Pandas with Encryption/Decryption along with the JSON – (Client API Access) along with Data Queue (A crossover between Space stone, Reality Stone & Power Stone)

Today, we’ll be discussing a new cross-over between API, JSON, Encryption along with data distribution through Queue.

The primary objective here is to distribute one csv file through API service & access our previously deployed Encryption/Decryption methods by accessing the parallel call through Queue. In this case, our primary objective is to allow asynchronous calls to Queue for data distributions & at this point we’re not really looking for performance improvement. Instead, our goal to achieve the target.

My upcoming posts will discuss the improvement of performance using Parallel calls.

Let’s discuss it now.

Please find the structure of our Windows & MAC directory are as follows –

Win_Vs_MAC

We’re not going to discuss any scripts, which we’ve already discussed in my previous posts. Please refer the relevant earlier posts from my blogs.

1. clsL.py (This script will create the split csv files or final merge file after the corresponding process. However, this can be used as usual verbose debug logging as well. Hence, the name comes into the picture.)

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###########################################
#### Written By: SATYAKI DE        ########
#### Written On: 25-Jan-2019       ########
####                               ########
#### Objective: Log File           ########
###########################################
import pandas as p
import platform as pl
from clsParam import clsParam as cf

class clsL(object):
    def __init__(self):
        self.path = cf.config['PATH']

    def logr(self, Filename, Ind, df, subdir=None):
        try:
            x = p.DataFrame()
            x = df
            sd = subdir

            os_det = pl.system()

            if sd == None:
                if os_det == "Windows":
                    fullFileName = self.path + '\\' + Filename
                else:
                    fullFileName = self.path + '/' + Filename
            else:
                if os_det == "Windows":
                    fullFileName = self.path + '\\' + sd + "\\" + Filename
                else:
                    fullFileName = self.path + '/' + sd + "/" + Filename

            if Ind == 'Y':
                x.to_csv(fullFileName, index=False)

            return 0

        except Exception as e:
            y = str(e)
            print(y)
            return 3

2. callRunServer.py (This script will create an instance of a server. Once, it is running – it will emulate the Server API functionalities. Hence, the name comes into the picture.)

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############################################
#### Written By: SATYAKI DE             ####
#### Written On: 10-Feb-2019            ####
#### Package Flask package needs to     ####
#### install in order to run this       ####
#### script.                            ####
####                                    ####
#### Objective: This script will        ####
#### initiate the encrypt/decrypt class ####
#### based on client supplied data.     ####
#### Also, this will create an instance ####
#### of the server & create an endpoint ####
#### or API using flask framework.      ####
############################################

from flask import Flask
from flask import jsonify
from flask import request
from flask import abort
from clsConfigServer import clsConfigServer as csf
import clsFlask as clf

app = Flask(__name__)

@app.route('/process/getEncrypt', methods=['POST'])
def getEncrypt():
    try:
        # If the server application doesn't have
        # valid json, it will throw 400 error
        if not request.get_json:
            abort(400)

        # Capturing the individual element
        content = request.get_json()

        dGroup = content['dataGroup']
        input_data = content['data']
        dTemplate = content['dataTemplate']

        # For debug purpose only
        print("-" * 157)
        print("Group: ", dGroup)
        print("Data: ", input_data)
        print("Template: ", dTemplate)
        print("-" * 157)

        ret_val = ''

        if ((dGroup != '') & (dTemplate != '')):
            y = clf.clsFlask()
            ret_val = y.getEncryptProcess(dGroup, input_data, dTemplate)
        else:
            abort(500)

        return jsonify({'status': 'success', 'encrypt_val': ret_val})
    except Exception as e:
        x = str(e)
        return jsonify({'status': 'error', 'detail': x})


@app.route('/process/getDecrypt', methods=['POST'])
def getDecrypt():
    try:
        # If the server application doesn't have
        # valid json, it will throw 400 error
        if not request.get_json:
            abort(400)

        # Capturing the individual element
        content = request.get_json()

        dGroup = content['dataGroup']
        input_data = content['data']
        dTemplate = content['dataTemplate']

        # For debug purpose only
        print("-" * 157)
        print("Group: ", dGroup)
        print("Data: ", input_data)
        print("Template: ", dTemplate)
        print("-" * 157)

        ret_val = ''

        if ((dGroup != '') & (dTemplate != '')):
            y = clf.clsFlask()
            ret_val = y.getDecryptProcess(dGroup, input_data, dTemplate)
        else:
            abort(500)

        return jsonify({'status': 'success', 'decrypt_val': ret_val})
    except Exception as e:
        x = str(e)
        return jsonify({'status': 'error', 'detail': x})


def main():
    try:
        print('Starting Encrypt/Decrypt Application!')

        # Calling Server Start-Up Script
        app.run(debug=True, host=str(csf.config['HOST_IP_ADDR']))
        ret_val = 0

        if ret_val == 0:
            print("Finished Returning Message!")
        else:
            raise IOError
    except Exception as e:
        print("Server Failed To Start!")

if __name__ == '__main__':
    main()

 

3. clsFlask.py (This script is part of the server process, which will categorize the encryption logic based on different groups. Hence, the name comes into the picture.)

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###########################################
#### Written By: SATYAKI DE            ####
#### Written On: 25-Jan-2019           ####
#### Package Flask package needs to    ####
#### install in order to run this      ####
#### script.                           ####
####                                   ####
#### Objective: This script will       ####
#### encrypt/decrypt based on the      ####
#### supplied salt value. Also,        ####
#### this will capture the individual  ####
#### element & stored them into JSON   ####
#### variables using flask framework.  ####
###########################################

from clsConfigServer import clsConfigServer as csf
import clsEnDecAuth as cen

class clsFlask(object):
    def __init__(self):
        self.xtoken = str(csf.config['DEF_SALT'])

    def getEncryptProcess(self, dGroup, input_data, dTemplate):
        try:
            # It is sending default salt value
            xtoken = self.xtoken

            # Capturing the individual element
            dGroup = dGroup
            input_data = input_data
            dTemplate = dTemplate

            # This will check the mandatory json elements
            if ((dGroup != '') & (dTemplate != '')):

                # Based on the Group & Element it will fetch the salt
                # Based on the specific salt it will encrypt the data
                if ((dGroup == 'GrDet') & (dTemplate == 'subGrAcct_Nbr')):
                    xtoken = str(csf.config['ACCT_NBR_SALT'])
                    print("xtoken: ", xtoken)
                    print("Flask Input Data: ", input_data)
                    x = cen.clsEnDec(xtoken)
                    ret_val = x.encrypt_str(input_data)
                elif ((dGroup == 'GrDet') & (dTemplate == 'subGrName')):
                    xtoken = str(csf.config['NAME_SALT'])
                    print("xtoken: ", xtoken)
                    print("Flask Input Data: ", input_data)
                    x = cen.clsEnDec(xtoken)
                    ret_val = x.encrypt_str(input_data)
                elif ((dGroup == 'GrDet') & (dTemplate == 'subGrPhone')):
                    xtoken = str(csf.config['PHONE_SALT'])
                    print("xtoken: ", xtoken)
                    print("Flask Input Data: ", input_data)
                    x = cen.clsEnDec(xtoken)
                    ret_val = x.encrypt_str(input_data)
                elif ((dGroup == 'GrDet') & (dTemplate == 'subGrEmail')):
                    xtoken = str(csf.config['EMAIL_SALT'])
                    print("xtoken: ", xtoken)
                    print("Flask Input Data: ", input_data)
                    x = cen.clsEnDec(xtoken)
                    ret_val = x.encrypt_str(input_data)
                else:
                    ret_val = ''
            else:
                ret_val = ''

