the runtime for each consecutive iteration is still roughly twice as long as
for the previous one -- is there a way to reduce whatever overhead is
accumulating?
On Feb 11, 2015, at 8:11 PM, Davies Liu <dav...@databricks.com> wrote:
> On Wed, Feb 11, 2015 at 10:47 AM, rok <rokros...@gmail.com> wrote:
>> I was having trouble with memory exceptions when broadcasting a large lookup
>> table, so I've resorted to processing it iteratively -- but how can I modify
>> an RDD iteratively?
>>
>> I'm trying something like :
>>
>> rdd = sc.parallelize(...)
>> lookup_tables = {...}
>>
>> for lookup_table in lookup_tables :
>> rdd = rdd.map(lambda x: func(x, lookup_table))
>>
>> If I leave it as is, then only the last "lookup_table" is applied instead of
>> stringing together all the maps. However, if add a .cache() to the .map then
>> it seems to work fine.
>
> This is the something related to Python closure implementation, you should
> do it like this:
>
> def create_func(lookup_table):
> return lambda x: func(x, lookup_table)
>
> for lookup_table in lookup_tables:
> rdd = rdd.map(create_func(lookup_table))
>
> The Python closure just remember the variable, not copy the value of it.
> In the loop, `lookup_table` is the same variable. When we serialize the final
> rdd, all the closures are referring to the same `lookup_table`, which points
> to the last value.
>
> When we create the closure in a function, Python create a variable for
> each closure, so it works.
>
>> A second problem is that the runtime for each iteration roughly doubles at
>> each iteration so this clearly doesn't seem to be the way to do it. What is
>> the preferred way of doing such repeated modifications to an RDD and how can
>> the accumulation of overhead be minimized?
>>
>> Thanks!
>>
>> Rok
>>
>>
>>
>> --
>> View this message in context:
>> http://apache-spark-user-list.1001560.n3.nabble.com/iteratively-modifying-an-RDD-tp21606.html
>> Sent from the Apache Spark User List mailing list archive at Nabble.com.
>>
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