参考资料: http://www.cloudera.com/content/cloudera-content/cloudera-docs/CDH4/latest/CDH4-Quick-Start/cdh4qs_topic_3_3.html http://www.cloudera.com/content/cloudera-content/cloudera-docs/Impala/latest/Installing-and-Using-Impala/Installing
参考资料:
http://www.cloudera.com/content/cloudera-content/cloudera-docs/CDH4/latest/CDH4-Quick-Start/cdh4qs_topic_3_3.html
http://www.cloudera.com/content/cloudera-content/cloudera-docs/Impala/latest/Installing-and-Using-Impala/Installing-and-Using-Impala.html
http://blog.cloudera.com/blog/2013/02/from-zero-to-impala-in-minutes/
什么是Impala？
Cloudera发布了实时查询开源项目Impala，根据多款产品实测表明，它比原来基于MapReduce的Hive SQL查询速度提升3～90倍。Impala是Google Dremel的模仿，但在SQL功能上青出于蓝胜于蓝。
1. 安装JDK
$ sudo yum install jdk-6u41-linux-amd64.rpm
2. 伪分布式模式安装CDH4
$ cd /etc/yum.repos.d/
$ sudo wget http://archive.cloudera.com/cdh4/redhat/6/x86_64/cdh/cloudera-cdh4.repo
$ sudo yum install hadoop-conf-pseudo
格式化NameNode.
$ sudo -u hdfs hdfs namenode -format
启动HDFS
$ for x in `cd /etc/init.d ; ls hadoop-hdfs-*` ; do sudo service $x start ; done
创建/tmp目录
$ sudo -u hdfs hadoop fs -rm -r /tmp
$ sudo -u hdfs hadoop fs -mkdir /tmp
$ sudo -u hdfs hadoop fs -chmod -R 1777 /tmp
创建YARN与日志目录
$ sudo -u hdfs hadoop fs -mkdir /tmp/hadoop-yarn/staging
$ sudo -u hdfs hadoop fs -chmod -R 1777 /tmp/hadoop-yarn/staging
$ sudo -u hdfs hadoop fs -mkdir /tmp/hadoop-yarn/staging/history/done_intermediate
$ sudo -u hdfs hadoop fs -chmod -R 1777 /tmp/hadoop-yarn/staging/history/done_intermediate
$ sudo -u hdfs hadoop fs -chown -R mapred:mapred /tmp/hadoop-yarn/staging
$ sudo -u hdfs hadoop fs -mkdir /var/log/hadoop-yarn
$ sudo -u hdfs hadoop fs -chown yarn:mapred /var/log/hadoop-yarn
检查HDFS文件树
$ sudo -u hdfs hadoop fs -ls -R /
drwxrwxrwt - hdfs supergroup 0 2012-05-31 15:31 /tmp drwxr-xr-x - hdfs supergroup 0 2012-05-31 15:31 /tmp/hadoop-yarn drwxrwxrwt - mapred mapred 0 2012-05-31 15:31 /tmp/hadoop-yarn/staging drwxr-xr-x - mapred mapred 0 2012-05-31 15:31 /tmp/hadoop-yarn/staging/history drwxrwxrwt - mapred mapred 0 2012-05-31 15:31 /tmp/hadoop-yarn/staging/history/done_intermediate drwxr-xr-x - hdfs supergroup 0 2012-05-31 15:31 /var drwxr-xr-x - hdfs supergroup 0 2012-05-31 15:31 /var/log drwxr-xr-x - yarn mapred 0 2012-05-31 15:31 /var/log/hadoop-yarn
启动YARN
$ sudo service hadoop-yarn-resourcemanager start
$ sudo service hadoop-yarn-nodemanager start
$ sudo service hadoop-mapreduce-historyserver start
创建用户目录(以用户dong.guo为例):
$ sudo -u hdfs hadoop fs -mkdir /user/dong.guo
$ sudo -u hdfs hadoop fs -chown dong.guo /user/dong.guo
测试上传文件
$ hadoop fs -mkdir input
$ hadoop fs -put /etc/hadoop/conf/*.xml input
$ hadoop fs -ls input
Found 4 items -rw-r--r-- 1 dong.guo supergroup 1461 2013-05-14 03:30 input/core-site.xml -rw-r--r-- 1 dong.guo supergroup 1854 2013-05-14 03:30 input/hdfs-site.xml -rw-r--r-- 1 dong.guo supergroup 1325 2013-05-14 03:30 input/mapred-site.xml -rw-r--r-- 1 dong.guo supergroup 2262 2013-05-14 03:30 input/yarn-site.xml
配置HADOOP_MAPRED_HOME环境变量
$ export HADOOP_MAPRED_HOME=/usr/lib/hadoop-mapreduce
运行一个测试Job
$ hadoop jar /usr/lib/hadoop-mapreduce/hadoop-mapreduce-examples.jar grep input output23 'dfs[a-z.]+'
Job完成后，可以看到以下目录
$ hadoop fs -ls