            # Return value
            return ret_val

        except Exception as e:
            ret_val = ''
            # Return the valid json Error Response
            return ret_val

    def getDecryptProcess(self, dGroup, input_data, dTemplate):
        try:
            xtoken = self.xtoken

            # Capturing the individual element
            dGroup = dGroup
            input_data = input_data
            dTemplate = dTemplate

            # This will check the mandatory json elements
            if ((dGroup != '') & (dTemplate != '')):

                # Based on the Group & Element it will fetch the salt
                # Based on the specific salt it will decrypt the data
                if ((dGroup == 'GrDet') & (dTemplate == 'subGrAcct_Nbr')):
                    xtoken = str(csf.config['ACCT_NBR_SALT'])
                    print("xtoken: ", xtoken)
                    print("Flask Input Data: ", input_data)
                    x = cen.clsEnDec(xtoken)
                    ret_val = x.decrypt_str(input_data)
                elif ((dGroup == 'GrDet') & (dTemplate == 'subGrName')):
                    xtoken = str(csf.config['NAME_SALT'])
                    print("xtoken: ", xtoken)
                    print("Flask Input Data: ", input_data)
                    x = cen.clsEnDec(xtoken)
                    ret_val = x.decrypt_str(input_data)
                elif ((dGroup == 'GrDet') & (dTemplate == 'subGrPhone')):
                    xtoken = str(csf.config['PHONE_SALT'])
                    print("xtoken: ", xtoken)
                    print("Flask Input Data: ", input_data)
                    x = cen.clsEnDec(xtoken)
                    ret_val = x.decrypt_str(input_data)
                elif ((dGroup == 'GrDet') & (dTemplate == 'subGrEmail')):
                    xtoken = str(csf.config['EMAIL_SALT'])
                    print("xtoken: ", xtoken)
                    print("Flask Input Data: ", input_data)
                    x = cen.clsEnDec(xtoken)
                    ret_val = x.decrypt_str(input_data)
                else:
                    ret_val = ''
            else:
                ret_val = ''

            # Return value
            return ret_val

        except Exception as e:
            ret_val = ''
            # Return the valid Error Response
            return ret_val

 

4. clsEnDec.py (This script will convert the string to encryption or decryption from its previous states based on the supplied group. Hence, the name comes into the picture.)

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###########################################
#### Written By: SATYAKI DE        ########
#### Written On: 25-Jan-2019       ########
#### Package Cryptography needs to ########
#### install in order to run this  ########
#### script.                       ########
####                               ########
#### Objective: This script will   ########
#### encrypt/decrypt based on the  ########
#### hidden supplied salt value.   ########
###########################################

from cryptography.fernet import Fernet

class clsEnDec(object):

    def __init__(self, token):
        # Calculating Key
        self.token = token

    def encrypt_str(self, data):
        try:
            # Capturing the Salt Information
            salt = self.token

            # Checking Individual Types inside the Dataframe
            cipher = Fernet(salt)
            encr_val = str(cipher.encrypt(bytes(data,'utf8'))).replace("b'","").replace("'","")

            return encr_val

        except Exception as e:
            x = str(e)
            print(x)
            encr_val = ''

            return encr_val

    def decrypt_str(self, data):
        try:
            # Capturing the Salt Information
            salt = self.token

            # Checking Individual Types inside the Dataframe
            cipher = Fernet(salt)
            decr_val = str(cipher.decrypt(bytes(data,'utf8'))).replace("b'","").replace("'","")

            return decr_val

        except Exception as e:
            x = str(e)
            print(x)
            decr_val = ''

            return decr_val

 

5. clsConfigServer.py (This script contains all the main parameter details of your emulated API server. Hence, the name comes into the picture.)

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###########################################
#### Written By: SATYAKI DE        ########
#### Written On: 10-Feb-2019       ########
####                               ########
#### Objective: Parameter File     ########
###########################################

import os
import platform as pl

# Checking with O/S system
os_det = pl.system()

class clsConfigServer(object):
    Curr_Path = os.path.dirname(os.path.realpath(__file__))

    if os_det == "Windows":
        config = {
            'FILE': 'acct_addr_20180112.csv',
            'SRC_FILE_PATH': Curr_Path + '\\' + 'src_file\\',
            'PROFILE_FILE_PATH': Curr_Path + '\\' + 'profile\\',
            'HOST_IP_ADDR': '0.0.0.0',
            'DEF_SALT': 'iooquzKtqLwUwXG3rModqj_fIl409vemWg9PekcKh2o=',
            'ACCT_NBR_SALT': 'iooquzKtqLwUwXG3rModqj_fIlpp1vemWg9PekcKh2o=',
            'NAME_SALT': 'iooquzKtqLwUwXG3rModqj_fIlpp1026Wg9PekcKh2o=',
            'PHONE_SALT': 'iooquzKtqLwUwXG3rMM0F5_fIlpp1026Wg9PekcKh2o=',
            'EMAIL_SALT': 'iooquzKtqLwU0653rMM0F5_fIlpp1026Wg9PekcKh2o='
        }
    else:
        config = {
            'FILE': 'acct_addr_20180112.csv',
            'SRC_FILE_PATH': Curr_Path + '/' + 'src_file/',
            'PROFILE_FILE_PATH': Curr_Path + '/' + 'profile/',
            'HOST_IP_ADDR': '0.0.0.0',
            'DEF_SALT': 'iooquzKtqLwUwXG3rModqj_fIl409vemWg9PekcKh2o=',
            'ACCT_NBR_SALT': 'iooquzKtqLwUwXG3rModqj_fIlpp1vemWg9PekcKh2o=',
            'NAME_SALT': 'iooquzKtqLwUwXG3rModqj_fIlpp1026Wg9PekcKh2o=',
            'PHONE_SALT': 'iooquzKtqLwUwXG3rMM0F5_fIlpp1026Wg9PekcKh2o=',
            'EMAIL_SALT': 'iooquzKtqLwU0653rMM0F5_fIlpp1026Wg9PekcKh2o='
        }

 

6. clsWeb.py (This script will receive the input Pandas dataframe & then convert it to JSON & then send it back to our Flask API Server for encryption/decryption. Hence, the name comes into the picture.)

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############################################
#### Written By: SATYAKI DE             ####
#### Written On: 09-Mar-2019            ####
#### Package Flask package needs to     ####
#### install in order to run this       ####
#### script.                            ####
####                                    ####
#### Objective: This script will        ####
#### initiate API based JSON requests   ####
#### at the server & receive the        ####
#### response from it & transform it    ####
#### back to the data-frame.            ####
############################################

import json
import requests
import datetime
import time
import ssl
import os
from clsParam import clsParam as cf

class clsWeb(object):
    def __init__(self, payload):
        self.payload = payload
        self.path = str(cf.config['PATH'])
        self.max_retries = int(cf.config['MAX_RETRY'])
        self.encrypt_ulr = str(cf.config['ENCRYPT_URL'])
        self.decrypt_ulr = str(cf.config['DECRYPT_URL'])

    def getResponse(self, mode):

        # Assigning Logging Info
        max_retries = self.max_retries
        encrypt_ulr = self.encrypt_ulr
        decrypt_ulr = self.decrypt_ulr
        En_Dec_Mode = mode

        try:

            # Bypassing SSL Authentication
            try:
                _create_unverified_https_context = ssl._create_unverified_context
            except AttributeError:
                # Legacy python that doesn't verify HTTPS certificates by default
                pass
            else:
                # Handle target environment that doesn't support HTTPS verification
                ssl._create_default_https_context = _create_unverified_https_context

            # Providing the url
            if En_Dec_Mode == 'En':
                url = encrypt_ulr
            else:
                url = decrypt_ulr

            print("URL::", url)

