spark（23）sparksql 操作hivesql、操作JDBC数据源、保存数据到不同类型文件

sparksql 操作hivesql

添加依赖

        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-hive_2.11</artifactId>
            <version>2.3.3</version>
        </dependency>

person.txt

1 zhangsan 43
2 lisi 21
3 laowang 47

Demo1.scala

import org.apache.spark.sql.SparkSession

object Demo1 {
  def main(args: Array[String]): Unit = {
    //注意：要开启对hive的支持：.enableHiveSupport()
    val spark=SparkSession.builder().appName("spark sql control hive sql").master("local[2]").enableHiveSupport().getOrCreate()
    val sc=spark.sparkContext
    sc.setLogLevel("Warn")

    spark.sql(
      """
        |create table if not exists person(id string,name string,age int)
        |row format delimited fields terminated by " "
        |""".stripMargin)    //stripMargin的作用是将 | 变成空格
      
    spark.sql("load data local inpath 'file:///F:/test/person.txt' into table person")
    spark.sql("select * from person").show()

    spark.stop()
  }
}

运行结果为：

+---+--------+---+
| id|    name|age|
+---+--------+---+
|  1|zhangsan| 43|
|  2|    lisi| 21|
|  3| laowang| 47|
+---+--------+---+

说明：

文件的路径一定要加上file:///，否则会报错
运行成功后，会在当前project的根目录下创建两个目录：metastore_db和spark-warehouse
metastore_db用于存放刚才在本地创建的表的元数据，spark-warehouse用于保存表的数据

spark sql 操作JDBC数据源(★★★★★)

spark sql可以通过 JDBC 从关系型数据库中读取数据的方式创建DataFrame，通过对DataFrame一系列的计算后，还可以将数据再写回关系型数据库中

通过sparksql加载mysql表中的数据

在node03创建表，准备数据

mysql> create database spark;
mysql> use spark;
mysql> create table user(id int,name varchar(15),age int);
mysql> insert into user(id,name,age) values(1,'krystal',21),(2,'jimmy',22);
mysql> select * from user;
+------+---------+------+
| id   | name    | age  |
+------+---------+------+
|    1 | krystal |   21 |
|    2 | jimmy   |   22 |
+------+---------+------+
2 rows in set (0.11 sec)

添加mysql连接驱动jar包

<dependency>
	<groupId>mysql</groupId>
	<artifactId>mysql-connector-java</artifactId>
	<version>5.1.38</version>
</dependency>

开发代码：

import java.util.Properties

import org.apache.spark.sql.{DataFrame, SparkSession}

object Demo2 {
  def main(args: Array[String]): Unit = {
    val spark=SparkSession.builder().appName("demo").master("local[2]").getOrCreate()

    val url="jdbc:mysql://node03:3306/spark"
    val tableName="user"
    val properties=new Properties()
    properties.setProperty("user","root")
    properties.setProperty("password","123456")

    val mysqlDF:DataFrame=spark.read.jdbc(url,tableName,properties)
    mysqlDF.printSchema()
    mysqlDF.show()

    mysqlDF.createTempView("user")
    spark.sql("select * from user")

    spark.stop()
  }
}

运行结果为：

root
 |-- id: integer (nullable = true)
 |-- name: string (nullable = true)
 |-- age: integer (nullable = true)
 
 +---+-------+---+
| id|   name|age|
+---+-------+---+
|  1|krystal| 21|
|  2|  jimmy| 22|
+---+-------+---+

通过sparksql保存结果数据到mysql表中(本地)

继续往user表插入数据：

mysql> use spark
mysql> insert into user(id,name,age) values(3,'zhangsan',34),(4,'lisi',46);
mysql> select * from user;
+------+----------+------+
| id   | name     | age  |
+------+----------+------+
|    1 | krystal  |   21 |
|    2 | jimmy    |   22 |
|    3 | zhangsan |   34 |
|    4 | lisi     |   46 |
+------+----------+------+
4 rows in set (0.00 sec)

代码开发(本地运行)

import java.util.Properties

import org.apache.spark.sql.{DataFrame, SparkSession}

object Demo2 {
  def main(args: Array[String]): Unit = {
    val spark=SparkSession.builder().appName("demo").master("local[2]").getOrCreate()

    val url="jdbc:mysql://node03:3306/spark"
    val tableName="user"
    val properties=new Properties()
    properties.setProperty("user","root")
    properties.setProperty("password","123456")

    val mysqlDF:DataFrame=spark.read.jdbc(url,tableName,properties)
    mysqlDF.createTempView("user")
    val resultDF:DataFrame=spark.sql("select * from user where age > 30")

    resultDF.write.mode("append").jdbc(url,"user2",properties)

    spark.stop()
  }
}

查询user2表：

mysql> select * from user2;
+------+----------+------+
| id   | name     | age  |
+------+----------+------+
|    3 | zhangsan |   34 |
|    4 | lisi     |   46 |
+------+----------+------+

