Spark Streaming scala performance drastic slow - apache-kafka

Question

I have following code :-
case class event(imei: String, date: String, gpsdt: String,dt: String,id: String)
case class historyevent(imei: String, date: String, gpsdt: String)
object kafkatesting {
def main(args: Array[String]) {
val clients = new RedisClientPool("192.168.0.40", 6379)
val conf = new SparkConf()
.setAppName("KafkaReceiver")
.set("spark.cassandra.connection.host", "192.168.0.40")
.set("spark.cassandra.connection.keep_alive_ms", "20000")
.set("spark.executor.memory", "3g")
.set("spark.driver.memory", "4g")
.set("spark.submit.deployMode", "cluster")
.set("spark.executor.instances", "4")
.set("spark.executor.cores", "3")
.set("spark.streaming.backpressure.enabled", "true")
.set("spark.streaming.backpressure.initialRate", "100")
.set("spark.streaming.kafka.maxRatePerPartition", "7")
val sc = SparkContext.getOrCreate(conf)
val ssc = new StreamingContext(sc, Seconds(10))
val sqlContext = new SQLContext(sc)
val kafkaParams = Map[String, String](
"bootstrap.servers" -> "192.168.0.113:9092",
"group.id" -> "test-group-aditya",
"auto.offset.reset" -> "largest")
val topics = Set("random")
val kafkaStream = KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder](ssc, kafkaParams, topics)
kafkaStream.foreachRDD { rdd =>
val updatedRDD = rdd.map(a =>
{
implicit val formats = DefaultFormats
val jValue = parse(a._2)
val fleetrecord = jValue.extract[historyevent]
val hash = fleetrecord.imei + fleetrecord.date + fleetrecord.gpsdt
val md5Hash = DigestUtils.md5Hex(hash).toUpperCase()
val now = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").format(Calendar.getInstance().getTime())
event(fleetrecord.imei, fleetrecord.date, fleetrecord.gpsdt, now, md5Hash)
})
.collect()
updatedRDD.foreach(f =>
{
clients.withClient {
client =>
{
val value = f.imei + " , " + f.gpsdt
val zscore = Calendar.getInstance().getTimeInMillis
val key = new SimpleDateFormat("yyyy-MM-dd").format(Calendar.getInstance().getTime())
val dt = new SimpleDateFormat("HH:mm:ss").format(Calendar.getInstance().getTime())
val q1 = "00:00:00"
val q2 = "06:00:00"
val q3 = "12:00:00"
val q4 = "18:00:00"
val quater = if (dt > q1 && dt < q2) {
System.out.println(dt + " lies in quarter 1");
" -> 1"
} else if (dt > q2 && dt < q3) {
System.out.println(dt + " lies in quarter 2");
" -> 2"
} else if (dt > q3 && dt < q4) {
System.out.println(dt + " lies in quarter 3");
" -> 3"
} else {
System.out.println(dt + " lies in quarter 4");
" -> 4"
}
client.zadd(key + quater, zscore, value)
println(f.toString())
}
}
})
val collection = sc.parallelize(updatedRDD)
collection.saveToCassandra("db", "table", SomeColumns("imei", "date", "gpsdt","dt","id"))
}
ssc.start()
ssc.awaitTermination()
}
}
I'm using this code to insert data from Kafka into Cassandra and Redis, but facing following issues:-
1) application creates a long queue of active batches while the previous batch is currently being processed. So, I want to have next batch only once the previous batch is finished executing.
2) I have four-node cluster which is processing each batch but it takes around 30-40 sec for executing 700 records.
Is my code is optimized or I need to work on my code for better performance?

Answer 1

Yes you can do all your stuff inside mapPartition. There are APIs from datastax that allow you to save the Dstream directly. Here is how you can do it for C*.
val partitionedDstream = kafkaStream.repartition(5) //change this value as per your data and spark cluster
//Now instead of iterating each RDD work on each partition.
val eventsStream: DStream[event] = partitionedDstream.mapPartitions(x => {
val lst = scala.collection.mutable.ListBuffer[event]()
while (x.hasNext) {
val a = x.next()
implicit val formats = DefaultFormats
val jValue = parse(a._2)
val fleetrecord = jValue.extract[historyevent]
val hash = fleetrecord.imei + fleetrecord.date + fleetrecord.gpsdt
val md5Hash = DigestUtils.md5Hex(hash).toUpperCase()
val now = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").format(Calendar.getInstance().getTime())
lst += event(fleetrecord.imei, fleetrecord.date, fleetrecord.gpsdt, now, md5Hash)
}
lst.toList.iterator
})
eventsStream.cache() //because you are using same Dstream for C* and Redis
//instead of collecting each RDD save whole Dstream at once
import com.datastax.spark.connector.streaming._
eventsStream.saveToCassandra("db", "table", SomeColumns("imei", "date", "gpsdt", "dt", "id"))
Also cassandra accepts timestamp as Long value, so you can also change some part of your code as below
val now = System.currentTimeMillis()
//also change your case class to take `Long` instead of `String`
case class event(imei: String, date: String, gpsdt: String, dt: Long, id: String)
Similarly you can change for Redis as well.