            # Capturing the payload
            data = self.payload

            # Converting String to Json
            # json_data = json.loads(data)
            json_data = json.loads(data)

            print("JSON:::::::", str(json_data))

            headers = {"Content-type": "application/json"}
            param = headers

            var1 = datetime.datetime.now().strftime("%H:%M:%S")
            print('Json Fetch Start Time:', var1)

            retries = 1
            success = False

            while not success:
                # Getting response from web service
                # response = requests.post(url, params=param, json=data, auth=(login, password), verify=False)
                response = requests.post(url, params=param, json=json_data, verify=False)
                print("Complete Return Code:: ", str(response.status_code))
                print("Return Code Initial::", str(response.status_code)[:1])

                if str(response.status_code)[:1] == '2':
                    # response = s.post(url, params=param, json=json_data, verify=False)
                    success = True
                else:
                    wait = retries * 2
                    print("Retry fails! Waiting " + str(wait) + " seconds and retrying.")
                    time.sleep(wait)
                    retries += 1
                    # print('Return Service::')

                # Checking Maximum Retries
                if retries == max_retries:
                    success = True
                    raise ValueError

                print("JSON RESPONSE:::", response.text)

                var2 = datetime.datetime.now().strftime("%H:%M:%S")
                print('Json Fetch End Time:', var2)

                # Capturing the response json from Web Service
                response_json = response.text
                load_val = json.loads(response_json)

                # Based on the mode application will send the return value
                if En_Dec_Mode == 'En':
                    encrypt_ele = str(load_val['encrypt_val'])
                    return_ele = encrypt_ele
                else:
                    decrypt_ele = str(load_val['decrypt_val'])
                    return_ele = decrypt_ele

            return return_ele

        except ValueError as v:
            raise ValueError

        except Exception as e:
            x = str(e)
            print(x)

            return 'Error'

Let’s discuss the key lines –

try:
    _create_unverified_https_context = ssl._create_unverified_context
except AttributeError:
    # Legacy python that doesn't verify HTTPS certificates by default
    pass
else:
    # Handle target environment that doesn't support HTTPS verification
    ssl._create_default_https_context = _create_unverified_https_context

If you are running in a secure environment. Sometimes, your proxy or firewall blocks you from accessing the API server – if they are using different networks. Hence, we need to bypass that. However, it is advisable not to use this in Prod environment for obvious reasons.

# Capturing the payload
data = self.payload

# Converting String to Json
json_data = json.loads(data)

This snippet will convert your data frame into a JSON object.

response = requests.post(url, params=param, json=json_data, verify=False)
print("Complete Return Code:: ", str(response.status_code))
print("Return Code Initial::", str(response.status_code)[:1])

if str(response.status_code)[:1] == '2':
    # response = s.post(url, params=param, json=json_data, verify=False)
    success = True
else:
    wait = retries * 2
    print("Retry fails! Waiting " + str(wait) + " seconds and retrying.")
    time.sleep(wait)
    retries += 1
    # print('Return Service::')

# Checking Maximum Retries
if retries == max_retries:
    success = True
    raise ValueError

In the first 3 lines, the application is building a JSON response, which will be sent to the API Server. And, it will capture the response from the server.

Next 8 lines will check the status code. And, based on the status code, it will continue or retry the requests in case if there is any failure or lousy response from the server.

Last 3 lines say if the application crosses the maximum allowable error limit, it will terminate the process by raising it as an error.

# Capturing the response json from Web Service
response_json = response.text
load_val = json.loads(response_json)

Once, it receives the valid response, the application will convert it back to the dataframe & send it to the calling methods.

7. clsParam.py (This script contains the fundamental parameter values to run your client application. Hence, the name comes into the picture.)

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###########################################
#### Written By: SATYAKI DE        ########
#### Written On: 20-Jan-2019       ########
###########################################

import os

class clsParam(object):

    config = {
        'MAX_RETRY' : 5,
        'ENCRYPT_MODE' : 'En',
        'DECRYPT_MODE': 'De',
        'PATH' : os.path.dirname(os.path.realpath(__file__)),
        'SRC_DIR' : os.path.dirname(os.path.realpath(__file__)) + '/' + 'src_files/',
        'FIN_DIR': os.path.dirname(os.path.realpath(__file__)) + '/' + 'finished/',
        'ENCRYPT_URL': "http://192.168.0.13:5000/process/getEncrypt",
        'DECRYPT_URL': "http://192.168.0.13:5000/process/getDecrypt",
        'NUM_OF_THREAD': 20
    }

 

8. clsSerial.py (This script will show the usual or serial way to convert your data into encryption & then to decrypts & store the result into two separate csv files. Hence, the name comes into the picture.)

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############################################
#### Written By: SATYAKI DE             ####
#### Written On: 10-Feb-2019            ####
#### Package Flask package needs to     ####
#### install in order to run this       ####
#### script.                            ####
####                                    ####
#### Objective: This script will        ####
#### initiate the encrypt/decrypt class ####
#### based on client supplied data      ####
#### using serial mode operation.       ####
############################################

import pandas as p
import clsWeb as cw
import datetime
from clsParam import clsParam as cf

# Disbling Warnings
def warn(*args, **kwargs):
    pass
import warnings
warnings.warn = warn

class clsSerial(object):
    def __init__(self):
        self.path = cf.config['PATH']
        self.EncryptMode = str(cf.config['ENCRYPT_MODE'])
        self.DecryptMode = str(cf.config['DECRYPT_MODE'])

    # Lookup Methods for Encryption
    def encrypt_acctNbr(self, row):
        # Declaring Local Variable
        en_AcctNbr = ''
        json_source_str = ''

        # Capturing essential values
        EncryptMode = self.EncryptMode
        lkp_acctNbr = row['Acct_Nbr']
        str_acct_nbr = str(lkp_acctNbr)
        fil_acct_nbr = str_acct_nbr.strip()

        # Forming JSON String for this field
        json_source_str = '{"dataGroup":"GrDet","data":"' + fil_acct_nbr + '","dataTemplate":"subGrAcct_Nbr"}'

        # Identifying Length of the field
        len_acct_nbr = len(fil_acct_nbr)

        # This will trigger the service if it has valid data
        if len_acct_nbr > 0:
            x = cw.clsWeb(json_source_str)
            en_AcctNbr = x.getResponse(EncryptMode)
        else:
            en_AcctNbr = ''

        fil_acct_nbr = ''
        fil_acct_nbr = ''

        return en_AcctNbr

    def encrypt_Name(self, row):
        # Declaring Local Variable
        en_AcctName = ''

        # Capturing essential values
        EncryptMode = self.EncryptMode
        lkp_acctName = row['Name']
        str_acct_name = str(lkp_acctName)
        fil_acct_name = str_acct_name.strip()

        # Forming JSON String for this field
        json_source_str = '{"dataGroup":"GrDet","data":"' + fil_acct_name + '","dataTemplate":"subGrName"}'

        # Identifying Length of the field
        len_acct_nbr = len(fil_acct_name)

        # This will trigger the service if it has valid data
        if len_acct_nbr > 0:
            x = cw.clsWeb(json_source_str)
            en_AcctName = x.getResponse(EncryptMode)
        else:
            en_AcctName = ''

        return en_AcctName

    def encrypt_Phone(self, row):
        # Declaring Local Variable
        en_Phone = ''

        # Capturing essential values
        EncryptMode = self.EncryptMode
        lkp_phone = row['Phone']
        str_phone = str(lkp_phone)
        fil_phone = str_phone.strip()

        # Forming JSON String for this field
        json_source_str = '{"dataGroup":"GrDet","data":"' + fil_phone + '","dataTemplate":"subGrPhone"}'

        # Identifying Length of the field
        len_acct_nbr = len(fil_phone)

        # This will trigger the service if it has valid data
        if len_acct_nbr > 0:
            x = cw.clsWeb(json_source_str)
            en_Phone = x.getResponse(EncryptMode)
        else:
            en_Phone = ''

        return en_Phone

    def encrypt_Email(self, row):
        # Declaring Local Variable
        en_Email = ''

