CDH5.12.1 安装部署

 

 

###通过http://192.168.50.200:7180/cmf/login 访问CM控制台

4.CDH安装

4.1CDH集群安装向导

1.admin/admin登陆到CM

2.同意license协议，点击继续

3.选择60试用，点击继续

4.点击“继续”

5.输入主机IP或者名称，点击搜索找到主机名后点击继续

6.点击“继续”

7.使用parcel选项，点击“更多选项”，点击“-”删除其他所有的地址，输入http://ip-192-168-50-200.hns.com/cdh5.12.1/点击“保存更改”

8.选择自定义存储库，输入cm的http地址

9.点击“继续”，进入下一步安装jdk

 

10.点击“继续”，进入下一步，默认多用户模式

11.点击“继续”，进入下一步配置ssh账号密码：

12.点击“继续”，进入下一步，安装Cloudera Manager相关到各个节点

13.点击“继续”，进入下一步安装cdh到各个节点

14.点击“继续”，进入下一步主机检查，确保所有检查项均通过

上述的解决方案：

在每台机器上执行如下操作：

[root@ip-192-168-50-200 ~]# echo never > /sys/kernel/mm/transparent_hugepage/enabled
[root@ip-192-168-50-200 ~]# echo never > /sys/kernel/mm/transparent_hugepage/defrag

[root@ip-192-168-50-200 ~]# echo "vm.swappiness = 10" >> /etc/sysctl.conf

[root@ip-192-168-50-200 ~]# sysctl -p

 

点击完成进入服务安装向导！！！

4.2 集群设置安装向导

1.选择需要安装的服务，此处使用自定义服务，如下图

2.点击“继续”，进入集群角色分配

HDFS角色分配：

Hive角色分配：

Cloudera Manager Service 角色分配：

Spark角色分配：（Spark on Yarn 所以没有spark的master和worker 角色）

Yarn角色分配：

Zookeeper角色分配：（至少3个Server）

3.角色分配完成点击“继续”，进入下一步，测试数据库连接

 

4.测试成功，点击“继续”，进入目录设置，此处使用默认默认目录，根据实际情况进行目录修改

5.点击“继续”，等待服务启动成功！！！

6.点击“继续”，显示集群安装成功！

7.安装成功后，进入home管理界面

5.快速组建服务验证

5.1HDFS验证（mkdir+put+cat +get）

mkdir操作：

put 操作：

cat 操作：

get 操作：

5.2 Hive 验证

使用hive命令行操作

hive> create external table test_table(
    > s1 string,
    > s2 string
    > )row format delimited fields terminated by ','
    > stored as textfile location '/hns/test';
OK
Time taken: 0.074 seconds
hive> show tables;
OK
test_table
Time taken: 0.012 seconds, Fetched: 1 row(s)

hive> select * from test_table;
OK
1 test
2 hns
3 zhangsan
Time taken: 0.054 seconds, Fetched: 3 row(s)
hive>

hive> insert into test_table values("4","lisi");
Query ID = hdfs_20181013220202_823a17d7-fb58-40e9-bf33-11f44d0de10a
Total jobs = 3
Launching Job 1 out of 3
Number of reduce tasks is set to 0 since there's no reduce operator
Starting Job = job_1539418452562_0003, Tracking URL = http://ip-192-168-50-200.hns.com:8088/proxy/application_1539418452562_0003/
Kill Command = /opt/cloudera/parcels/CDH-5.12.1-1.cdh5.12.1.p0.3/lib/hadoop/bin/hadoop job  -kill job_1539418452562_0003
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 0
2018-10-13 22:02:42,009 Stage-1 map = 0%,  reduce = 0%
2018-10-13 22:02:49,308 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 0.93 sec
MapReduce Total cumulative CPU time: 930 msec
Ended Job = job_1539418452562_0003
Stage-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://ip-192-168-50-200.hns.com:8020/hns/test/.hive-staging_hive_2018-10-13_22-02-31_572_2687237229927791201-1/-ext-10000
Loading data to table default.test_table
Table default.test_table stats: [numFiles=2, numRows=1, totalSize=31, rawDataSize=6]
MapReduce Jobs Launched: 
Stage-Stage-1: Map: 1   Cumulative CPU: 0.93 sec   HDFS Read: 3658 HDFS Write: 81 SUCCESS
Total MapReduce CPU Time Spent: 930 msec
OK
Time taken: 19.016 seconds
hive> select * from test_table;
OK
4       lisi
1       test
2       hns
3       zhangsan
Time taken: 0.121 seconds, Fetched: 4 row(s)
hive> 

