这篇文章主要介绍“Apache下Flink transformation的用法”,在日常操作中,相信很多人在Apache下Flink transformation的用法问题上存在疑惑,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”Apache下Flink transformation的用法”的疑惑有所帮助!接下来,请跟着小编一起来学习吧!

10年积累的成都网站设计、成都做网站、外贸网站建设经验,可以快速应对客户对网站的新想法和需求。提供各种问题对应的解决方案。让选择我们的客户得到更好、更有力的网络服务。我虽然不认识你,你也不认识我。但先网站设计制作后付款的网站建设流程,更有崖州免费网站建设让你可以放心的选择与我们合作。
Map Function
Scala
新建一个Object
object DataSetTransformationApp {
def main(args: Array[String]): Unit = {
val environment = ExecutionEnvironment.getExecutionEnvironment
}
def mapFunction(env: ExecutionEnvironment): Unit = {
val data = env.fromCollection(List(1,2,3,4,5,6,7,8,9,10))
}
}这里的数据源是一个1到10的list集合。Map的原理是:假设data数据集中有N个元素,将每一个元素进行转化:
data.map { x => x.toInt }好比:y=f(x)
// 对data中的每一个元素都去做一个+1操作 data.map((x:Int) => x + 1 ).print()
然后对每一个元素都做一个+1操作。
简单写法:
如果这个里面只有一个元素,就可以直接写成下面形式:
data.map((x) => x + 1).print()
更简洁的写法:
data.map(x => x + 1).print()
更简洁的方法:
data.map(_ + 1).print()
输出结果:
2 3 4 5 6 7 8 9 10 11
Java
public static void main(String[] args) throws Exception {
ExecutionEnvironment executionEnvironment = ExecutionEnvironment.getExecutionEnvironment();
mapFunction(executionEnvironment);
}
public static void mapFunction(ExecutionEnvironment executionEnvironment) throws Exception {
List list = new ArrayList<>();
for (int i = 1; i <= 10; i++) {
list.add(i + "");
}
DataSource data = executionEnvironment.fromCollection(list);
data.map(new MapFunction() {
public Integer map(String input) {
return Integer.parseInt(input) + 1;
}
}).print();
} 因为我们定义的List是一个String的泛型,因此MapFunction的泛型是
Filter Function
将每个元素执行+1操作,并取出大于5的元素。
Scala
def filterFunction(env: ExecutionEnvironment): Unit = {
val data = env.fromCollection(List(1, 2, 3, 4, 5, 6, 7, 8, 9, 10))
data.map(_ + 1).filter(_ > 5).print()
}filter只会返回满足条件的记录。
Java
public static void filterFunction(ExecutionEnvironment env) throws Exception {
List list = new ArrayList<>();
for (int i = 1; i <= 10; i++) {
list.add(i);
}
DataSource data = env.fromCollection(list);
data.map(new MapFunction() {
public Integer map(Integer input) {
return input + 1;
}
}).filter(new FilterFunction() {
@Override
public boolean filter(Integer input) throws Exception {
return input > 5;
}
}).print();
} MapPartition Function
map function 与 MapPartition function有什么区别?
