you used ZipWithUniqueID?
On 24 April 2015 at 21:28, Michal Michalski <michal.michal...@boxever.com>
wrote:
> I somehow missed zipWithIndex (and Sean's email), thanks for hint. I mean
> - I saw it before, but I just thought it's not doing what I want. I've
> re-read the description now and it looks like it might be actually what I
> need. Thanks.
>
> Kind regards,
> Michał Michalski,
> michal.michal...@boxever.com
>
> On 24 April 2015 at 16:26, Ganelin, Ilya <ilya.gane...@capitalone.com>
> wrote:
>
>> To maintain the order you can use zipWithIndex as Sean Owen pointed out.
>> This is the same as zipWithUniqueId except the assigned number is the index
>> of the data in the RDD which I believe matches the order of data as it's
>> stored on HDFS.
>>
>>
>>
>> Sent with Good (www.good.com)
>>
>>
>> -----Original Message-----
>> *From: *Michal Michalski [michal.michal...@boxever.com]
>> *Sent: *Friday, April 24, 2015 11:18 AM Eastern Standard Time
>> *To: *Ganelin, Ilya
>> *Cc: *Spico Florin; user
>> *Subject: *Re: Does HadoopRDD.zipWithIndex method preserve the order of
>> the input data from Hadoop?
>>
>> I read it one by one as I need to maintain the order, but it doesn't mean
>> that I process them one by one later. Input lines refer to different
>> entities I update, so once I read them in order, I group them by the id of
>> the entity I want to update, sort the updates on per-entity basis and
>> process them further in parallel (including writing data to C* and Kafka at
>> the very end). That's what I use Spark for - the first step I ask about is
>> just a requirement related to the input format I get and need to support.
>> Everything what happens after that is just a normal data processing job
>> that you want to distribute.
>>
>> Kind regards,
>> Michał Michalski,
>> michal.michal...@boxever.com
>>
>> On 24 April 2015 at 16:10, Ganelin, Ilya <ilya.gane...@capitalone.com>
>> wrote:
>>
>>> If you're reading a file one by line then you should simply use Java's
>>> Hadoop FileSystem class to read the file with a BuffereInputStream. I don't
>>> think you need an RDD here.
>>>
>>>
>>>
>>> Sent with Good (www.good.com)
>>>
>>>
>>> -----Original Message-----
>>> *From: *Michal Michalski [michal.michal...@boxever.com]
>>> *Sent: *Friday, April 24, 2015 11:04 AM Eastern Standard Time
>>> *To: *Ganelin, Ilya
>>> *Cc: *Spico Florin; user
>>> *Subject: *Re: Does HadoopRDD.zipWithIndex method preserve the order of
>>> the input data from Hadoop?
>>>
>>> The problem I'm facing is that I need to process lines from input file
>>> in the order they're stored in the file, as they define the order of
>>> updates I need to apply on some data and these updates are not commutative
>>> so that order matters. Unfortunately the input is purely order-based,
>>> theres no timestamp per line etc. in the file and I'd prefer to avoid
>>> preparing the file in advance by adding ordinals before / after each line.
>>> From the approaches you suggested first two won't work as there's nothing I
>>> could sort by. I'm not sure about the third one - I'm just not sure what
>>> you meant there to be honest :-)
>>>
>>> Kind regards,
>>> Michał Michalski,
>>> michal.michal...@boxever.com
>>>
>>> On 24 April 2015 at 15:48, Ganelin, Ilya <ilya.gane...@capitalone.com>
>>> wrote:
>>>
>>>> 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]
>>>> *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
>>>>
>>>> On 24 April 2015 at 15:28, Michal Michalski <
>>>> 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
>>>>>
>>>>> On 24 April 2015 at 14:05, Spico Florin <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
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>
>>>>
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>>>
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