jar:file:/usr/local/src/hive/lib/hive-common-2.0.0.jar!/hive-log4j2.propertiesHive-on-MR is deprecated in Hive 2 and may not be available in the future versions. Consider using a different execution engine (i.e. spark, tez) or using Hive 1.X releases.hive> 

当显示 hive>时，表示启动成功，进入到了 Hive shell 状态。

步骤二：Hive 操作基本命令

注意：Hive 命令行语句后面一定要加分号。

（1）查看数据库

hive> show databases;OKdefaultsampleTime taken: 0.973 seconds, Fetched: 2 row(s)

显示默认的数据库 default。

（2）查看 default 数据库所有表

hive> use default;OKTime taken: 0.02 secondshive> show tables;OKtestTime taken: 2.201 seconds, Fetched: 1 row(s)

显示 default 数据中没有任何表。

（3）创建表 stu，表的 id 为整数型，name 为字符型

hive> create table stu(id int,name string);OKTime taken: 0.432 seconds

（4）为表 stu 插入一条信息，id 号为 001，name 为liuyaling

hive> insert into stu values (001,"liuyaling");WARNING: Hive-on-MR is deprecated in Hive 2 and may not be available in the future versions. Consider using a different execution engine (i.e. spark, tez) or using Hive 1.X releases.Query ID = hadoop_20230423222915_a95e9891-fdf5-4739-a63e-fcadecc85e28Total jobs = 3Launching Job 1 out of 3Number of reduce tasks is set to 0 since there"s no reduce operatorStarting Job = job_1682258121749_0001, Tracking URL = http://master:8088/proxy/application_1682258121749_0001/Kill Command = /usr/local/src/hadoop/bin/hadoop job  -kill job_1682258121749_0001Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 02023-04-23 22:31:36,420 Stage-1 map = 0%,  reduce = 0%2023-04-23 22:31:43,892 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 2.72 secMapReduce Total cumulative CPU time: 2 seconds 720 msecEnded Job = job_1682258121749_0001Stage-4 is selected by condition resolver.Stage-3 is filtered out by condition resolver.Stage-5 is filtered out by condition resolver.Moving data to: hdfs://master:9000/user/hive/warehouse/stu/.hive-staging_hive_2023-04-23_22-30-32_985_529079703757687911-1/-ext-10000Loading data to table default.stuMapReduce Jobs Launched: Stage-Stage-1: Map: 1   Cumulative CPU: 2.72 sec   HDFS Read: 4135 HDFS Write: 79 SUCCESSTotal MapReduce CPU Time Spent: 2 seconds 720 msecOKTime taken: 72.401 seconds

按照以上操作，继续插入两条信息：id 和 name 分别为 1002、1003 和 yanhaoxiang、tnt。

（5）插入数据后查看表的信息

hive> show tables;OKstutestvalues__tmp__table__1values__tmp__table__2Time taken: 0.026 seconds, Fetched: 4 row(s)

（6）查看表 stu 结构

hive> desc stu;OKid                  int                                     name                string                                  Time taken: 0.041 seconds, Fetched: 2 row(s)

（7）查看表 stu 的内容

hive> select * from stu;OK1liuyaling1002yanhaoxiang1002yanhaoxiang1003tntTime taken: 0.118 seconds, Fetched: 4 row(s)

步骤三：通过 Hive 命令行界面查看文件系统和历史命令

（1）查看本地文件系统，执行命令 ! ls /usr/local/src;

hive> ! ls /usr/local/src;flumehadoophbasehivejdksqoopzookeeper

（2）查看 HDFS 文件系统，执行命令 dfs -ls /;

hive> dfs -ls /;Found 5 itemsdrwxr-xr-x   - hadoop supergroup          0 2023-04-23 21:58 /hbasedrwxr-xr-x   - hadoop supergroup          0 2023-03-17 17:33 /inputdrwxr-xr-x   - hadoop supergroup          0 2023-03-17 18:45 /outputdrwx------   - hadoop supergroup          0 2023-04-21 16:02 /tmpdrwxr-xr-x   - hadoop supergroup          0 2023-04-14 20:48 /userhive> exit;