需求:DataSource 中有100个元素,把结果存储在数据库中
如果使用map function ,那么实现方法如下:
// DataSource 中有100个元素,把结果存储在数据库中
def mapPartitionFunction(env: ExecutionEnvironment): Unit = {
val students = new ListBuffer[String]
for (i <- 1 to 100) {
students.append("Student" + i)
}
val data = env.fromCollection(students)
data.map(x=>{
// 每一个元素要存储到数据库中去,肯定需要先获取到connection
val connection = DBUtils.getConnection()
println(connection + " ... ")
// TODO .... 保存数据到DB
DBUtils.returnConnection(connection)
}).print()
}打印结果,将会打印100个获取DBUtils.getConnection()的请求。如果数据量增多,显然不停的获取连接是不现实的。
因此MapPartition就应运而生了,转换一个分区里面的数据,也就是说一个分区中的数据调用一次。
因此要首先设置分区:
val data = env.fromCollection(students).setParallelism(4)
设置4个分区,也就是并行度,然后使用mapPartition来处理:
data.mapPartition(x => {
val connection = DBUtils.getConnection()
println(connection + " ... ")
// TODO .... 保存数据到DB
DBUtils.returnConnection(connection)
x
}).print()那么就会的到4次连接请求,每一个分区获取一个connection。
Java
public static void mapPartitionFunction(ExecutionEnvironment env) throws Exception {
List list = new ArrayList<>();
for (int i = 1; i <= 100; i++) {
list.add("student:" + i);
}
DataSource data = env.fromCollection(list);
/*data.map(new MapFunction() {
@Override
public String map(String input) throws Exception {
String connection = DBUtils.getConnection();
System.out.println("connection = [" + connection + "]");
DBUtils.returnConnection(connection);
return input;
}
}).print();*/
data.mapPartition(new MapPartitionFunction() {
@Override
public void mapPartition(Iterable values, Collector first groupBy sortGroup
Scala
first表示获取前几个,groupBy表示分组,sortGroup表示分组内排序
def firstFunction(env:ExecutionEnvironment): Unit = {
val info = ListBuffer[(Int, String)]()
info.append((1, "hadoop"))
info.append((1, "spark"))
info.append((1, "flink"))
info.append((2, "java"))
info.append((2, "springboot"))
info.append((3, "linux"))
info.append((4, "vue"))
val data = env.fromCollection(info)
data.first(3).print()
//输出:(1,hadoop)
//(1,spark)
//(1,flink)
data.groupBy(0).first(2).print()//根据第一个字段分组,每个分组获取前两个数据
//(3,linux)
//(1,hadoop)
//(1,spark)
//(2,java)
//(2,springboot)
//(4,vue)
data.groupBy(0).sortGroup(1, Order.ASCENDING).first(2).print() //根据第一个字段分组,然后在分组内根据第二个字段升序排序,并取出前两个数据
//输出(3,linux)
//(1,flink)
//(1,hadoop)
//(2,java)
//(2,springboot)
//(4,vue)
}Java
public static void firstFunction(ExecutionEnvironment env) throws Exception {
List> info = new ArrayList<>();
info.add(new Tuple2<>(1, "hadoop"));
info.add(new Tuple2<>(1, "spark"));
info.add(new Tuple2<>(1, "flink"));
info.add(new Tuple2<>(2, "java"));
info.add(new Tuple2<>(2, "springboot"));
info.add(new Tuple2<>(3, "linux"));
info.add(new Tuple2<>(4, "vue"));
DataSource> data = env.fromCollection(info);
data.first(3).print();
data.groupBy(0).first(2).print();
data.groupBy(0).sortGroup(1, Order.ASCENDING).first(2).print();
} FlatMap Function
获取一个元素,然后产生0个、1个或多个元素
Scala
def flatMapFunction(env: ExecutionEnvironment): Unit = {
val info = ListBuffer[(String)]()
info.append("hadoop,spark");
info.append("hadoop,flink");
info.append("flink,flink");
val data = env.fromCollection(info)
data.flatMap(_.split(",")).print()
}输出:
hadoop spark hadoop flink flink flink
FlatMap将每个元素都用逗号分割,然后变成多个。
经典例子:
data.flatMap(_.split(",")).map((_,1)).groupBy(0).sum(1).print()将每个元素用逗号分割,然后每个元素做map,然后根据第一个字段分组,然后根据第二个字段求和。
输出结果如下:
(hadoop,2) (flink,3) (spark,1)
Java
同样实现一个经典案例wordcount
public static void flatMapFunction(ExecutionEnvironment env) throws Exception {
List info = new ArrayList<>();
info.add("hadoop,spark");
info.add("hadoop,flink");
info.add("flink,flink");
DataSource data = env.fromCollection(info);
data.flatMap(new FlatMapFunction() {
@Override
public void flatMap(String input, Collector out) throws Exception {
String[] splits = input.split(",");
for(String split: splits) {
//发送出去
out.collect(split);
}
}
}).map(new MapFunction>() {
@Override
public Tuple2 map(String value) throws Exception {
return new Tuple2<>(value,1);
}
}).groupBy(0).sum(1).print();
} Distinct
去重操作
Scala
def distinctFunction(env: ExecutionEnvironment): Unit = {
val info = ListBuffer[(String)]()
info.append("hadoop,spark");
info.append("hadoop,flink");
info.append("flink,flink");
val data = env.fromCollection(info)
data.flatMap(_.split(",")).distinct().print()
}这样就将每一个元素都做了去重操作。输出如下:
hadoop flink spark
Java
public static void distinctFunction(ExecutionEnvironment env) throws Exception {
List info = new ArrayList<>();
info.add("hadoop,spark");
info.add("hadoop,flink");
info.add("flink,flink");
DataSource data = env.fromCollection(info);
data.flatMap(new FlatMapFunction() {
@Override
public void flatMap(String input, Collector out) throws Exception {
String[] splits = input.split(",");
for(String split: splits) {
//发送出去
out.collect(split);
}
}
}).distinct().print();
} Join
Joins two data sets by creating all pairs of elements that are equal on their keys. Optionally uses a JoinFunction to turn the pair of elements into a single element, or a FlatJoinFunction to turn the pair of elements into arbitrarily many (including none) elements. See the keys section to learn how to define join keys.
