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。