【慕课网实战】五、以慕课网日志分析为例 进入大数据 Spark SQL 的世界

提交Spark Application到环境中运行
spark-submit \
--name SQLContextApp \
--class com.imooc.spark.SQLContextApp \
--master local[2] \
/home/hadoop/lib/sql-1.0.jar \
/home/hadoop/app/spark-2.1.0-bin-2.6.0-cdh5.7.0/examples/src/main/resources/people.json

注意：
1）To use a HiveContext, you do not need to have an existing Hive setup
2）hive-site.xml

create table t(key string, value string);
explain extended select a.key*(2+3), b.value from t a join t b on a.key = b.key and a.key > 3;

== Parsed Logical Plan ==
'Project [unresolvedalias(('a.key * (2 + 3)), None), 'b.value]
+- 'Join Inner, (('a.key = 'b.key) && ('a.key > 3))
:- 'UnresolvedRelation `t`, a
+- 'UnresolvedRelation `t`, b

== Analyzed Logical Plan ==
(CAST(key AS DOUBLE) * CAST((2 + 3) AS DOUBLE)): double, value: string
Project [(cast(key#321 as double) * cast((2 + 3) as double)) AS (CAST(key AS DOUBLE) * CAST((2 + 3) AS DOUBLE))#325, value#324]
+- Join Inner, ((key#321 = key#323) && (cast(key#321 as double) > cast(3 as double)))
:- SubqueryAlias a
: +- MetastoreRelation default, t
+- SubqueryAlias b
+- MetastoreRelation default, t

== Optimized Logical Plan ==
Project [(cast(key#321 as double) * 5.0) AS (CAST(key AS DOUBLE) * CAST((2 + 3) AS DOUBLE))#325, value#324]
+- Join Inner, (key#321 = key#323)
:- Project [key#321]
: +- Filter (isnotnull(key#321) && (cast(key#321 as double) > 3.0))
: +- MetastoreRelation default, t
+- Filter (isnotnull(key#323) && (cast(key#323 as double) > 3.0))
+- MetastoreRelation default, t

== Physical Plan ==
*Project [(cast(key#321 as double) * 5.0) AS (CAST(key AS DOUBLE) * CAST((2 + 3) AS DOUBLE))#325, value#324]
+- *SortMergeJoin [key#321], [key#323], Inner
:- *Sort [key#321 ASC NULLS FIRST], false, 0
: +- Exchange hashpartitioning(key#321, 200)
: +- *Filter (isnotnull(key#321) && (cast(key#321 as double) > 3.0))
: +- HiveTableScan [key#321], MetastoreRelation default, t
+- *Sort [key#323 ASC NULLS FIRST], false, 0
+- Exchange hashpartitioning(key#323, 200)
+- *Filter (isnotnull(key#323) && (cast(key#323 as double) > 3.0))
+- HiveTableScan [key#323, value#324], MetastoreRelation default, t

thriftserver/beeline的使用
1) 启动thriftserver: 默认端口是10000 ，可以修改
2）启动beeline
beeline -u jdbc:hive2://localhost:10000 -n hadoop

修改thriftserver启动占用的默认端口号：
./start-thriftserver.sh \
--master local[2] \
--jars ~/software/mysql-connector-java-5.1.27-bin.jar \
--hiveconf hive.server2.thrift.port=14000

beeline -u jdbc:hive2://localhost:14000 -n hadoop

thriftserver和普通的spark-shell/spark-sql有什么区别？
1）spark-shell、spark-sql都是一个spark application；
2）thriftserver， 不管你启动多少个客户端(beeline/code)，永远都是一个spark application
解决了一个数据共享的问题，多个客户端可以共享数据；

注意事项：在使用jdbc开发时，一定要先启动thriftserver
Exception in thread "main" java.sql.SQLException:
Could not open client transport with JDBC Uri: jdbc:hive2://hadoop001:14000:
java.net.ConnectException: Connection refused