Spark2.3(三十七)：Stream join Stream（res文件每天更新一份）

Posted 2021-01-27 yy3b2007com

kafka测试数据生成：

package com.dx.kafka;

import java.util.Properties;
import java.util.Random;

import org.apache.kafka.clients.producer.Producer;
import org.apache.kafka.clients.producer.ProducerRecord;

public class KafkaProducer {
    public static void main(String[] args) throws InterruptedException {
        Properties props = new Properties();
        props.put("bootstrap.servers", "192.168.0.141:9092,192.168.0.142:9092,192.168.0.143:9092,192.168.0.144:9092");
        props.put("acks", "all");
        props.put("retries", 0);
        props.put("batch.size", 16384);
        props.put("linger.ms", 1);
        props.put("buffer.memory", 33554432);
        props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
        props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");
        Producer<String, String> producer = new org.apache.kafka.clients.producer.KafkaProducer(props);
        int i = 0;
        Random random=new Random();
        while (true) {
            i++;
            producer.send(new ProducerRecord<String, String>("my-topic", "key-" + Integer.toString(i),
                    i%3+","+random.nextInt(100)));
            System.out.println(i);
            Thread.sleep(1000);
            
            if(i%100==0) {
                Thread.sleep(60*1000);                
            }
        }
        // producer.close();

    }
}

Stream join Stream测试代码：

要求：使用spark structured streaming实时读取kafka中的数据，kafka中的数据包含字段int_id；kafka上数据需要关联资源信息（通过kafka的int_id与资源的int_id进行关联），同时要求资源每天都更新。

使用spark structured streaming实时读取kafka中的数据

        Dataset<Row> linesDF = this.sparkSession.readStream()//
                .format("kafka")//
                .option("failOnDataLoss", false)//
                .option("kafka.bootstrap.servers", "192.168.0.141:9092,192.168.0.142:9092,192.168.0.143:9092,192.168.0.144:9092")//
                .option("subscribe", "my-topic")//
                .option("startingOffsets", "earliest")//
                .option("maxOffsetsPerTrigger", 10)//
                .load();

        StructType structType = new StructType();
        structType = structType.add("int_id", DataTypes.StringType, false);
        structType = structType.add("rsrp", DataTypes.StringType, false);
        structType = structType.add("mro_timestamp", DataTypes.TimestampType, false);
        ExpressionEncoder<Row> encoder = RowEncoder.apply(structType);
        Dataset<Row> mro = linesDF.select("value").as(Encoders.STRING()).map(new MapFunction<String, Row>() {
            private static final long serialVersionUID = 1L;

            @Override
            public Row call(String t) throws Exception {
                List<Object> values = new ArrayList<Object>();
                String[] fields = t.split(",");
                values.add(fields.length >= 1 ? fields[0] : "null");
                values.add(fields.length >= 2 ? fields[1] : "null");
                values.add(new Timestamp(new Date().getTime()));

                return RowFactory.create(values.toArray());
            }
        }, encoder);
        mro=mro.withWatermark("mro_timestamp", "15 minutes");
        mro.printSchema();

加载资源信息

        StructType resulStructType = new StructType();
        resulStructType = resulStructType.add("int_id", DataTypes.StringType, false);
        resulStructType = resulStructType.add("enodeb_id", DataTypes.StringType, false);
        resulStructType = resulStructType.add("res_timestamp", DataTypes.TimestampType, false);
        ExpressionEncoder<Row> resultEncoder = RowEncoder.apply(resulStructType);
        Dataset<Row> resDs = sparkSession.readStream().option("maxFileAge", "1ms").textFile(resourceDir)
                .map(new MapFunction<String, Row>() {
                    private static final long serialVersionUID = 1L;

                    @Override
                    public Row call(String value) throws Exception {
                        String[] fields = value.split(",");
                        Object[] objItems = new Object[3];
                        objItems[0] = fields[0];
                        objItems[1] = fields[1];
                        objItems[2] = Timestamp.valueOf(fields[2]);

                        return RowFactory.create(objItems);
                    }
                }, resultEncoder);
        resDs = resDs.withWatermark("res_timestamp", "1 seconds");
        resDs.printSchema();

kafka上数据与资源关联

关联条件int_id相同，同时要求res.timestamp<=mro.timestmap & res.timestamp<(mro.timestmap-1天)

res如果放入broadcast经过测试发现也是可行的。

        // JavaSparkContext jsc =
        // JavaSparkContext.fromSparkContext(sparkSession.sparkContext());
        Dataset<Row> cellJoinMro = mro.as("t10")//
                .join(resDs.as("t11"),// jsc.broadcast(resDs).getValue()
                        functions.expr("t11.int_id=t10.int_id "//
                                + "and t11.res_timestamp<=t10.mro_timestamp "//
                                + "and timestamp_diff(t11.res_timestamp,t10.mro_timestamp,‘>‘,‘-86400000‘)"),//
                        "left_outer")//
                .selectExpr("t10.int_id", "t10.rsrp", "t11.enodeb_id", "t10.mro_timestamp", "t11.res_timestamp");

        StreamingQuery query = cellJoinMro.writeStream().format("console").outputMode("update") //
                .trigger(Trigger.ProcessingTime(1, TimeUnit.MINUTES))//
                .start();

udf：timestamp_diff定义

        sparkSession.udf().register("timestamp_diff", new UDF4<Timestamp, Timestamp, String, String, Boolean>() {
            private static final long serialVersionUID = 1L;

            @Override
            public Boolean call(Timestamp t1, Timestamp t2, String operator, String intervalMsStr) throws Exception {
                long diffValue=t1.getTime()-t2.getTime();
                long intervalMs=Long.valueOf(intervalMsStr);
                
                if(operator.equalsIgnoreCase(">")){
                    return diffValue>intervalMs;
                }else if(operator.equalsIgnoreCase(">=")){
                    return diffValue>=intervalMs;
                }else if(operator.equalsIgnoreCase("<")){
                    return diffValue<intervalMs;
                }else if(operator.equalsIgnoreCase("<=")){
                    return diffValue<=intervalMs;
                }else if(operator.equalsIgnoreCase("=")){
                    return diffValue==intervalMs;
                }else{
                    throw new RuntimeException("unknown error");
                }
            }
        },DataTypes.BooleanType);

如果删除资源历史数据，不会导致正在运行的程序抛出异常；当添加新文件到res hdfs路径下时，可以自动被加载进来。

备注：要求必须每天资源文件只能有一份，否则会导致kafka上数据关联后结果重复，同时，res上的每天的文件中包含timestmap字段格式都为yyyy-MM-dd 00:00:00。