（3）查看在 Hive 中输入的所有历史命令

进入到当前用户 Hadoop 的目录/home/hadoop，查看.hivehistory 文件。

[hadoop@master hive]$ cd /home/hadoop/[hadoop@master ~]$ cat .hivehistory show databases;create database sample;show databases;use sample;create table student(number string,name string);exitshow databases;exit;use sample;select * from student;sqoop export --connect "jdbc:mysql://master:3306/sample?useUnicode=true&characterEncoding=utf-8" --username root --password Password@123! --table student --input-fields-terminated-by "|" --export-dir /user/hive/warehouse/sample.db/student/*sqoop export --connect "jdbc:mysql://master:3306/sample?useUnicode=true&characterEncoding=utf-8" --username root --password Password@123! --table student --input-fields-terminated-by "|" --export-dir /user/hive/warehouse/sample.db/student/*;sqoop export --connect "jdbc:mysql://master3306/sample?useUnicode=true&characterEncoding=utf-8" --username root --password Password@123! --table student --input-fields-terminated-by "|" --export-dir /user/hive/warehouse/sample.db/student/*sqoop export --connect "jdbc:mysql://master3306/sample?useUnicode=true&characterEncoding=utf-8" --username root --password Password@123! --table student --input-fields-terminated-by "|" --export-dir /user/hive/warehouse/sample.db/student/*;sqoop export --connect "jdbc:mysql://master3306/sample?useUnicode=true&characterEncoding=utf-8" --username root --password Password@123! --table student --input-fields-terminated-by "|" --export-dir /user/hive/warehouse/sample.db/student/*exitshow databases;use sample;show tables;use default;show tables;quit"select*from default.test;quit;show databases;use default;show tables;insert into stu values (001,"liuyaling");create table stu(id int,name string);insert into stu values (001,"liuyaling");insert into stu values (1002,"yanhaoxiang");hiveinsert into stu values (1003,"tnt");quitexitexit;quit;show tables;insert into stu values (1002,"yanhaoxiang");insert into stu values (1003,"tnt");show tables;desc stu;select * from stu;! ls /usr/local/src;dfs -ls /;exit;

结果显示，之前在 Hive 命令行界面下运行的所有命令（含错误命令）都显示了出 来，有助于维护、故障排查等工作。

步骤一: 查看 ZooKeeper 状态，执行命令 zkServer.sh status，结果显示如下

[hadoop@master ~]$ zkServer.sh statusZooKeeper JMX enabled by defaultUsing config: /usr/local/src/zookeeper/bin/../conf/zoo.cfgMode: follower

以上结果中，Mode:follower 表示为 ZooKeeper 的跟随者。

步骤二: 查看运行进程

QuorumPeerMain：QuorumPeerMain 是 ZooKeeper 集群的启动入口类，是用来加载配 置启动 QuorumPeer 线程的。

执行命令 jps 以查看进程情况。

[hadoop@master ~]$ jps2642 QuorumPeerMain2994 SecondaryNameNode3154 ResourceManager5400 Jps2795 NameNode

