Michael - you need to sort your RDD. Check out the shuffle documentation on the Spark Programming Guide. It talks about this specifically. You can resolve this in a couple of ways - either by collecting your RDD and sorting it, using sortBy, or not worrying about the internal ordering. You can still extract elements in order by using a filter with the zip if e.g RDD.filter(s => s._2 < 50).sortBy(_._1)
Sent with Good (www.good.com) -----Original Message----- From: Michal Michalski [michal.michal...@boxever.com<mailto:michal.michal...@boxever.com>] Sent: Friday, April 24, 2015 10:41 AM Eastern Standard Time To: Spico Florin Cc: user Subject: Re: Does HadoopRDD.zipWithIndex method preserve the order of the input data from Hadoop? Of course after you do it, you probably want to call repartition(somevalue) on your RDD to "get your paralellism back". Kind regards, Michał Michalski, michal.michal...@boxever.com<mailto:michal.michal...@boxever.com> On 24 April 2015 at 15:28, Michal Michalski <michal.michal...@boxever.com<mailto:michal.michal...@boxever.com>> wrote: I did a quick test as I was curious about it too. I created a file with numbers from 0 to 999, in order, line by line. Then I did: scala> val numbers = sc.textFile("./numbers.txt") scala> val zipped = numbers.zipWithUniqueId scala> zipped.foreach(i => println(i)) Expected result if the order was preserved would be something like: (0, 0), (1, 1) etc. Unfortunately, the output looks like this: (126,1) (223,2) (320,3) (1,0) (127,11) (2,10) (...) The workaround I found that works for me for my specific use case (relatively small input files) is setting explicitly the number of partitions to 1 when reading a single *text* file: scala> val numbers_sp = sc.textFile("./numbers.txt", 1) Than the output is exactly as I would expect. I didn't dive into the code too much, but I took a very quick look at it and figured out - correct me if I missed something, it's Friday afternoon! ;-) - that this workaround will work fine for all the input formats inheriting from org.apache.hadoop.mapred.FileInputFormat including TextInputFormat, of course - see the implementation of getSplits() method there ( http://grepcode.com/file/repo1.maven.org/maven2/org.jvnet.hudson.hadoop/hadoop-core/0.19.1-hudson-2/org/apache/hadoop/mapred/FileInputFormat.java#FileInputFormat.getSplits%28org.apache.hadoop.mapred.JobConf%2Cint%29 ). The numSplits variable passed there is exactly the same value as you provide as a second argument to textFile, which is minPartitions. However, while *min* suggests that we can only define a minimal number of partitions, while we have no control over the max, from what I can see in the code, that value specifies the *exact* number of partitions per the FileInputFormat.getSplits implementation. Of course it can differ for other input formats, but in this case it should work just fine. Kind regards, Michał Michalski, michal.michal...@boxever.com<mailto:michal.michal...@boxever.com> On 24 April 2015 at 14:05, Spico Florin <spicoflo...@gmail.com<mailto:spicoflo...@gmail.com>> wrote: Hello! I know that HadoopRDD partitions are built based on the number of splits in HDFS. I'm wondering if these partitions preserve the initial order of data in file. As an example, if I have an HDFS (myTextFile) file that has these splits: split 0-> line 1, ..., line k split 1->line k+1,..., line k+n splt 2->line k+n, line k+n+m and the code val lines=sc.textFile("hdfs://mytextFile") lines.zipWithIndex() will the order of lines preserved? (line 1, zipIndex 1) , .. (line k, zipIndex k), and so one. I found this question on stackoverflow (http://stackoverflow.com/questions/26046410/how-can-i-obtain-an-element-position-in-sparks-rdd) whose answer intrigued me: "Essentially, RDD's zipWithIndex() method seems to do this, but it won't preserve the original ordering of the data the RDD was created from" Can you please confirm that is this the correct answer? Thanks. Florin ________________________________________________________ The information contained in this e-mail is confidential and/or proprietary to Capital One and/or its affiliates. The information transmitted herewith is intended only for use by the individual or entity to which it is addressed. If the reader of this message is not the intended recipient, you are hereby notified that any review, retransmission, dissemination, distribution, copying or other use of, or taking of any action in reliance upon this information is strictly prohibited. If you have received this communication in error, please contact the sender and delete the material from your computer.
