大数据进阶之路——Spark SQL基本配置_孙中明

MapReduce的局限性：
1）代码繁琐；
2）只能够支持map和reduce方法；
3）执行效率低下；
4）不适合迭代多次、交互式、流式的处理；

框架多样化：
1）批处理（离线）：MapReduce、Hive、Pig
2）流式处理（实时）： Storm、JStorm
3）交互式计算：Impala

学习、运维成本无形中都提高了很多

===> Spark

Spark安装

前置要求：

1）Building Spark using Maven requires Maven 3.3.9 or newer and Java 7+
2）export MAVEN_OPTS="-Xmx2g -XX:ReservedCodeCacheSize=512m"

mvn编译命令：

./build/mvn -Pyarn -Phadoop-2.4 -Dhadoop.version=2.4.0 -DskipTests clean package

[hadoop@hadoop001 spark-2.1.0]$ cat pom.xml 
[hadoop@hadoop001 spark-2.1.0]$ pwd
/home/hadoop/source/spark-2.1.0

<properties>
    <hadoop.version>2.2.0</hadoop.version>
    <protobuf.version>2.5.0</protobuf.version>
    <yarn.version>${hadoop.version}</yarn.version>
......
</properties>

...............
<profile>
  <id>hadoop-2.6</id>
  <properties>
    <hadoop.version>2.6.4</hadoop.version>
    <jets3t.version>0.9.3</jets3t.version>
    <zookeeper.version>3.4.6</zookeeper.version>
    <curator.version>2.6.0</curator.version>
  </properties>
</profile>

路径下执行

[hadoop@hadoop001 spark-2.1.0]$ pwd
/home/hadoop/source/spark-2.1.0

==> ./build/mvn -Pyarn -Phadoop-2.6 -Phive -Phive-thriftserver -Dhadoop.version=2.6.0-cdh5.7.0 -DskipTests clean package

编译可以运行的包

./dev/make-distribution.sh --name 2.6.0-cdh5.7.0 --tgz -Pyarn -Phadoop-2.6 -Phive -Phive-thriftserver -Dhadoop.version=2.6.0-cdh5.7.0

make-distribution.sh

spark-$VERSION-bin-$NAME.tgz

—>spark-2.1.0-bin-2.6.0-cdh5.7.0.tgz

编译失败

Failed to execute goal on project ...: Could not resolve dependencies for project ...

在pom.xml中添加

<repository>
      <id>cloudera</id>
      <url>https://repository.cloudera.com/artifactory/cloudera-repos/</url>
</repository>

如果scala是2.10
需要添加./dev/change-scala-version.sh 2.10

环境搭建

local

• tar -zxvf park-2.1.0-bin-2.6.0-cdh5.7.0.tgz -C ~/app/

• 配置环境SPARK_HOME

• source ~./bash_profile

运行
spark-shell --master local[2]

	at org.apache.spark.repl.SparkILoop$$anonfun$initializeSpark$1.apply(SparkILoop.scala:37)
	at org.apache.spark.repl.SparkILoop$$anonfun$initializeSpark$1.apply(SparkILoop.scala:37)
a)
Caused by: org.datanucleus.exceptions.NucleusException: Attempt to invoke the "BONECP" plugin to create a ConnectionPool gave an error : The specified datastore driver ("com.mysql.jdbc.Driver") was not found in the CLASSPATH. Please check your CLASSPATH specification, and the name of the driver.
	at org.datanucleus.store.rdbms.ConnectionFactoryImpl.generateDataSources(ConnectionFactoryImpl.java:259)
	
 java:104)

.............................................

  at org.apache.hadoop.hive.ql.metadata.Hive.createMetaStoreClient(Hive.java:3005)
 571)
  at org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.createDefaultDB(HiveMetaStore.java:624)
  
  at org.datanucleus.plugin.NonManagedPluginRegistry.createExecutableExtension(NonManagedPluginRegistry.java:631)
  at org.datanucleus.plugin.PluginManager.createExecutableExtension(PluginManager.java:325)
  at org.datanucleus.store.AbstractStoreManager.registerConnectionFactory(AbstractStoreManager.java:282)
  at org.datanucleus.store.AbstractStoreManager.<init>(AbstractStoreManager.java:240)

