I think what you want is to make each bucket as a new RDD as what you mentioned in Pig syntax.
gs = ORDER g BY group ASC, g.timestamp ASC // 'group' is the rounded timestamp for each bucket From my understanding, currently in Spark there’s no such kind of API to achieve this, maybe you have to create a customized RDD by yourself. For the code why cannot executed, .map(sc.parallelize(_._2.sortBy(_._1))) // nested RDD, hmm... This “sc.parallelize(_._2.sortBy(_._1))”will be serialized as a closure to execute in remote side, which obviously do not has SparkContext, I think Spark cannot support nested RDD in closure. Thanks Jerry From: Jianshi Huang [mailto:jianshi.hu...@gmail.com] Sent: Monday, October 27, 2014 3:30 PM To: Shao, Saisai Cc: user@spark.apache.org; Tathagata Das (t...@databricks.com) Subject: Re: RDD to DStream Ok, back to Scala code, I'm wondering why I cannot do this: data.groupBy(timestamp / window) .sortByKey() // no sort method available here .map(sc.parallelize(_._2.sortBy(_._1))) // nested RDD, hmm... .collect() // returns Seq[RDD[(Timestamp, T)]] Jianshi On Mon, Oct 27, 2014 at 3:24 PM, Jianshi Huang <jianshi.hu...@gmail.com<mailto:jianshi.hu...@gmail.com>> wrote: You're absolutely right, it's not 'scalable' as I'm using collect(). However, it's important to have the RDDs ordered by the timestamp of the time window (groupBy puts data to corresponding timewindow). It's fairly easy to do in Pig, but somehow I have no idea how to express it in RDD... Something like (in Pig, pseudo code :): g = GROUP data BY (timestamp / windowSize) // group data into buckets in the same time window gs = ORDER g BY group ASC, g.timestamp ASC // 'group' is the rounded timestamp for each bucket stream = FOREACH gs GENERATE toRDD(g) No idea how to do the order by part in RDD. Jianshi On Mon, Oct 27, 2014 at 3:07 PM, Shao, Saisai <saisai.s...@intel.com<mailto:saisai.s...@intel.com>> wrote: I think you solution may not be extendable if the data size is increasing, since you have to collect all your data back to driver node, so the memory usage of driver will be a problem. why not filter out specific time-range data as a rdd, after filtering the whole time-range, you will get a series of RDD with timestamp divided, and then feed into queue. Still it is not an efficient way, but it is not limited by driver memory. Also there may have some other solutions like shuffle to arrange data, but you cannot avoid scanning the whole data. Basically we need to avoid fetching large amount of data back to driver. Thanks Jerry From: Jianshi Huang [mailto:jianshi.hu...@gmail.com<mailto:jianshi.hu...@gmail.com>] Sent: Monday, October 27, 2014 2:39 PM To: Shao, Saisai Cc: user@spark.apache.org<mailto:user@spark.apache.org>; Tathagata Das (t...@databricks.com<mailto:t...@databricks.com>) Subject: Re: RDD to DStream Hi Saisai, I understand it's non-trivial, but the requirement of simulating offline data as stream is also fair. :) I just wrote a prototype, however, I need to do a collect and a bunch of parallelize... // RDD of (timestamp, value) def rddToDStream[T: ClassTag](data: RDD[(Long, T)], timeWindow: Long, ssc: StreamingContext): DStream[T] = { val sc = ssc.sparkContext val d = data.groupBy(_._1 / timeWindow) .map(e => (e._1, e._2.toSeq.sortBy(_._1).map(_._2))) .collect() .map(e => (e._1, sc.parallelize(e._2))) .sortBy(_._1) val queue = new mutable.SynchronizedQueue[RDD[T]] queue ++= d.map(_._2) ssc.queueStream(queue) } Any way to get a list of RDDs sorted by group key just after groupBy? Jianshi On Mon, Oct 27, 2014 at 2:00 PM, Shao, Saisai <saisai.s...@intel.com<mailto:saisai.s...@intel.com>> wrote: Hi Jianshi, For simulation purpose, I think you can try ConstantInputDStream and QueueInputDStream to convert one RDD or series of RDD into DStream, the first one output the same RDD in each batch duration, and the second one just output a RDD in a queue in each batch duration. You can take a look at it. For your case, I think TD’s comment are quite meaningful, it’s not trivial to do so, often requires a job to scan all the records, it’s also not the design purpose of Spark Streaming, I guess it’s hard to achieve what you want. Thanks Jerry From: Jianshi Huang [mailto:jianshi.hu...@gmail.com<mailto:jianshi.hu...@gmail.com>] Sent: Monday, October 27, 2014 1:42 PM To: Tathagata Das Cc: Aniket Bhatnagar; user@spark.apache.org<mailto:user@spark.apache.org> Subject: Re: RDD to DStream I have a similar requirement. But instead of grouping it by chunkSize, I would have the timeStamp be part of the data. So the function I want has the following signature: // RDD of (timestamp, value) def rddToDStream[T](data: RDD[(Long, T)], timeWindow: Long)(implicit ssc: StreamingContext): DStream[T] And DStream should respect the timestamp part. This is important for simulation, right? Do you have any good solution for this? Jianshi On Thu, Aug 7, 2014 at 9:32 AM, Tathagata Das <tathagata.das1...@gmail.com<mailto:tathagata.das1...@gmail.com>> wrote: Hey Aniket, Great thoughts! I understand the usecase. But as you have realized yourself it is not trivial to cleanly stream a RDD as a DStream. Since RDD operations are defined to be scan based, it is not efficient to define RDD based on slices of data within a partition of another RDD, using pure RDD transformations. What you have done is a decent, and probably the only feasible solution, with its limitations. Also the requirements of converting a batch of data to a stream of data can be pretty diverse. What rate, what # of events per batch, how many batches, is it efficient? Hence, it is not trivial to define a good, clean public API for that. If any one has any thoughts, ideas, etc on this, you are more than welcome to share them. TD On Mon, Aug 4, 2014 at 12:43 AM, Aniket Bhatnagar <aniket.bhatna...