Flink算子
map
Map 算子会遍历数据流的每一个元素产生一个新的元素。
public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setParallelism(2);
SingleOutputStreamOperator<Integer> source = env.socketTextStream("192.168.235.130", 8888).map(new MapFunction<String, Integer>() {
@Override
public Integer map(String s) throws Exception {
return Integer.valueOf(s)*10;
}
});
source.print();
env.execute();
}
filter
filter算子通过一个布尔表达式对数据流的元素进行过滤，若为true则正常输出该元素，若为false则过滤掉该元素。
public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setParallelism(2);
SingleOutputStreamOperator<String> filter = env.socketTextStream("192.168.235.130", 8888).filter(new FilterFunction<String>() {
@Override
public boolean filter(String s) throws Exception {
String[] data = s.split(",");
return "10".equals(data[1]);
}
});
filter.print();
env.execute();
}
flatMap
flatMap遍历数据流中的每一个元素产生N(N >= 0)个元素。
public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setParallelism(2);
SingleOutputStreamOperator<String> flatMap = env.socketTextStream("192.168.235.130", 8888).flatMap(new FlatMapFunction<String, String>() {
@Override
public void flatMap(String s, Collector<String> collector) throws Exception {
String[] data = s.split(",");
for (String str : data) {
collector.collect(str);
}
}
});
flatMap.print();
env.execute();
}
keyBy
在使用聚合算子之前通常要经过keyBy分组，keyBy通过指定的key将数据流中的数据划分到不同的分区，那么具有相同key的数据都被发送到同一个分区，但一个分区中可能存在不同key的数据，底层原理是通过计算key的哈希值对分区数取模来实现的，如果key是POJO类型必须重写hashCode()方法。
public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setParallelism(10);
KeyedStream<String, String> keyedStream = env.socketTextStream("192.168.235.130", 8888).keyBy(new KeySelector<String, String>() {
@Override
public String getKey(String s) throws Exception {
String[] data = s.split(",");
return data[1];
}
});
keyedStream.print();
env.execute();
}
aggregations
aggregations包含以下聚合算子，在数据流中，sum()用于对指定的字段求和，min()对指定的字段求最小值，max()对指定的字段求最大值，maxby()取比较字段的最大值，同时非比较字段 取 最大值这条数据的值，minBy()同理，取比较字段的最小值，同时非比较字段 取 最小值这条数据的值。
public class WaterSensor {
public String id;
public Long ts;
public Integer vc;
// 要提供一个空参的构造器
public WaterSensor() {
}
public WaterSensor(String id, Long ts, Integer vc) {
this.id = id;
this.ts = ts;
this.vc = vc;
}
}
public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setParallelism(2);
DataStreamSource<WaterSensor> sensorDS = env.fromElements(
new WaterSensor("s1", 10L, 10),
new WaterSensor("s1", 20L, 11),
new WaterSensor("s1", 30L, 10),
new WaterSensor("s2", 40L, 2),
new WaterSensor("s3", 50L, 3)
);
KeyedStream<WaterSensor, String> sensorKS = sensorDS
.keyBy(new KeySelector<WaterSensor, String>() {
@Override
public String getKey(WaterSensor value) throws Exception {
return value.getId();
}
});
SingleOutputStreamOperator<WaterSensor> result = sensorKS.maxBy("vc");
// SingleOutputStreamOperator<WaterSensor> result = sensorKS.max("vc");
// SingleOutputStreamOperator<WaterSensor> result = sensorKS.min("vc");
// SingleOutputStreamOperator<WaterSensor> result = sensorKS.maxBy("vc");
// SingleOutputStreamOperator<WaterSensor> result = sensorKS.minby("vc");
result.print();
env.execute();
}
reduce
reduce用于对分组完的数据流进行聚合处理，把新输入的数据和当前已经归约出来的数据进行聚合计算，因此每组的第一个元素不会执行reduce操作，需要等待同组的下一个元素到来后再进行计算。
public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setParallelism(2);
DataStreamSource<WaterSensor> sensorDS = env.fromElements(
new WaterSensor("s1", 10L, 1),
new WaterSensor("s1", 20L, 11),
new WaterSensor("s1", 30L, 21),
new WaterSensor("s2", 40L, 2),
new WaterSensor("s3", 50L, 3)
);
KeyedStream<WaterSensor, String> sensorKS = sensorDS
.keyBy(new KeySelector<WaterSensor, String>() {
@Override
public String getKey(WaterSensor value) throws Exception {
return value.getId();
}
});
SingleOutputStreamOperator<WaterSensor> reduce = sensorKS.reduce(new ReduceFunction<WaterSensor>() {
@Override
public WaterSensor reduce(WaterSensor value1, WaterSensor value2) throws Exception {
System.out.println("value1=" + value1);
System.out.println("value2=" + value2);
return new WaterSensor(value1.id, value2.ts, value1.vc + value2.vc);
}
});
reduce.print();
env.execute();
}
物理分区算子
常见的物理分区策略包含以下几种：随机分区、轮询分区、重缩放，广播，全局分区和自定义分区。
public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.createLocalEnvironmentWithWebUI(new Configuration());
env.setParallelism(8);
DataStreamSource<String> socketDS = env.socketTextStream("192.168.235.130", 8888);
// shuffle随机分区
socketDS.shuffle().print();
// rebalance轮询
// 如果是数据源倾斜的场景，调用rebalance，就可以解决数据源的数据倾斜
// socketDS.rebalance().print();
//rescale缩放：实现轮询，比rebalance更高效
// socketDS.rescale().print();
// broadcast广播：发送给下游所有的子任务
// socketDS.broadcast().print();
// global全局：全部发往第一个子任务
// socketDS.global().print();
// keyby: 按指定key去发送，相同key发往同一个子任务
