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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
2. 伪分布式模式安装CDH4
$ 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.
启动HDFS
代码如下 $ for x in `cd /etc/init.d ; ls hadoop-hdfs-*` ; do sudo service $x start ; done创建/tmp目录
代码如下 $ 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
代码如下$ 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
代码如下$ 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
代码如下 $ 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):
代码如下 $ 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
代码如下$ 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
代码如下 $ export HADOOP_MAPRED_HOME=/usr/lib/hadoop-mapreduce运行一个测试Job
代码如下 $ 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
代码如下$ 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
代码如下$ 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
代码如下$ 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
代码如下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.111cn.net |
| 2 | www.111cn.net |
| 3 | mb.111cn.net |
| 4 | www.111cn.net |
| 5 | baidu.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。