【云星数据---Apache Flink实战系列(精品版)】：Apache Flink批处理API详解与编程实战025--DateSet实用API详解025

一、Flink DateSet定制API详解(JAVA版) -002

flatMap

以element为粒度，对element进行1：n的转化。

执行程序：

package code.book.batch.dataset.advance.api;import org.apache.flink.api.common.functions.FlatMapFunction;import org.apache.flink.api.java.DataSet;import org.apache.flink.api.java.ExecutionEnvironment;import org.apache.flink.util.Collector;import java.util.List;public class FlatMapFunction001java {    public static void main(String[] args) throws Exception {        // 1.设置运行环境，准备运行的数据        final ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();        DataSet<String> text = env.fromElements("flink vs spark", "buffer vs  shuffle");        // 2.以element为粒度，将element进行map操作，转化为大写并添加后缀字符串"--##bigdata##"        DataSet<String> text2 = text.flatMap(new FlatMapFunction<String, String>() {            @Override            public void flatMap(String s, Collector<String> collector) throws Exception {                collector.collect(s.toUpperCase() + "--##bigdata##");            }        });        text2.print();        // 3.以element为粒度，将element进行map操作，转化为大写并添加后缀字符串"--##bigdata##"        DataSet<String[]> text3 = text.flatMap(new FlatMapFunction<String, String[]>() {            @Override            public void flatMap(String s, Collector<String[]> collector) throws Exception {                collector.collect(s.toUpperCase().split("\\s+"));            }        });        final List<String[]> collect = text3.collect();        //显示结果，使用Lambda表达式的写法        collect.forEach(arr -> {            for (String token : arr) {                System.out.println(token);            }        });        //显示结果，不使用Lambda表达式的写法        for (String[] arr : collect) {            for (String token : arr) {                System.out.println(token);            }        }    }}

执行结果：

text2.print();FLINK VS SPARK--##bigdata##BUFFER VS  SHUFFLE--##bigdata##collect.forEach(arr -> {for (String token : arr) {System.out.println(token);}});FLINKVSSPARKBUFFERVSSHUFFLE

filter

以element为粒度，对element进行过滤操作。将满足过滤条件的element组成新的DataSet

执行程序：

package code.book.batch.dataset.advance.api;import org.apache.flink.api.common.functions.FilterFunction;import org.apache.flink.api.java.DataSet;import org.apache.flink.api.java.ExecutionEnvironment;public class FilterFunction001java {    public static void main(String[] args) throws Exception {        // 1.设置运行环境，准备运行的数据        final ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();        DataSet<Integer> text = env.fromElements(2, 4, 7, 8, 9, 6);        //2.对DataSet的元素进行过滤，筛选出偶数元素        DataSet<Integer> text2 =text.filter(new FilterFunction<Integer>() {            @Override            public boolean filter(Integer e) throws Exception {                return e%2==0;            }        });        text2.print();        //3.对DataSet的元素进行过滤，筛选出大于5的元素        DataSet<Integer> text3 =text.filter(new FilterFunction<Integer>() {            @Override            public boolean filter(Integer e) throws Exception {                return e>5;            }        });        text3.print();    }}

执行结果：

text2.print()2486text3.print()7896