// one-to-one: Forward分区器
env.execute();
}
富函数
Flink函数类都有对应的Rich版本，例如RichMapFunction、RichFilterFunction、RichReduceFunction等，富函数类与常规函数类的主要区别在于，富函数类可以获取运行环境的上下文，并且拥有生命周期的方法，所以富函数类能够实现更复杂的功能。
public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.createLocalEnvironmentWithWebUI(new Configuration());
env.setParallelism(3);
DataStreamSource<String> source = env.socketTextStream("192.168.235.130", 8888);
SingleOutputStreamOperator<Integer> map = source.map(new RichMapFunction<String, Integer>() {
@Override
public void open(Configuration parameters) throws Exception {
super.open(parameters);
System.out.println(
"子任务编号=" + getRuntimeContext().getIndexOfThisSubtask()
+ "，子任务名称=" + getRuntimeContext().getTaskNameWithSubtasks()
+ ",调用open()");
}
@Override
public void close() throws Exception {
super.close();
System.out.println(
"子任务编号=" + getRuntimeContext().getIndexOfThisSubtask()
+ "，子任务名称=" + getRuntimeContext().getTaskNameWithSubtasks()
+ ",调用close()");
}
@Override
public Integer map(String value) throws Exception {
return Integer.parseInt(value) + 1;
}
});
map.print();
env.execute();
}
注： 富函数在启动时，open()调用一次，结束时，close()调用一次。
split
split与side output都是分流算子，分流就是定义一些筛选条件，将一条数据流拆分成多条数据流。
public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setParallelism(2);
DataStreamSource<String> source = env.socketTextStream("192.168.235.130", 8888);
SingleOutputStreamOperator<String> even = source.filter(value -> Integer.valueOf(value) % 2 == 0);
SingleOutputStreamOperator<String> odd = source.filter(value -> Integer.valueOf(value) % 2 == 1);
even.print("偶数流");
odd.print("奇数流");
env.execute();
}
split的缺点：每一个数据都要调用两次filter处理，效率低，一般不用。
side output
side output在处理数据流时，可以将数据流中的元素根据条件发送到额外的输出流中，而不需要复制整个数据流。
public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setParallelism(2);
DataStreamSource<String> source = env.socketTextStream("192.168.235.130", 8888);
SingleOutputStreamOperator<WaterSensor> map = source.map(new MapFunction<String, WaterSensor>() {
@Override
public WaterSensor map(String s) throws Exception {
String[] data = s.split(",");
return new WaterSensor(data[0], Long.valueOf(data[1]), Integer.valueOf(data[1]));
}
});
OutputTag<WaterSensor> tag1 = new OutputTag<>("s1", Types.POJO(WaterSensor.class));
OutputTag<WaterSensor> tag2 = new OutputTag<>("s2", Types.POJO(WaterSensor.class));
SingleOutputStreamOperator<WaterSensor> process = map.process(new ProcessFunction<WaterSensor, WaterSensor>() {
@Override
public void processElement(WaterSensor value, Context ctx, Collector<WaterSensor> out) throws Exception {
String id = value.getId();
if ("s1".equals(id)) {
ctx.output(tag1, value);
} else if ("s2".equals(id)) {
ctx.output(tag2, value);
} else {
out.collect(value);
}
}
});
SideOutputDataStream<WaterSensor> sideOutput1 = process.getSideOutput(tag1);
SideOutputDataStream<WaterSensor> sideOutput2 = process.getSideOutput(tag2);
process.print("主流");
sideOutput1.printToErr("s1");
sideOutput2.printToErr("s2");
env.execute();
}
union(联合)
union是最简单的合流操作，可以直接将多条数据流合在一起，但要求流中的数据类型必须相同，
public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setParallelism(2);
DataStreamSource<Integer> source1 = env.fromElements(10, 20, 30, 40);
DataStreamSource<Integer> source2 = env.fromElements(5, 6, 7, 8);
DataStreamSource<String> source3 = env.fromElements("100", "200", "300", "400");
// DataStream<Integer> union1 = source1.union(source2, source3.map(value -> Integer.valueOf(value)));
DataStream<Integer> union2 = source1.union(source3.map(value -> Integer.valueOf(value)));
// union1.print("union1");
union2.print("union2");
env.execute();
}
union的缺点：要求数据类型必须相同，不能改变，缺少灵活性，所以很少用。
connect(连接）
connect每次能连接2条流，流的数据类型可以不一样，两条流连接后可以各自调用函数map、flatmap、process等处理。
public static void main(String[] args) throws Exception {
StreamExecutionEnvironment environment = StreamExecutionEnvironment.getExecutionEnvironment();
environment.setParallelism(2);
SingleOutputStreamOperator<Integer> source1 = environment.socketTextStream("192.168.235.130", 9999).map(value -> Integer.valueOf(value));
DataStreamSource<String> source2 = environment.socketTextStream("192.168.235.130", 8888);
ConnectedStreams<Integer, String> connect = source1.connect(source2);
SingleOutputStreamOperator<Object> map = connect.map(new CoMapFunction<Integer, String, Object>() {
@Override
public Object map1(Integer value) throws Exception {
value *= 10;
return "来源于数字流"+value.toString();
}
@Override
public Object map2(String value) throws Exception {
return "来源于字母流"+value;
}
});
map.print();
environment.execute();
}
原文地址：https://blog.csdn.net/weixin_42828342/article/details/143727004
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