Caused by: org.datanucleus.store.rdbms.connectionpool.DatastoreDriverNotFoundException: The specified datastore driver ("com.mysql.jdbc.Driver") was not found in the CLASSPATH. Please check your CLASSPATH specification, and the name of the driver.
  at org.datanucleus.store.rdbms.connectionpool.AbstractConnectionPoolFactory.loadDriver(AbstractConnectionPoolFactory.java:58)
  at org.datanucleus.store.rdbms.connectionpool.BoneCPConnectionPoolFactory.createConnectionPool(BoneCPConnectionPoolFactory.java:54)
  at org.datanucleus.store.rdbms.ConnectionFactoryImpl.generateDataSources(ConnectionFactoryImpl.java:238)
  ... 145 more

原因没有引入mysql驱动

spark-shell --master local[2] --jar /home/hadoop/software/mysql-connector-java-5.1.27-bin.jar

[hadoop@hadoop001 software]$ spark-shell --master local[2] --jars /home/hadoop/software/mysql-connector-java-5.1.27-bin.jar 
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
20/10/16 20:42:32 WARN SparkContext: Support for Java 7 is deprecated as of Spark 2.0.0
20/10/16 20:42:33 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
20/10/16 20:42:35 WARN Utils: Service 'SparkUI' could not bind on port 4040. Attempting port 4041.
20/10/16 20:42:50 ERROR ObjectStore: Version information found in metastore differs 1.1.0 from expected schema version 1.2.0. Schema verififcation is disabled hive.metastore.schema.verification so setting version.
20/10/16 20:42:53 WARN ObjectStore: Failed to get database global_temp, returning NoSuchObjectException
Spark context Web UI available at http://192.168.43.214:4041
Spark context available as 'sc' (master = local[2], app id = local-1602906155852).
Spark session available as 'spark'.
Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /___/ .__/\_,_/_/ /_/\_\   version 2.1.0
      /_/
         
Using Scala version 2.11.8 (Java HotSpot(TM) 64-Bit Server VM, Java 1.7.0_51)
Type in expressions to have them evaluated.
Type :help for more information.

Standalone

Spark Standalone模式的架构和Hadoop HDFS/YARN很类似的
1 master + n worker

spark-env.sh

SPARK_MASTER_HOST=hadoop001
SPARK_WORKER_CORES=2
SPARK_WORKER_MEMORY=2g
SPARK_WORKER_INSTANCES=1

master:

hadoop1 

slaves:

hadoop2
hadoop3
hadoop4
....
hadoop10

==> start-all.sh 会在 hadoop1机器上启动master进程，在slaves文件配置的所有hostname的机器上启动worker进程

Spark WordCount统计
val file = spark.sparkContext.textFile(“file:///home/hadoop/data/wc.txt”)
val wordCounts = file.flatMap(line => line.split(",")).map((word => (word, 1))).reduceByKey(_ + _)
wordCounts.collect

本地IDE

A master URL must be set in your configuration

点击edit configuration，在左侧点击该项目。在右侧VM options中输入“-Dspark.master=local”，指示本程序本地单线程运行，再次运行即可。

package org.example

import org.apache.spark.sql.SQLContext
import org.apache.spark.{SparkConf, SparkContext}

object SQLContextAPP {
  def main(args: Array[String]): Unit = {
    //1创建相应的Spark
    val sparkConf = new SparkConf()
    sparkConf.setAppName("SQLContextAPP")
    val sc = new SparkContext(sparkConf)
    val sqlContext = new SQLContext(sc)

    //2数据处理
    val people = sqlContext.read.format("json").load("people.json")
    people.printSchema()
    people.show()

    //3关闭资源
    sc.stop()


  }

}

root
 |-- age: long (nullable = true)
 |-- name: string (nullable = true)

.........................


| age|   name|
+----+-------+
|null|Michael|
|  30|   Andy|
|  19| Justin|
+----+-------+

配置maven环境变量cmd控制台提示：mvn不是内部或外部命令，也不是可运行的程序或批处理文件

首先maven环境变量：

变量名：MAVEN_HOME

变量值：E:\apache-maven-3.2.3

变量名：Path

变量值：;%MAVEN_HOME%\bin

然后到项目的目录下直接执行

C:\Users\jacksun\IdeaProjects\SqarkSQL\ mvn clean package -DskipTests

在集群上测试

spark-submit \
--name SQLContextApp \
--class org.example.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

HiveContextAPP

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

package org.example

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.sql.SQLContext
import org.apache.spark.sql.hive.HiveContext

object HiveContextAPP {
  def main(args: Array[String]): Unit = {
    //1创建相应的Spark
    val path =args(0)
    val sparkConf = new SparkConf()

