Alright, so I've upped the minSplits parameter on my call to textFile, but the resulting RDD still has only 1 partition, which I assume means it was read in on a single process. I am checking the number of partitions in pyspark by using the rdd._jrdd.splits().size() trick I picked up on this list.
The source file is a gzipped text file. I have heard things about gzipped files not being splittable. Is this the reason that opening the file with minSplits = 10 still gives me an RDD with one partition? If so, I guess the only way to speed up the load would be to change the source file's format to something splittable. Otherwise, if I want to speed up subsequent computation on the RDD, I should explicitly partition it with a call to RDD.partitionBy(10). Is that correct? On Mon, Mar 31, 2014 at 1:15 PM, Nicholas Chammas < nicholas.cham...@gmail.com> wrote: > OK sweet. Thanks for walking me through that. > > I wish this were StackOverflow so I could bestow some nice rep on all you > helpful people. > > > On Mon, Mar 31, 2014 at 1:06 PM, Aaron Davidson <ilike...@gmail.com>wrote: > >> Note that you may have minSplits set to more than the number of cores in >> the cluster, and Spark will just run as many as possible at a time. This is >> better if certain nodes may be slow, for instance. >> >> In general, it is not necessarily the case that doubling the number of >> cores doing IO will double the throughput, because you could be saturating >> the throughput with fewer cores. However, S3 is odd in that each connection >> gets way less bandwidth than your network link can provide, and it does >> seem to scale linearly with the number of connections. So, yes, taking >> minSplits up to 4 (or higher) will likely result in a 2x performance >> improvement. >> >> saveAsTextFile() will use as many partitions (aka splits) as the RDD it's >> being called on. So for instance: >> >> sc.textFile(myInputFile, 15).map(lambda x: x + >> "!!!").saveAsTextFile(myOutputFile) >> >> will use 15 partitions to read the text file (i.e., up to 15 cores at a >> time) and then again to save back to S3. >> >> >> >> On Mon, Mar 31, 2014 at 9:46 AM, Nicholas Chammas < >> nicholas.cham...@gmail.com> wrote: >> >>> So setting >>> minSplits<http://spark.incubator.apache.org/docs/latest/api/pyspark/pyspark.context.SparkContext-class.html#textFile> >>> will >>> set the parallelism on the read in SparkContext.textFile(), assuming I have >>> the cores in the cluster to deliver that level of parallelism. And if I >>> don't explicitly provide it, Spark will set the minSplits to 2. >>> >>> So for example, say I have a cluster with 4 cores total, and it takes 40 >>> minutes to read a single file from S3 with minSplits at 2. Tt should take >>> roughly 20 minutes to read the same file if I up minSplits to 4. >>> >>> Did I understand that correctly? >>> >>> RDD.saveAsTextFile() doesn't have an analog to minSplits, so I'm >>> guessing that's not an operation the user can tune. >>> >>> >>> On Mon, Mar 31, 2014 at 12:29 PM, Aaron Davidson <ilike...@gmail.com>wrote: >>> >>>> Spark will only use each core for one task at a time, so doing >>>> >>>> sc.textFile(<s3 location>, <num reducers>) >>>> >>>> where you set "num reducers" to at least as many as the total number of >>>> cores in your cluster, is about as fast you can get out of the box. Same >>>> goes for saveAsTextFile. >>>> >>>> >>>> On Mon, Mar 31, 2014 at 8:49 AM, Nicholas Chammas < >>>> nicholas.cham...@gmail.com> wrote: >>>> >>>>> Howdy-doody, >>>>> >>>>> I have a single, very large file sitting in S3 that I want to read in >>>>> with sc.textFile(). What are the best practices for reading in this file >>>>> as >>>>> quickly as possible? How do I parallelize the read as much as possible? >>>>> >>>>> Similarly, say I have a single, very large RDD sitting in memory that >>>>> I want to write out to S3 with RDD.saveAsTextFile(). What are the best >>>>> practices for writing this file out as quickly as possible? >>>>> >>>>> Nick >>>>> >>>>> >>>>> ------------------------------ >>>>> View this message in context: Best practices: Parallelized write to / >>>>> read from >>>>> S3<http://apache-spark-user-list.1001560.n3.nabble.com/Best-practices-Parallelized-write-to-read-from-S3-tp3516.html> >>>>> Sent from the Apache Spark User List mailing list >>>>> archive<http://apache-spark-user-list.1001560.n3.nabble.com/>at >>>>> Nabble.com. >>>>> >>>> >>>> >>> >> >
