SparkSQL使用之Spark SQL CLI

Spark SQL CLI描述

Spark SQL CLI的引入使得在SparkSQL中通过hive metastore就可以直接对hive进行查询更加方便；当前版本中还不能使用Spark SQL CLI与ThriftServer进行交互。

 

使用Spark SQL CLI前需要注意：

1、将hive-site.xml配置文件拷贝到$SPARK_HOME/conf目录下；

2、需要在$SPARK_HOME/conf/spark-env.sh中的SPARK_CLASSPATH添加jdbc驱动的jar包

export SPARK_CLASSPATH=$SPARK_CLASSPATH:/home/hadoop/software/mysql-connector-java-5.1.27-bin.jar

 

Spark SQL CLI命令参数介绍：

cd $SPARK_HOME/bin

spark-sql --help

Usage: ./bin/spark-sql [options] [cli option]

Spark assembly has been built with Hive, including Datanucleus jars on classpath

Options:

  --master MASTER_URL spark://host:port, mesos://host:port, yarn, or local.

  --deploy-mode DEPLOY_MODE   Whether to launch the driver program locally ("client") or

                              on one of the worker machines inside the cluster ("cluster")

                              (Default: client).

  --class CLASS_NAME          Your application's main class (for Java / Scala apps).

  --name NAME                 A name of your application.

  --jars JARS                 Comma-separated list of local jars to include on the driver

                              and executor classpaths.

  --py-files PY_FILES         Comma-separated list of .zip, .egg, or .py files to place

                              on the PYTHONPATH for Python apps.

  --files FILES               Comma-separated list of files to be placed in the working

                              directory of each executor.



  --conf PROP=VALUE           Arbitrary Spark configuration property.

  --properties-file FILE      Path to a file from which to load extra properties. If not

                              specified, this will look for conf/spark-defaults.conf.



  --driver-memory MEM         Memory for driver (e.g. 1000M, 2G) (Default: 512M).

  --driver-java-options       Extra Java options to pass to the driver.

  --driver-library-path       Extra library path entries to pass to the driver.

  --driver-class-path         Extra class path entries to pass to the driver. Note that

                              jars added with --jars are automatically included in the

                              classpath.



  --executor-memory MEM       Memory per executor (e.g. 1000M, 2G) (Default: 1G).



  --help, -h                  Show this help message and exit

  --verbose, -v               Print additional debug output



 Spark standalone with cluster deploy mode only:

  --driver-cores NUM          Cores for driver (Default: 1).

  --supervise                 If given, restarts the driver on failure.



 Spark standalone and Mesos only:

  --total-executor-cores NUM  Total cores for all executors.



 YARN-only:

  --executor-cores NUM        Number of cores per executor (Default: 1).

  --queue QUEUE_NAME          The YARN queue to submit to (Default: "default").

  --num-executors NUM         Number of executors to launch (Default: 2).

  --archives ARCHIVES         Comma separated list of archives to be extracted into the

                              working directory of each executor.



CLI options:

-d,--define <key=value>          Variable subsitution to apply to hive

                                  commands. e.g. -d A=B or --define A=B

    --database <databasename>     Specify the database to use

 -e <quoted-query-string>         SQL from command line

 -f <filename>                    SQL from files

 -h <hostname>                    connecting to Hive Server on remote host

    --hiveconf <property=value>   Use value for given property

    --hivevar <key=value>         Variable subsitution to apply to hive

                                  commands. e.g. --hivevar A=B

 -i <filename>                    Initialization SQL file

 -p <port>                        connecting to Hive Server on port number

 -S,--silent                      Silent mode in interactive shell

 -v,--verbose                     Verbose mode (echo executed SQL to the console)

在启动spark-sql时，如果不指定master，则以local的方式运行，master既可以指定standalone的地址，也可以指定yarn；

当设定master为yarn时(spark-sql --master yarn)时，可以通过http://hadoop000:8088页面监控到整个job的执行过程；

注：如果在$SPARK_HOME/conf/spark-defaults.conf中配置了spark.master spark://hadoop000:7077，那么在启动spark-sql时不指定master也是运行在standalone集群之上。

 

spark-sql使用

启动spark-sql： 由于我已经在spark-defaults.conf中配置了spark.master spark://hadoop000:7077，就没在spark-sql启动时指定master了

cd $SPARK_HOME/bin

spark-sql

SELECT track_time, url, session_id, referer, ip, end_user_id, city_id FROM page_views WHERE city_id = -1000 limit 10;


SELECT session_id, count(*) c FROM page_views group by session_id order by c desc limit 10;

上面两个sql语句用到的表现在存在hive中了，如果没有则手工创建下，创建脚本以及导入数据脚本如下：

create table page_views(

track_time string,

url string,

session_id string,

referer string,

ip string,

end_user_id string,

city_id string

)

ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t';

load data local inpath '/home/spark/software/data/page_views.dat' overwrite into table page_views;