Hive MapReduce操作：

hive> select count(*) from test_table;
Query ID = hdfs_20181013220606_1011d0ce-9ddd-43ec-a103-18b3a32ea292
Total jobs = 1
Launching Job 1 out of 1
Number of reduce tasks determined at compile time: 1
In order to change the average load for a reducer (in bytes):
  set hive.exec.reducers.bytes.per.reducer=
In order to limit the maximum number of reducers:
  set hive.exec.reducers.max=
In order to set a constant number of reducers:
  set mapreduce.job.reduces=
Starting Job = job_1539418452562_0004, Tracking URL = http://ip-192-168-50-200.hns.com:8088/proxy/application_1539418452562_0004/
Kill Command = /opt/cloudera/parcels/CDH-5.12.1-1.cdh5.12.1.p0.3/lib/hadoop/bin/hadoop job  -kill job_1539418452562_0004
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 1
2018-10-13 22:06:45,367 Stage-1 map = 0%,  reduce = 0%
2018-10-13 22:06:52,595 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 1.12 sec
2018-10-13 22:07:00,998 Stage-1 map = 100%,  reduce = 100%, Cumulative CPU 2.28 sec
MapReduce Total cumulative CPU time: 2 seconds 280 msec
Ended Job = job_1539418452562_0004
MapReduce Jobs Launched: 
Stage-Stage-1: Map: 1  Reduce: 1   Cumulative CPU: 2.28 sec   HDFS Read: 7350 HDFS Write: 2 SUCCESS
Total MapReduce CPU Time Spent: 2 seconds 280 msec
OK
4
Time taken: 24.471 seconds, Fetched: 1 row(s)

5.3 MapReduce 验证：

[hdfs@ip-192-168-50-200 hadoop-mapreduce]$ pwd
/opt/cloudera/parcels/CDH/lib/hadoop-mapreduce

[hdfs@ip-192-168-50-200 hadoop-mapreduce]$ hadoop jar hadoop-mapreduce-examples.jar pi 5 5
Number of Maps  = 5
Samples per Map = 5
Wrote input for Map #0
Wrote input for Map #1
Wrote input for Map #2
Wrote input for Map #3
Wrote input for Map #4
Starting Job
.
.
.
18/10/13 22:12:33 INFO mapreduce.Job: Running job: job_1539418452562_0005
18/10/13 22:12:41 INFO mapreduce.Job: Job job_1539418452562_0005 running in uber mode : false
18/10/13 22:12:41 INFO mapreduce.Job:  map 0% reduce 0%
18/10/13 22:12:49 INFO mapreduce.Job:  map 40% reduce 0%
18/10/13 22:12:54 INFO mapreduce.Job:  map 80% reduce 0%
18/10/13 22:12:59 INFO mapreduce.Job:  map 100% reduce 0%
18/10/13 22:13:03 INFO mapreduce.Job:  map 100% reduce 100%
18/10/13 22:13:03 INFO mapreduce.Job: Job job_1539418452562_0005 completed successfully
18/10/13 22:13:03 INFO mapreduce.Job: Counters: 49
        File System Counters
.
.
.

5.4 Spark 验证

scala> val testFile=sc.textFile("hdfs://ip-192-168-50-200.hns.com:8020/hns/test/a.txt")
testFile: org.apache.spark.rdd.RDD[String] = hdfs://ip-192-168-50-200.hns.com:8020/hns/test/a.txt MapPartitionsRDD[1] at textFile at :27

scala> testFile.count()
res2: Long = 3 

 

转载于:https://www.cnblogs.com/shanhua-fu/p/9783504.html