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spark sql简单示例java
2018-11-23 01:28:37 】 浏览:476
Tags:spark sql 简单 示例 java

运行环境


集群环境:CDH5.3.0


具体JAR版本如下:


spark版本:1.2.0-cdh5.3.0


hive版本:0.13.1-cdh5.3.0


hadoop版本:2.5.0-cdh5.3.0


spark sql的JAVA版简单示例


spark sql直接查询JSON格式的数据


spark sql的自定义函数


spark sql查询hive上面的表


import java.util.ArrayList;
import java.util.List;


import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.sql.api.java.DataType;
import org.apache.spark.sql.api.java.JavaSQLContext;
import org.apache.spark.sql.api.java.JavaSchemaRDD;
import org.apache.spark.sql.api.java.Row;
import org.apache.spark.sql.api.java.UDF1;
import org.apache.spark.sql.hive.api.java.JavaHiveContext;




/**
* 注意:
* 使用JavaHiveContext时
* 1:需要在classpath下面增加三个配置文件:hive-site.xml,core-site.xml,hdfs-site.xml
* 2:需要增加postgresql或mysql驱动包的依赖
* 3:需要增加hive-jdbc,hive-exec的依赖
*
*/
public class SimpleDemo {
public static void main(String[] args) {
SparkConf conf = new SparkConf().setAppName("simpledemo").setMaster("local");
JavaSparkContext sc = new JavaSparkContext(conf);
JavaSQLContext sqlCtx = new JavaSQLContext(sc);
JavaHiveContext hiveCtx = new JavaHiveContext(sc);
// testQueryJson(sqlCtx);
// testUDF(sc, sqlCtx);
testHive(hiveCtx);
sc.stop();
sc.close();
}


//测试spark sql直接查询JSON格式的数据
public static void testQueryJson(JavaSQLContext sqlCtx) {
JavaSchemaRDD rdd = sqlCtx.jsonFile("file:///D:/tmp/tmp/json.txt");
rdd.printSchema();


// Register the input schema RDD
rdd.registerTempTable("account");


JavaSchemaRDD accs = sqlCtx.sql("SELECT address, email,id,name FROM account ORDER BY id LIMIT 10");
List<Row> result = accs.collect();
for (Row row : result) {
System.out.println(row.getString(0) + "," + row.getString(1) + "," + row.getInt(2) + ","
+ row.getString(3));
}


JavaRDD<String> names = accs.map(new Function<Row, String>() {
@Override
public String call(Row row) throws Exception {
return row.getString(3);
}
});
System.out.println(names.collect());
}




//测试spark sql的自定义函数
public static void testUDF(JavaSparkContext sc, JavaSQLContext sqlCtx) {
// Create a account and turn it into a Schema RDD
ArrayList<AccountBean> accList = new ArrayList<AccountBean>();
accList.add(new AccountBean(1, "lily", "lily@163.com", "gz tianhe"));
JavaRDD<AccountBean> accRDD = sc.parallelize(accList);


JavaSchemaRDD rdd = sqlCtx.applySchema(accRDD, AccountBean.class);


rdd.registerTempTable("acc");


// 编写自定义函数UDF
sqlCtx.registerFunction("strlength", new UDF1<String, Integer>() {
@Override
public Integer call(String str) throws Exception {
return str.length();
}
}, DataType.IntegerType);


// 数据查询
List<Row> result = sqlCtx.sql("SELECT strlength('name'),name,address FROM acc LIMIT 10").collect();
for (Row row : result) {
System.out.println(row.getInt(0) + "," + row.getString(1) + "," + row.getString(2));
}
}


//测试spark sql查询hive上面的表
public static void testHive(JavaHiveContext hiveCtx) {
List<Row> result = hiveCtx.sql("SELECT foo,bar,name from pokes2 limit 10").collect();
for (Row row : result) {
System.out.println(row.getString(0) + "," + row.getString(1) + "," + row.getString(2));
}
}
}


spark sql简单示例java https://www.cppentry.com/bencandy.php?fid=116&id=185527

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