Flink时间窗口
给出一个FLink 1.15的Flink时间窗口的处理代码
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
| import org.apache.flink.streaming.api.datastream.DataStream; import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment; import org.apache.flink.streaming.api.windowing.time.Time;
public class WindowProcessing {
public static void main(String[] args) throws Exception { final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
DataStream<String> dataStream = env.socketTextStream("localhost", 9999);
dataStream.flatMap(new WordCountSplitter()) .keyBy(0) .timeWindow(Time.seconds(10)) .sum(1) .print();
env.execute("Window Processing Example"); } }
|
在这个例子中,我们使用了一个10秒的时间窗口,并对每个窗口内的数据进行求和操作。WordCountSplitter是一个自定义的FlatMapFunction,用于将输入的字符串分割成单词并输出 (不包含在代码片段中)。最后,我们通过调用print()方法将结果打印出来。
给出一段Flink 1.15处理读取Kafka并对应调整数据格式的代码
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
| import org.apache.flink.streaming.api.datastream.DataStream; import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment; import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer;
public class KafkaStreamProcessing {
public static void main(String[] args) throws Exception { final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
Properties properties = new Properties(); properties.setProperty("bootstrap.servers", "localhost:9092"); properties.setProperty("group.id", "flink-consumer-group");
FlinkKafkaConsumer<String> kafkaConsumer = new FlinkKafkaConsumer<>("topic-name", new SimpleStringSchema(), properties); DataStream<String> kafkaDataStream = env.addSource(kafkaConsumer);
DataStream<String> processedDataStream = kafkaDataStream.map(new MapFunction<String, String>() { @Override public String map(String value) throws Exception { return value.toUpperCase(); } });
processedDataStream.print();
env.execute("Kafka Stream Processing"); } }
|
在这段代码中,我们首先创建了一个StreamExecutionEnvironment
对象,并设置了Kafka的连接属性。然后创建一个FlinkKafkaConsumer
对象来读取指定的Kafka主题中的数据。接着对读取到的数据进行处理,这里简单地将数据转换为大写字母,并使用processedDataStream.print()
方法打印出来。最后调用env.execute()
方法来执行Flink作业。
给出一个Flink从Kafka队列中读取Nginx日志, 并统计日志PV数据的代码
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42
| import org.apache.flink.api.common.functions.FlatMapFunction; import org.apache.flink.api.java.tuple.Tuple2; import org.apache.flink.streaming.api.datastream.DataStream; import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment; import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer; import org.apache.flink.util.Collector;
import java.util.Properties;
public class NginxLogPVCounter {
public static void main(String[] args) throws Exception { final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
Properties properties = new Properties(); properties.setProperty("bootstrap.servers", "localhost:9092"); properties.setProperty("group.id", "nginx-log-group");
FlinkKafkaConsumer<String> kafkaConsumer = new FlinkKafkaConsumer<>("nginx-logs", new SimpleStringSchema(), properties); DataStream<String> nginxLogs = env.addSource(kafkaConsumer);
DataStream<Tuple2<String, Integer>> pvCounts = nginxLogs.flatMap(new LogPVCounter()) .keyBy(0) .sum(1);
pvCounts.print();
env.execute("Nginx Log PV Counter"); }
public static class LogPVCounter implements FlatMapFunction<String, Tuple2<String, Integer>> { @Override public void flatMap(String log, Collector<Tuple2<String, Integer>> out) { String[] tokens = log.split("\\s+"); if (tokens.length >= 3) { String requestUrl = tokens[2]; out.collect(new Tuple2<>(requestUrl, 1)); } } } }
|
这段代码实现了从Kafka队列中读取Nginx日志数据,统计每个请求URL的PV数据并输出。需要引入相关的Flink和Kafka依赖包。在代码中,假设每个日志条目是以空格分隔的格式,包含时间戳、客户端IP、请求URL和响应码。通过flatMap函数将每条日志拆分并提取请求URL信息,然后进行计数并累加,最后通过keyBy和sum函数实现对PV数据的统计。最后将结果打印出来。
给出Flink读取Kafka队列数据的Java代码
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
| import org.apache.flink.streaming.api.datastream.DataStream; import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment; import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer;
import java.util.Properties;
public class KafkaReader {
public static void main(String[] args) throws Exception { final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
Properties properties = new Properties(); properties.setProperty("bootstrap.servers", "localhost:9092"); properties.setProperty("group.id", "test-consumer-group");
FlinkKafkaConsumer<String> kafkaConsumer = new FlinkKafkaConsumer<>("topic-name", new SimpleStringSchema(), properties);
DataStream<String> stream = env.addSource(kafkaConsumer);
stream.print();
env.execute("Kafka Reader"); } }
|
请注意替换代码中的”localhost:9092”为您的Kafka服务器地址,”test-consumer-group”为您的消费者组名称,以及”topic-name”为您要读取数据的主题名称。
给出Flink的DataStream,基于Sink向ElasticSearch写入数据的Java代码
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
| import org.apache.flink.streaming.api.datastream.DataStream; import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment; import org.apache.flink.streaming.connectors.elasticsearch.ElasticsearchSinkFunction; import org.apache.flink.streaming.connectors.elasticsearch6.ElasticsearchSink; import org.apache.http.HttpHost; import org.elasticsearch.client.Requests; import org.elasticsearch.common.xcontent.XContentBuilder;
import java.util.ArrayList; import java.util.List;
public class FlinkElasticsearchSink {
public static void main(String[] args) throws Exception { final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
DataStream<String> dataStream = ...