        # Capturing essential values
        EncryptMode = self.EncryptMode
        lkp_email = row['Email']
        str_email = str(lkp_email)
        fil_email = str_email.strip()

        # Forming JSON String for this field
        json_source_str = '{"dataGroup":"GrDet","data":"' + fil_email + '","dataTemplate":"subGrEmail"}'

        # Identifying Length of the field
        len_acct_nbr = len(fil_email)

        # This will trigger the service if it has valid data
        if len_acct_nbr > 0:
            x = cw.clsWeb(json_source_str)
            en_Email = x.getResponse(EncryptMode)
        else:
            en_Email = ''

        return en_Email

    # Lookup Methods for Decryption
    def decrypt_acctNbr(self, row):
        # Declaring Local Variable
        de_AcctNbr = ''
        json_source_str = ''

        # Capturing essential values
        EncryptMode = self.DecryptMode
        lkp_acctNbr = row['Acct_Nbr']
        str_acct_nbr = str(lkp_acctNbr)
        fil_acct_nbr = str_acct_nbr.strip()

        # Forming JSON String for this field
        json_source_str = '{"dataGroup":"GrDet","data":"' + fil_acct_nbr + '","dataTemplate":"subGrAcct_Nbr"}'

        # Identifying Length of the field
        len_acct_nbr = len(fil_acct_nbr)

        # This will trigger the service if it has valid data
        if len_acct_nbr > 0:
            x = cw.clsWeb(json_source_str)
            de_AcctNbr = x.getResponse(EncryptMode)
        else:
            de_AcctNbr = ''

        return de_AcctNbr

    def decrypt_Name(self, row):
        # Declaring Local Variable
        de_AcctName = ''

        # Capturing essential values
        EncryptMode = self.DecryptMode
        lkp_acctName = row['Name']
        str_acct_name = str(lkp_acctName)
        fil_acct_name = str_acct_name.strip()

        # Forming JSON String for this field
        json_source_str = '{"dataGroup":"GrDet","data":"' + fil_acct_name + '","dataTemplate":"subGrName"}'

        # Identifying Length of the field
        len_acct_nbr = len(fil_acct_name)

        # This will trigger the service if it has valid data
        if len_acct_nbr > 0:
            x = cw.clsWeb(json_source_str)
            de_AcctName = x.getResponse(EncryptMode)
        else:
            de_AcctName = ''

        return de_AcctName

    def decrypt_Phone(self, row):
        # Declaring Local Variable
        de_Phone = ''

        # Capturing essential values
        EncryptMode = self.DecryptMode
        lkp_phone = row['Phone']
        str_phone = str(lkp_phone)
        fil_phone = str_phone.strip()

        # Forming JSON String for this field
        json_source_str = '{"dataGroup":"GrDet","data":"' + fil_phone + '","dataTemplate":"subGrPhone"}'

        # Identifying Length of the field
        len_acct_nbr = len(fil_phone)

        # This will trigger the service if it has valid data
        if len_acct_nbr > 0:
            x = cw.clsWeb(json_source_str)
            de_Phone = x.getResponse(EncryptMode)
        else:
            de_Phone = ''

        return de_Phone

    def decrypt_Email(self, row):
        # Declaring Local Variable
        de_Email = ''

        # Capturing essential values
        EncryptMode = self.DecryptMode
        lkp_email = row['Email']
        str_email = str(lkp_email)
        fil_email = str_email.strip()

        # Forming JSON String for this field
        json_source_str = '{"dataGroup":"GrDet","data":"' + fil_email + '","dataTemplate":"subGrEmail"}'

        # Identifying Length of the field
        len_acct_nbr = len(fil_email)

        # This will trigger the service if it has valid data
        if len_acct_nbr > 0:
            x = cw.clsWeb(json_source_str)
            de_Email = x.getResponse(EncryptMode)
        else:
            de_Email = ''

        return de_Email

    def getEncrypt(self, df_payload):
        try:
            df_input = p.DataFrame()
            df_fin = p.DataFrame()

            # Assigning Target File Basic Name
            df_input = df_payload

            # Checking total count of rows
            count_row = df_input.shape[0]
            print('Total number of records to process:: ', count_row)

            # Deriving rows
            df_input['Encrypt_Acct_Nbr'] = df_input.apply(lambda row: self.encrypt_acctNbr(row), axis=1)
            df_input['Encrypt_Name'] = df_input.apply(lambda row: self.encrypt_Name(row), axis=1)
            df_input['Encrypt_Phone'] = df_input.apply(lambda row: self.encrypt_Phone(row), axis=1)
            df_input['Encrypt_Email'] = df_input.apply(lambda row: self.encrypt_Email(row), axis=1)

            # Dropping original columns
            df_input.drop(['Acct_Nbr', 'Name', 'Phone', 'Email'], axis=1, inplace=True)

            # Renaming new columns with the old column names
            df_input.rename(columns={'Encrypt_Acct_Nbr':'Acct_Nbr'}, inplace=True)
            df_input.rename(columns={'Encrypt_Name': 'Name'}, inplace=True)
            df_input.rename(columns={'Encrypt_Phone': 'Phone'}, inplace=True)
            df_input.rename(columns={'Encrypt_Email': 'Email'}, inplace=True)

            # New Column List Orders
            column_order = ['Acct_Nbr', 'Name', 'Acct_Addr_1', 'Acct_Addr_2', 'Phone', 'Email', 'Serial_No']
            df_fin = df_input.reindex(column_order, axis=1)

            return df_fin
        except Exception as e:
            df_error = p.DataFrame({'Acct_Nbr':str(e), 'Name':'', 'Acct_Addr_1':'', 'Acct_Addr_2':'', 'Phone':'', 'Email':'', 'Serial_No':''})

            return df_error


    def getDecrypt(self, df_encrypted_payload):
        try:
            df_input = p.DataFrame()
            df_fin = p.DataFrame()

            # Assigning Target File Basic Name
            df_input = df_encrypted_payload

            # Checking total count of rows
            count_row = df_input.shape[0]
            print('Total number of records to process:: ', count_row)


            # Deriving rows
            df_input['Decrypt_Acct_Nbr'] = df_input.apply(lambda row: self.decrypt_acctNbr(row), axis=1)
            df_input['Decrypt_Name'] = df_input.apply(lambda row: self.decrypt_Name(row), axis=1)
            df_input['Decrypt_Phone'] = df_input.apply(lambda row: self.decrypt_Phone(row), axis=1)
            df_input['Decrypt_Email'] = df_input.apply(lambda row: self.decrypt_Email(row), axis=1)

            # Dropping original columns
            df_input.drop(['Acct_Nbr', 'Name', 'Phone', 'Email'], axis=1, inplace=True)

            # Renaming new columns with the old column names
            df_input.rename(columns={'Decrypt_Acct_Nbr':'Acct_Nbr'}, inplace=True)
            df_input.rename(columns={'Decrypt_Name': 'Name'}, inplace=True)
            df_input.rename(columns={'Decrypt_Phone': 'Phone'}, inplace=True)
            df_input.rename(columns={'Decrypt_Email': 'Email'}, inplace=True)

            # New Column List Orders
            column_order = ['Acct_Nbr', 'Name', 'Acct_Addr_1', 'Acct_Addr_2', 'Phone', 'Email']
            df_fin = df_input.reindex(column_order, axis=1)

            return df_fin
        except Exception as e:
            df_error = p.DataFrame({'Acct_Nbr':str(e), 'Name':'', 'Acct_Addr_1':'', 'Acct_Addr_2':'', 'Phone':'', 'Email':''})

            return df_error

Key lines to discuss –

Main two methods, we’ll be looking into & they are –

a. getEncrypt

b. getDecrypt

However, these two functions constructions are identical in nature. One is for encryption & the other one is decryption.

