One of the things I love about running Hive is the ability to run Python and leverage the power of the parallel processing. Below I’m going to show a stripped down example of how to integrate a Hive statement & Python together to aggregate data to prepare it for modeling. Keep in mind, you can also use Hive & Python to transform data line by line as well, and it extremely handy for data transformation.
Use case: print out an array of products sold to a particular user. Again is a basic example, but you can build upon this and generate products sold for every user, then use KNN to generate clusters of users, or perhaps Association Rules to generate baskets.
Here is the Python script, which will have to be saved in local Hadoop path:
#!/usr/bin/python import sys items_sold =  # create global list variable class Items: # create class to store and access items added def __init__(self, x): self.x = x def set_x(self, x): self.x = x def get_x(self): return self.x def print_results(): # print output in Hive result_set = [item.get_x() for item in items_sold]; print (result_set) # Hive submits each record to stdin # The record/line is stripped of extra characters and submitted for line in sys.stdin: line = line.strip() purchased_item = line.split('\t') items_sold.append(Items(purchased_item)) print_results()
Here is the hive statement:
add file blog_hive.py; select TRANSFORM (a.purchased_item) using 'blog_hive.py' AS array_purchased from (select purchased_item from company_purchases where user_id = 'u1') a;
Result in Hive will be similar to this: [‘s_123’, ‘s_234’, ‘s890’]