Found 2 items drwxr-xr-x - dong.guo supergroup 0 2013-05-14 03:30 input drwxr-xr-x - dong.guo supergroup 0 2013-05-14 03:32 output23
$ hadoop fs -ls output23
Found 2 items -rw-r--r-- 1 dong.guo supergroup 0 2013-05-14 03:32 output23/_SUCCESS -rw-r--r-- 1 dong.guo supergroup 150 2013-05-14 03:32 output23/part-r-00000
$ hadoop fs -cat output23/part-r-00000 | head
1 dfs.safemode.min.datanodes 1 dfs.safemode.extension 1 dfs.replication 1 dfs.namenode.name.dir 1 dfs.namenode.checkpoint.dir 1 dfs.datanode.data.dir
3. 安装 Hive
$ sudo yum install hive hive-metastore hive-server
$ sudo yum install mysql-server
$ sudo service mysqld start
$ cd ~
$ wget 'http://cdn.mysql.com/Downloads/Connector-J/mysql-connector-java-5.1.25.tar.gz'
$ tar xzf mysql-connector-java-5.1.25.tar.gz
$ sudo cp mysql-connector-java-5.1.25/mysql-connector-java-5.1.25-bin.jar /usr/lib/hive/lib/
$ sudo /usr/bin/mysql_secure_installation
[...] Enter current password for root (enter for none): OK, successfully used password, moving on... [...] Set root password? [Y/n] y New password:hadoophive Re-enter new password:hadoophive Remove anonymous users? [Y/n] Y [...] Disallow root login remotely? [Y/n] N [...] Remove test database and access to it [Y/n] Y [...] Reload privilege tables now? [Y/n] Y All done!
$ mysql -u root -phadoophive
mysql> CREATE DATABASE metastore; mysql> USE metastore; mysql> SOURCE /usr/lib/hive/scripts/metastore/upgrade/mysql/hive-schema-0.10.0.mysql.sql; mysql> CREATE USER 'hive'@'%' IDENTIFIED BY 'hadoophive'; mysql> CREATE USER 'hive'@'localhost' IDENTIFIED BY 'hadoophive'; mysql> REVOKE ALL PRIVILEGES, GRANT OPTION FROM 'hive'@'%'; mysql> REVOKE ALL PRIVILEGES, GRANT OPTION FROM 'hive'@'localhost'; mysql> GRANT SELECT,INSERT,UPDATE,DELETE,LOCK TABLES,EXECUTE ON metastore.* TO 'hive'@'%'; mysql> GRANT SELECT,INSERT,UPDATE,DELETE,LOCK TABLES,EXECUTE ON metastore.* TO 'hive'@'localhost'; mysql> FLUSH PRIVILEGES; mysql> quit;
$ sudo mv /etc/hive/conf/hive-site.xml /etc/hive/conf/hive-site.xml.bak
$ sudo vim /etc/hive/conf/hive-site.xml
javax.jdo.option.ConnectionURL jdbc:mysql://localhost/metastore the URL of the MySQL database javax.jdo.option.ConnectionDriverName com.mysql.jdbc.Driver javax.jdo.option.ConnectionUserName hive javax.jdo.option.ConnectionPassword hadoophive datanucleus.autoCreateSchema false datanucleus.fixedDatastore true hive.metastore.uris thrift://127.0.0.1:9083 IP address (or fully-qualified domain name) and port of the metastore host hive.aux.jars.path file:///usr/lib/hive/lib/zookeeper.jar,file:///usr/lib/hive/lib/hbase.jar,file:///usr/lib/hive/lib/hive-hbase-handler-0.10.0-cdh4.2.0.jar,file:///usr/lib/hive/lib/guava-11.0.2.jar
$ sudo service hive-metastore start
Starting (hive-metastore): [ OK ]
$ sudo service hive-server start
Starting (hive-server): [ OK ]
$ sudo -u hdfs hadoop fs -mkdir /user/hive
$ sudo -u hdfs hadoop fs -chown hive /user/hive
$ sudo -u hdfs hadoop fs -mkdir /tmp
$ sudo -u hdfs hadoop fs -chmod 777 /tmp
$ sudo -u hdfs hadoop fs -chmod o+t /tmp
$ sudo -u hdfs hadoop fs -mkdir /data
$ sudo -u hdfs hadoop fs -chown hdfs /data
$ sudo -u hdfs hadoop fs -chmod 777 /data
$ sudo -u hdfs hadoop fs -chmod o+t /data
$ sudo chown -R hive:hive /var/lib/hive
$ sudo vim /tmp/kv1.txt
