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