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From Aaron Davidson <ilike...@gmail.com>
Subject Re: Iterator over RDD in PySpark
Date Sat, 02 Aug 2014 01:46:55 GMT
Ah, that's unfortunate, that definitely should be added. Using a
pyspark-internal method, you could try something like

javaIterator = rdd._jrdd.toLocalIterator()
it = rdd._collect_iterator_through_file(javaIterator)


On Fri, Aug 1, 2014 at 3:04 PM, Andrei <faithlessfriend@gmail.com> wrote:

> Thanks, Aaron, it should be fine with partitions (I can repartition it
> anyway, right?).
> But rdd.toLocalIterator is purely Java/Scala method. Is there Python
> interface to it?
> I can get Java iterator though rdd._jrdd, but it isn't converted to Python
> iterator automatically. E.g.:
>
>   >>> rdd = sc.parallelize([1, 2, 3, 4, 5])
>   >>> it = rdd._jrdd.toLocalIterator()
>   >>> next(it)
>   14/08/02 01:02:32 INFO SparkContext: Starting job: apply at
> Iterator.scala:371
>   ...
>   14/08/02 01:02:32 INFO SparkContext: Job finished: apply at
> Iterator.scala:371, took 0.02064317 s
>   bytearray(b'\x80\x02K\x01.')
>
> I understand that returned byte array somehow corresponds to actual data,
> but how can I get it?
>
>
>
> On Fri, Aug 1, 2014 at 8:49 PM, Aaron Davidson <ilikerps@gmail.com> wrote:
>
>> rdd.toLocalIterator will do almost what you want, but requires that each
>> individual partition fits in memory (rather than each individual line).
>> Hopefully that's sufficient, though.
>>
>>
>> On Fri, Aug 1, 2014 at 1:38 AM, Andrei <faithlessfriend@gmail.com> wrote:
>>
>>> Is there a way to get iterator from RDD? Something like rdd.collect(),
>>> but returning lazy sequence and not single array.
>>>
>>> Context: I need to GZip processed data to upload it to Amazon S3. Since
>>> archive should be a single file, I want to iterate over RDD, writing each
>>> line to a local .gz file. File is small enough to fit local disk, but still
>>> large enough not to fit into memory.
>>>
>>
>>
>

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