kafka版本：<kafka.version> 0.8.2.1</kafka.version>
spark版本 <artifactId>spark-streaming-kafka-0-8_2.11</artifactId>
object DmRealStat {
def main(args: Array[String]): Unit = {
/**
* 1.集成kafka进行数据进行数据读取
* 程序第一次启动从数据库获取偏移量,开始读取
*/
val sparkConf = new SparkConf().setMaster("local[4]").setAppName("实时监控")
//开启背压 开启后spark自动根据系统负载选择最优消费速率
sparkConf.set("spark.streaming.backpressure.enabled", "true")
//spark.streaming.backpressure.initialRate （整数） 默认直接读取所有
sparkConf.set(" spark.streaming.backpressure.initialRate", "1000")
//（4）限制每秒每个消费线程读取每个kafka分区最大的数据量 （整数） 默认直接读取所有
sparkConf.set(" spark.streaming.kafka.maxRatePerPartition ", "500")
sparkConf.set("spark.streaming.stopGracefullyOnShutdown", "true")
// sparkConf.set("spark.driver.memory","2G")
val ssc = new StreamingContext(sparkConf, Seconds(2))
val sc = ssc.sparkContext
//sparksql
val spark = SparkSession.builder().config(sparkConf).enableHiveSupport().getOrCreate()
//程序第一次启动,无偏移量
/*
def createDirectStream[
K: ClassTag, key的类型
V: ClassTag, value的类型
KD <: Decoder[K]: ClassTag,
VD <: Decoder[V]: ClassTag] (
ssc: StreamingContext,
kafkaParams: Map[String, String],
topics: Set[String]
): InputDStream[(K, V)] = {
val messageHandler = (mmd: MessageAndMetadata[K, V]) => (mmd.key, mmd.message)
val kc = new KafkaCluster(kafkaParams)
val fromOffsets = getFromOffsets(kc, kafkaParams, topics)
new DirectKafkaInputDStream[K, V, KD, VD, (K, V)](
ssc, kafkaParams, fromOffsets, messageHandler)
}
*/
val conf = ConfigFactory.load()
val brokers = conf.getString("kafka.broker.list")
val topic = conf.getString("kafka.topic")
val groupid = "11"
val kafkaParams = Map(
"metadata.broker.list" -> brokers,
"auto.offset.reset" -> "smallest",
"group.id" -> groupid
)
//加载配置信息 默认加载default.jdbc 如需设置生产环境 scalajdbcTest
DBs.setup()
val fromOffsets: Map[TopicAndPartition, Long] = DB.readOnly { implicit session =>
sql"select topic,partitions,offset from stream_offset where groupid=? and topic=? and brokerlist=?".bind(groupid, topic, brokers).map(rs => {
(TopicAndPartition(rs.get[String]("topic"), rs.get[Int]("partitions")), rs.long("offset"))
}).list().apply()
}.toMap
val topics = Set(topic)
val stream = if (fromOffsets.size == 0) {
// 程序第一次启动
KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder](ssc, kafkaParams, topics)
}
else {
//程序非第一次启动
var checkOffset = Map[TopicAndPartition, Long]()
//思考:kafka默认的保存数据是7天 但在过程中在没有启动过消费者 ,保存的offset是过期的偏移量 所以
// 必须查询偏移量与当前有效的最早的偏移量进行比较 如果保存的比当前的小,说明过期了
val kafkaCluste = new KafkaCluster(kafkaParams);
//传进去TopicAndPartition
val earliestLeaderOffsets = kafkaCluste.getEarliestLeaderOffsets(fromOffsets.keySet)
if (earliestLeaderOffsets.isRight) {
//得到了分区和对应的偏移量
val topicAndOffset: Map[TopicAndPartition, KafkaCluster.LeaderOffset] = earliestLeaderOffsets.right.get
checkOffset = fromOffsets.map(selectOffset => {
//拿到当前集群的分区 最早偏移量
val currentOffset = topicAndOffset.get(selectOffset._1).get.offset
if (selectOffset._2 >= currentOffset) {
//数据库的大于当前集群的 就使用数据库offfset
selectOffset
} else {
(selectOffset._1, currentOffset)
// val a= new KafkaConsumer(Map[String,Object](""->"")
}
})
checkOffset
}
//此处从数据库获取偏移量 ,程序启动从此处开始往后消费
val messageHandler = (mm: MessageAndMetadata[String, String]) => {
