Great - that's even easier. Maybe we could have a simple example in the doc.
On Wed, Mar 25, 2015 at 7:06 PM, Sandy Ryza <sandy.r...@cloudera.com> wrote: > Regarding Patrick's question, you can just do "new Configuration(oldConf)" > to get a cloned Configuration object and add any new properties to it. > > -Sandy > > On Wed, Mar 25, 2015 at 4:42 PM, Imran Rashid <iras...@cloudera.com> wrote: > >> Hi Nick, >> >> I don't remember the exact details of these scenarios, but I think the user >> wanted a lot more control over how the files got grouped into partitions, >> to group the files together by some arbitrary function. I didn't think >> that was possible w/ CombineFileInputFormat, but maybe there is a way? >> >> thanks >> >> On Tue, Mar 24, 2015 at 1:50 PM, Nick Pentreath <nick.pentre...@gmail.com> >> wrote: >> >> > Imran, on your point to read multiple files together in a partition, is >> it >> > not simpler to use the approach of copy Hadoop conf and set per-RDD >> > settings for min split to control the input size per partition, together >> > with something like CombineFileInputFormat? >> > >> > On Tue, Mar 24, 2015 at 5:28 PM, Imran Rashid <iras...@cloudera.com> >> > wrote: >> > >> > > I think this would be a great addition, I totally agree that you need >> to >> > be >> > > able to set these at a finer context than just the SparkContext. >> > > >> > > Just to play devil's advocate, though -- the alternative is for you >> just >> > > subclass HadoopRDD yourself, or make a totally new RDD, and then you >> > could >> > > expose whatever you need. Why is this solution better? IMO the >> criteria >> > > are: >> > > (a) common operations >> > > (b) error-prone / difficult to implement >> > > (c) non-obvious, but important for performance >> > > >> > > I think this case fits (a) & (c), so I think its still worthwhile. But >> > its >> > > also worth asking whether or not its too difficult for a user to extend >> > > HadoopRDD right now. There have been several cases in the past week >> > where >> > > we've suggested that a user should read from hdfs themselves (eg., to >> > read >> > > multiple files together in one partition) -- with*out* reusing the code >> > in >> > > HadoopRDD, though they would lose things like the metric tracking & >> > > preferred locations you get from HadoopRDD. Does HadoopRDD need to >> some >> > > refactoring to make that easier to do? Or do we just need a good >> > example? >> > > >> > > Imran >> > > >> > > (sorry for hijacking your thread, Koert) >> > > >> > > >> > > >> > > On Mon, Mar 23, 2015 at 3:52 PM, Koert Kuipers <ko...@tresata.com> >> > wrote: >> > > >> > > > see email below. reynold suggested i send it to dev instead of user >> > > > >> > > > ---------- Forwarded message ---------- >> > > > From: Koert Kuipers <ko...@tresata.com> >> > > > Date: Mon, Mar 23, 2015 at 4:36 PM >> > > > Subject: hadoop input/output format advanced control >> > > > To: "u...@spark.apache.org" <u...@spark.apache.org> >> > > > >> > > > >> > > > currently its pretty hard to control the Hadoop Input/Output formats >> > used >> > > > in Spark. The conventions seems to be to add extra parameters to all >> > > > methods and then somewhere deep inside the code (for example in >> > > > PairRDDFunctions.saveAsHadoopFile) all these parameters get >> translated >> > > into >> > > > settings on the Hadoop Configuration object. >> > > > >> > > > for example for compression i see "codec: Option[Class[_ <: >> > > > CompressionCodec]] = None" added to a bunch of methods. >> > > > >> > > > how scalable is this solution really? >> > > > >> > > > for example i need to read from a hadoop dataset and i dont want the >> > > input >> > > > (part) files to get split up. the way to do this is to set >> > > > "mapred.min.split.size". now i dont want to set this at the level of >> > the >> > > > SparkContext (which can be done), since i dont want it to apply to >> > input >> > > > formats in general. i want it to apply to just this one specific >> input >> > > > dataset i need to read. which leaves me with no options currently. i >> > > could >> > > > go add yet another input parameter to all the methods >> > > > (SparkContext.textFile, SparkContext.hadoopFile, >> > SparkContext.objectFile, >> > > > etc.). but that seems ineffective. >> > > > >> > > > why can we not expose a Map[String, String] or some other generic way >> > to >> > > > manipulate settings for hadoop input/output formats? it would require >> > > > adding one more parameter to all methods to deal with hadoop >> > input/output >> > > > formats, but after that its done. one parameter to rule them all.... >> > > > >> > > > then i could do: >> > > > val x = sc.textFile("/some/path", formatSettings = >> > > > Map("mapred.min.split.size" -> "12345")) >> > > > >> > > > or >> > > > rdd.saveAsTextFile("/some/path, formatSettings = >> > > > Map(mapred.output.compress" -> "true", >> > "mapred.output.compression.codec" >> > > -> >> > > > "somecodec")) >> > > > >> > > >> > >> --------------------------------------------------------------------- To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org For additional commands, e-mail: dev-h...@spark.apache.org