此时 QuorumPeerMain 进程已启动。

步骤四: 在成功启动 ZooKeeper 服务后，输入命令 zkCli.sh，连接到 ZooKeeper 服务。

[hadoop@master ~]$ zkCli.sh Connecting to localhost:21812023-04-23 22:39:17,093 [myid:] - INFO  [main:Environment@100] - Client environment:zookeeper.version=3.4.8--1, built on 02/06/2016 03:18 GMT2023-04-23 22:39:17,096 [myid:] - INFO  [main:Environment@100] - Client environment:host.name=master2023-04-23 22:39:17,096 [myid:] - INFO  [main:Environment@100] - Client environment:java.version=1.8.0_1522023-04-23 22:39:17,097 [myid:] - INFO  [main:Environment@100] - Client environment:java.vendor=Oracle Corporation2023-04-23 22:39:17,097 [myid:] - INFO  [main:Environment@100] - Client environment:java.home=/usr/local/src/jdk/jre2023-04-23 22:39:17,097 [myid:] - INFO  [main:Environment@100] - Client environment:java.class.path=/usr/local/src/zookeeper/bin/../build/classes:/usr/local/src/zookeeper/bin/../build/lib/*.jar:/usr/local/src/zookeeper/bin/../lib/slf4j-log4j12-1.6.1.jar:/usr/local/src/zookeeper/bin/../lib/slf4j-api-1.6.1.jar:/usr/local/src/zookeeper/bin/../lib/netty-3.7.0.Final.jar:/usr/local/src/zookeeper/bin/../lib/log4j-1.2.16.jar:/usr/local/src/zookeeper/bin/../lib/jline-0.9.94.jar:/usr/local/src/zookeeper/bin/../zookeeper-3.4.8.jar:/usr/local/src/zookeeper/bin/../src/java/lib/*.jar:/usr/local/src/zookeeper/bin/../conf:/usr/local/src/sqoop/lib:2023-04-23 22:39:17,097 [myid:] - INFO  [main:Environment@100] - Client environment:java.library.path=/usr/java/packages/lib/amd64:/usr/lib64:/lib64:/lib:/usr/lib2023-04-23 22:39:17,097 [myid:] - INFO  [main:Environment@100] - Client environment:java.io.tmpdir=/tmp2023-04-23 22:39:17,097 [myid:] - INFO  [main:Environment@100] - Client environment:java.compiler=2023-04-23 22:39:17,097 [myid:] - INFO  [main:Environment@100] - Client environment:os.name=Linux2023-04-23 22:39:17,097 [myid:] - INFO  [main:Environment@100] - Client environment:os.arch=amd642023-04-23 22:39:17,097 [myid:] - INFO  [main:Environment@100] - Client environment:os.version=3.10.0-862.el7.x86_642023-04-23 22:39:17,097 [myid:] - INFO  [main:Environment@100] - Client environment:user.name=hadoop2023-04-23 22:39:17,097 [myid:] - INFO  [main:Environment@100] - Client environment:user.home=/home/hadoop2023-04-23 22:39:17,097 [myid:] - INFO  [main:Environment@100] - Client environment:user.dir=/home/hadoop2023-04-23 22:39:17,099 [myid:] - INFO  [main:ZooKeeper@438] - Initiating client connection, connectString=localhost:2181 sessionTimeout=30000 watcher=org.apache.zookeeper.ZooKeeperMain$MyWatcher@69d0a921Welcome to ZooKeeper!2023-04-23 22:39:17,122 [myid:] - INFO  [main-SendThread(localhost:2181):ClientCnxn$SendThread@1032] - Opening socket connection to server localhost/0:0:0:0:0:0:0:1:2181. Will not attempt to authenticate using SASL (unknown error)JLine support is enabled2023-04-23 22:39:17,208 [myid:] - INFO  [main-SendThread(localhost:2181):ClientCnxn$SendThread@876] - Socket connection established to localhost/0:0:0:0:0:0:0:1:2181, initiating session2023-04-23 22:39:17,223 [myid:] - INFO  [main-SendThread(localhost:2181):ClientCnxn$SendThread@1299] - Session establishment complete on server localhost/0:0:0:0:0:0:0:1:2181, sessionid = 0x187ae88b8390000, negotiated timeout = 30000WATCHER::WatchedEvent state:SyncConnected type:None path:null[zk: localhost:2181(CONNECTED) 0] 

步骤五: 使用 Watch 监听/hbase 目录，一旦/hbase 内容有变化，将会有提 示。打开监视，执行命令 get /hbase 1。

[zk: localhost:2181(CONNECTED) 0] get /hbase 1cZxid = 0x500000002ctime = Sun Apr 23 21:58:42 CST 2023mZxid = 0x500000002mtime = Sun Apr 23 21:58:42 CST 2023pZxid = 0x500000062cversion = 18dataVersion = 0aclVersion = 0ephemeralOwner = 0x0dataLength = 0numChildren = 14[zk: localhost:2181(CONNECTED) 1] quitQuitting...2023-04-23 22:40:16,816 [myid:] - INFO  [main:ZooKeeper@684] - Session: 0x187ae88b8390001 closed2023-04-23 22:40:16,817 [myid:] - INFO  [main-EventThread:ClientCnxn$EventThread@519] - EventThread shut down for session: 0x187ae88b8390001[hadoop@master ~]$ 

结果显示，当执行命令 set /hbase value-update 后，数据版本由 0 变成 1，说明 /hbase 处于监控中。

步骤一: 查询 Sqoop 版本号，验证 Sqoop 是否启动成功。

首先切换到/usr/local/src/sqoop 目录，执行命令：./bin/sqoop-version

[hadoop@master ~]$ cd /usr/local/src/sqoop/[hadoop@master sqoop]$  ./bin/sqoop-version Warning: /usr/local/src/sqoop/../hcatalog does not exist! HCatalog jobs will fail.Please set $HCAT_HOME to the root of your HCatalog installation.Warning: /usr/local/src/sqoop/../accumulo does not exist! Accumulo imports will fail.Please set $ACCUMULO_HOME to the root of your Accumulo installation.23/04/23 22:40:59 INFO sqoop.Sqoop: Running Sqoop version: 1.4.7Sqoop 1.4.7git commit id 2328971411f57f0cb683dfb79d19d4d19d185dd8Compiled by maugli on Thu Dec 21 15:59:58 STD 2017