result = input1.join(input2) .where(0) // key of the first input (tuple field 0) .equalTo(1); // key of the second input (tuple field 1)
表示第一个tuple input1中的第0个字段,与第二个tuple input2中的第一个字段进行join。
def joinFunction(env: ExecutionEnvironment): Unit = {
val info1 = ListBuffer[(Int, String)]() //编号 名字
info1.append((1, "hadoop"))
info1.append((2, "spark"))
info1.append((3, "flink"))
info1.append((4, "java"))
val info2 = ListBuffer[(Int, String)]() //编号 城市
info2.append((1, "北京"))
info2.append((2, "上海"))
info2.append((3, "深圳"))
info2.append((5, "广州"))
val data1 = env.fromCollection(info1)
val data2 = env.fromCollection(info2)
data1.join(data2).where(0).equalTo(0).apply((first, second)=>{
(first._1, first._2, second._2)
}).print()
}输出结果如下:
(3,flink,深圳) (1,hadoop,北京) (2,spark,上海)
Java
public static void joinFunction(ExecutionEnvironment env) throws Exception {
List> info1 = new ArrayList<>(); //编号 名字
info1.add(new Tuple2<>(1, "hadoop"));
info1.add(new Tuple2<>(2, "spark"));
info1.add(new Tuple2<>(3, "flink"));
info1.add(new Tuple2<>(4, "java"));
List> info2 = new ArrayList<>(); //编号 城市
info2.add(new Tuple2<>(1, "北京"));
info2.add(new Tuple2<>(2, "上海"));
info2.add(new Tuple2<>(3, "深圳"));
info2.add(new Tuple2<>(5, "广州"));
DataSource> data1 = env.fromCollection(info1);
DataSource> data2 = env.fromCollection(info2);
data1.join(data2).where(0).equalTo(0).with(new JoinFunction, Tuple2, Tuple3>() {
@Override
public Tuple3 join(Tuple2 first, Tuple2 second) throws Exception {
return new Tuple3(first.f0, first.f1,second.f1);
}
}).print();
} Tuple2
OuterJoin
上面讲的join是内连接,这个OuterJoin是外连接,包括左外连接,右外连接,全连接在两个数据集上。
def outJoinFunction(env: ExecutionEnvironment): Unit = {
val info1 = ListBuffer[(Int, String)]() //编号 名字
info1.append((1, "hadoop"))
info1.append((2, "spark"))
info1.append((3, "flink"))
info1.append((4, "java"))
val info2 = ListBuffer[(Int, String)]() //编号 城市
info2.append((1, "北京"))
info2.append((2, "上海"))
info2.append((3, "深圳"))
info2.append((5, "广州"))
val data1 = env.fromCollection(info1)
val data2 = env.fromCollection(info2)
data1.leftOuterJoin(data2).where(0).equalTo(0).apply((first, second) => {
if (second == null) {
(first._1, first._2, "-")
}else {
(first._1, first._2, second._2)
}
}).print() //左外连接 把左边的所有数据展示出来
}左外连接,当左边的数据在右边没有对应的数据时,需要进行处理,否则会出现空指针异常。输出如下:
(3,flink,深圳) (1,hadoop,北京) (2,spark,上海) (4,java,-)
右外连接:
data1.rightOuterJoin(data2).where(0).equalTo(0).apply((first, second) => {
if (first == null) {
(second._1, "-", second._2)
}else {
(first._1, first._2, second._2)
}
}).print()右外连接,输出:
(3,flink,深圳) (1,hadoop,北京) (5,-,广州) (2,spark,上海)
全连接:
data1.fullOuterJoin(data2).where(0).equalTo(0).apply((first, second) => {
