目录
0、 相关文章链接;
1、 union和connect算子;
2、 split、select和SideOutputs;
3、 rebalance重平衡分区;
4、 其他分区算子;
0. 相关文章链接
1. union和connect算子
API:
- union:union算子可以合并多个同类型的数据流,并生成同类型的数据流,即可以将多个DataStream[T]合并为一个新的DataStream[T]。数据将按照先进先出(First In First Out)的模式合并,且不去重。
-
connect:
-
connect提供了和union类似的功能,用来连接两个数据流,它与union的区别在于:connect只能连接两个数据流,union可以连接多个数据流。
-
connect所连接的两个数据流的数据类型可以不一致,union所连接的两个数据流的数据类型必须一致。
-
两个DataStream经过connect之后被转化为ConnectedStreams,ConnectedStreams会对两个流的数据应用不同的处理方法,且双流之间可以共享状态。
需求举例:
将两个String类型的流进行union
将一个String类型和一个Long类型的流进行connect
代码实现:
import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.streaming.api.datastream.ConnectedStreams;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.co.CoMapFunction;
/**
* Author itcast
* Desc
*/
public class TransformationDemo02 {
public static void main(String[] args) throws Exception {
//1.env
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);
//2.Source
DataStream<String> ds1 = env.fromElements("hadoop", "spark", "flink");
DataStream<String> ds2 = env.fromElements("hadoop", "spark", "flink");
DataStream<Long> ds3 = env.fromElements(1L, 2L, 3L);
//3.Transformation
DataStream<String> result1 = ds1.union(ds2);//合并但不去重 https://blog.csdn.net/valada/article/details/104367378
ConnectedStreams<String, Long> tempResult = ds1.connect(ds3);
//interface CoMapFunction<IN1, IN2, OUT>
DataStream<String> result2 = tempResult.map(new CoMapFunction<String, Long, String>() {
@Override
public String map1(String value) throws Exception {
return "String->String:" + value;
}
@Override
public String map2(Long value) throws Exception {
return "Long->String:" + value.toString();
}
});
//4.Sink
result1.print();
result2.print();
//5.execute
env.execute();
}
}
2. split、select和Side Outputs
API:
- Split就是将一个流分成多个流,注意:split函数已过期并移除
- Select就是获取分流后对应的数据
- Side Outputs:可以使用process方法对流中数据进行处理,并针对不同的处理结果将数据收集到不同的OutputTag中
需求举例:
对流中的数据按照奇数和偶数进行分流,并获取分流后的数据
代码实现:
import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.api.common.typeinfo.TypeInformation;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.ProcessFunction;
import org.apache.flink.util.Collector;
import org.apache.flink.util.OutputTag;
/**
* Author itcast
* Desc
*/
public class TransformationDemo03 {
public static void main(String[] args) throws Exception {
//1.env
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);
//2.Source
DataStreamSource<Integer> ds = env.fromElements(1, 2, 3, 4, 5, 6, 7, 8, 9, 10);
//3.Transformation
/*SplitStream<Integer> splitResult = ds.split(new OutputSelector<Integer>() {
@Override
public Iterable<String> select(Integer value) {
//value是进来的数字
if (value % 2 == 0) {
//偶数
ArrayList<String> list = new ArrayList<>();
list.add("偶数");
return list;
} else {
//奇数
ArrayList<String> list = new ArrayList<>();
list.add("奇数");
return list;
}
}
});
DataStream<Integer> evenResult = splitResult.select("偶数");
DataStream<Integer> oddResult = splitResult.select("奇数");*/
//定义两个输出标签
OutputTag<Integer> tag_even = new OutputTag<Integer>("偶数", TypeInformation.of(Integer.class));
OutputTag<Integer> tag_odd = new OutputTag<Integer>("奇数"){};
//对ds中的数据进行处理
SingleOutputStreamOperator<Integer> tagResult = ds.process(new ProcessFunction<Integer, Integer>() {
@Override
public void processElement(Integer value, Context ctx, Collector<Integer> out) throws Exception {
if (value % 2 == 0) {
//偶数
ctx.output(tag_even, value);
} else {
//奇数
ctx.output(tag_odd, value);
}
}
});
//取出标记好的数据
DataStream<Integer> evenResult = tagResult.getSideOutput(tag_even);
DataStream<Integer> oddResult = tagResult.getSideOutput(tag_odd);
//4.Sink
evenResult.print("偶数");
oddResult.print("奇数");
//5.execute
