Spark MySQL使用 （java&scala) 大表问题

如果是大表, 会出现OOM

原本是这样，直接写Table名称，就会读整张Table 
.option("dbtable", "test_table")
可以改写成：
.option("dbtable", "(select * from test_table where dt >= '2017-05-01') as T")
PS记得一定要用左右括号包起来，因为dbtable的value会被当成一张table作查询，mysql connector会自动dbtable后面加上where 1=1，如果没包起来就会出现SQL Syntax Error之类的错误

参考：https://blog.csdn.net/Damionew/article/details/103918688

参考Spark Doc：https://spark.apache.org/docs/latest/sql-getting-started.html

1.首先Maven需要引入spark依赖和mysql驱动:

<dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_2.12</artifactId>
            <version>2.4.4</version>
        </dependency>
 
        <!-- https://mvnrepository.com/artifact/org.apache.spark/spark-sql -->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-sql_2.12</artifactId>
            <version>2.4.4</version>
        </dependency>
 
        <dependency>
            <groupId>mysql</groupId>
            <artifactId>mysql-connector-java</artifactId>
            <version>8.0.16</version>
        </dependency>

读取mysql 数据(scala)　方式１

def main(args: Array[String]): Unit = {
    val spark = SparkSession.builder().appName("SparkSQLAndMySQL").master("local").getOrCreate()
    //读取方式一
    val connectionProperties = new Properties()
    connectionProperties.put("user", "root")
    connectionProperties.put("password", "root")
    val jdbcDF = spark.read.jdbc("jdbc:mysql://localhost:3306/lzdcase",
                                                "wktableallstatisc",connectionProperties)
    jdbcDF.show();
    import  spark.implicits._
    import  org.apache.spark.sql.functions._
    //read 方式二
    val jdbcDF2 =
      spark.read.format("jdbc")
        .option("url","jdbc:mysql://localhost:3306/lzdcase")
        .option("dbtable","afsa2staticstb")
        .option("user","root")
        .option("password","root").load()
    jdbcDF2.show(2)
    jdbcDF2.groupBy("mawb").agg(count(jdbcDF2("bookingNo")) ,sum(jdbcDF2("totalcouriers"))).show()
   //write method1
    val jdbcProperties = new Properties()
    jdbcProperties.put("user","root")
    jdbcProperties.put("password","root")
   // def jdbc(url: String, table: String, connectionProperties: Properties)
    jdbcDF2.write.mode(SaveMode.Overwrite).jdbc("jdbc:mysql://localhost:3306/lzdcase","sparktest",jdbcProperties)
    //write method2
    jdbcDF2.write.mode(SaveMode.Overwrite)
        .format("jdbc")
        .option("user","root")
        .option("password","root")
        .option("dbtable","sparkwrite2")
        .option("url","jdbc:mysql://localhost:3306/lzdcase")
        .save()

    //输出方式三 执行创建表的列名和数据类型 数据类型不能大写
    jdbcDF2.write
      .option("createTableColumnTypes", "name varchar(200),salary int")
      .jdbc("jdbc:mysql://localhost:3306/db1", "employees2", jdbcProperties)
    spark.stop()
  }

读取mysql 数据(java)　方式２

import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SparkSession;
 
public class JavaSparkSqlDemo {
    public static void main(String[] args){
        SparkSession sparkSession = SparkSession
                .builder()
                .appName("JavaSparkSqlDemo")  //Sets a name for the application
                .master("local")    //Sets the Spark master URL to connect to
                .getOrCreate();     //获取或者新建一个 sparkSession
        //设置sparkSession数据连接
        Dataset userDataset = sparkSession.read()
                .format("jdbc")
                .option("url","jdbc:mysql://localhost:3306/test?useUnicode=true&characterEncoding=utf-8")
                .option("dbtable","user")
                .option("driver","com.mysql.cj.jdbc.Driver")
                .option("user","root")
                .option("password","root")
                .load();
        Dataset roleDataset = sparkSession.read()
                .format("jdbc")
                .option("url","jdbc:mysql://localhost:3306/test?useUnicode=true&characterEncoding=utf-8")
                .option("dbtable","role")
                .option("driver","com.mysql.cj.jdbc.Driver")
                .option("user","root")
                .option("password","root")
                .load();
        //注册临时表后才能进行select等操作，必需，否则not found in database 'default'
        userDataset.registerTempTable("user");
        roleDataset.registerTempTable("role");
        //SQL查询操作
        //注意：1.所有用到的表需要在option和registerTempTable注册
        Dataset<Row> sqlDF = sparkSession.sql("SELECT t1.id,t1.name,t2.role FROM USER t1 LEFT JOIN role t2 ON t1.id = t2.id ");
        sqlDF.show();
       
    }
}