# Deriving rows
df_input['Encrypt_Acct_Nbr'] = df_input.apply(lambda row: self.encrypt_acctNbr(row), axis=1)
df_input['Encrypt_Name'] = df_input.apply(lambda row: self.encrypt_Name(row), axis=1)
df_input['Encrypt_Phone'] = df_input.apply(lambda row: self.encrypt_Phone(row), axis=1)
df_input['Encrypt_Email'] = df_input.apply(lambda row: self.encrypt_Email(row), axis=1)

As you can see, the application is processing row-by-row & column-by-column data transformations using look-up functions.

# Dropping original columns
df_input.drop(['Acct_Nbr', 'Name', 'Phone', 'Email'], axis=1, inplace=True)

As the comment suggested, the application is dropping all the unencrypted source columns.

# Renaming new columns with the old column names
df_input.rename(columns={'Encrypt_Acct_Nbr':'Acct_Nbr'}, inplace=True)
df_input.rename(columns={'Encrypt_Name': 'Name'}, inplace=True)
df_input.rename(columns={'Encrypt_Phone': 'Phone'}, inplace=True)
df_input.rename(columns={'Encrypt_Email': 'Email'}, inplace=True)

Once, the application drops all the source columns, it will rename the new column names back to old columns & based on this data will be merged with the rest of the data from the source csv.

# New Column List Orders
column_order = ['Acct_Nbr', 'Name', 'Acct_Addr_1', 'Acct_Addr_2', 'Phone', 'Email', 'Serial_No']
df_fin = df_input.reindex(column_order, axis=1)

Once, the application finished doing all these transformations, it will now re-sequence the order of the columns, which will create the same column order as it’s source csv files.

Similar logic is applicable for the decryption as well.

As we know, there are many look-up methods take part as part of this drive.

encrypt_acctNbr, encrypt_Name, encrypt_Phone, encrypt_Email
decrypt_acctNbr, decrypt_Name, decrypt_Phone, decrypt_Email

We’ll discuss only one method as these are completely identical.

# Capturing essential values
EncryptMode = self.EncryptMode
lkp_acctNbr = row['Acct_Nbr']
str_acct_nbr = str(lkp_acctNbr)
fil_acct_nbr = str_acct_nbr.strip()

From the row, our application is extracting the relevant column. In this case, it is Acct_Nbr. And, then converts it to string & remove any unnecessary white space from it.

# Forming JSON String for this field
json_source_str = '{"dataGroup":"GrDet","data":"' + fil_acct_nbr + '","dataTemplate":"subGrAcct_Nbr"}'

Once extracted, the application will build the target JON string as per column data.

# Identifying Length of the field
len_acct_nbr = len(fil_acct_nbr)

# This will trigger the service if it has valid data
if len_acct_nbr > 0:
    x = cw.clsWeb(json_source_str)
    en_AcctNbr = x.getResponse(EncryptMode)
else:
    en_AcctNbr = ''

Based on the length of the extracted value, our application will trigger the individual JSON requests & will receive the data frame in response.

9. clsParallel.py (This script will use the queue to make asynchronous calls & perform the same encryption & decryption. Hence, the name comes into the picture.)

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############################################
#### Written By: SATYAKI DE             ####
#### Written On: 10-Feb-2019            ####
#### Package Flask package needs to     ####
#### install in order to run this       ####
#### script.                            ####
####                                    ####
#### Objective: This script will        ####
#### initiate the encrypt/decrypt class ####
#### based on client supplied data.     ####
#### This script will use the advance   ####
#### queue & asynchronus calls to the   ####
#### API Server to process Encryption & ####
#### Decryption on our csv files.       ####
############################################
import pandas as p
import clsWebService as cw
import datetime
from clsParam import clsParam as cf
from multiprocessing import Lock, Process, Queue, freeze_support, JoinableQueue
import gc
import signal
import time
import os
import queue
import asyncio

# Declaring Global Variable
q = Queue()
lock = Lock()

finished_task = JoinableQueue()
pending_task = JoinableQueue()

sp_fin_dict = {}
dp_fin_dict = {}

# Disbling Warnings
def warn(*args, **kwargs):
    pass
import warnings
warnings.warn = warn

class clsParallel(object):
    def __init__(self):
        self.path = cf.config['PATH']
        self.EncryptMode = str(cf.config['ENCRYPT_MODE'])
        self.DecryptMode = str(cf.config['DECRYPT_MODE'])
        self.num_worker_process = int(cf.config['NUM_OF_THREAD'])
        self.lock = Lock()

    # Lookup Methods for Encryption
    def encrypt_acctNbr(self, row):
        # Declaring Local Variable
        en_AcctNbr = ''
        json_source_str = ''

        # Capturing essential values
        EncryptMode = self.EncryptMode
        lkp_acctNbr = row['Acct_Nbr']
        str_acct_nbr = str(lkp_acctNbr)
        fil_acct_nbr = str_acct_nbr.strip()

        # Forming JSON String for this field
        json_source_str = '{"dataGroup":"GrDet","data":"' + fil_acct_nbr + '","dataTemplate":"subGrAcct_Nbr"}'

        # Identifying Length of the field
        len_acct_nbr = len(fil_acct_nbr)

        # This will trigger the service if it has valid data
        if len_acct_nbr > 0:
            x = cw.clsWeb(json_source_str)
            en_AcctNbr = x.getResponse(EncryptMode)
        else:
            en_AcctNbr = ''

        fil_acct_nbr = ''

        return en_AcctNbr

    def encrypt_Name(self, row):
        # Declaring Local Variable
        en_AcctName = ''

        # Capturing essential values
        EncryptMode = self.EncryptMode
        lkp_acctName = row['Name']
        str_acct_name = str(lkp_acctName)
        fil_acct_name = str_acct_name.strip()

        # Forming JSON String for this field
        json_source_str = '{"dataGroup":"GrDet","data":"' + fil_acct_name + '","dataTemplate":"subGrName"}'

        # Identifying Length of the field
        len_acct_nbr = len(fil_acct_name)

        # This will trigger the service if it has valid data
        if len_acct_nbr > 0:
            x = cw.clsWeb(json_source_str)
            en_AcctName = x.getResponse(EncryptMode)
        else:
            en_AcctName = ''

        return en_AcctName

    def encrypt_Phone(self, row):
        # Declaring Local Variable
        en_Phone = ''

        # Capturing essential values
        EncryptMode = self.EncryptMode
        lkp_phone = row['Phone']
        str_phone = str(lkp_phone)
        fil_phone = str_phone.strip()

        # Forming JSON String for this field
        json_source_str = '{"dataGroup":"GrDet","data":"' + fil_phone + '","dataTemplate":"subGrPhone"}'

        # Identifying Length of the field
        len_acct_nbr = len(fil_phone)

        # This will trigger the service if it has valid data
        if len_acct_nbr > 0:
            x = cw.clsWeb(json_source_str)
            en_Phone = x.getResponse(EncryptMode)
        else:
            en_Phone = ''

        return en_Phone

    def encrypt_Email(self, row):
        # Declaring Local Variable
        en_Email = ''

        # Capturing essential values
        EncryptMode = self.EncryptMode
        lkp_email = row['Email']
        str_email = str(lkp_email)
        fil_email = str_email.strip()

        # Forming JSON String for this field
        json_source_str = '{"dataGroup":"GrDet","data":"' + fil_email + '","dataTemplate":"subGrEmail"}'

