这篇文章主要介绍“Flink DataSet算子的作用是什么”，在日常操作中，相信很多人在Flink DataSet算子的作用是什么问题上存在疑惑，小编查阅了各式资料，整理出简单好用的操作方法，希望对大家解答”Flink DataSet算子的作用是什么”的疑惑有所帮助！接下来，请跟着小编一起来学习吧！
Flink为了能够处理有边界的数据集和无边界的数据集，提供了对应的DataSet API和DataStream API。我们可以开发对应的Java程序或者Scala程序来完成相应的功能。下面举例了一些DataSet API中的基本的算子。
下面我们通过具体的代码来为大家演示每个算子的作用。
//获取运行环境
ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
ArrayList<String> data = new ArrayList<String>();
data.add("I love Beijing");
data.add("I love China");
data.add("Beijing is the capital of China");
DataSource<String> text = env.fromCollection(data);
DataSet<List<String>> mapData = text.map(new MapFunction<String, List<String>>() {
public List<String> map(String data) throws Exception {
String[] words = data.split(" ");
//创建一个List
List<String> result = new ArrayList<String>();
for(String w:words){
result.add(w);
}
return result;
}
});
mapData.print();
System.out.println("*****************************************");
DataSet<String> flatMapData = text.flatMap(new FlatMapFunction<String, String>() {
public void flatMap(String data, Collector<String> collection) throws Exception {
String[] words = data.split(" ");
for(String w:words){
collection.collect(w);
}
}
});
flatMapData.print();
System.out.println("*****************************************");
/* new MapPartitionFunction<String, String>
第一个String：表示分区中的数据元素类型
第二个String：表示处理后的数据元素类型*/
DataSet<String> mapPartitionData = text.mapPartition(new MapPartitionFunction<String, String>() {
public void mapPartition(Iterable<String> values, Collector<String> out) throws Exception {
//针对分区进行操作的好处是：比如要进行数据库的操作，一个分区只需要创建一个Connection
//values中保存了一个分区的数据
Iterator<String> it = values.iterator();
while (it.hasNext()) {
String next = it.next();
String[] split = next.split(" ");
for (String word : split) {
out.collect(word);
}
}
//关闭链接
}
});
mapPartitionData.print();//获取运行环境
ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
ArrayList<String> data = new ArrayList<String>();
data.add("I love Beijing");
data.add("I love China");
data.add("Beijing is the capital of China");
DataSource<String> text = env.fromCollection(data);
DataSet<String> flatMapData = text.flatMap(new FlatMapFunction<String, String>() {
public void flatMap(String data, Collector<String> collection) throws Exception {
String[] words = data.split(" ");
for(String w:words){
collection.collect(w);
}
}
});
//去掉重复的单词
flatMapData.distinct().print();
System.out.println("*********************");
//选出长度大于3的单词
flatMapData.filter(new FilterFunction<String>() {
public boolean filter(String word) throws Exception {
int length = word.length();
return length>3?true:false;
}
}).print();//获取运行的环境
ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
//创建第一张表：用户ID 姓名
ArrayList<Tuple2<Integer, String>> data1 = new ArrayList<Tuple2<Integer,String>>();
data1.add(new Tuple2(1,"Tom"));
data1.add(new Tuple2(2,"Mike"));
data1.add(new Tuple2(3,"Mary"));
data1.add(new Tuple2(4,"Jone"));
//创建第二张表：用户ID 所在的城市
ArrayList<Tuple2<Integer, String>> data2 = new ArrayList<Tuple2<Integer,String>>();
data2.add(new Tuple2(1,"北京"));
data2.add(new Tuple2(2,"上海"));
data2.add(new Tuple2(3,"广州"));
data2.add(new Tuple2(4,"重庆"));
//实现join的多表查询：用户ID 姓名 所在的程序
DataSet<Tuple2<Integer, String>> table1 = env.fromCollection(data1);
DataSet<Tuple2<Integer, String>> table2 = env.fromCollection(data2);
table1.join(table2).where(0).equalTo(0)
/*第一个Tuple2<Integer,String>：表示第一张表
* 第二个Tuple2<Integer,String>：表示第二张表
* Tuple3<Integer,String, String>：多表join连接查询后的返回结果 */
.with(new JoinFunction<Tuple2<Integer,String>, Tuple2<Integer,String>, Tuple3<Integer,String, String>>() {
public Tuple3<Integer, String, String> join(Tuple2<Integer, String> table1,
Tuple2<Integer, String> table2) throws Exception {
return new Tuple3<Integer, String, String>(table1.f0,table1.f1,table2.f1);
