Flink DataStream API 介绍

Flink DataStream API 介绍

StreamExecutionEnvironment

DataStram数据源

Datastream 基本数据源

//从给定的数据元素中转换
DatastreamSource<OUT> fromElements(OUT... data)
//从指定的集合中转换成DataStream
DatastreamSource<OUT> flomCollection(Collection<OUT> data)
//读取文件并转换
DataStreamSource<OUT> readFile(FileInputFormat<OUT> inputFormat, String filePath)
//从Scocket端口中读取
DataStreamSource<String> socketTextStream(String hostname, int port, String delimiter)
//直接通过InputFormat创建
DataStreamSource<OUT> createInput(InputFormat<OUT, ?> inputFormat)
1
2
3
4
5
6
7
8
9
10

最终都是通过 ExecutionEnvironment 创建 fromSource() 方法转换成DataStreamSource

Datastream 数据源连接器

Flink 内 置 Connector:

• Apache Kafka (source/sink)

• Apache Cassandra (Sink)

• Amazon Kinesis Streams (source/sink)

• Elasticsearch(Sink)

• Hadoop FileSystem (sink)

• RabbitMQ (source/sink)

• Apache NiFi (source/sink)

• Twitter Streaming API (source)

• Google PubSub (source/sink)

• JDBC (sinkJ

Apache Bahir 项 目 :

• Apache ActiveMQ (source/sink)
• Apache Flume (sink)
• Redis (sink)
• Akka (sink)
• Netty (source)

以Kafka 连接器为例 :

<dependency>
    <groupld>org.apache.flinkgroupld>
    <artifactId>flink-connector-kafka_2.11artifactId>
    <version>1.11.0version>
dependency>
1
2
3
4
5

Datastream 数据源连接器 - Source

Properties properties = new Properties();
properties.setProperty("bootstrap.servers", "localhost:9092);
properties.setProperty("group.id", "test0");

Datastream<String> stream = env.addSource(new FlinkKafkaConsumer<>("topic",new SimpleStringSchema(), properties));
1
2
3
4
5

Datastream 数据源连接器

以Kafka 连接器为例 :

Map<KafkaTopicPartition, Long> specificStartOffsets = new HashMap<>();

specificStartOffsets.put(new KafkaTopicPartition(“myTopic“, 0), 23L);
specificStartOffsets.put(new KafkaTopicPartition(“myTopic“, 1), 31L);
specificStartOffsets.put(new KafkaTopicPartition(“myTopic“, 2), 43L);

myConsumer.setStartFromSpecificOffsets(specificstartOffsets);
1
2
3
4
5
6
7

Datastream 数据源连接器 - Sink

Datastream<string> stream = …

Properties properties = new Properties();
properties.setpProperty("bootstrap.servers", "localhost:9092");

FlinkKafkaProducer<String> myProducer = new FlinkKafkaProducer<>("my-topic",//target topic
new SimpleStringSchema(), // serialization schema
properties, // producer config
FlinkKafkaProducer.Semantic.EXACTLY_ONCE); // fault-tolerance
stream.addsSink(myProducer);
1
2
3
4
5
6
7
8
9
10

Datastream 主要转换操作

理解Keyedstream

Datastream 之间的转换

物理分组操作

public DataStream<T> shuffle(){
    return setConnectionType(new ShufflePartitioner<T>());
}
1
2
3

DataStream Kafka 实例

public class KafkaExample{
    public static void main(String[] args) throws Exception {
        // parse inputarg umenlts
        final ParameterTool parameterTool = ParameterTooLfromArgs(args);
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.getConfig().setRestartStrategy(RestartStrategies.fixedDelayRestart(4, 10000));
        env.enableCheckpointing(5000); // create a checkpoint every 5 seconds
        env.getConfig().setGlobalobParameters(parameterTool); // make parameters available in the web interface
        env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);
        DataStream<kafkaEvent> input = env
            .addSource(
            new FlinkKafKaConsumer<>(
                parameterTool.getRedquired("input-topic"),
                new KafkaEventSchema(),
                parameterTool.getPropelties())
            .assignTimestampsAndWatermarks(new CustomWatermarkExtractor()))
            .keyBy("word")
            .map(new RollingAdditionMapper())
            .shuffle();
        input.addSink(
            new FlinKafkaProduCer<>(
                parameterTool.getRequired("output-topic"),
                new KeyedSerializationSchemaWrapper<>(new KafKaEventSChema()),
                parameterTool.getProperties(),
                FlinkKafkaProducer.Semantic.EXACTLY_ONCE));
        env.execute("Modern Kafka ExamPle");
    }
}
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