Hi Marcelo,If you are using spark 2.3+ and dataset API/SparkSQL,you can use this inbuilt function "monotonically_increasing_id" in Spark.A little tweaking using Spark sql inbuilt functions can enable you to achieve this without having to write code or define RDDs with map/reduce functions.Akshay Bhardwaj+91-97111-33849On Thu, May 30, 2019 at 4:05 AM Marcelo Valle <email@example.com> wrote:Hi all,I am new to spark and I am trying to write an application using dataframes that normalize data.So I have a dataframe `denormalized_cities` with 3 columns: COUNTRY, CITY, CITY_NICKNAMEHere is what I want to do:
- Map by country, then for each country generate a new ID and write to a new dataframe `countries`, which would have COUNTRY_ID, COUNTRY - country ID would be generated, probably using `monotonically_increasing_id`.
- For each country, write several lines on a new dataframe `cities`, which would have COUNTRY_ID, ID, CITY, CITY_NICKNAME. COUNTRY_ID would be the same generated on country table and ID would be another ID I generate.What's the best way to do this, hopefully using only dataframes (no low level RDDs) unless it's not possible?I clearly see a MAP/Reduce process where for each KEY mapped I generate a row in countries table with COUNTRY_ID and for every value I write a row in cities table. But how to implement this in an easy and efficient way?I thought about using a `GroupBy Country` and then using `collect` to collect all values for that country, but then I don't know how to generate the country id and I am not sure about memory efficiency of `collect` for a country with too many cities (bare in mind country/city is just an example, my real entities are different).Could anyone point me to the direction of a good solution?Thanks,Marcelo.
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