package com.zdjizhi.topology;

import cn.hutool.log.Log;
import cn.hutool.log.LogFactory;
import com.zdjizhi.common.FlowWriteConfig;
import com.zdjizhi.utils.functions.DealFileProcessFunction;
import com.zdjizhi.utils.functions.FilterNullFunction;
import com.zdjizhi.utils.functions.MapCompletedFunction;
import com.zdjizhi.utils.functions.TypeMapCompletedFunction;
import com.zdjizhi.utils.kafka.KafkaConsumer;
import com.zdjizhi.utils.kafka.KafkaProducer;
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 java.util.Map;

/**
 * @author 王成成
 * @Package com.zdjizhi.topology
 * @Description:
 * @date 2022.06.01
 */
public class LogFlowWriteTopology {
    private static final Log logger = LogFactory.get();

    public static void main(String[] args) {
        final StreamExecutionEnvironment environment = StreamExecutionEnvironment.getExecutionEnvironment();
        //两个输出之间的最大时间 (单位milliseconds)
        environment.setBufferTimeout(FlowWriteConfig.BUFFER_TIMEOUT);

        if (FlowWriteConfig.LOG_NEED_COMPLETE == 1) {

            SingleOutputStreamOperator<Map<String, Object>> streamSource = environment.addSource(KafkaConsumer.myDeserializationConsumer())
                    .setParallelism(FlowWriteConfig.SOURCE_PARALLELISM).name(FlowWriteConfig.SOURCE_KAFKA_TOPIC);

            DataStream<Map<String, Object>> cleaningLog;
            switch (FlowWriteConfig.LOG_TRANSFORM_TYPE) {
                case 0:
                    //对原始日志进行处理补全转换等，不对日志字段类型做校验。
                    cleaningLog = streamSource.map(new MapCompletedFunction()).name("MapCompletedFunction")
                            .setParallelism(FlowWriteConfig.TRANSFORM_PARALLELISM);
                    break;
                case 1:
                    //对原始日志进行处理补全转换等，对日志字段类型做若校验，可根据schema进行强转。
                    cleaningLog = streamSource.map(new TypeMapCompletedFunction()).name("TypeMapCompletedFunction")
                            .setParallelism(FlowWriteConfig.TRANSFORM_PARALLELISM);
                    break;
                default:
                    //对原始日志进行处理补全转换等，不对日志字段类型做校验。
                    cleaningLog = streamSource.map(new MapCompletedFunction()).name("MapCompletedFunction")
                            .setParallelism(FlowWriteConfig.TRANSFORM_PARALLELISM);
            }
            //处理带有非结构化日志的数据
            SingleOutputStreamOperator<String> process = cleaningLog.process(new DealFileProcessFunction());
            SingleOutputStreamOperator<String> resultFileMetaData = process.getSideOutput(DealFileProcessFunction.metaToKafa).filter(new FilterNullFunction()).name("FilterAbnormalTrafficFileMetaData").setParallelism(FlowWriteConfig.TRANSFORM_PARALLELISM);
            SingleOutputStreamOperator<String> result = process.filter(new FilterNullFunction()).name("FilterAbnormalData").setParallelism(FlowWriteConfig.TRANSFORM_PARALLELISM);
            //文件元数据发送至TRAFFIC-FILE-METADATA
            resultFileMetaData.addSink(KafkaProducer.getTrafficFileMetaKafkaProducer()).name("toTrafficFileMeta")
                    .setParallelism(FlowWriteConfig.FILE_DATA_SINK_PARALLELISM);
            //补全后的数据发送给百分点的kafka
            result.addSink(KafkaProducer.getPercentKafkaProducer()).name("toPercentKafka")
                    .setParallelism(FlowWriteConfig.PERCENT_SINK_PARALLELISM);
        }
        try {
            environment.execute(args[0]);
        } catch (Exception e) {
            logger.error("This Flink task start ERROR! Exception information is :" + e);
            e.printStackTrace();
        }

    }

}