我需要Flink广播流的代码
时间: 2023-09-17 15:06:17 浏览: 129
### 回答1:
以下是一个简单的 Flink 广播流程例子:
```
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.BroadcastStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.co.KeyedBroadcastProcessFunction;
import org.apache.flink.util.Collector;
public class BroadCastExample {
public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
DataStream<Tuple2<String, Integer>> dataStream = env
.fromElements(Tuple2.of("A", 1), Tuple2.of("B", 2), Tuple2.of("C", 3));
DataStream<String> broadcastStream = env.fromElements("A", "B");
broadcastStream.broadcast().connect(dataStream)
.process(new KeyedBroadcastProcessFunction<String, String, Tuple2<String, Integer>, Tuple2<String, Integer>>() {
@Override
public void processElement(Tuple2<String, Integer> value, ReadOnlyContext ctx, Collector<Tuple2<String, Integer>> out) throws Exception {
for (String s : ctx.getBroadcastState(new MapStateDescriptor<>("broadcast", Types.STRING, Types.STRING)).immutableEntries()) {
if (s.equals(value.f0)) {
out.collect(value);
}
}
}
@Override
public void processBroadcastElement(String value, Context ctx, Collector<Tuple2<String, Integer>> out) throws Exception {
ctx.getBroadcastState(new MapStateDescriptor<>("broadcast", Types.STRING, Types.STRING)).put(value, value);
}
})
.print();
env.execute();
}
}
```
这个例子中,我们创建了两个数据流:一个是 `dataStream`,包含了 (字符串, 整数) 的元组;另一个是 `broadcastStream`,包含了字符串。我们使用 `broadcastStream.broadcast().connect(dataStream)` 方法将广播流与数据流连接,并使用 `KeyedBroadcastProcessFunction` 处理数据。最后,我们使用 `print()` 方
### 回答2:
Flink广播流可以通过将数据广播到所有并行任务中,使每个任务都能访问到相同的广播数据。以下是一个使用Flink广播流的简单示例代码:
```java
import org.apache.flink.api.common.functions.RichFlatMapFunction;
import org.apache.flink.api.common.state.BroadcastState;
import org.apache.flink.api.common.state.MapStateDescriptor;
import org.apache.flink.api.common.typeinfo.BasicTypeInfo;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.streaming.api.datastream.BroadcastStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.util.Collector;
public class BroadcastStreamExample {
public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
// 创建广播流
MapStateDescriptor<String, Integer> broadcastStateDescriptor = new MapStateDescriptor<>(
"broadcastConfig",
BasicTypeInfo.STRING_TYPE_INFO,
BasicTypeInfo.INT_TYPE_INFO
);
BroadcastStream<String> broadcastStream = env.fromElements("apple", "banana", "orange")
.broadcast(broadcastStateDescriptor);
// 在主流中使用广播流
env.fromElements("apple", "orange", "grape")
.flatMap(new RichFlatMapFunction<String, String>() {
private transient BroadcastState<String, Integer> broadcastState;
@Override
public void open(Configuration parameters) throws Exception {
broadcastState = getRuntimeContext().getBroadcastState(broadcastStateDescriptor);
}
@Override
public void flatMap(String value, Collector<String> out) throws Exception {
Integer count = broadcastState.get(value);
if (count != null) {
out.collect(value + ":" + count);
} else {
out.collect(value + ":0");
}
}
})
.print();
// 执行作业
env.execute("Broadcast Stream Example");
}
}
```
在这个示例代码中,首先创建了一个广播流`broadcastStream`,其中包含了要广播的数据("apple", "banana", "orange")。
然后在主流中使用了广播流`broadcastStream`,通过`flatMap`函数访问广播数据。`open`函数中通过`getRuntimeContext().getBroadcastState()`方法获取了`broadcastState`,可以通过该对象访问广播数据。
在`flatMap`函数中,通过`broadcastState.get(value)`方法获取了广播数据中与当前元素相对应的值,如果找到了则输出相应的计数值,否则输出0。
最后通过`env.execute()`方法执行整个作业。运行结果会输出每个元素的计数值。
### 回答3:
Flink广播流是一种特殊的流处理模式,它能够在流处理任务中进行信息的广播,使得多个任务共享同一份数据。以下是一个使用Flink广播流的简单示例代码:
```java
import org.apache.flink.api.common.functions.RichMapFunction;
import org.apache.flink.api.common.state.BroadcastState;
import org.apache.flink.api.common.state.MapStateDescriptor;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.streaming.api.datastream.BroadcastStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.util.Collector;
import java.util.HashMap;
import java.util.Map;
public class BroadcastStreamExample {
public static void main(String[] args) throws Exception {
// 创建流执行环境
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
// 定义广播流描述符和初始值
MapStateDescriptor<String, String> mapStateDescriptor = new MapStateDescriptor<>(
"broadcast-state",
String.class,
String.class
);
BroadcastStream<String> broadcastStream = env.fromCollection(getBroadcastData())
.broadcast(mapStateDescriptor);
// 主数据流处理
env.fromCollection(getMainData())
.map(new RichMapFunction<String, String>() {
private transient BroadcastState<String, String> broadcastState;
@Override
public void open(Configuration parameters) throws Exception {
broadcastState = getRuntimeContext().getBroadcastState(mapStateDescriptor);
}
@Override
public String map(String value) throws Exception {
// 使用广播流的数据进行处理
String broadcastData = broadcastState.get("broadcast-key");
return value + " - " + broadcastData;
}
})
.print();
// 执行任务
env.execute("Broadcast Stream Example");
}
// 获取广播数据
public static Map<String, String> getBroadcastData() {
Map<String, String> broadcastData = new HashMap<>();
broadcastData.put("broadcast-key", "broadcast-value");
return broadcastData;
}
// 获取主数据
public static String[] getMainData() {
return new String[]{"data1", "data2", "data3"};
}
}
```
这个示例代码中,我们首先创建了一个流执行环境,并使用`fromCollection`方法生成了一个广播流`broadcastStream`。在主数据流处理部分,通过`map`函数使用广播流中的数据进行处理。在`open`函数中,我们获取了广播流的状态,并在`map`函数中使用该状态进行处理。
需要注意的是,由于该示例是单机模拟的,所以广播流的数据是保存在内存中的。在实际生产环境中,通常会将广播数据存储在外部系统(如数据库或文件系统)中,并通过Flink的`BroadcastState`接口进行读取和更新。