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From Patrick McCarthy <pmccar...@dstillery.com>
Subject Re: PySpark - Expand rows into dataframes via function
Date Tue, 03 Oct 2017 13:30:33 GMT
Thanks Sathish.

Before you responded, I came up with this solution:

# A function to take in one row and return the expanded ranges:
def processRow(x):

...
return zip(list_of_ip_ranges, list_of_registry_ids)

# and then in spark,

processed_rdds = spark_df_of_input_data.rdd.flatMap(lambda x: processRow(x))

processed_df =
(processed_rdds.toDF().withColumnRenamed('_1','ip').withColumnRenamed('_2','registryid'))

And then after that I split and subset the IP column into what I wanted.

On Mon, Oct 2, 2017 at 7:52 PM, Sathish Kumaran Vairavelu <
vsathishkumaran@gmail.com> wrote:

> It's possible with array function combined with struct construct. Below is
> a SQL example
>
> select Array(struct(ip1,hashkey), struct(ip2,hashkey))
> from (select substr(col1,1,2) as ip1, substr(col1,3,3) as ip2, etc,
> hashkey from object) a
>
> If you want dynamic ip ranges; you need to dynamically construct structs
> based on the range values. Hope this helps.
>
>
> Thanks
>
> Sathish
>
> On Mon, Oct 2, 2017 at 9:01 AM Patrick McCarthy <pmccarthy@dstillery.com>
> wrote:
>
>> Hello,
>>
>> I'm trying to map ARIN registry files into more explicit IP ranges. They
>> provide a number of IPs in the range (here it's 8192) and a starting IP,
>> and I'm trying to map it into all the included /24 subnets. For example,
>>
>> Input:
>>
>> array(['arin', 'US', 'ipv4', '23.239.160.0', 8192, 20131104.0, 'allocated',
>>
>>        'ff26920a408f15613096aa7fe0ddaa57'], dtype=object)
>>
>>
>> Output:
>>
>> array([['23', '239', '160', 'ff26920a408f15613096aa7fe0ddaa57'],
>>        ['23', '239', '161', 'ff26920a408f15613096aa7fe0ddaa57'],
>>        ['23', '239', '162', 'ff26920a408f15613096aa7fe0ddaa57'],
>>
>> ...
>>
>>
>> I have the input lookup table in a pyspark DF, and a python function to do the conversion
into the mapped output. I think to produce the full mapping I need a UDTF but this concept
doesn't seem to exist in PySpark. What's the best approach to do this mapping and recombine
into a new DataFrame?
>>
>>
>> Thanks,
>>
>> Patrick
>>
>>

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