结果显示：Sqoop 1.4.7，说明 Sqoop 版本号为 1.4.7，并启动成功。

步骤二: 测试 Sqoop 是否能够成功连接数据库

切换到 Sqoop 的 目 录 ， 执 行 命 令 bin/sqoop list-databases --connect jdbc:mysql://master:3306/ --username root --password Password123$，命令中 “master:3306”为数据库主机名和端口。

[hadoop@master sqoop]$  bin/sqoop list-databases --connect jdbc:mysql://master:3306/ --username root --password Password@123!Warning: /usr/local/src/sqoop/../hcatalog does not exist! HCatalog jobs will fail.Please set $HCAT_HOME to the root of your HCatalog installation.Warning: /usr/local/src/sqoop/../accumulo does not exist! Accumulo imports will fail.Please set $ACCUMULO_HOME to the root of your Accumulo installation.23/04/23 22:42:16 INFO sqoop.Sqoop: Running Sqoop version: 1.4.723/04/23 22:42:16 WARN tool.BaseSqoopTool: Setting your password on the command-line is insecure. Consider using -P instead.23/04/23 22:42:16 INFO manager.MySQLManager: Preparing to use a MySQL streaming resultset.Sun Apr 23 22:42:16 CST 2023 WARN: Establishing SSL connection without server"s identity verification is not recommended. According to MySQL 5.5.45+, 5.6.26+ and 5.7.6+ requirements SSL connection must be established by default if explicit option isn"t set. For compliance with existing applications not using SSL the verifyServerCertificate property is set to "false". You need either to explicitly disable SSL by setting useSSL=false, or set useSSL=true and provide truststore for server certificate verification.information_schemahivemysqlperformance_schemasamplesys

结果显示，可以连接到 MySQL，并查看到 Master 主机中 MySQL 的所有库实例，如 information_schema、hive、mysql、performance_schema 和 sys 等数据库。

步骤三: 执行命令 sqoop help，可以看到如下内容，代表 Sqoop 启动成功。

[hadoop@master sqoop]$ sqoop helpWarning: /usr/local/src/sqoop/../hcatalog does not exist! HCatalog jobs will fail.Please set $HCAT_HOME to the root of your HCatalog installation.Warning: /usr/local/src/sqoop/../accumulo does not exist! Accumulo imports will fail.Please set $ACCUMULO_HOME to the root of your Accumulo installation.23/04/23 22:42:37 INFO sqoop.Sqoop: Running Sqoop version: 1.4.7usage: sqoop COMMAND [ARGS]Available commands:  codegen            Generate code to interact with database records  create-hive-table  Import a table definition into Hive  eval               Evaluate a SQL statement and display the results  export             Export an HDFS directory to a database table  help               List available commands  import             Import a table from a database to HDFS  import-all-tables  Import tables from a database to HDFS  import-mainframe   Import datasets from a mainframe server to HDFS  job                Work with saved jobs  list-databases     List available databases on a server  list-tables        List available tables in a database  merge              Merge results of incremental imports  metastore          Run a standalone Sqoop metastore  version            Display version informationSee "sqoop help COMMAND" for information on a specific command.

结果显示了 Sqoop 的常用命令和功能，如下表所示。

步骤一:检查 Flume 安装是否成功，执行 flume-ng version 命令，查看 Flume 的版本。

[hadoop@master sqoop]$ cd /usr/local/src/flume/[hadoop@master flume]$ flume-ng versionFlume 1.6.0Source code repository: https://git-wip-us.apache.org/repos/asf/flume.gitRevision: 2561a23240a71ba20bf288c7c2cda88f443c2080Compiled by hshreedharan on Mon May 11 11:15:44 PDT 2015From source with checksum b29e416802ce9ece3269d34233baf43f[hadoop@master flume]$ 

步骤二:添加 example.conf 到/usr/local/src/flume

[hadoop@master flume]$ vim /usr/local/src/flume/example.confa1.sources=r1a1.sinks=k1a1.channels=c1a1.sources.r1.type=spooldira1.sources.r1.spoolDir=/usr/local/src/flume/a1.sources.r1.fileHeader=truea1.sinks.k1.type=hdfsa1.sinks.k1.hdfs.path=hdfs://master:9000/flumea1.sinks.k1.hdfs.rollsize=1048760a1.sinks.k1.hdfs.rollCount=0a1.sinks.k1.hdfs.rollInterval=900a1.sinks.k1.hdfs.useLocalTimeStamp=truea1.channels.c1.type=filea1.channels.c1.capacity=1000a1.channels.c1.transactionCapacity=100a1.sources.r1.channels = c1a1.sinks.k1.channel = c1

