Spark Streaming Job time increasing gradually - scala

Question

I have a streaming job ,
1. Load the data from hdfs file and register it as a temp table.
2. Then join the temp table with tables present in the hive database.
3. Then send the record to Kafka.
initially it was taking 12s to do complete first cycle , then it increases to the 50s after 10 hrs. I don't understand the issue here. Also i noticed that shuffle write is also increasing in every node after 10 hrs it is 200GB+
Sample code is ,
val rowRDD = hContext.read.format("com.databricks.spark.csv").option("header", "false").
option("delimiter", delimiter).load(path).map(col => dosomething)
//Add filter to rdd to convert the data in a time range.
val filteredRDD = rowRDD.filter { col =>{dosomething}}
//Create the data from changed data RDD and schema to new DataFrame
val tblDF = hContext.createDataFrame(filteredRDD, tblSchema).where("crud_status IN ('U','D','I')")
//Register all the records into the temporary tables.
tblDF.registerTempTable("name_changed")
val userDF = hContext.sql("SELECT id,name,account FROM name_changed JOIN account ON(name_changed.id=account.id) JOIN question
on (account.question=question.question)")
userDF.foreachPartition { records =>
val producer = getKafkaProducer(kafkaBootstrap)
records.foreach { rowData =>
producer.send(new ProducerRecord[String, Array[Byte]](topicName, rowData) )
}
}
producer.close()
}