分享一下spark streaming与flume集成的scala代码。

时间:2023-03-09 23:11:25
分享一下spark streaming与flume集成的scala代码。
文章来自:http://www.cnblogs.com/hark0623/p/4172462.html   转发请注明

object LogicHandle {
def main(args: Array[String]) {
//添加这个不会报执行错误
val path = new File(".").getCanonicalPath()
System.getProperties().put("hadoop.home.dir", path);
new File("./bin").mkdirs();
new File("./bin/winutils.exe").createNewFile(); //val sparkConf = new SparkConf().setAppName("SensorRealTime").setMaster("local[2]")
val sparkConf = new SparkConf().setAppName("SensorRealTime") val ssc = new StreamingContext(sparkConf, Seconds(20)) val hostname = "localhost"
val port = 2345
val storageLevel = StorageLevel.MEMORY_ONLY
val flumeStream = FlumeUtils.createStream(ssc, hostname, port, storageLevel) val lhc = new LogicHandleClass(); //日志格式化模板
val sdf = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss");
val sdfHour = new SimpleDateFormat("HH");
val sdfMinute = new SimpleDateFormat("mm") //存储数据的hash对象 key/value存储 根据文档规则,使用各统计指标的key/value
var redisMap = new HashMap[String, String]

  
    
flumeStream.foreachRDD(rdd => {
val events = rdd.collect()
//println("event count:" + events.length)
var i = 1
for (event <- events) {
val sensorInfo = new String(event.event.getBody.array()) //单行记录
//单行记录格式化
val arrayFileds = sensorInfo.split(",")
if (arrayFileds.length == 6) {
val shopId = arrayFileds(0) //店内编号 val floorId = shopId.substring(0, 5) //楼层编号
val mac = arrayFileds(1)
val ts = arrayFileds(2).toLong //时间戳
val time = sdf.format(ts * 1000)
var hour = sdfHour.format(ts * 1000)
var minute = sdfMinute.format(ts * 1000)
var allMinute = hour.toInt * 60 + minute.toInt val x = arrayFileds(3)
val y = arrayFileds(4)
val level = arrayFileds(5) //后边就是我的业务代码了,省略了
}
} //存储至redis中
lhc.SetAll(redisMap)
}) ssc.start()
ssc.awaitTermination()
}
}