大数据IMF传奇行动绝密课程第58课：使用Java和Scala在IDE中开发DataFrame实战

使用Java和Scala在IDE中开发DataFrame实战

1、使用Java开发DataFrame

2、使用Scala开发DataFrame

创建DataFrame的时候可以来自于其它RDD，来源于Hive表，以及其他数据来源，例如json文件

SQLContext只支持SQL一种方言（delax?），HiveContext支持SQL方言以及其它方言，通过设置都可以支持。

//F:\sparkData\people.json文件
{"name":"Michael"}
{"name":"Andy","age":31}
{"name":"Justin","age":20}

一、使用Java开发DataFrame

package com.tom.spark.SparkApps.sql;

import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.sql.DataFrame;
import org.apache.spark.sql.SQLContext;

/**
*
*/
public class DataFrameOps {

/**
* @param args
*/
public static void main(String[] args) {
//创建SparkConf用于读取系统配置信息并设置当前应用程序的名字
SparkConf conf = new SparkConf().setAppName("DataFrameOps").setMaster("local");
//创建JavaSparkContext对象实例作为整个Driver的核心基石
JavaSparkContext sc = new JavaSparkContext(conf);
//设置日志级别为WARN
sc.setLogLevel("WARN");
//创建SQLContext上下文对象用于SQL的分析
SQLContext sqlContext = new SQLContext(sc);
//创建Data Frame，可以简单的认为DataFrame是一张表
DataFrame df = sqlContext.read().json("F:\\sparkData\\people.json");
//select * from table
df.show();
//desc table
df.printSchema();
//select name from table
df.select(df.col("name")).show();
//select name, age+10 from table
df.select(df.col("name"), df.col("age").plus(10)).show();
//select * from table where age > 21
df.filter(df.col("age").gt(21)).show();
//select age, count(1) from table group by age
df.groupBy("age").count().show(); //df.groupBy(df.col("age")).count().show();
}

}

以下为程序输出：

+----+-------+
| age|   name|
+----+-------+
|null|Michael|
|  31|   Andy|
|  20| Justin|
+----+-------+

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

+-------+
|   name|
+-------+
|Michael|
|   Andy|
| Justin|
+-------+

+-------+----------+
|   name|(age + 10)|
+-------+----------+
|Michael|      null|
|   Andy|        41|
| Justin|        30|
+-------+----------+

+---+----+
|age|name|
+---+----+
| 31|Andy|
+---+----+

+----+-----+
| age|count|
+----+-----+
|  31|    1|
|null|    1|
|  20|    1|
+----+-----+

二、使用Scala开发DataFrame

package com.tom.spark.sql

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

/**
*
*/
object DataFrameOps {
def main(args: Array[String]): Unit = {
val conf = new SparkConf().setAppName("DataFrameOps").setMaster("local")
val sc = new SparkContext(conf)
sc.setLogLevel("WARN")
val sqlContext = new SQLContext(sc)
val df = sqlContext.read.json("F:\\sparkData\\people.json")
df.show()
df.printSchema()
df.select("name").show()
df.select(df("name"),df("age")+10).show()
df.filter(df("age")>21).show()
df.groupBy("age").count().show()
}
}

以下为程序输出

+----+-------+
| age|   name|
+----+-------+
|null|Michael|
|  31|   Andy|
|  20| Justin|
+----+-------+

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

+-------+
|   name|
+-------+
|Michael|
|   Andy|
| Justin|
+-------+

+-------+----------+
|   name|(age + 10)|
+-------+----------+
|Michael|      null|
|   Andy|        41|
| Justin|        30|
+-------+----------+

+---+----+
|age|name|
+---+----+
| 31|Andy|
+---+----+

+----+-----+
| age|count|
+----+-----+
|  31|    1|
|null|    1|
|  20|    1|
+----+-----+

spark-submit可以指定–file参数，可以把hive-site.xml中指定的hive文件夹添加进来

spark-submit --class com.dt.spark.sql.DataFrameOps
--files /usr/local/hive/apache-hive-1.2.1-bin/conf/hive-site.xml
--driver-class-path /usr/local/hive/apace-hive-1.2.1-bin/mysql-connector-java-5.1.35-bin.jar
--master spark://Master:7077 /root/Documents/SparkApps/WordCount.jar