env.execute();
}
}
3. rebalance重平衡分区
功能概述:
- 类似于Spark中的repartition,但是功能更强大,可以直接解决数据倾斜。
- Flink也有数据倾斜的时候,比如当前有数据量大概10亿条数据需要处理,在处理过程中可能会发生如图所示的状况,出现了数据倾斜,其他3台机器执行完毕也要等待机器1执行完毕后才算整体将任务完成。
- 所以在实际的工作中,出现这种情况比较好的解决方案就是rebalance(内部使用round robin方法将数据均匀打散)。
代码演示:
import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.api.common.functions.FilterFunction;
import org.apache.flink.api.common.functions.RichMapFunction;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
/**
* Author itcast
* Desc
*/
public class TransformationDemo04 {
public static void main(String[] args) throws Exception {
//1.env
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC).setParallelism(3);
//2.source
DataStream<Long> longDS = env.fromSequence(0, 100);
//3.Transformation
//下面的操作相当于将数据随机分配一下,有可能出现数据倾斜
DataStream<Long> filterDS = longDS.filter(new FilterFunction<Long>() {
@Override
public boolean filter(Long num) throws Exception {
return num > 10;
}
});
//接下来使用map操作,将数据转为(分区编号/子任务编号, 数据)
//Rich表示多功能的,比MapFunction要多一些API可以供我们使用
DataStream<Tuple2<Integer, Integer>> result1 = filterDS
.map(new RichMapFunction<Long, Tuple2<Integer, Integer>>() {
@Override
public Tuple2<Integer, Integer> map(Long value) throws Exception {
//获取分区编号/子任务编号
int id = getRuntimeContext().getIndexOfThisSubtask();
return Tuple2.of(id, 1);
}
}).keyBy(t -> t.f0).sum(1);
DataStream<Tuple2<Integer, Integer>> result2 = filterDS.rebalance()
.map(new RichMapFunction<Long, Tuple2<Integer, Integer>>() {
@Override
public Tuple2<Integer, Integer> map(Long value) throws Exception {
//获取分区编号/子任务编号
int id = getRuntimeContext().getIndexOfThisSubtask();
return Tuple2.of(id, 1);
}
}).keyBy(t -> t.f0).sum(1);
//4.sink
//result1.print();//有可能出现数据倾斜
result2.print();//在输出前进行了rebalance重分区平衡,解决了数据倾斜
//5.execute
env.execute();
}
}
4. 其他分区算子
API:
说明:
recale分区。基于上下游Operator的并行度,将记录以循环的方式输出到下游Operator的每个实例。
举例:
上游并行度是2,下游是4,则上游一个并行度以循环的方式将记录输出到下游的两个并行度上;上游另一个并行度以循环的方式将记录输出到下游另两个并行度上。若上游并行度是4,下游并行度是2,则上游两个并行度将记录输出到下游一个并行度上;上游另两个并行度将记录输出到下游另一个并行度上。
代码演示:
import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.common.functions.Partitioner;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.util.Collector;
/**
* Author itcast
* Desc
*/
public class TransformationDemo05 {
public static void main(String[] args) throws Exception {
//1.env
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);
//2.Source
DataStream<String> linesDS = env.readTextFile("data/input/words.txt");
SingleOutputStreamOperator<Tuple2<String, Integer>> tupleDS = linesDS.flatMap(new FlatMapFunction<String, Tuple2<String, Integer>>() {
@Override
public void flatMap(String value, Collector<Tuple2<String, Integer>> out) throws Exception {
String[] words = value.split(" ");
for (String word : words) {
out.collect(Tuple2.of(word, 1));
}
}
});
//3.Transformation
DataStream<Tuple2<String, Integer>> result1 = tupleDS.global();
DataStream<Tuple2<String, Integer>> result2 = tupleDS.broadcast();
DataStream<Tuple2<String, Integer>> result3 = tupleDS.forward();
DataStream<Tuple2<String, Integer>> result4 = tupleDS.shuffle();
DataStream<Tuple2<String, Integer>> result5 = tupleDS.rebalance();
DataStream<Tuple2<String, Integer>> result6 = tupleDS.rescale();
DataStream<Tuple2<String, Integer>> result7 = tupleDS.partitionCustom(new Partitioner<String>() {
@Override
public int partition(String key, int numPartitions) {
return key.equals("hello") ? 0 : 1;
}
}, t -> t.f0);
//4.sink
//result1.print();
//result2.print();
//result3.print();
//result4.print();
//result5.print();
//result6.print();
result7.print();
//5.execute
env.execute();
}
}
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