also check out wholeTextFiles https://spark.apache.org/docs/1.4.0/api/java/org/apache/spark/SparkContext.html#wholeTextFiles(java.lang.String,%20int)
On 20 October 2015 at 15:04, Lan Jiang <ljia...@gmail.com> wrote: > As Francois pointed out, you are encountering a classic small file > anti-pattern. One solution I used in the past is to wrap all these small > binary files into a sequence file or avro file. For example, the avro > schema can have two fields: filename: string and binaryname:byte[]. Thus > your file is splittable and will not create so many partitions. > > Lan > > > On Oct 20, 2015, at 8:03 AM, François Pelletier < > newslett...@francoispelletier.org> wrote: > > You should aggregate your files in larger chunks before doing anything > else. HDFS is not fit for small files. It will bloat it and cause you a lot > of performance issues. Target a few hundred MB chunks partition size and > then save those files back to hdfs and then delete the original ones. You > can read, use coalesce and the saveAsXXX on the result. > > I had the same kind of problem once and solved it in bunching 100's of > files together in larger ones. I used text files with bzip2 compression. > > > > Le 2015-10-20 08:42, Sean Owen a écrit : > > coalesce without a shuffle? it shouldn't be an action. It just treats many > partitions as one. > > On Tue, Oct 20, 2015 at 1:00 PM, t3l <t...@threelights.de> wrote: > >> >> I have dataset consisting of 50000 binary files (each between 500kb and >> 2MB). They are stored in HDFS on a Hadoop cluster. The datanodes of the >> cluster are also the workers for Spark. I open the files as a RDD using >> sc.binaryFiles("hdfs:///path_to_directory").When I run the first action >> that >> involves this RDD, Spark spawns a RDD with more than 30000 partitions. And >> this takes ages to process these partitions even if you simply run >> "count". >> Performing a "repartition" directly after loading does not help, because >> Spark seems to insist on materializing the RDD created by binaryFiles >> first. >> >> How I can get around this? >> >> >> >> -- >> View this message in context: >> http://apache-spark-user-list.1001560.n3.nabble.com/Ahhhh-Spark-creates-30000-partitions-What-can-I-do-tp25140.html >> Sent from the Apache Spark User List mailing list archive at Nabble.com. >> >> --------------------------------------------------------------------- >> To unsubscribe, e-mail: user-unsubscr...@spark.apache.org >> For additional commands, e-mail: user-h...@spark.apache.org >> >> > > >