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package com.zdjizhi.topology;
import cn.hutool.core.convert.Convert;
import cn.hutool.core.util.ObjectUtil;
import cn.hutool.log.Log;
import cn.hutool.log.LogFactory;
import com.zdjizhi.common.*;
import com.zdjizhi.enums.DnsType;
import com.zdjizhi.etl.*;
import com.zdjizhi.utils.arangodb.ArangoDBSink;
import com.zdjizhi.utils.ck.ClickhouseSink;
import com.zdjizhi.utils.kafka.KafkaConsumer;
import org.apache.flink.api.common.eventtime.WatermarkStrategy;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.windowing.assigners.TumblingProcessingTimeWindows;
import org.apache.flink.streaming.api.windowing.time.Time;
import java.time.Duration;
import java.util.Map;
import java.util.Objects;
import static com.zdjizhi.common.FlowWriteConfig.*;
public class LogFlowWriteTopology {
private static final Log logger = LogFactory.get();
public static void main(String[] args) {
try {
final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
//两个输出之间的最大时间 (单位milliseconds)
env.setBufferTimeout(FlowWriteConfig.BUFFER_TIMEOUT);
//1 connection，2 dns
if (FlowWriteConfig.LOG_NEED_COMPLETE == 1) {
//connection
DataStream<Map<String, Object>> connSource = env.addSource(KafkaConsumer.myDeserializationConsumer(SOURCE_KAFKA_TOPIC_CONNECTION))
.filter(Objects::nonNull)
.setParallelism(SOURCE_PARALLELISM)
.name(SOURCE_KAFKA_TOPIC_CONNECTION);
//sketch
DataStream<Map<String, Object>> sketchSource = env.addSource(KafkaConsumer.myDeserializationConsumer(SOURCE_KAFKA_TOPIC_SKETCH))
.filter(Objects::nonNull)
.setParallelism(FlowWriteConfig.SOURCE_PARALLELISM)
.name(SOURCE_KAFKA_TOPIC_SKETCH);
//transform
DataStream<Map<String, Object>> connTransformStream = connSource
.assignTimestampsAndWatermarks(WatermarkStrategy
.<Map<String, Object>>forBoundedOutOfOrderness(Duration.ofSeconds(FlowWriteConfig.FLINK_WATERMARK_MAX_ORDERNESS))
.withTimestampAssigner((event, timestamp) -> Convert.toLong(event.get("conn_start_time")) * 1000))
.keyBy(new IpKeysSelector())
.window(TumblingProcessingTimeWindows.of(Time.seconds(FlowWriteConfig.LOG_AGGREGATE_DURATION)))
.process(new ConnProcessFunction())
.filter(x -> Objects.nonNull(x))
.setParallelism(FlowWriteConfig.TRANSFORM_PARALLELISM);
DataStream<Map<String, Object>> sketchTransformStream = sketchSource.assignTimestampsAndWatermarks(WatermarkStrategy
.<Map<String, Object>>forBoundedOutOfOrderness(Duration.ofSeconds(FlowWriteConfig.FLINK_WATERMARK_MAX_ORDERNESS))
.withTimestampAssigner((event, timestamp) -> Convert.toLong(event.get("sketch_start_time")) * 1000))
.keyBy(new IpKeysSelector())
.window(TumblingProcessingTimeWindows.of(Time.seconds(FlowWriteConfig.LOG_AGGREGATE_DURATION)))
.process(new SketchProcessFunction())
.filter(Objects::nonNull)
.setParallelism(FlowWriteConfig.TRANSFORM_PARALLELISM);
//入Arangodb
DataStream<Map<String, Object>> ip2ipGraph = connTransformStream.union(sketchTransformStream)
.keyBy(new IpKeysSelector())
.window(TumblingProcessingTimeWindows.of(Time.seconds(LOG_AGGREGATE_DURATION_GRAPH)))
.process(new Ip2IpGraphProcessFunction())
// .filter(Objects::nonNull)
