Hi,
did you try exploding the arrays, then doing the aggregation/count and
at the end applying a udf to add the 0 values ?
my experience is working on arrays is usually a bad idea.
sakag <lead2sh...@gmail.com> writes:
> Hi all,
>
> We have a rather interesting use case, and are struggling to come up with an
> approach that scales. Reaching out to seek your expert opinion/feedback and
> tips.
>
> What we are trying to do is to find the count of numerical ids over a
> sliding time window where each of our data records has a timestamp and a set
> of numerical ids in the below format.
>
> timestamp | ids
> 1 [1,2,3,8]
> 1 [1,2]
> 2 [1,2,3,4]
> 2 [1, 10]
>
> What we are looking to get as output is:
>
> timestamp | id_count_map
> 1 | {1: 2, 2: 2, 3: 1, 4:0, 5:0, 6:0, 8:1}
> 2 | {1: 2, 2:1, 3: 1, 4: 1, 5:0, 6:0, 7:0, 8:0, 9:0, 10:1}
>
> This gives us the frequency of occurrence of these ids over time periods.
> Please note that the output expected is in a dense format.
>
> However, we are running into scale issues with the data that has these
> characteristics.
>
> - 500 million records - Total ~100 GB
> - Each record can have 500 elements in the ids column
> - Max id value (length of id_count_map) is 750K
>
> We have tried the below approaches to achieve this
> 1) Expanding ids to a dense, frequency-based vector and then doing a
> row-wise sum over a Window partitioned by timestamp
> 2) Converting ids into a SparseVector and computing the L1 norm (using
> Summarizer) over a Window partitioned by timestamp
> 3) GroupBy/aggregating ids by timestamp, converting to a sparse,
> frequency-based vector using collections.Counter, and expanding to a dense
> format
> 4) GroupBy/aggregating ids by timestamp, converting to a sparse,
> frequency-based vector using CountVectorizer, and then expanding to a dense
> format
>
> Any other approaches we could try?
>
> Thanks!
> Sakshi
--
nicolas paris
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