        # Identifying Length of the field
        len_acct_nbr = len(fil_email)

        # This will trigger the service if it has valid data
        if len_acct_nbr > 0:
            x = cw.clsWeb(json_source_str)
            en_Email = x.getResponse(EncryptMode)
        else:
            en_Email = ''

        return en_Email

    # Lookup Methods for Decryption
    def decrypt_acctNbr(self, row):
        # Declaring Local Variable
        de_AcctNbr = ''
        json_source_str = ''

        # Capturing essential values
        EncryptMode = self.DecryptMode
        lkp_acctNbr = row['Acct_Nbr']
        str_acct_nbr = str(lkp_acctNbr)
        fil_acct_nbr = str_acct_nbr.strip()

        # Forming JSON String for this field
        json_source_str = '{"dataGroup":"GrDet","data":"' + fil_acct_nbr + '","dataTemplate":"subGrAcct_Nbr"}'

        # Identifying Length of the field
        len_acct_nbr = len(fil_acct_nbr)

        # This will trigger the service if it has valid data
        if len_acct_nbr > 0:
            x = cw.clsWeb(json_source_str)
            de_AcctNbr = x.getResponse(EncryptMode)
        else:
            de_AcctNbr = ''

        return de_AcctNbr

    def decrypt_Name(self, row):
        # Declaring Local Variable
        de_AcctName = ''

        # Capturing essential values
        EncryptMode = self.DecryptMode
        lkp_acctName = row['Name']
        str_acct_name = str(lkp_acctName)
        fil_acct_name = str_acct_name.strip()

        # Forming JSON String for this field
        json_source_str = '{"dataGroup":"GrDet","data":"' + fil_acct_name + '","dataTemplate":"subGrName"}'

        # Identifying Length of the field
        len_acct_nbr = len(fil_acct_name)

        # This will trigger the service if it has valid data
        if len_acct_nbr > 0:
            x = cw.clsWeb(json_source_str)
            de_AcctName = x.getResponse(EncryptMode)
        else:
            de_AcctName = ''

        return de_AcctName

    def decrypt_Phone(self, row):
        # Declaring Local Variable
        de_Phone = ''

        # Capturing essential values
        EncryptMode = self.DecryptMode
        lkp_phone = row['Phone']
        str_phone = str(lkp_phone)
        fil_phone = str_phone.strip()

        # Forming JSON String for this field
        json_source_str = '{"dataGroup":"GrDet","data":"' + fil_phone + '","dataTemplate":"subGrPhone"}'

        # Identifying Length of the field
        len_acct_nbr = len(fil_phone)

        # This will trigger the service if it has valid data
        if len_acct_nbr > 0:
            x = cw.clsWeb(json_source_str)
            de_Phone = x.getResponse(EncryptMode)
        else:
            de_Phone = ''

        return de_Phone

    def decrypt_Email(self, row):
        # Declaring Local Variable
        de_Email = ''

        # Capturing essential values
        EncryptMode = self.DecryptMode
        lkp_email = row['Email']
        str_email = str(lkp_email)
        fil_email = str_email.strip()

        # Forming JSON String for this field
        json_source_str = '{"dataGroup":"GrDet","data":"' + fil_email + '","dataTemplate":"subGrEmail"}'

        # Identifying Length of the field
        len_acct_nbr = len(fil_email)

        # This will trigger the service if it has valid data
        if len_acct_nbr > 0:
            x = cw.clsWeb(json_source_str)
            de_Email = x.getResponse(EncryptMode)
        else:
            de_Email = ''

        return de_Email

    def getEncrypt(self, df_dict):
        try:
            en_fin_dict = {}

            df_input = p.DataFrame()
            df_fin = p.DataFrame()

            # Assigning Target File Basic Name
            for k, v in df_dict.items():
                Process_Name = k
                df_input = v

            # Checking total count of rows
            count_row = int(df_input.shape[0])
            print('Part number of records to process:: ', count_row)

            if count_row > 0:

                # Deriving rows
                df_input['Encrypt_Acct_Nbr'] = df_input.apply(lambda row: self.encrypt_acctNbr(row), axis=1)
                df_input['Encrypt_Name'] = df_input.apply(lambda row: self.encrypt_Name(row), axis=1)
                df_input['Encrypt_Phone'] = df_input.apply(lambda row: self.encrypt_Phone(row), axis=1)
                df_input['Encrypt_Email'] = df_input.apply(lambda row: self.encrypt_Email(row), axis=1)

                # Dropping original columns
                df_input.drop(['Acct_Nbr', 'Name', 'Phone', 'Email'], axis=1, inplace=True)

                # Renaming new columns with the old column names
                df_input.rename(columns={'Encrypt_Acct_Nbr':'Acct_Nbr'}, inplace=True)
                df_input.rename(columns={'Encrypt_Name': 'Name'}, inplace=True)
                df_input.rename(columns={'Encrypt_Phone': 'Phone'}, inplace=True)
                df_input.rename(columns={'Encrypt_Email': 'Email'}, inplace=True)

                # New Column List Orders
                column_order = ['Acct_Nbr', 'Name', 'Acct_Addr_1', 'Acct_Addr_2', 'Phone', 'Email', 'Serial_No']
                df_fin = df_input.reindex(column_order, axis=1)

                sp_fin_dict[Process_Name] = df_fin

            return sp_fin_dict
        except Exception as e:
            df_error = p.DataFrame({'Acct_Nbr':str(e), 'Name':'', 'Acct_Addr_1':'', 'Acct_Addr_2':'', 'Phone':'', 'Email':'', 'Serial_No':''})
            sp_fin_dict[Process_Name] = df_error

            return sp_fin_dict

    async def produceEncr(self, queue, l_dict):

        m_dict = {}

        m_dict = self.getEncrypt(l_dict)

        for k, v in m_dict.items():
            item = k
            print('producing {}...'.format(item))

        await queue.put(m_dict)


    async def consumeEncr(self, queue):
        result_dict = {}

        while True:
            # wait for an item from the producer
            sp_fin_dict.update(await queue.get())

            # process the item
            for k, v in sp_fin_dict.items():
                item = k
                print('consuming {}...'.format(item))

            # Notify the queue that the item has been processed
            queue.task_done()


    async def runEncrypt(self, n, df_input):
        l_dict = {}

        queue = asyncio.Queue()
        # schedule the consumer
        consumer = asyncio.ensure_future(self.consumeEncr(queue))

        start_pos = 0
        end_pos = 0

        num_worker_process = n

        count_row = df_input.shape[0]
        print('Total number of records to process:: ', count_row)

        interval = int(count_row / num_worker_process) + 1
        actual_worker_task = int(count_row / interval) + 1

        for i in range(actual_worker_task):
            name = 'Task-' + str(i)

            if ((start_pos + interval) < count_row):
                end_pos = start_pos + interval
            else:
                end_pos = start_pos + (count_row - start_pos)

            print("start_pos: ", start_pos)
            print("end_pos: ", end_pos)

            split_df = df_input.iloc[start_pos:end_pos]
            l_dict[name] = split_df

            if ((start_pos > count_row) | (start_pos == count_row)):
                break
            else:
                start_pos = start_pos + interval

            # run the producer and wait for completion
            await self.produceEncr(queue, l_dict)
            # wait until the consumer has processed all items
            await queue.join()

        # the consumer is still awaiting for an item, cancel it
        consumer.cancel()

        return sp_fin_dict


    def getEncryptParallel(self, df_payload):

        l_dict = {}
        data_dict = {}
        min_val_list = {}
        cnt = 1
        num_worker_process = self.num_worker_process
        actual_worker_task = 0
        number_of_processes = 4

        processes = []

        split_df = p.DataFrame()
        df_ret = p.DataFrame()
        dummy_df = p.DataFrame()