    //测试和生产中AppName和Master是通过脚本执行的
    //sparkConf.setAppName("HiveContextAPP").setMaster("local[2]")



    val sc = new SparkContext(sparkConf)
    val hiveContext = new HiveContext(sc)

    //2数据处理
    hiveContext.table("emp").show

    //3关闭资源
    sc.stop()


  }
}

spark-submit \
--name HiveContextApp \
--class org.example.HiveContextApp \
--master local[2] \
/home/hadoop/lib/sql-1.0.jar \
--jars /home/hadoop/software/mysql-connector-java-5.1.27-bin.jar 

SparkSessinon

package org.example

import org.apache.spark.sql.SparkSession

object SparkSessionApp {
  def main(args: Array[String]) {

    val spark = SparkSession.builder().appName("SparkSessionApp")
      .master("local[2]").getOrCreate()

    val people = spark.read.json("people.json")
    people.show()

    spark.stop()
  }
}

Spark Shell

• 启动hive

[hadoop@hadoop001 bin]$ pwd
/home/hadoop/app/hive-1.1.0-cdh5.7.0/bin
[hadoop@hadoop001 bin]$ hive
ls: cannot access /home/hadoop/app/spark-2.1.0-bin-2.6.0-cdh5.7.0/lib/spark-assembly-*.jar: No such file or directory
which: no hbase in (/home/hadoop/app/spark-2.1.0-bin-2.6.0-cdh5.7.0/bin:/home/hadoop/app/scala-2.11.8/bin:/home/hadoop/app/hive-1.1.0-cdh5.7.0/bin:/home/hadoop/app/hadoop-2.6.0-cdh5.7.0/bin:/home/hadoop/app/apache-maven-3.3.9/bin:/home/hadoop/app/jdk1.7.0_51/bin:/usr/local/bin:/bin:/usr/bin:/usr/local/sbin:/usr/sbin:/sbin)

Logging initialized using configuration in jar:file:/home/hadoop/app/hive-1.1.0-cdh5.7.0/lib/hive-common-1.1.0-cdh5.7.0.jar!/hive-log4j.properties
WARNING: Hive CLI is deprecated and migration to Beeline is recommended.
hive> 

• 拷贝
  [hadoop@hadoop001 conf]$ cp hive-site.xml ~/app/spark-2.1.0-bin-2.6.0-cdh5.7.0/conf/

• 启动Spark
  spark-shell --master local[2] --jars /home/hadoop/software/mysql-connector-java-5.1.27-bin.jar

scala> spark.sql("show tables").show
+--------+------------+-----------+
|database|   tableName|isTemporary|
+--------+------------+-----------+
| default|        dept|      false|
| default|         emp|      false|
| default|hive_table_1|      false|
| default|hive_table_2|      false|
| default|           t|      false|
+--------+------------+-----------+


hive> show tables;
OK
dept
emp
hive_wordcount

scala> spark.sql("select * from emp e join dept d on e.deptno=d.deptno").show
+-----+------+---------+----+----------+------+------+------+------+----------+--------+
|empno| ename|      job| mgr|  hiredate|   sal|  comm|deptno|deptno|     dname|     loc|
+-----+------+---------+----+----------+------+------+------+------+----------+--------+
| 7369| SMITH|    CLERK|7902|1980-12-17| 800.0|  null|    20|    20|  RESEARCH|  DALLAS|
| 7499| ALLEN| SALESMAN|7698| 1981-2-20|1600.0| 300.0|    30|    30|     SALES| CHICAGO|
| 7521|  WARD| SALESMAN|7698| 1981-2-22|1250.0| 500.0|    30|    30|     SALES| CHICAGO|
| 7566| JONES|  MANAGER|7839|  1981-4-2|2975.0|  null|    20|    20|  RESEARCH|  DALLAS|
| 7654|MARTIN| SALESMAN|7698| 1981-9-28|1250.0|1400.0|    30|    30|     SALES| CHICAGO|
| 7698| BLAKE|  MANAGER|7839|  1981-5-1|2850.0|  null|    30|    30|     SALES| CHICAGO|
| 7782| CLARK|  MANAGER|7839|  1981-6-9|2450.0|  null|    10|    10|ACCOUNTING|NEW YORK|
| 7788| SCOTT|  ANALYST|7566| 1987-4-19|3000.0|  null|    20|    20|  RESEARCH|  DALLAS|
| 7839|  KING|PRESIDENT|null|1981-11-17|5000.0|  null|    10|    10|ACCOUNTING|NEW YORK|
| 7844|TURNER| SALESMAN|7698|  1981-9-8|1500.0|   0.0|    30|    30|     SALES| CHICAGO|
| 7876| ADAMS|    CLERK|7788| 1987-5-23|1100.0|  null|    20|    20|  RESEARCH|  DALLAS|
| 7900| JAMES|    CLERK|7698| 1981-12-3| 950.0|  null|    30|    30|     SALES| CHICAGO|
| 7902|  FORD|  ANALYST|7566| 1981-12-3|3000.0|  null|    20|    20|  RESEARCH|  DALLAS|
| 7934|MILLER|    CLERK|7782| 1982-1-23|1300.0|  null|    10|    10|ACCOUNTING|NEW YORK|
+-----+------+---------+----+----------+------+------+------+------+----------+--------+

