Hi All, 


yes ,i need to add the below scenario based code to the executing spark job,while executing this it took lot of time to complete,please suggest best way to get below requirement without using UDF  


Thanks, 

Ankamma Rao B 


From: Sean Owen <srowen@gmail.com>
Sent: Friday, April 9, 2021 6:11 PM
To: ayan guha <guha.ayan@gmail.com>
Cc: Rao Bandaru <rao.msbi@outlook.com>; User <user@spark.apache.org>
Subject: Re: [Spark SQL]:to calculate distance between four coordinates(Latitude1, Longtitude1, Latitude2, Longtitude2) in the pysaprk dataframe
 
This can be significantly faster with a pandas UDF, note, because you can vectorize the operations. 

On Fri, Apr 9, 2021, 7:32 AM ayan guha <guha.ayan@gmail.com> wrote:
Hi

We are using a haversine distance function for this, and wrapping it in udf. 

from pyspark.sql.functions import acos, cos, sin, lit, toRadians, udf
from pyspark.sql.types import *

def haversine_distance(long_x, lat_x, long_y, lat_y):
    return acos(
        sin(toRadians(lat_x)) * sin(toRadians(lat_y)) +
        cos(toRadians(lat_x)) * cos(toRadians(lat_y)) *
            cos(toRadians(long_x) - toRadians(long_y))
    ) * lit(6371.0)
 
distudf = udf(haversine_distance, FloatType()) 

in case you just want to use just Spark SQL, you can still utilize the functions shown above to implement in SQL. 

Any reason you do not want to use UDF?


On Fri, Apr 9, 2021 at 10:19 PM Rao Bandaru <rao.msbi@outlook.com> wrote:
Hi All, 

 

I have a requirement to calculate distance between four coordinates(Latitude1, Longtitude1, Latitude2, Longtitude2) in the pysaprk dataframe with the help of from geopy import distance without using UDF (user defined function),Please help how to achieve this scenario and do the needful. 

 

Thanks, 

Ankamma Rao B 



--
Best Regards,
Ayan Guha