        # Assigning Target File Basic Name
        df_input = df_payload

        # Checking total count of rows
        count_row = df_input.shape[0]
        print('Total number of records to process:: ', count_row)

        interval = int(count_row / num_worker_process) + 1
        actual_worker_task = int(count_row/interval) + 1

        loop = asyncio.get_event_loop()
        loop.run_until_complete(self.runEncrypt(actual_worker_task, df_input))
        loop.close()

        for k, v in sp_fin_dict.items():
            min_val_list[int(k.replace('Task-', ''))] = v

        min_val = min(min_val_list, key=int)
        print("Minimum Index Value: ", min_val)

        for k, v in sorted(sp_fin_dict.items(), key=lambda k: int(k[0].replace('Task-', ''))):
            if int(k.replace('Task-', '')) == min_val:
                df_ret = sp_fin_dict[k]
            else:
                d_frames = [df_ret, sp_fin_dict[k]]
                df_ret = p.concat(d_frames)

        return df_ret

    def getDecrypt(self, df_encrypted_dict):
        try:
            de_fin_dict = {}

            df_input = p.DataFrame()
            df_fin = p.DataFrame()

            # Assigning Target File Basic Name
            for k, v in df_encrypted_dict.items():
                Process_Name = k
                df_input = v

            # Checking total count of rows
            count_row = int(df_input.shape[0])
            print('Part number of records to process:: ', count_row)

            if count_row > 0:

                # Deriving rows
                df_input['Decrypt_Acct_Nbr'] = df_input.apply(lambda row: self.decrypt_acctNbr(row), axis=1)
                df_input['Decrypt_Name'] = df_input.apply(lambda row: self.decrypt_Name(row), axis=1)
                df_input['Decrypt_Phone'] = df_input.apply(lambda row: self.decrypt_Phone(row), axis=1)
                df_input['Decrypt_Email'] = df_input.apply(lambda row: self.decrypt_Email(row), axis=1)

                # Dropping original columns
                df_input.drop(['Acct_Nbr', 'Name', 'Phone', 'Email'], axis=1, inplace=True)

                # Renaming new columns with the old column names
                df_input.rename(columns={'Decrypt_Acct_Nbr':'Acct_Nbr'}, inplace=True)
                df_input.rename(columns={'Decrypt_Name': 'Name'}, inplace=True)
                df_input.rename(columns={'Decrypt_Phone': 'Phone'}, inplace=True)
                df_input.rename(columns={'Decrypt_Email': 'Email'}, inplace=True)

                # New Column List Orders
                column_order = ['Acct_Nbr', 'Name', 'Acct_Addr_1', 'Acct_Addr_2', 'Phone', 'Email', 'Serial_No']
                df_fin = df_input.reindex(column_order, axis=1)

                de_fin_dict[Process_Name] = df_fin

            return de_fin_dict

        except Exception as e:
            df_error = p.DataFrame({'Acct_Nbr': str(e), 'Name': '', 'Acct_Addr_1': '', 'Acct_Addr_2': '', 'Phone': '', 'Email': '', 'Serial_No': ''})
            de_fin_dict[Process_Name] = df_error

            return de_fin_dict

    async def produceDecr(self, queue, l_dict):

        m_dict = {}

        m_dict = self.getDecrypt(l_dict)

        for k, v in m_dict.items():
            item = k
            print('producing {}...'.format(item))

        await queue.put(m_dict)


    async def consumeDecr(self, queue):
        result_dict = {}

        while True:
            # wait for an item from the producer
            dp_fin_dict.update(await queue.get())

            # process the item
            for k, v in dp_fin_dict.items():
                item = k
                print('consuming {}...'.format(item))

            # Notify the queue that the item has been processed
            queue.task_done()


    async def runDecrypt(self, n, df_input):
        l_dict = {}

        queue = asyncio.Queue()
        # schedule the consumer
        consumerDe = asyncio.ensure_future(self.consumeDecr(queue))

        start_pos = 0
        end_pos = 0

        num_worker_process = n

        count_row = df_input.shape[0]
        print('Total number of records to process:: ', count_row)

        interval = int(count_row / num_worker_process) + 1
        actual_worker_task = int(count_row / interval) + 1

        for i in range(actual_worker_task):
            name = 'Task-' + str(i)

            if ((start_pos + interval) < count_row):
                end_pos = start_pos + interval
            else:
                end_pos = start_pos + (count_row - start_pos)

            print("start_pos: ", start_pos)
            print("end_pos: ", end_pos)

            split_df = df_input.iloc[start_pos:end_pos]
            l_dict[name] = split_df

            if ((start_pos > count_row) | (start_pos == count_row)):
                break
            else:
                start_pos = start_pos + interval

            # run the producer and wait for completion
            await self.produceDecr(queue, l_dict)
            # wait until the consumer has processed all items
            await queue.join()

        # the consumer is still awaiting for an item, cancel it
        consumerDe.cancel()

        return dp_fin_dict


    def getDecryptParallel(self, df_payload):

        l_dict = {}
        data_dict = {}
        min_val_list = {}
        cnt = 1
        num_worker_process = self.num_worker_process
        actual_worker_task = 0
        number_of_processes = 4

        processes = []

        split_df = p.DataFrame()
        df_ret_1 = p.DataFrame()
        dummy_df = p.DataFrame()

        # Assigning Target File Basic Name
        df_input = df_payload

        # Checking total count of rows
        count_row = df_input.shape[0]
        print('Total number of records to process:: ', count_row)

        interval = int(count_row / num_worker_process) + 1
        actual_worker_task = int(count_row/interval) + 1

        loop_1 = asyncio.new_event_loop()
        asyncio.set_event_loop(asyncio.new_event_loop())
        loop_2 = asyncio.get_event_loop()
        loop_2.run_until_complete(self.runDecrypt(actual_worker_task, df_input))
        loop_2.close()

        for k, v in dp_fin_dict.items():
            min_val_list[int(k.replace('Task-', ''))] = v

        min_val = min(min_val_list, key=int)
        print("Minimum Index Value: ", min_val)

        for k, v in sorted(dp_fin_dict.items(), key=lambda k: int(k[0].replace('Task-', ''))):
            if int(k.replace('Task-', '')) == min_val:
                df_ret_1 = dp_fin_dict[k]
            else:
                d_frames = [df_ret_1, dp_fin_dict[k]]
                df_ret_1 = p.concat(d_frames)

        return df_ret_1

I don’t want to discuss any more look-up methods as the post is already pretty big. Only address a few critical lines

Under getEncryptParallel, the following lines are essential –

# Checking total count of rows
count_row = df_input.shape[0]
print('Total number of records to process:: ', count_row)

interval = int(count_row / num_worker_process) + 1
actual_worker_task = int(count_row/interval) + 1

Based on the dataframe total number of records, our application will split that main dataframe into parts of sub dataframe & then pass them using queue by asynchronous queue calls.

loop = asyncio.get_event_loop()
loop.run_until_complete(self.runEncrypt(actual_worker_task, df_input))
loop.close()

Initiating our queue methods & passing our dataframe to it.

for k, v in sorted(sp_fin_dict.items(), key=lambda k: int(k[0].replace('Task-', ''))):
    if int(k.replace('Task-', '')) == min_val:
        df_ret = sp_fin_dict[k]
    else:
        d_frames = [df_ret, sp_fin_dict[k]]
        df_ret = p.concat(d_frames)

Our application is sending & receiving data using the dictionary. The reason is – we’re not expecting data that we may get it from our server in sequence. Instead, we’re hoping the data will be random. Hence, using keys, we’re maintaining our final sequence & that will ensure our application to joining back to the correct sets of source data, which won’t be the candidate for any encryption/decryption.

