Flatmap works too.. Explode function is a SQL/Dataframe way of one to many
operation. Both should work. Thanks
On Tue, Oct 3, 2017 at 8:30 AM Patrick McCarthy <pmccar...@dstillery.com>
wrote:
> 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 <
> vsathishkuma...@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 <pmccar...@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
>>>
>>>
>
