That suggestion got lost along the way and IIRC the patch didn't have that. It's a good idea though, if nothing else to provide a simple means for backwards compatibility.
I created a JIRA for this. It's very straightforward so maybe someone can pick it up quickly: https://issues.apache.org/jira/browse/SPARK-1677 On Tue, Apr 29, 2014 at 2:20 PM, Dean Wampler <deanwamp...@gmail.com> wrote: > Thanks. I'm fine with the logic change, although I was a bit surprised to > see Hadoop used for file I/O. > > Anyway, the jira issue and pull request discussions mention a flag to > enable overwrites. That would be very convenient for a tutorial I'm > writing, although I wouldn't recommend it for normal use, of course. > However, I can't figure out if this actually exists. I found the > spark.files.overwrite property, but that doesn't apply. Does this override > flag, method call, or method argument actually exist? > > Thanks, > Dean > > > On Tue, Apr 29, 2014 at 1:54 PM, Patrick Wendell <pwend...@gmail.com> wrote: > >> Hi Dean, >> >> We always used the Hadoop libraries here to read and write local >> files. In Spark 1.0 we started enforcing the rule that you can't >> over-write an existing directory because it can cause >> confusing/undefined behavior if multiple jobs output to the directory >> (they partially clobber each other's output). >> >> https://issues.apache.org/jira/browse/SPARK-1100 >> https://github.com/apache/spark/pull/11 >> >> In the JIRA I actually proposed slightly deviating from Hadoop >> semantics and allowing the directory to exist if it is empty, but I >> think in the end we decided to just go with the exact same semantics >> as Hadoop (i.e. empty directories are a problem). >> >> - Patrick >> >> On Tue, Apr 29, 2014 at 9:43 AM, Dean Wampler <deanwamp...@gmail.com> >> wrote: >> > I'm observing one anomalous behavior. With the 1.0.0 libraries, it's >> using >> > HDFS classes for file I/O, while the same script compiled and running >> with >> > 0.9.1 uses only the local-mode File IO. >> > >> > The script is a variation of the Word Count script. Here are the "guts": >> > >> > object WordCount2 { >> > def main(args: Array[String]) = { >> > >> > val sc = new SparkContext("local", "Word Count (2)") >> > >> > val input = sc.textFile(".../some/local/file").map(line => >> > line.toLowerCase) >> > input.cache >> > >> > val wc2 = input >> > .flatMap(line => line.split("""\W+""")) >> > .map(word => (word, 1)) >> > .reduceByKey((count1, count2) => count1 + count2) >> > >> > wc2.saveAsTextFile("output/some/directory") >> > >> > sc.stop() >> > >> > It works fine compiled and executed with 0.9.1. If I recompile and run >> with >> > 1.0.0-RC1, where the same output directory still exists, I get this >> > familiar Hadoop-ish exception: >> > >> > [error] (run-main-0) org.apache.hadoop.mapred.FileAlreadyExistsException: >> > Output directory >> > >> file:/Users/deanwampler/projects/typesafe/activator/activator-spark/output/kjv-wc >> > already exists >> > org.apache.hadoop.mapred.FileAlreadyExistsException: Output directory >> > >> file:/Users/deanwampler/projects/typesafe/activator/activator-spark/output/kjv-wc >> > already exists >> > at >> > >> org.apache.hadoop.mapred.FileOutputFormat.checkOutputSpecs(FileOutputFormat.java:121) >> > at >> > >> org.apache.spark.rdd.PairRDDFunctions.saveAsHadoopDataset(PairRDDFunctions.scala:749) >> > at >> > >> org.apache.spark.rdd.PairRDDFunctions.saveAsHadoopFile(PairRDDFunctions.scala:662) >> > at >> > >> org.apache.spark.rdd.PairRDDFunctions.saveAsHadoopFile(PairRDDFunctions.scala:581) >> > at org.apache.spark.rdd.RDD.saveAsTextFile(RDD.scala:1057) >> > at spark.activator.WordCount2$.main(WordCount2.scala:42) >> > at spark.activator.WordCount2.main(WordCount2.scala) >> > ... >> > >> > Thoughts? >> > >> > >> > On Tue, Apr 29, 2014 at 3:05 AM, Patrick Wendell <pwend...@gmail.com> >> wrote: >> > >> >> Hey All, >> >> >> >> This is not an official vote, but I wanted to cut an RC so that people >> can >> >> test against the Maven artifacts, test building with their >> configuration, >> >> etc. We are still chasing down a few issues and updating docs, etc. >> >> >> >> If you have issues or bug reports for this release, please send an >> e-mail >> >> to the Spark dev list and/or file a JIRA. >> >> >> >> Commit: d636772 (v1.0.0-rc3) >> >> >> >> >> https://git-wip-us.apache.org/repos/asf?p=spark.git;a=commit;h=d636772ea9f98e449a038567b7975b1a07de3221 >> >> >> >> Binaries: >> >> http://people.apache.org/~pwendell/spark-1.0.0-rc3/ >> >> >> >> Docs: >> >> http://people.apache.org/~pwendell/spark-1.0.0-rc3-docs/ >> >> >> >> Repository: >> >> https://repository.apache.org/content/repositories/orgapachespark-1012/ >> >> >> >> == API Changes == >> >> If you want to test building against Spark there are some minor API >> >> changes. We'll get these written up for the final release but I'm >> noting a >> >> few here (not comprehensive): >> >> >> >> changes to ML vector specification: >> >> >> >> >> http://people.apache.org/~pwendell/spark-1.0.0-rc3-docs/mllib-guide.html#from-09-to-10 >> >> >> >> changes to the Java API: >> >> >> >> >> http://people.apache.org/~pwendell/spark-1.0.0-rc3-docs/java-programming-guide.html#upgrading-from-pre-10-versions-of-spark >> >> >> >> coGroup and related functions now return Iterable[T] instead of Seq[T] >> >> ==> Call toSeq on the result to restore the old behavior >> >> >> >> SparkContext.jarOfClass returns Option[String] instead of Seq[String] >> >> ==> Call toSeq on the result to restore old behavior >> >> >> >> Streaming classes have been renamed: >> >> NetworkReceiver -> Receiver >> >> >> > >> > >> > >> > -- >> > Dean Wampler, Ph.D. >> > Typesafe >> > @deanwampler >> > http://typesafe.com >> > http://polyglotprogramming.com >> > > > > -- > Dean Wampler, Ph.D. > Typesafe > @deanwampler > http://typesafe.com > http://polyglotprogramming.com