说明：

resultDF.wirte.mode("...")的参数有四种，mode用来指定数据的插入模式。
//overwrite: 表示覆盖，如果表不存在，事先帮我们创建
//append :表示追加，如果表不存在，事先帮我们创建
//ignore :表示忽略，如果表事先存在，就不进行任何操作
//error :如果表事先存在就报错（默认选项）
将数据处理后保存到MySQL中，还可以使用rdd方法，详情查看spqrk.md
如果要将本地运行改成打jar包到集群运行，只需要修改2个地方：
1. 删掉.master()
2. 将resultDF.write.mDFode(args(0)).jdbc()的第2个参数设为args(1)

通过sparksql保存结果数据到mysql表中(集群)

import java.util.Properties

import org.apache.spark.sql.{DataFrame, SparkSession}

object Demo2 {
  def main(args: Array[String]): Unit = {
    val spark=SparkSession.builder().appName("demo").getOrCreate()

    val url="jdbc:mysql://node03:3306/spark"
    val tableName="user"
    val properties=new Properties()
    properties.setProperty("user","root")
    properties.setProperty("password","123456")

    val mysqlDF:DataFrame=spark.read.jdbc(url,tableName,properties)
    mysqlDF.createTempView("user")
    val resultDF:DataFrame=spark.sql("select * from user where age > 30")

    resultDF.write.mode("append").jdbc(url,args(1),properties)

    spark.stop()
  }
}

提交任务脚本

spark-submit \
--master spark://node01:7077 \
--class com.kaikeba.sql.Data2Mysql \
--executor-memory 1g \
--total-executor-cores 4 \
--driver-class-path /home/hadoop/jars/mysql-connector-java-5.1.38.jar \
--jars /home/hadoop/jars/mysql-connector-java-5.1.38.jar \
original-spark_class05-1.0-SNAPSHOT.jar \
append  t_kaikeba


--driver-class-path：指定一个Driver端所需要的额外jar
--jars ：指定executor端所需要的额外jar

sparksql 保存数据到不同类型文件

创建F:\test\score.json文件：

{"name":"zhangsan1","classNum":"10","score":90}
{"name":"zhangsan11","classNum":"10","score":90}
{"name":"zhangsan2","classNum":"10","score":80}
{"name":"zhangsan3","classNum":"10","score":95}
{"name":"zhangsan4","classNum":"20","score":90}
{"name":"zhangsan5","classNum":"20","score":91}
{"name":"zhangsan6","classNum":"20","score":86}
{"name":"zhangsan7","classNum":"20","score":78}
{"name":"zhangsan8","classNum":"30","score":60}
{"name":"zhangsan9","classNum":"30","score":88}
{"name":"zhangsan10","classNum":"30","score":95}

代码开发：

import java.util.Properties

import org.apache.spark.sql.{DataFrame, SparkSession}

object Demo3 {
  def main(args: Array[String]): Unit = {
    val spark=SparkSession.builder().appName("demo").master("local[2]")getOrCreate()
    val dataDF:DataFrame=spark.read.json("file:///F:/test/score.json")

    //处理数据：
    dataDF.createTempView("tableDemo")
    val result=spark.sql("select * from tableDemo where score > 80")

    //保存数据：
    result.write.json("file:///F:/test/out_json")
    result.write.parquet("file:///F:/test/out_parquet")
    result.write.save("file:///F:/test/out_save")
    result.write.csv("file:///F:/test/out_csv")
    result.write.saveAsTable("t1")
    result.write.partitionBy("classNum").json("file:///F:/test/out_partition_json")
    result.write.partitionBy("classNum","name").json("file:///F:/test/out_partition2_json")

    spark.stop()
  }
}

说明：

out_xxx等都是目录来的，而且是由程序来创建，不需要事先创建，否则报错
write.save()默认保存为parquet格式
经过partitionBy分区后保存的数据文件如下：

result.write.saveAsTable("t1")保存表t1在spark-warehouse目录下：

posted @ 2020-08-25 06:09 Whatever_It_Takes 阅读(797) 评论(0) 编辑收藏举报

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