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", "[e-mail]", "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));
        }
    }
}