Spark : cleaner way to build Dataset out of Spark streaming - apache-spark

Question

I want to create an API which looks like this
public Dataset<Row> getDataFromKafka(SparkContext sc, String topic, StructType schema);
here
topic - is Kafka topic name from which the data is going to be consumed.
schema - is schema information for Dataset
so my function contains following code :
JavaStreamingContext jsc = new JavaStreamingContext(javaSparkContext, Durations.milliseconds(2000L));
JavaPairInputDStream<String, String> directStream = KafkaUtils.createDirectStream(
jsc, String.class, String.class,
StringDecoder.class, StringDecoder.class,
kafkaConsumerConfig(), topics
);
Dataset<Row> dataSet = sqlContext.createDataFrame(javaSparkContext.emptyRDD(), schema);
DataSetHolder holder = new DataSetHolder(dataSet);
LongAccumulator stopStreaming = sc.longAccumulator("stop");
directStream.foreachRDD(rdd -> {
RDD<Row> rows = rdd.values().map(value -> {
//get type of message from value
Row row = null;
if (END == msg) {
stopStreaming.add(1);
row = null;
} else {
row = new GenericRow(/*row data created from values*/);
}
return row;
}).filter(row -> row != null).rdd();
holder.union(sqlContext.createDataFrame(rows, schema));
holder.get().count();
});
jsc.start();
//stop stream if stopStreaming value is greater than 0 its spawned as new thread.
return holder.get();
Here DatasetHolder is a wrapper class around Dataset to combine the result of all the rdds.
class DataSetHolder {
private Dataset<Row> df = null;
public DataSetHolder(Dataset<Row> df) {
this.df = df;
}
public void union(Dataset<Row> frame) {
this.df = df.union(frame);
}
public Dataset<Row> get() {
return df;
}
}
This doesn't looks good at all but I had to do it. I am wondering what is the good way to do it. Or is there any provision for this by Spark?
Update
So after consuming all the data from stream i.e. from kafka topic, we create a dataframe out of it so that the data analyst can register it as a temp table and can fire any query to get the meaningful result.