Actually wildcards work too, e.g. s3n://bucket/file1*, and I believe so do
comma-separated lists (e.g. s3n://file1,s3n://file2). These are all inherited
from FileInputFormat in Hadoop.
Matei
On Apr 28, 2014, at 6:05 PM, Andrew Ash <and...@andrewash.com> wrote:
> This is already possible with the sc.textFile("/path/to/filedir/*") call.
> Does that work for you?
>
> Sent from my mobile phone
>
> On Apr 29, 2014 2:46 AM, "Nicholas Chammas" <nicholas.cham...@gmail.com>
> wrote:
> It would be useful to have some way to open multiple files at once into a
> single RDD (e.g. sc.textFile(iterable_over_uris)). Logically, it would be
> equivalent to opening a single file which is made by concatenating the
> various files together. This would only be useful, of course, if the source
> files were all in the same format.
>
> Nick
>
>
>
> On Mon, Apr 28, 2014 at 7:40 PM, Andrew Ash <and...@andrewash.com> wrote:
> The way you've written it there, I would expect that to be serial runs. The
> reason is, you're looping over matches with a driver-level map, which is
> serialized. Then calling foreachWith on the RDDs executes the action in a
> blocking way, so you don't get a result back until the cluster finishes.
>
> You can have multiple jobs running at the same time though by sharing a
> SparkContext among threads. Rather than run all the foreachWith()s in serial
> on a single thread in the driver, try running each in its own thread.
>
>
>
>
> On Tue, Apr 29, 2014 at 1:35 AM, Art Peel <found...@gmail.com> wrote:
>
> I’m trying to process 3 S3 files in parallel, but they always get processed
> serially.
> Am I going about this the wrong way? Full details below.
>
> Regards,
> Art
>
>
>
> I’m trying to do the following on a Spark cluster with 3 slave nodes.
>
> Given a list of N URLS for files in S3 (with s3n:// urls),
>
> For each file:
> 1. search for some items
> 2. record the findings from step 1 in an external data store.
>
> I’d like to process the 3 URLs in parallel on 3 different slave nodes, but
> instead, they are getting processed serially on one node.
>
> I’ve tried various versions of my code to no avail. I’m also creating the
> SparkContext with "spark.scheduler.mode", "FAIR”.
>
> Have I made a fundamental mistake?
>
> I’m running Spark 0.9.1 and my code looks roughly like this:
>
> def processFiles(sc: SparkContext) {
>
> val urls = List("s3n://foo/file.txt", "s3n://bar/file.txt",
> "s3n://baz/file.txt")
>
> val hadoopFiles = urls.map(url => {
> sc.hadoopFile(url, classOf[FooInputFormat], classOf[LongWritable],
> classOf[WritableFooRecord])
> })
>
> val matches = hadoopFiles.par.map((hadoopFile) => {
>
> findMatches(hadoopFile)
> })
>
>
> matches.map((matchRDD) => {
> recordMatches(matchRDD)
> })
> }
>
>
> def findMatches(hadoopFile: RDD): RDD = {
>
> hadoopFile.map( record => caseClassResultFromSearch(record) )
>
> }
>
> def recordMatches(matchRDD: RDD) {
>
>
> matchRDD.foreachWith(_ => {
>
> makeRestClient(config)
>
> // I get 3 jobs referring to the line number of the next line, but the
> jobs run serially on one node.
> })((matchRecord, client) => {
>
> client.storeResult(matchRecord)
>
> }
>
>
> }
>
>
>
>