步骤三:启动 Flume Agent a1 日志控制台

[hadoop@master flume]$ flume-ng agent --conf-file example.conf --name a1 -Dflume.root.logger=INFO,consoleWarning: No configuration directory set! Use --conf  to override.Info: Including Hadoop libraries found via (/usr/local/src/hadoop/bin/hadoop) for HDFS accessInfo: Excluding /usr/local/src/hadoop/share/hadoop/common/lib/slf4j-api-1.7.10.jar from classpathInfo: Excluding /usr/local/src/hadoop/share/hadoop/common/lib/slf4j-log4j12-1.7.10.jar from classpathInfo: Including HBASE libraries found via (/usr/local/src/hbase/bin/hbase) for HBASE accessInfo: Excluding /usr/local/src/hbase/lib/slf4j-api-1.7.7.jar from classpathInfo: Excluding /usr/local/src/hbase/lib/slf4j-log4j12-1.7.5.jar from classpathxxxxxxxxxx[hadoop@master flume]$ flume-ng agent --conf-file example.conf --name a1 -Dflume.root.logger=INFO,consoleWarning: No configuration directory set! Use --conf  to override.Info: Including Hadoop libraries found via (/usr/local/src/hadoop/bin/hadoop) for HDFS accessInfo: Excluding /usr/local/src/hadoop/share/hadoop/common/lib/slf4j-api-1.7.10.jar from classpathInfo: Excluding /usr/local/src/hadoop/share/hadoop/common/lib/slf4j-log4j12-1.7.10.jar from classpathInfo: Including HBASE libraries found via (/usr/local/src/hbase/bin/hbase) for HBASE accessInfo: Excluding /usr/local/src/hbase/lib/slf4j-api-1.7.7.jar from classpathInfo: Excluding /usr/local/src/hbase/lib/slf4j-log4j12-1.7.5.jar from classpath[hadoop@master flume]$  /usr/local/src/flume/bin/flume-ng agent --conf ./conf --conf-file ./example.conf --name a1 -Dflume.root.logger=INFO,consoleInfo: Sourcing environment configuration script /usr/local/src/flume/conf/flume-env.shInfo: Including Hadoop libraries found via (/usr/local/src/hadoop/bin/hadoop) for HDFS access.../lib/native:/usr/local/src/hadoop/lib/native org.apache.flume.node.Application --conf-file ./example.conf --name a1/usr/local/src/flume/bin/flume-ng: line 241: /usr/loocal/src/jdk/bin/java: No such file or directory...23/04/23 22:52:56 INFO instrumentation.MonitoredCounterGroup: Shutdown Metric for type: SINK, name: k1. sink.connection.failed.count == 023/04/23 22:52:56 INFO instrumentation.MonitoredCounterGroup: Shutdown Metric for type: SINK, name: k1. sink.event.drain.attempt == 191823/04/23 22:52:56 INFO instrumentation.MonitoredCounterGroup: Shutdown Metric for type: SINK, name: k1. sink.event.drain.sucess == 1918[hadoop@master flume]$ ^C

步骤四: 查看结果

[hadoop@master flume]$ hdfs dfs -lsr /flumelsr: DEPRECATED: Please use "ls -R" instead.-rw-r--r--   2 hadoop supergroup       1499 2023-04-23 22:52 /flume/FlumeData.1682261559722-rw-r--r--   2 hadoop supergroup       1419 2023-04-23 22:52 /flume/FlumeData.1682261559723-rw-r--r--   2 hadoop supergroup       1468 2023-04-23 22:52 /flume/FlumeData.1682261559724...-rw-r--r--   2 hadoop supergroup       1795 2023-04-23 22:52 /flume/FlumeData.1682261559817-rw-r--r--   2 hadoop supergroup       1841 2023-04-23 22:52 /flume/FlumeData.1682261559818-rw-r--r--   2 hadoop supergroup       1665 2023-04-23 22:52 /flume/FlumeData.1682261559819-rw-r--r--   2 hadoop supergroup       1439 2023-04-23 22:52 /flume/FlumeData.1682261559820

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