RaMSFT

def calculate_power(x, n):
    """calculate the power

    Args:
        x (integer): integr value
        n (integer): integer value

    Returns:
        _type_: power of x to the n
    """

RaMSFT

def string_int(text):
    """_summary_

    Args:
        text (string): a number enclosed in " or ' quotes as string

    Returns:
        integer: cast the string to number and return
    """
    

RaMSFT

#if you would like to know how tage works, un commete these
# list(range(5)) #0, 1, 2, 3, 4
# list(range(2, 8)) #2, 3, 4, 5, 6, 7
# list(range(2, 8, 2)) #2, 4, 6

firstdf = spark.range(100)

firstdf.count()

firstdf.show()

RaMSFT

from multiprocessing.pool import ThreadPool
import time

## Change the database name of your choice
databse_name = 'your_database_name'
#create a list of all tables to generate metrics
table_list = spark.sql(f"show tables in {databse_name}").select('database','tablename').collect()
print(table_list)

RaMSFT

import time
from multiprocessing.pool import ThreadPool

table_list = ['customers','orders','adress','payments','shipment','sellers','products','tax','fraud','revenue']

print(table_list)

def create_table_loop(table_name):
  spark.sql(f"create table loop_db.{table_name}(id int, name string)")

RaMSFT

import time
from multiprocessing.pool import ThreadPool

table_list = ['customers','orders','adress','payments','shipment','sellers','products','tax','fraud','revenue']

print(table_list)

def create_table_fanout(table_name):
  spark.sql(f"create table fanout_db.{table_name}(id int, name string)")

RaMSFT

import time

table_list = ['customers','orders','adress','payments','shipment','sellers','products','tax','fraud','revenue']

def create_table_loop(table_name):
  spark.sql(f"create table loop_db.{table_name}(id int, name string)")

loop_start_time = time.time()

for table in table_list:

RaMSFT

def positive_negative(input_list):
    # Write your code here
    zeros = input_list.count(0)
    total = len(input_list)

    if zeros == 0:
        input_list.append(0)

    input_list.sort()
    neg_list = input_list[:input_list.index(0)]

RaMSFT

import http
import json
def call_publicapi(table_name):
  """
  purpose:
    This function does bla bla bla. 
  params: reads 3 global parameters 
    status, execution_log: for sttaus and loogging
    database name: for creating database
  response:

RaMSFT

#Creating an empty DF (This is kind of an Hack...)
tbl_df = spark.sql("show tables in user_data like 'xxx'")
#Loop through all databases
for db in spark.sql("show databases like '*data'").collect():
  #create a dataframe with list of tables from the database
  df = spark.sql(f"show tables in {db.databaseName}")
  #union the tables list dataframe with main dataframe 
  tbl_df = tbl_df.union(df)
  
#After the loop, show the results