@gmail.com<mailto:aniket.bhatna...@gmail.com>> wrote: The use case for converting RDD into DStream is that I want to simulate a stream from an already persisted data for testing analytics. It is trivial to create a RDD from any persisted data but not so much for DStream. Therefore, my idea to create DStream from RDD. For example, lets say you are trying to implement analytics on time series data using Lambda architecture. This means you would have to implement the same analytics on streaming data (in streaming mode) as well as persisted data (in batch mode). The workflow for implementing the anlytics would be to first implement it in batch mode using RDD operations and then simulate stream to test the analytics in stream mode. The simulated stream should produce the elements at a specified rate. So the solution maybe to read data in a RDD, split (chunk) it into multiple RDDs with each RDD having the size of elements that need to be streamed per time unit and then finally stream each RDD using the compute function. The problem with using QueueInputDStream is that it will stream data as per the batch duration specified in the streaming context and one cannot specify a custom slide duration. Moreover, the class QueueInputDStream is private to streaming package, so I can't really use it/extend it from an external package. Also, I could not find a good solution split a RDD into equal sized smaller RDDs that can be fed into an extended version of QueueInputDStream. Finally, here is what I came up with: class RDDExtension[T: ClassTag](rdd: RDD[T]) { def toStream(streamingContext: StreamingContext, chunkSize: Int, slideDurationMilli: Option[Long] = None): DStream[T] = { new InputDStream[T](streamingContext) { private val iterator = rdd.toLocalIterator // WARNING: each partition much fit in RAM of local machine. private val grouped = iterator.grouped(chunkSize) override def start(): Unit = {} override def stop(): Unit = {} override def compute(validTime: Time): Option[RDD[T]] = { if (grouped.hasNext) { Some(rdd.sparkContext.parallelize(grouped.next())) } else { None } } override def slideDuration = { slideDurationMilli.map(duration => new Duration(duration)). getOrElse(super.slideDuration) } } } This aims to stream chunkSize elements every slideDurationMilli milliseconds (defaults to batch size in streaming context). It's still not perfect (for example, the streaming is not precise) but given that this will only be used for testing purposes, I don't look for ways to further optimize it. Thanks, Aniket On 2 August 2014 04:07, Mayur Rustagi <mayur.rust...@gmail.com<mailto:mayur.rust...@gmail.com>> wrote: Nice question :) Ideally you should use a queuestream interface to push RDD into a queue & then spark streaming can handle the rest. Though why are you looking to convert RDD to DStream, another workaround folks use is to source DStream from folders & move files that they need reprocessed back into the folder, its a hack but much less headache . Mayur Rustagi Ph: +1 (760) 203 3257<tel:%2B1%20%28760%29%20203%203257> http://www.sigmoidanalytics.com @mayur_rustagi<https://twitter.com/mayur_rustagi> On Fri, Aug 1, 2014 at 10:21 AM, Aniket Bhatnagar <aniket.bhatna...@gmail.com<mailto:aniket.bhatna...@gmail.com>> wrote: Hi everyone I haven't been receiving replies to my queries in the distribution list. Not pissed but I am actually curious to know if my messages are actually going through or not. Can someone please confirm that my msgs are getting delivered via this distribution list? Thanks, Aniket On 1 August 2014 13:55, Aniket Bhatnagar <aniket.bhatna...@gmail.com<mailto:aniket.bhatna...@gmail.com>> wrote: Sometimes it is useful to convert a RDD into a DStream for testing purposes (generating DStreams from historical data, etc). Is there an easy way to do this? I could come up with the following inefficient way but no sure if there is a better way to achieve this. Thoughts? class RDDExtension[T](rdd: RDD[T]) { def chunked(chunkSize: Int): RDD[Seq[T]] = { rdd.mapPartitions(partitionItr => partitionItr.grouped(chunkSize)) } def skipFirst(): RDD[T] = { rdd.zipWithIndex().filter(tuple => tuple._2 > 0).map(_._1) } def toStream(streamingContext: StreamingContext, chunkSize: Int, slideDurationMilli: Option[Long] = None): DStream[T] = { new InputDStream[T](streamingContext) { @volatile private var currentRDD: RDD[Seq[T]] = rdd.chunked(chunkSize) override def start(): Unit = {} override def stop(): Unit = {} override def compute(validTime: Time): Option[RDD[T]] = { val chunk = currentRDD.take(1) currentRDD = currentRDD.skipFirst() Some(rdd.sparkContext.parallelize(chunk)) } override def slideDuration = { slideDurationMilli.map(duration => new Duration(duration)). getOrElse(super.slideDuration) } } } -- Jianshi Huang LinkedIn: jianshi Twitter: @jshuang Github & Blog: http://huangjs.github.com/ -- Jianshi Huang LinkedIn: jianshi Twitter: @jshuang Github & Blog: http://huangjs.github.com/ -- Jianshi Huang LinkedIn: jianshi Twitter: @jshuang Github & Blog: http://huangjs.github.com/ -- Jianshi Huang LinkedIn: jianshi Twitter: @jshuang Github & Blog: http://huangjs.github.com/