.setParallelism(TRANSFORM_PARALLELISM);
//写入CKsink,批量处理
connSource.windowAll(TumblingProcessingTimeWindows.of(Time.seconds(SINK_BATCH_TIME_OUT))).apply(new CKWindow()).addSink(new ClickhouseSink(SINK_CK_TABLE_CONNECTION)).name("CKSink");
sketchSource.windowAll(TumblingProcessingTimeWindows.of(Time.seconds(SINK_BATCH_TIME_OUT))).apply(new CKWindow()).addSink(new ClickhouseSink(SINK_CK_TABLE_SKETCH)).name("CKSink");
sketchTransformStream.windowAll(TumblingProcessingTimeWindows.of(Time.seconds(SINK_BATCH_TIME_OUT))).apply(new CKWindow()).addSink(new ClickhouseSink(SINK_CK_TABLE_RELATION_CONNECTION)).name("CKSink");
//写入arangodb
ip2ipGraph.windowAll(TumblingProcessingTimeWindows.of(Time.seconds(SINK_BATCH_TIME_OUT))).apply(new ArangodbIPWindow()).addSink(new ArangoDBSink(R_VISIT_IP2IP)).name(R_VISIT_IP2IP);
} else if (FlowWriteConfig.LOG_NEED_COMPLETE == 2) {
DataStream<Map<String, Object>> dnsSource = env.addSource(KafkaConsumer.myDeserializationConsumer(SOURCE_KAFKA_TOPIC_DNS))
.filter(Objects::nonNull)
.map(new DnsMapFunction())
.setParallelism(FlowWriteConfig.SOURCE_PARALLELISM)
.name(FlowWriteConfig.SOURCE_KAFKA_TOPIC_DNS);
DataStream<Map<String, Object>> dnsTransform = dnsSource.assignTimestampsAndWatermarks(WatermarkStrategy
.<Map<String, Object>>forBoundedOutOfOrderness(Duration.ofSeconds(FLINK_WATERMARK_MAX_ORDERNESS))
.withTimestampAssigner((event, timestamp) -> Convert.toLong(event.get("capture_time")) * 1000))
.flatMap(new DnsSplitFlatMapFunction())
.keyBy(new DnsGraphKeysSelector())
.window(TumblingProcessingTimeWindows.of(Time.seconds(LOG_AGGREGATE_DURATION)))
.process(new DnsRelationProcessFunction())
.filter(Objects::nonNull)
.setParallelism(TRANSFORM_PARALLELISM);
//dns 原始日志 ck入库
dnsSource.filter(Objects::nonNull)
.setParallelism(FlowWriteConfig.SINK_PARALLELISM)
.windowAll(TumblingProcessingTimeWindows.of(Time.seconds(SINK_BATCH_TIME_OUT))).apply(new CKWindow())
.addSink(new ClickhouseSink(SINK_CK_TABLE_DNS))
.setParallelism(FlowWriteConfig.SINK_PARALLELISM)
.name("CKSink");
//dns 拆分后relation日志 ck入库
dnsTransform.windowAll(TumblingProcessingTimeWindows.of(Time.seconds(SINK_BATCH_TIME_OUT))).apply(new CKWindow())
.addSink(new ClickhouseSink(SINK_CK_TABLE_RELATION_DNS))
.setParallelism(SINK_PARALLELISM)
.name("CKSink");
//arango 入库,按record_type分组入不同的表
DataStream<Map<String, Object>> dnsGraph = dnsTransform.keyBy(new DnsGraphKeysSelector())
.window(TumblingProcessingTimeWindows.of(Time.seconds(LOG_AGGREGATE_DURATION_GRAPH)))
.process(new DnsGraphProcessFunction())
.setParallelism(SINK_PARALLELISM)
.filter(Objects::nonNull);
for (DnsType dnsEnum : DnsType.values()) {
dnsGraph.filter(x -> ObjectUtil.equal(dnsEnum.getType(), x.get("record_type")))
.windowAll(TumblingProcessingTimeWindows.of(Time.seconds(SINK_BATCH_TIME_OUT))).apply(new ArangodbDnsWindow())
.addSink(new ArangoDBSink(dnsEnum.getSink()))
.setParallelism(SINK_PARALLELISM)
.name("ArangodbSink");
}
}
env.execute(args[0]);
} catch (Exception e) {
logger.error("This Flink task start ERROR! Exception information is : {}", e);
}
}
}
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