Sean
Dstream.saveAsTextFiles internally calls foreachRDD and saveAsTextFile for
each interval
def saveAsTextFiles(prefix: String, suffix: String = "") {
val saveFunc = (rdd: RDD[T], time: Time) => {
val file = rddToFileName(prefix, suffix, time)
rdd.saveAsTextFile(file)
}
this.foreachRDD(saveFunc)
}
val sparkConf = new SparkConf().setAppName("TwitterRawJSON")
val ssc = new StreamingContext(sparkConf, Seconds(30))
stream.saveAsTextFiles("hdfs://localhost:9000/twitterRawJSON")
1) if there are no sliding window calls in this streaming context, will
there just one file written per interval?
2) if there is a sliding window call in the same context, such as
val hashTags = stream.flatMap(json =>
DataObjectFactory.createStatus(json).getText.split("
").filter(_.startsWith("#")))
val topCounts60 = hashTags.map((_, 1)).reduceByKeyAndWindow(_ + _,
Seconds(600))
.map{case (topic, count) => (count, topic)}
.transform(_.sortByKey(false))
will the some files get written multiples time (as long as the interval is
in the batch)
Deenar
>>DStream.foreachRDD gives you an RDD[String] for each interval of
course. I don't think it makes sense to say a DStream can be converted
into one RDD since it is a stream. The past elements are inherently
not supposed to stick around for a long time, and future elements
aren't known. You may consider saving each RDD[String] to HDFS, and
then simply loading it from HDFS as an RDD[String].
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