75 lines
3.9 KiB
Java
75 lines
3.9 KiB
Java
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package com.zdjizhi.topology;
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import cn.hutool.log.Log;
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import cn.hutool.log.LogFactory;
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import com.zdjizhi.common.FlowWriteConfig;
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import com.zdjizhi.utils.functions.DealFileProcessFunction;
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import com.zdjizhi.utils.functions.FilterNullFunction;
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import com.zdjizhi.utils.functions.MapCompletedFunction;
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import com.zdjizhi.utils.functions.TypeMapCompletedFunction;
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import com.zdjizhi.utils.kafka.KafkaConsumer;
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import com.zdjizhi.utils.kafka.KafkaProducer;
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import org.apache.flink.streaming.api.datastream.DataStream;
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import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
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import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
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import java.util.Map;
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/**
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* @author 王成成
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* @Package com.zdjizhi.topology
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* @Description:
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* @date 2022.06.01
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*/
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public class LogFlowWriteTopology {
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private static final Log logger = LogFactory.get();
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public static void main(String[] args) {
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final StreamExecutionEnvironment environment = StreamExecutionEnvironment.getExecutionEnvironment();
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//两个输出之间的最大时间 (单位milliseconds)
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environment.setBufferTimeout(FlowWriteConfig.BUFFER_TIMEOUT);
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if (FlowWriteConfig.LOG_NEED_COMPLETE == 1) {
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SingleOutputStreamOperator<Map<String, Object>> streamSource = environment.addSource(KafkaConsumer.myDeserializationConsumer())
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.setParallelism(FlowWriteConfig.SOURCE_PARALLELISM).name(FlowWriteConfig.SOURCE_KAFKA_TOPIC);
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DataStream<Map<String, Object>> cleaningLog;
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switch (FlowWriteConfig.LOG_TRANSFORM_TYPE) {
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case 0:
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//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。
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cleaningLog = streamSource.map(new MapCompletedFunction()).name("MapCompletedFunction")
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.setParallelism(FlowWriteConfig.TRANSFORM_PARALLELISM);
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break;
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case 1:
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//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。
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cleaningLog = streamSource.map(new TypeMapCompletedFunction()).name("TypeMapCompletedFunction")
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.setParallelism(FlowWriteConfig.TRANSFORM_PARALLELISM);
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break;
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default:
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//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。
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cleaningLog = streamSource.map(new MapCompletedFunction()).name("MapCompletedFunction")
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.setParallelism(FlowWriteConfig.TRANSFORM_PARALLELISM);
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}
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//处理带有非结构化日志的数据
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SingleOutputStreamOperator<String> process = cleaningLog.process(new DealFileProcessFunction());
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SingleOutputStreamOperator<String> resultFileMetaData = process.getSideOutput(DealFileProcessFunction.metaToKafa).filter(new FilterNullFunction()).name("FilterAbnormalTrafficFileMetaData").setParallelism(FlowWriteConfig.TRANSFORM_PARALLELISM);
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SingleOutputStreamOperator<String> result = process.filter(new FilterNullFunction()).name("FilterAbnormalData").setParallelism(FlowWriteConfig.TRANSFORM_PARALLELISM);
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//文件元数据发送至TRAFFIC-FILE-METADATA
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resultFileMetaData.addSink(KafkaProducer.getTrafficFileMetaKafkaProducer()).name("toTrafficFileMeta")
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.setParallelism(FlowWriteConfig.FILE_DATA_SINK_PARALLELISM);
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//补全后的数据发送给百分点的kafka
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result.addSink(KafkaProducer.getPercentKafkaProducer()).name("toPercentKafka")
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.setParallelism(FlowWriteConfig.PERCENT_SINK_PARALLELISM);
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}
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try {
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environment.execute(args[0]);
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} catch (Exception e) {
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logger.error("This Flink task start ERROR! Exception information is :" + e);
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e.printStackTrace();
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}
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}
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}
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