hive> select * from emp e join dept d on e.deptno=d.deptno
    > ;
Query ID = hadoop_20201020054545_f7fbda3e-439e-409e-b2ce-3c553d969ed4
Total jobs = 1
20/10/20 05:48:46 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Execution log at: /tmp/hadoop/hadoop_20201020054545_f7fbda3e-439e-409e-b2ce-3c553d969ed4.log
2020-10-20 05:48:49	Starting to launch local task to process map join;	maximum memory = 477102080
2020-10-20 05:48:51	Dump the side-table for tag: 1 with group count: 4 into file: file:/tmp/hadoop/5c8577b3-c00d-4ece-9899-c0e3de66f2f2/hive_2020-10-20_05-48-27_437_556791932773953494-1/-local-10003/HashTable-Stage-3/MapJoin-mapfile01--.hashtable
2020-10-20 05:48:51	Uploaded 1 File to: file:/tmp/hadoop/5c8577b3-c00d-4ece-9899-c0e3de66f2f2/hive_2020-10-20_05-48-27_437_556791932773953494-1/-local-10003/HashTable-Stage-3/MapJoin-mapfile01--.hashtable (404 bytes)
2020-10-20 05:48:51	End of local task; Time Taken: 2.691 sec.
Execution completed successfully
MapredLocal task succeeded
Launching Job 1 out of 1
Number of reduce tasks is set to 0 since there's no reduce operator
Starting Job = job_1602849227137_0002, Tracking URL = http://hadoop001:8088/proxy/application_1602849227137_0002/
Kill Command = /home/hadoop/app/hadoop-2.6.0-cdh5.7.0/bin/hadoop job  -kill job_1602849227137_0002
Hadoop job information for Stage-3: number of mappers: 1; number of reducers: 0
2020-10-20 05:49:13,663 Stage-3 map = 0%,  reduce = 0%
2020-10-20 05:49:36,950 Stage-3 map = 100%,  reduce = 0%, Cumulative CPU 13.08 sec
MapReduce Total cumulative CPU time: 13 seconds 80 msec
Ended Job = job_1602849227137_0002
MapReduce Jobs Launched: 
Stage-Stage-3: Map: 1   Cumulative CPU: 13.08 sec   HDFS Read: 7639 HDFS Write: 927 SUCCESS
Total MapReduce CPU Time Spent: 13 seconds 80 msec
OK
7369	SMITH	CLERK	7902	1980-12-17	800.0	NULL	20	20	RESEARCH	DALLAS
7499	ALLEN	SALESMAN	7698	1981-2-20	1600.0	300.0	30	30	SALES	CHICAGO
7521	WARD	SALESMAN	7698	1981-2-22	1250.0	500.0	30	30	SALES	CHICAGO
7566	JONES	MANAGER	7839	1981-4-2	2975.0	NULL	20	20	RESEARCH	DALLAS
7654	MARTIN	SALESMAN	7698	1981-9-28	1250.0	1400.0	30	30	SALES	CHICAGO
7698	BLAKE	MANAGER	7839	1981-5-1	2850.0	NULL	30	30	SALES	CHICAGO
7782	CLARK	MANAGER	7839	1981-6-9	2450.0	NULL	10	10	ACCOUNTING	NEW YORK
7788	SCOTT	ANALYST	7566	1987-4-19	3000.0	NULL	20	20	RESEARCH	DALLAS
7839	KING	PRESIDENT	NULL	1981-11-17	5000.0	NULL	10	10	ACCOUNTINGNEW YORK
7844	TURNER	SALESMAN	7698	1981-9-8	1500.0	0.0	30	30	SALES	CHICAGO
7876	ADAMS	CLERK	7788	1987-5-23	1100.0	NULL	20	20	RESEARCH	DALLAS
7900	JAMES	CLERK	7698	1981-12-3	950.0	NULL	30	30	SALES	CHICAGO
7902	FORD	ANALYST	7566	1981-12-3	3000.0	NULL	20	20	RESEARCH	DALLAS
7934	MILLER	CLERK	7782	1982-1-23	1300.0	NULL	10	10	ACCOUNTING	NEW YORK
Time taken: 71.998 seconds, Fetched: 14 row(s)
hive> 