Let’s discuss runEncrypt method.

for i in range(actual_worker_task):
    name = 'Task-' + str(i)

    if ((start_pos + interval) < count_row):
        end_pos = start_pos + interval
    else:
        end_pos = start_pos + (count_row - start_pos)

    print("start_pos: ", start_pos)
    print("end_pos: ", end_pos)

    split_df = df_input.iloc[start_pos:end_pos]
    l_dict[name] = split_df

    if ((start_pos > count_row) | (start_pos == count_row)):
        break
    else:
        start_pos = start_pos + interval

Here, our application is splitting our source data frame into multiple sub dataframe & then it can be processed in parallel using queues.

# run the producer and wait for completion
await self.produceEncr(queue, l_dict)
# wait until the consumer has processed all items
await queue.join()

Invoking the encryption-decryption process using queues. The last line is significant. The queue will not destroy until all the item produced/place into the queue are not consumed. Hence, your main program will wait until it processes all the records of your dataframe.

Two methods named produceEncr & consumeEncr mainly used for placing an item inside the queue & then after encryption/decryption it will retrieve it from the queue.

Few important lines from both the methods are –

#produceEncr
await queue.put(m_dict)

#consumeEncr
# wait for an item from the producer
sp_fin_dict.update(await queue.get())
# Notify the queue that the item has been processed
queue.task_done()

From the first two lines, one can see that the application will place its item into the queue. Rests are the lines from the other methods. Our application is pouring the data into the dictionary, which will be returned to our calling methods. The last line is significantly essential. Without the task_done process, the queue will continue to wait for upcoming items. Hence, that will trigger infinite wait or sometimes deadlock.

10. callClient.py (This script will trigger both the serial & parallel process of encryption one by one & finally capture some statistics. Hence, the name comes into the picture.)

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############################################
#### Written By: SATYAKI DE             ####
#### Written On: 10-Feb-2019            ####
#### Package Flask package needs to     ####
#### install in order to run this       ####
#### script.                            ####
####                                    ####
#### Objective: This script will        ####
#### initiate the encrypt/decrypt class ####
#### based on client supplied data.     ####
############################################
import pandas as p
import clsSerial as cs
import time
import datetime
from clsParam import clsParam as cf
import clsParallel as cp
import sys

def main():
    source_df = p.DataFrame()
    encrypted_df = p.DataFrame()
    source_encrypted_df = p.DataFrame()
    decrypted_df = p.DataFrame()
    encrypted_parallel_df = p.DataFrame()
    source_encrypted_parallel_df = p.DataFrame()
    decrypted_parallel_df = p.DataFrame()

    ###############################################################################
    #####                Start Of Serial Encryption Methods                  ######
    ###############################################################################

    print("-" * 157)

    startEnTime = time.time()
    srcFile = 'acct_addr_20180106'
    srcFileWithPath = str(cf.config['SRC_DIR']) + srcFile + '.csv'

    print("Calling Serial Process to Encrypt!")

    # Reading source file
    source_df = p.read_csv(srcFileWithPath, index_col=False)

    # Calling Encrypt Methods
    x = cs.clsSerial()
    encrypted_df = x.getEncrypt(source_df)

    # Handling Multiple source files
    var = datetime.datetime.now().strftime("%H.%M.%S")
    print('Target File Extension will contain the following:: ', var)

    targetFile = srcFile + '_Serial_'
    taregetFileWithPath = str(cf.config['FIN_DIR']) + targetFile + var + '.csv'

    # Finally Storing them into csv
    encrypted_df.to_csv(taregetFileWithPath, index=False)

    endEnTime = time.time()
    z1 = str(endEnTime - startEnTime)
    print("Over All Encrypt Process Time:", z1)

    time.sleep(20)

    ###############################################################################
    #####                Start Of Serial Decryption Methods                  ######
    ###############################################################################

    print("-" * 157)

    startDeTime = time.time()
    srcFileWithPath = taregetFileWithPath

    print("Calling Serial Process to Decrypt!")

    # Reading source file
    source_encrypted_df = p.read_csv(srcFileWithPath, index_col=False)

    # Calling Encrypt Methods
    x = cs.clsSerial()
    decrypted_df = x.getDecrypt(source_encrypted_df)

    targetFile = srcFile + '_restored_'
    taregetFileWithPath = str(cf.config['FIN_DIR']) + targetFile + var + '.csv'

    # Finally Storing them into csv
    decrypted_df.to_csv(taregetFileWithPath, index=False)

    endDeTime = time.time()
    z2 = str(endDeTime - startDeTime)
    print("Over All Decrypt Process Time:", z2)

    print("-" * 157)

    ###############################################################################
    #####        End Of Serial Encryption/Decryption Methods                 ######
    ###############################################################################

    time.sleep(20)

    ###############################################################################
    #####                Start Of Parallel Encryption Methods                ######
    ###############################################################################

    print("-" * 157)

    startEnTime = time.time()
    srcFileWithPath = str(cf.config['SRC_DIR']) + srcFile + '.csv'

    print("Calling Serial Process to Encrypt!")

    # Reading source file
    source_df = p.read_csv(srcFileWithPath, index_col=False)

    # Calling Encrypt Methods
    x = cp.clsParallel()
    encrypted_parallel_df = x.getEncryptParallel(source_df)

    # Handling Multiple source files
    var = datetime.datetime.now().strftime("%H.%M.%S")
    print('Target File Extension will contain the following:: ', var)

    targetFile = srcFile + '_Parallel_'
    taregetFileWithPath = str(cf.config['FIN_DIR']) + targetFile + var + '.csv'

    # Finally Storing them into csv
    encrypted_parallel_df.to_csv(taregetFileWithPath, index=False)

    endEnTime = time.time()
    z3 = str(endEnTime - startEnTime)
    print("Over All Encrypt Process Time:", z3)

    time.sleep(20)

    ###############################################################################
    #####                Start Of Serial Decryption Methods                  ######
    ###############################################################################

    print("-" * 157)

    startDeTime = time.time()
    srcFileWithPath = taregetFileWithPath

    print("Calling Parallel Process to Decrypt!")

    # Reading source file
    source_encrypted_parallel_df = p.read_csv(srcFileWithPath, index_col=False)

    # Calling Encrypt Methods
    x = cp.clsParallel()
    decrypted_parallel_df = x.getDecryptParallel(source_encrypted_parallel_df)

    targetFile = srcFile + '_restored_'
    taregetFileWithPath = str(cf.config['FIN_DIR']) + targetFile + var + '.csv'

    # Finally Storing them into csv
    decrypted_parallel_df.to_csv(taregetFileWithPath, index=False)

    endDeTime = time.time()
    z4 = str(endDeTime - startDeTime)
    print("Over All Decrypt Process Time:", z4)

    print("-" * 157)

    ###############################################################################
    #####        End Of Parallel Encryption/Decryption Methods               ######
    ###############################################################################

    ###############################################################################
    #####    Final Statistics between Serial & Parallel loading.             ######
    ###############################################################################

    print("-" * 157)
    print("Serial Encryption:: ", z1)
    print("Serial Decryption:: ", z2)
    print("-" * 157)
    print("Parallel Encryption:: ", z3)
    print("Parallel Decryption:: ", z4)
    print("-" * 157)


if __name__ == '__main__':
    main()

As you can see, we’ve triggered both the application using the main callable scripts.

Let’s explore the output –

Windows:

Win_Files

Mac:

MAC_Files

Note that you have to open two different windows or MAC terminal. One will trigger the server & others will trigger the client to simulate this.

Server:

Win_Server

Clients:

Win:

Win_Run

MAC:

MAC_Run

So, finally, we’ve achieved our goal. So, today we’ve done a bit long but beneficial & advanced concepts of crossover stones from our python verse. 🙂

Lot more innovative posts are coming.

Till then – Happy Avenging!

One thought on “Pandas with Encryption/Decryption along with the JSON – (Client API Access) along with Data Queue (A crossover between Space stone, Reality Stone & Power Stone)

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