1 www.baidu.com 2 www.google.com 3 www.sina.com.cn 4 www.163.com 5 heylinx.com
$ sudo -u hive hive
Logging initialized using configuration in file:/etc/hive/conf.dist/hive-log4j.properties Hive history file=/tmp/root/hive_job_log_root_201305140801_825709760.txt hive> CREATE TABLE IF NOT EXISTS pokes ( foo INT,bar STRING ) ROW FORMAT DELIMITED FIELDS TERMINATED BY "\t" LINES TERMINATED BY "\n"; hive> show tables; OK pokes Time taken: 0.415 seconds hive> LOAD DATA LOCAL INPATH '/tmp/kv1.txt' OVERWRITE INTO TABLE pokes; Copying data from file:/tmp/kv1.txt Copying file: file:/tmp/kv1.txt Loading data to table default.pokes rmr: DEPRECATED: Please use 'rm -r' instead. Deleted /user/hive/warehouse/pokes Table default.pokes stats: [num_partitions: 0, num_files: 1, num_rows: 0, total_size: 79, raw_data_size: 0] OK Time taken: 1.681 seconds
$ export HADOOP_MAPRED_HOME=/usr/lib/hadoop-mapreduce
4. 安装 Impala
$ cd /etc/yum.repos.d/
$ sudo wget http://archive.cloudera.com/impala/redhat/6/x86_64/impala/cloudera-impala.repo
$ sudo yum install impala impala-shell
$ sudo yum install impala-server impala-state-store
$ sudo vim /etc/hadoop/conf/hdfs-site.xml
... dfs.client.read.shortcircuit true dfs.domain.socket.path /var/run/hadoop-hdfs/dn._PORT dfs.client.file-block-storage-locations.timeout 3000 dfs.datanode.hdfs-blocks-metadata.enabled true
$ sudo cp -rpa /etc/hadoop/conf/core-site.xml /etc/impala/conf/
$ sudo cp -rpa /etc/hadoop/conf/hdfs-site.xml /etc/impala/conf/
$ sudo service hadoop-hdfs-datanode restart
$ sudo service impala-state-store restart
$ sudo service impala-server restart
$ sudo /usr/java/default/bin/jps
5. 安装 Hbase
$ sudo yum install hbase
$ sudo vim /etc/security/limits.conf
hdfs - nofile 32768 hbase - nofile 32768
$ sudo vim /etc/pam.d/common-session
session required pam_limits.so
$ sudo vim /etc/hadoop/conf/hdfs-site.xml
dfs.datanode.max.xcievers 4096
$ sudo cp /usr/lib/impala/lib/hive-hbase-handler-0.10.0-cdh4.2.0.jar /usr/lib/hive/lib/hive-hbase-handler-0.10.0-cdh4.2.0.jar
$ sudo /etc/init.d/hadoop-hdfs-namenode restart
$ sudo /etc/init.d/hadoop-hdfs-datanode restart
$ sudo yum install hbase-master
$ sudo service hbase-master start
$ sudo -u hive hive
Logging initialized using configuration in file:/etc/hive/conf.dist/hive-log4j.properties
Hive history file=/tmp/hive/hive_job_log_hive_201305140905_2005531704.txt
hive> CREATE TABLE hbase_table_1(key int, value string) STORED BY 'org.apache.hadoop.hive.hbase.HBaseStorageHandler' WITH SERDEPROPERTIES ("hbase.columns.mapping" = ":key,cf1:val") TBLPROPERTIES ("hbase.table.name" = "xyz");
OK
Time taken: 3.587 seconds
hive> INSERT OVERWRITE TABLE hbase_table_1 SELECT * FROM pokes WHERE foo=5;
Total MapReduce jobs = 1
Launching Job 1 out of 1
Number of reduce tasks is set to 0 since there's no reduce operator
Starting Job = job_1368502088579_0004, Tracking URL = http://ip-10-197-10-4:8088/proxy/application_1368502088579_0004/
Kill Command = /usr/lib/hadoop/bin/hadoop job -kill job_1368502088579_0004
Hadoop job information for Stage-0: number of mappers: 1; number of reducers: 0
2013-05-14 09:12:45,340 Stage-0 map = 0%, reduce = 0%
2013-05-14 09:12:53,165 Stage-0 map = 100%, reduce = 0%, Cumulative CPU 2.63 sec
