sparkstreaming整合kafka参数设置，message偏移量写入mysql

 kafka高级数据源拉取到spark，偏移量自我维护，借助scalikejdbc写入到mysql。

需要导入

<dependency>
<groupId>org.scalikejdbc</groupId>
<artifactId>scalikejdbc_2.11</artifactId>
<version>2.5.0</version>
</dependency><!-- scalikejdbc-config_2.11 -->
<dependency>
<groupId>org.scalikejdbc</groupId>
<artifactId>scalikejdbc-config_2.11</artifactId>
<version>2.5.0</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming-kafka-0-10_2.11</artifactId>
<version>2.2.1</version>
</dependency>

import org.apache.kafka.common.TopicPartition
import org.apache.kafka.common.serialization.StringDeserializer
import org.apache.log4j.{Level, Logger}
import org.apache.spark.SparkConf
import org.apache.spark.rdd.RDD
import org.apache.spark.streaming.kafka010._
import org.apache.spark.streaming.{Seconds, StreamingContext}
import scalikejdbc.config.DBs
import scalikejdbc.{DB, SQL}

/**
* kafka数据读取，偏移量自己管理，偏移量数据传入mysql。
* log数据可以使用其他的进行保存。
*/
object WCKafkaMysqlDB_offset {

Logger.getLogger("org").setLevel(Level.WARN)

def main(args: Array[String]): Unit = {

val conf = new SparkConf().setMaster("local[*]").setAppName("xx")
//每秒钟每个分区kafka拉取消息的速率
.set("spark.streaming.kafka.maxRatePerPartition", "100")
// 序列化
.set("spark.serilizer", "org.apache.spark.serializer.KryoSerializer")
// 建议开启rdd的压缩
.set("spark.rdd.compress", "true")
val ssc = new StreamingContext(conf, Seconds(2))

//一参数设置
val groupId = "1"
val kafkaParams = Map[String, Object](
"bootstrap.servers" -> "hdp01:9092,hdp02:9092,hdp03:9092",
"key.deserializer" -> classOf[StringDeserializer],
"value.deserializer" -> classOf[StringDeserializer],
"group.id" -> groupId,
"auto.offset.reset" -> "earliest",
"enable.auto.commit" -> (false: java.lang.Boolean) //自己维护偏移量。连接kafka的集群。
)
val topics = Array("test")

//二参数设置
DBs.setup()
val fromdbOffset: Map[TopicPartition, Long] =
DB.readOnly { implicit session =>
SQL(s"select * from `offset` where groupId = '${groupId}'")
.map(rs => (new TopicPartition(rs.string("topic"), rs.int("partition")), rs.long("untilOffset")))
.list().apply()

}.toMap

//程序启动，拉取kafka的消息。
val stream = if (fromdbOffset.size == 0) {
KafkaUtils.createDirectStream[String, String](
ssc,
LocationStrategies.PreferConsistent,
ConsumerStrategies.Subscribe[String, String](topics, kafkaParams)
)
} else {
KafkaUtils.createDirectStream(
ssc,
LocationStrategies.PreferConsistent,
ConsumerStrategies.Assign[String, String](fromdbOffset.keys, kafkaParams, fromdbOffset)
)
}

stream.foreachRDD({
rdd =>
val offsetRanges: Array[OffsetRange] = rdd.asInstanceOf[HasOffsetRanges].offsetRanges

//数据处理
val resout: RDD[(String, Int)] = rdd.flatMap(_.value().split(" ")).map((_, 1)).reduceByKey(_ + _)
resout.foreach(println)
resout.foreachPartition({
it =>
val jedis = RedisUtils.getJedis
it.foreach({
va =>
jedis.hincrBy("wc", va._1, va._2)
})
jedis.close()
})
//偏移量存入mysql，使用scalikejdbc框架事务
DB.localTx { implicit session =>
for (or <- offsetRanges) {
SQL("replace into `offset`(groupId,topic,partition,untilOffset) values(?,?,?,?)")
.bind(groupId, or.topic, or.partition, or.untilOffset).update().apply()
}
}
})

ssc.start()
ssc.awaitTermination()

}
}

2.scalikejdbc配置可参考官网。