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Mock data of people

Loading fake information about people into cassandra table

This Script loads generated mock data of fake people into Cassandra “mock-data” mock_data_imp.py table, whithin “examples” keyspace.

Dependencies

Some python packages are needed:

from cassandra.cluster import Cluster
from cassandra.policies import TokenAwarePolicy, RoundRobinPolicy
from cassandra.cqlengine.models import Model
from cassandra.cqlengine.management import sync_table
from cassandra.cqlengine import connection
from cassandra.cqlengine.columns import *

More information about python driver for Cassandra

Dataset structure

COLUMNS = ['id','first_name','last_name','email','gender','birth_date','ip_address','probability','smoker_bool','drinker','language','image']

Defined constant COLUMNS synthetize the structure of the CSV file mock_data.csv

This file was generated with online freemium tool Mockaroo which is able to generate ramdom values into CSV format with several avalaible types.

I’ve choosed a set of frequently used types:

• id an autoincremental id
• first_name first name of person
• last_name last name of person
• email a fake but well formed email
• gender “Male” or “Female” gender
• birth_date Birth date yyyy-mm-dd
• ip_address an x.x.x.x IPv4 formatted field
• probability classify based on Binomial distribution
• smoker_bool smoker, true or false
• drinker alcoholic habits frequence: Never, Once, Seldom, Often, Daily, Weekly, Monthly, Yearly
• language mother tongue: German, English, Spanish
• image a fake image URL with different sizes

More details about data template on Mockaroo

Loading data from file system to memory

I use python pandas library to load CSV file into memory and to be able to work with.

df = read_csv(os.path.abspath(FICHERO_DATOS),header=0,names=COLUMNS,quotechar='"',decimal=',',encoding=ENCODING)

read_csv is very flexible method within the powerful pandas library which empower you to make multiple things. Load data into Dataframe structure, that is similar with Spark Dataframe; an important difference is that the last one is a distributed dataset and in pandas is locally stored in memory, but is relatively easy to convert between each other, so you could work with both at your convenience.

Storing into Cassandra

Using this class you can do object mapping with records into Cassandra table

## Object Mapper
class MockData(Model):
  __keyspace__ = KEYSPACE
  id = Integer(primary_key=True)
  first_name = Text()
  last_name = Text()
  email = Text()
  gender = Text()
  birth_date = Date()
  ip_address = Text()
  probability = Float()
  smoker_bool = Boolean()
  drinker = Text()
  language = Text()
  image = Text()

Simply with sync_table(MockCars) you can manage record persistence. Model descendant classes inherit a method to create records which will be posted into Cassandra table.

for ind, row in tqdm(df.iterrows(), total=df.shape[0]):
  MockCars.create(
    car_id = row['car_id'],
    registration = row['registration'],
    car_make = row['car_make'],
    car_model = row['car_model'],
    car_model_year = row['car_model_year'],
    color = row['color'],
    id_owner = row['id_owner']
)