MapReduce Total cumulative CPU time: 2 seconds 630 msec
Ended Job = job_1368502088579_0004
1 Rows loaded to hbase_table_1
MapReduce Jobs Launched:
Job 0: Map: 1 Cumulative CPU: 2.63 sec HDFS Read: 288 HDFS Write: 0 SUCCESS
Total MapReduce CPU Time Spent: 2 seconds 630 msec
OK
Time taken: 21.063 seconds
hive> select * from hbase_table_1;
OK
5 heylinx.com
Time taken: 0.685 seconds
hive> SELECT COUNT (*) FROM pokes;
Total MapReduce 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 mapred.reduce.tasks=
Starting Job = job_1368502088579_0005, Tracking URL = http://ip-10-197-10-4:8088/proxy/application_1368502088579_0005/
Kill Command = /usr/lib/hadoop/bin/hadoop job -kill job_1368502088579_0005
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 1
2013-05-14 10:32:04,711 Stage-1 map = 0%, reduce = 0%
2013-05-14 10:32:11,461 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.22 sec
2013-05-14 10:32:12,554 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.22 sec
2013-05-14 10:32:13,642 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.22 sec
2013-05-14 10:32:14,760 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.22 sec
2013-05-14 10:32:15,918 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.22 sec
2013-05-14 10:32:16,991 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.22 sec
2013-05-14 10:32:18,111 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.22 sec
2013-05-14 10:32:19,188 Stage-1 map = 100%, reduce = 100%, Cumulative CPU 4.04 sec
MapReduce Total cumulative CPU time: 4 seconds 40 msec
Ended Job = job_1368502088579_0005
MapReduce Jobs Launched:
Job 0: Map: 1 Reduce: 1 Cumulative CPU: 4.04 sec HDFS Read: 288 HDFS Write: 2 SUCCESS
Total MapReduce CPU Time Spent: 4 seconds 40 msec
OK
5
Time taken: 28.195 seconds
6. 测试Impala性能
View parameters on http://ec2-204-236-182-78.us-west-1.compute.amazonaws.com:25000
$ impala-shell
[ip-10-197-10-4.us-west-1.compute.internal:21000] > CREATE TABLE IF NOT EXISTS pokes ( foo INT,bar STRING ) ROW FORMAT DELIMITED FIELDS TERMINATED BY "\t" LINES TERMINATED BY "\n"; Query: create TABLE IF NOT EXISTS pokes ( foo INT,bar STRING ) ROW FORMAT DELIMITED FIELDS TERMINATED BY "\t" LINES TERMINATED BY "\n" [ip-10-197-10-4.us-west-1.compute.internal:21000] > show tables; Query: show tables Query finished, fetching results ... +-------+ | name | +-------+ | pokes | +-------+ Returned 1 row(s) in 0.00s [ip-10-197-10-4.us-west-1.compute.internal:21000] > SELECT * from pokes; Query: select * from pokes Query finished, fetching results ... +-----+-----------------+ | foo | bar | +-----+-----------------+ | 1 | www.baidu.com | | 2 | www.google.com | | 3 | www.sina.com.cn | | 4 | www.163.com | | 5 | heylinx.com | +-----+-----------------+ Returned 5 row(s) in 0.28s [ip-10-197-10-4.us-west-1.compute.internal:21000] > SELECT COUNT (*) from pokes; Query: select COUNT (*) from pokes Query finished, fetching results ... +----------+ | count(*) | +----------+ | 5 | +----------+ Returned 1 row(s) in 0.34s
通过两个COUNT的结果来看，Hive使用了 28.195 seconds 而 Impala仅使用了0.34s，由此可以看出Impala的性能确实要优于Hive。
原文地址：伪分布式安装部署CDH4.2.1与Impala[原创实践], 感谢原作者分享。
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