今天就跟大家聊聊有关如何进行flink中的kafka源码分析，可能很多人都不太了解，为了让大家更加了解，小编给大家总结了以下内容，希望大家根据这篇文章可以有所收获。

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最近一直在弄flink sql相关的东西，第一阶段的目标是从解决kafka的消费和写入的问题。不过也有些同学并不是很了解，今天我们来详细分析一下包的继承层次。

flink源码如下：

public class KafkaTableSourceFactory implements StreamTableSourceFactory{

    private ConcurrentHashMap kafkaTableSources = new ConcurrentHashMap<>();

    @Override
    public Map requiredContext() {
        Map context = new HashMap<>();
        context.put(CONNECTOR_TYPE(), KafkaConnectorDescriptor.CONNECTOR_TYPE);
        context.put(CONNECTOR_PROPERTY_VERSION(),String.valueOf(KafkaConnectorDescriptor.CONNECTOR_PROPERTY_VERSION));
        return context;
    }

    @Override
    public List supportedProperties() {
        List properties = new ArrayList<>();
        properties.add(KafkaConnectorDescriptor.DATABASE_KEY);
        properties.add(KafkaConnectorDescriptor.TABLE_KEY);
        return properties;
    }

    @Override
    public StreamTableSource createStreamTableSource(Map properties) {
        //避免频繁的触发 是否需要加缓存
        KafkaTableSource kafkaTableSource;
        String dataBase = properties.get(KafkaConnectorDescriptor.DATABASE_KEY);
        String table = properties.get(KafkaConnectorDescriptor.TABLE_KEY);
        if (!kafkaTableSources.containsKey(dataBase + table)) {
            Kafka08UDMPBTableSource.Builder builder = new Kafka08UDMPBTableSource.Builder();
            kafkaTableSource = builder
                    .cluster(dataBase)
                    .subject(table)
                    .build();
            kafkaTableSources.put(dataBase + table,kafkaTableSource);
        } else {
            kafkaTableSource = kafkaTableSources.get(dataBase + table);
        }
        return kafkaTableSource;
    }

}

class Kafka08PBTableSource protected(topic: String,
                                     properties: Properties,
                                     schema: TableSchema,
                                     typeInformation: TypeInformation[Row],
                                     paramMap: util.LinkedHashMap[String, AnyRef],
                                     entryClass: String)
  extends KafkaTableSource(schema, topic, properties, new PBRowDeserializationSchema(typeInformation, paramMap,entryClass)) {

  override def createKafkaConsumer(topic: String, properties: Properties, deserializationSchema: DeserializationSchema[Row]): FlinkKafkaConsumerBase[Row] = {
    this.setStartupMode(StartupMode.EARLIEST)
    new FlinkKafkaConsumer08(topic, deserializationSchema, properties).setStartFromEarliest()

  }
}

下面用户自定义的kafka的sink类：

class Kafka08UDMPBTableSink (topic: String,
                              properties: Properties,
                              partitioner: Optional[FlinkKafkaPartitioner[Row]],
                              paramMap: util.LinkedHashMap[String, AnyRef],
                              serializationSchema: SerializationSchema[Row],
                              fieldNames: Array[String],
                              fieldTypes: Array[TypeInformation[_]]
                            ) extends KafkaTableSink(topic, properties, partitioner.orElse(new FlinkFixedPartitioner[Row])) {

  override def createKafkaProducer(topic: String, properties: Properties, serializationSchema: SerializationSchema[Row], partitioner: Optional[FlinkKafkaPartitioner[Row]]): SinkFunction[Row]={
    new FlinkKafkaProducer08[Row](topic, serializationSchema, properties, partitioner.orElse(new FlinkFixedPartitioner[Row]))
  }

  override def createSerializationSchema(rowSchema: RowTypeInfo) = serializationSchema

  override def createCopy = new Kafka08UDMPBTableSink(topic, properties, this.partitioner, paramMap, serializationSchema, fieldNames, fieldTypes)

  override def configure(fieldNames: Array[String], fieldTypes: Array[TypeInformation[_]]): KafkaTableSink = {
    super.configure(this.fieldNames, this.fieldTypes)
  }

  override def getFieldNames: Array[String]=this.fieldNames

  /** Returns the types of the table fields. */
  override def getFieldTypes: Array[TypeInformation[_]]=this.fieldTypes


  override def emitDataStream(dataStream: DataStream[Row]): Unit = {
    val kafkaProducer = createKafkaProducer(topic, properties, serializationSchema, partitioner)
    dataStream.addSink(kafkaProducer).name(TableConnectorUtil.generateRuntimeName(this.getClass, fieldNames))
  }

}

public class TrackRowDeserializationSchema implements SerializationSchema, DeserializationSchema {
    private static final long serialVersionUID = -2885556750743978636L;

    /** Type information describing the input type. */
    private TypeInformation typeInfo = null;

    private LinkedHashMap paraMap;

    private String inSchema;
    private String outSchema;
    private String inClass;
    private String outClass;
}

public class TrackRowFormatFactory extends TableFormatFactoryBase
        implements SerializationSchemaFactory, DeserializationSchemaFactory {

    public TrackRowFormatFactory() {
        super(TrackValidator.FORMAT_TYPE_VALUE, 1, false);
    }

    public TrackRowFormatFactory(String type, int version, boolean supportsSchemaDerivation) {
        super(type, version, supportsSchemaDerivation);
    }

    @Override
    protected List supportedFormatProperties() {
        final List properties = new ArrayList<>();
        properties.add(TrackValidator.FORMAT_IN_SCHEMA);
        properties.add(TrackValidator.FORMAT_IN_CLASS);
        properties.add(TrackValidator.FORMAT_OUT_CLASS);
        properties.add(TrackValidator.FORMAT_OUT_SCHEMA);
        properties.add(TrackValidator.FORMAT_TYPE_INFORMATION);
        properties.add(TrackValidator.FORMAT_TYPE_VALUE);
        return properties;
    }
}

看完上述内容，你们对如何进行flink中的kafka源码分析有进一步的了解吗？如果还想了解更多知识或者相关内容，请关注创新互联行业资讯频道，感谢大家的支持。

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