} }).print();ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment(); //创建第一张表：用户ID 姓名 ArrayList<Tuple2<Integer, String>> data1 = new ArrayList<Tuple2<Integer,String>>(); data1.add(new Tuple2(1,"Tom")); data1.add(new Tuple2(2,"Mike")); data1.add(new Tuple2(3,"Mary")); data1.add(new Tuple2(4,"Jone")); //创建第二张表：用户ID 所在的城市 ArrayList<Tuple2<Integer, String>> data2 = new ArrayList<Tuple2<Integer,String>>(); data2.add(new Tuple2(1,"北京")); data2.add(new Tuple2(2,"上海")); data2.add(new Tuple2(3,"广州")); data2.add(new Tuple2(4,"重庆")); //实现join的多表查询：用户ID 姓名 所在的程序 DataSet<Tuple2<Integer, String>> table1 = env.fromCollection(data1); DataSet<Tuple2<Integer, String>> table2 = env.fromCollection(data2); //生成笛卡尔积 table1.cross(table2).print();
ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
//这里的数据是：员工姓名、薪水、部门号
DataSet<Tuple3<String, Integer,Integer>> grade =
env.fromElements(new Tuple3<String, Integer,Integer>("Tom",1000,10),
new Tuple3<String, Integer,Integer>("Mary",1500,20),
new Tuple3<String, Integer,Integer>("Mike",1200,30),
new Tuple3<String, Integer,Integer>("Jerry",2000,10));
//按照插入顺序取前三条记录
grade.first(3).print();
System.out.println("**********************");
//先按照部门号排序，在按照薪水排序
grade.sortPartition(2, Order.ASCENDING).sortPartition(1, Order.ASCENDING).print();
System.out.println("**********************");
//按照部门号分组，求每组的第一条记录
grade.groupBy(2).first(1).print();ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
//创建第一张表：用户ID 姓名
ArrayList<Tuple2<Integer, String>> data1 = new ArrayList<Tuple2<Integer,String>>();
data1.add(new Tuple2(1,"Tom"));
data1.add(new Tuple2(3,"Mary"));
data1.add(new Tuple2(4,"Jone"));
//创建第二张表：用户ID 所在的城市
ArrayList<Tuple2<Integer, String>> data2 = new ArrayList<Tuple2<Integer,String>>();
data2.add(new Tuple2(1,"北京"));
data2.add(new Tuple2(2,"上海"));
data2.add(new Tuple2(4,"重庆"));
//实现join的多表查询：用户ID 姓名 所在的程序
DataSet<Tuple2<Integer, String>> table1 = env.fromCollection(data1);
DataSet<Tuple2<Integer, String>> table2 = env.fromCollection(data2);
//左外连接
table1.leftOuterJoin(table2).where(0).equalTo(0)
.with(new JoinFunction<Tuple2<Integer,String>, Tuple2<Integer,String>, Tuple3<Integer,String,String>>() {
public Tuple3<Integer, String, String> join(Tuple2<Integer, String> table1,
Tuple2<Integer, String> table2) throws Exception {
// 左外连接表示等号左边的信息会被包含
if(table2 == null){
return new Tuple3<Integer, String, String>(table1.f0,table1.f1,null);
}else{
return new Tuple3<Integer, String, String>(table1.f0,table1.f1,table2.f1);
}
}
}).print();
System.out.println("***********************************");
//右外连接
table1.rightOuterJoin(table2).where(0).equalTo(0)
.with(new JoinFunction<Tuple2<Integer,String>, Tuple2<Integer,String>, Tuple3<Integer,String,String>>() {
public Tuple3<Integer, String, String> join(Tuple2<Integer, String> table1,
Tuple2<Integer, String> table2) throws Exception {
//右外链接表示等号右边的表的信息会被包含
if(table1 == null){
return new Tuple3<Integer, String, String>(table2.f0,null,table2.f1);
}else{
return new Tuple3<Integer, String, String>(table2.f0,table1.f1,table2.f1);
}
}
}).print();
System.out.println("***********************************");
//全外连接
table1.fullOuterJoin(table2).where(0).equalTo(0)
.with(new JoinFunction<Tuple2<Integer,String>, Tuple2<Integer,String>, Tuple3<Integer,String,String>>() {
public Tuple3<Integer, String, String> join(Tuple2<Integer, String> table1, Tuple2<Integer, String> table2)
throws Exception {
if(table1 == null){
return new Tuple3<Integer, String, String>(table2.f0,null,table2.f1);
}else if(table2 == null){
return new Tuple3<Integer, String, String>(table1.f0,table1.f1,null);
}else{
return new Tuple3<Integer, String, String>(table1.f0,table1.f1,table2.f1);
}
}
}).print();到此，关于“Flink DataSet算子的作用是什么”的学习就结束了，希望能够解决大家的疑惑。理论与实践的搭配能更好的帮助大家学习，快去试试吧！若想继续学习更多相关知识，请继续关注亿速云网站，小编会继续努力为大家带来更多实用的文章！
免责声明：本站发布的内容（图片、视频和文字）以原创、转载和分享为主，文章观点不代表本网站立场，如果涉及侵权请联系站长邮箱：is@yisu.com进行举报，并提供相关证据，一经查实，将立刻删除涉嫌侵权内容。