(mm.key(), mm.message())
}
KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder, (String, String)](ssc, kafkaParams, checkOffset, messageHandler)
}
//2.处理数据
stream
.foreachRDD(kafkardd => {
// val a: RDD[(String, String)] =kafkardd
val mapdata = LogUtils.logParse(kafkardd.map(_._2)).filter(log => log.contains("en") && log("en") == "e_dm")
mapdata.foreach(println(_))
var minute = ""
//2实时进行审核信息统计
//看一下偏移量
//3.自主管理偏移量存入redis/或者mysql
val offsetRanges = kafkardd.asInstanceOf[HasOffsetRanges].offsetRanges
offsetRanges.foreach(offsetRange => {
DB.autoCommit(implicit session =>
sql"replace into stream_offset(topic,partitions,groupid,brokerlist,offset)values (?,?,?,?,?)".bind(
offsetRange.topic,
offsetRange.partition,
groupid,
brokers,
offsetRange.untilOffset
).update().apply()
)
println("topic:" + offsetRange.topic + "分区:" + offsetRange.partition + "开始消费" + offsetRange.fromOffset + "消费到" + offsetRange.untilOffset + "共计" + offsetRange.count())
}
)
})
ssc.start()
ssc.awaitTermination()
}
def dongmanStat(mapdata:RDD[mutable.Map[String,String]]): Unit ={
val baseData = mapdata.filter(map => map.contains("c_type_name") && map.contains("status")).map(_map => {
val baseData = mapdata.map(_map => {
// String contId = _map.get("c_id");
// String cpId = _map.get("cp_id");
// String contTypeName = _map.get("c_type_name");
// String status = _map.get("status");
// String duration = _map.get("dura");
// String operator = _map.get("operator");
// String bcTime = _map.get("bc_time");
val minute = _map("s_time").substring(0, 12)
val day = _map("s_time").substring(0, 8)
val c_type = _map("c_type_name");
val progId = _map("cp_id");
val bcTotal = if (_map("status").toInt >= 8) 1 else 0
val receive = if (_map("status").toInt == 8) 1 else 0
val waitingBc = if (_map("status").toInt == 8) 1 else 0
val bcPerson = _map.getOrElse("operator", " ");
val syncTime = _map.getOrElse("sync_time", "");
// val srcLog = _map.getOrElse("src_log");
// val isDel = _map.getOrElse("is_delete",0)
// val isBcReview = _map.getOrElse("is_bc_review","")
(day, c_type, progId, bcPerson, syncTime, List[Int](bcTotal, receive, waitingBc))
})
// //内容统计
// val contBcStat = baseData.map {
// case (day, contId, progId, bcPerson, syncTime, list) => {
// ((day, contId), list)
// }
// }.distinct().reduceByKey((list1, list2) => {
// list1.zip(list2).map(i => {
// i._1 + i._2
// })
// }).foreachPartition(rdd => {
// val jedis = JedisUtil.getJedisClient()
// rdd.foreach(data => {
// val key: String = "cidStat" + "_" + data._1._1
// val a = jedis.hincrBy(key, "bcTotal", data._2(0))
// if (a > 0) println("自增成功") else println("自增失败")
// jedis.hincrBy(key, "receive", data._2(1))
// jedis.hincrBy(key, "waitingBc", data._2(2) - data._2(0))
// })
// jedis.close()
// })
//播控人内容统计 如果是相同的内容播控 条数去重
val bcPersonStat = baseData.map(t => ((t._1, t._4, t._2))).distinct()
// .updateStateByKey[Long]((seq: Seq[Int], state: Option[Long]) => {
// //seq:Seq[Long] 当前批次中每个相同key的value组成的Seq
// val currentValue = seq.sum
// //state:Option[Long] 代表当前批次之前的所有批次的累计的结果，val对于wordcount而言就是先前所有批次中相同单词出现的总次数
// val preValue = state.getOrElse(0L)
// Some(currentValue + preValue)
// })
.map(t => ((t._1, t._2), 1))