if (first == null) {
(second._1, "-", second._2)
}else if (second == null){
(second._1, "-", second._2)
} else {
(first._1, first._2, second._2)
}
}).print()(3,flink,深圳) (1,hadoop,北京) (5,-,广州) (2,spark,上海) (4,java,-)
Java
左外连接:
public static void outjoinFunction(ExecutionEnvironment env) throws Exception {
List> info1 = new ArrayList<>(); //编号 名字
info1.add(new Tuple2<>(1, "hadoop"));
info1.add(new Tuple2<>(2, "spark"));
info1.add(new Tuple2<>(3, "flink"));
info1.add(new Tuple2<>(4, "java"));
List> info2 = new ArrayList<>(); //编号 城市
info2.add(new Tuple2<>(1, "北京"));
info2.add(new Tuple2<>(2, "上海"));
info2.add(new Tuple2<>(3, "深圳"));
info2.add(new Tuple2<>(5, "广州"));
DataSource> data1 = env.fromCollection(info1);
DataSource> data2 = env.fromCollection(info2);
data1.leftOuterJoin(data2).where(0).equalTo(0).with(new JoinFunction, Tuple2, Tuple3>() {
@Override
public Tuple3 join(Tuple2 first, Tuple2 second) throws Exception {
if(second == null) {
return new Tuple3(first.f0, first.f1, "-");
}
return new Tuple3(first.f0, first.f1,second.f1);
}
}).print();
} 右外连接:
data1.rightOuterJoin(data2).where(0).equalTo(0).with(new JoinFunction, Tuple2 , Tuple3 >() { @Override public Tuple3 join(Tuple2 first, Tuple2 second) throws Exception { if (first == null) { return new Tuple3 (second.f0, "-", second.f1); } return new Tuple3 (first.f0, first.f1, second.f1); } }).print();
全连接:
data1.fullOuterJoin(data2).where(0).equalTo(0).with(new JoinFunction, Tuple2 , Tuple3 >() { @Override public Tuple3 join(Tuple2 first, Tuple2 second) throws Exception { if (first == null) { return new Tuple3 (second.f0, "-", second.f1); } else if (second == null) { return new Tuple3 (first.f0, first.f1, "-"); } return new Tuple3 (first.f0, first.f1, second.f1); } }).print();
cross function
Scala
笛卡尔积,左边与右边交叉处理
def crossFunction(env: ExecutionEnvironment): Unit = {
val info1 = List("乔峰", "慕容复")
val info2 = List(3,1,0)
val data1 = env.fromCollection(info1)
val data2 = env.fromCollection(info2)
data1.cross(data2).print()
}输出:
(乔峰,3) (乔峰,1) (乔峰,0) (慕容复,3) (慕容复,1) (慕容复,0)
Java
public static void crossFunction(ExecutionEnvironment env) throws Exception {
List info1 = new ArrayList<>();
info1.add("乔峰");
info1.add("慕容复");
List info2 = new ArrayList<>();
info2.add("3");
info2.add("1");
info2.add("0");
DataSource data1 = env.fromCollection(info1);
DataSource data2 = env.fromCollection(info2);
data1.cross(data2).print();
} 到此,关于“Apache下Flink transformation的用法”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,请继续关注创新互联网站,小编会继续努力为大家带来更多实用的文章!
网站题目:Apache下Flinktransformation的用法
本文URL:http://www.jxjierui.cn/article/pshoog.html


咨询
建站咨询