SPARK SQL 基本秒出结果,hive比较耗时

• hive-site.xml
  删除警告

<property>
  <name>hive.metastore.schema.verification</name>
  <value>false</value>
</property>

Spark Sql

20/10/20 06:20:09 INFO DAGScheduler: Job 1 finished: processCmd at CliDriver.java:376, took 0.261151 s
7369	SMITH	CLERK	7902	1980-12-17	800.0	NULL	20	20	RESEARCH	DALLAS
7499	ALLEN	SALESMAN	7698	1981-2-20	1600.0	300.0	30	30	SALES	CHICAGO
7521	WARD	SALESMAN	7698	1981-2-22	1250.0	500.0	30	30	SALES	CHICAGO
7566	JONES	MANAGER	7839	1981-4-2	2975.0	NULL	20	20	RESEARCH	DALLAS
7654	MARTIN	SALESMAN	7698	1981-9-28	1250.0	1400.0	30	30	SALES	CHICAGO
7698	BLAKE	MANAGER	7839	1981-5-1	2850.0	NULL	30	30	SALES	CHICAGO
7782	CLARK	MANAGER	7839	1981-6-9	2450.0	NULL	10	10	ACCOUNTING	NEW YORK
7788	SCOTT	ANALYST	7566	1987-4-19	3000.0	NULL	20	20	RESEARCH	DALLAS
7839	KING	PRESIDENT	NULL	1981-11-17	5000.0	NULL	10	10	ACCOUNTINGNEW YORK
7844	TURNER	SALESMAN	7698	1981-9-8	1500.0	0.0	30	30	SALES	CHICAGO
7876	ADAMS	CLERK	7788	1987-5-23	1100.0	NULL	20	20	RESEARCH	DALLAS
7900	JAMES	CLERK	7698	1981-12-3	950.0	NULL	30	30	SALES	CHICAGO
7902	FORD	ANALYST	7566	1981-12-3	3000.0	NULL	20	20	RESEARCH	DALLAS
7934	MILLER	CLERK	7782	1982-1-23	1300.0	NULL	10	10	ACCOUNTING	NEW YORK
Time taken: 13.625 seconds, Fetched 14 row(s)
20/10/20 06:20:09 INFO CliDriver: Time taken: 13.625 seconds, Fetched 14 row(s)

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

spark下的sbin

1. 启动thriftserver:
   ./start-thriftserver.sh --master local[2] --jars /home/hadoop/software/mysql-connector-java-5.1.27-bin.jar
   

默认端口是10000 ，可以修改

./start-thriftserver.sh  \
--master local[2] \
--jars ~/software/mysql-connector-java-5.1.27-bin.jar  \
--hiveconf hive.server2.thrift.port=14000 

2）启动beeline
beeline -u jdbc:hive2://localhost:10000 -n hadoop

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；

jdbc

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

<dependency>
      <groupId>org.spark-project.hive</groupId>
      <artifactId>hive-jdbc</artifactId>
      <version>1.2.1.spark2</version>
      <!--
      <scope>provided</scope>
      -->
    </dependency>

package org.example
import java.sql.DriverManager
object JDBCApp {

  def main(args: Array[String]) {

    Class.forName("org.apache.hive.jdbc.HiveDriver")

    val conn = DriverManager.getConnection("jdbc:hive2://192.168.43.214:10000","hadoop","")
    val pstmt = conn.prepareStatement("select empno, ename, sal from emp")
    val rs = pstmt.executeQuery()
    while (rs.next()) {
      println("empno:" + rs.getInt("empno") +
        " , ename:" + rs.getString("ename") +
        " , sal:" + rs.getDouble("sal"))

    }

    rs.close()
    pstmt.close()
    conn.close()


  }


}