List<HttpHost> httpHosts = new ArrayList<>(); httpHosts.add(new HttpHost("localhost", 9200, "http"));
ElasticsearchSink.Builder<String> esSinkBuilder = new ElasticsearchSink.Builder<>( httpHosts, new ElasticsearchSinkFunction<String>() { public void process(String element, RuntimeContext ctx, RequestIndexer indexer) { XContentBuilder jsonBuilder = ... indexer.add(Requests.indexRequest().index("your_index").type("your_type").source(jsonBuilder)); } } );
esSinkBuilder.setBulkFlushMaxActions(1);
dataStream.addSink(esSinkBuilder.build());
env.execute("Flink Elasticsearch Sink"); } }
|
请注意,上述代码中的...
部分需要根据实际情况进行替换和补充。在实际应用中,您可能需要从外部数据源读取数据,构建XContentBuilder对象以表示要写入Elasticsearch的数据,设置索引名称和类型等。
DataStream的source、sink处理类之外,那个类是用于处理中间的数据,具体的回调函数名称是什么?
处理中间数据的类是DataStream的operator类,具体的回调函数名称是processElement。
给出DataStream的Operator类中processElement回调函数的使用案例的代码
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
| import org.apache.flink.api.common.functions.MapFunction; import org.apache.flink.streaming.api.datastream.DataStream; import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment; import org.apache.flink.streaming.api.functions.ProcessFunction; import org.apache.flink.util.Collector;
public class ProcessElementExample {
public static void main(String[] args) throws Exception { final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
DataStream<Integer> dataStream = env.fromElements(1, 2, 3, 4, 5);
DataStream<Integer> processedStream = dataStream.process(new ProcessFunction<Integer, Integer>() { @Override public void processElement(Integer value, Context ctx, Collector<Integer> out) throws Exception { out.collect(value * 2); } });
processedStream.print();
env.execute("Process Element Example"); } }
|
给出DataStream的Operator类对Kafka数据操作的代码实例。
下面是一个使用Kafka作为数据源的DataStream操作示例代码:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
| import org.apache.flink.api.common.serialization.SimpleStringSchema; import org.apache.flink.streaming.api.datastream.DataStream; import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment; import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer;
import java.util.Properties;
public class KafkaDataStreamOperator {
public static void main(String[] args) throws Exception { final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
Properties properties = new Properties(); properties.setProperty("bootstrap.servers", "localhost:9092"); properties.setProperty("group.id", "test-group");
FlinkKafkaConsumer<String> kafkaConsumer = new FlinkKafkaConsumer<>("test-topic", new SimpleStringSchema(), properties);
DataStream<String> kafkaStream = env.addSource(kafkaConsumer);
DataStream<String> processedStream = kafkaStream.map(value -> value.toUpperCase());
processedStream.print();
env.execute("Kafka Data Stream Operator"); } }
|
在这个示例中,我们首先创建了一个Flink的执行环境env
和用于连接到Kafka的配置属性properties
。然后创建了一个FlinkKafkaConsumer
实例,并使用addSource()
方法将其添加到Flink流中。
接下来,我们对从Kafka读取的数据进行处理,这里简单地将每条消息都转换为大写形式。最后,我们调用print()
方法将处理后的数据打印出来,并执行Flink作业。
请注意,上述示例中只是对从Kafka读取的数据进行了简单处理,实际应用中可以根据需求进行更复杂的操作。