Author: Aaron VanSledright

Building Dynamic DNS with Route53 and PFSense

I use PFSense as my home router, firewall, VPN and much more. I’m sure a lot of my readers do as well. One thing that I have always set up is an entry in Route53 that points to my public IP address on my PFSense box. However, I use Comcast so, my IP address is changing every so often.

Typically this isn’t a big deal because only a few applications utilize the DNS entry I have setup. But, what if I could automate the changes by scheduling a job that automatically checks my IP address on the PFSense side and then updates the Route53 record automatically?

A couple of requirements:
– PFSense with the API package installed
– A subdomain setup in Route53 that points to your PFSense box

Some Python to do some magic:

import requests
import json
import boto3


clientid = "<pfsense clientID here>"
key = "<pfsense api key here>"
route53 = boto3.client('route53')
zoneID = "<route53 hosted zone here>"
# be sure to include a trailing "." as this is how Route53 formats things
# EX: https://google.com.
pfsenseDNS = "<Your subdomain>"

headers = {
    "Authorization": f"{clientid} {key}",
    "Content-type": 'application/json'
    }
#GET Pfsense IP
def getWanIP():
    response = requests.get('https://<your subdomain>/api/v1/system/arp', headers=headers)
    arptable = json.loads(response.content)
    entries = arptable['data']
    wan = []

    for entry in entries:
        # change the interface code if necessary
        if entry['interface'] == 'igb0':
            wan.append(entry)
    for entry in wan:
        if entry['status'] == 'permanent':
            wanIP = entry['ip']
            return wanIP

record_set = route53.list_resource_record_sets(
    HostedZoneId=zoneID
)

for record in record_set['ResourceRecordSets']:
    if record['Name'] == pfsenseDNS:
        #pprint.pprint(record)
        if record['Type'] == 'A':
            for entry in record['ResourceRecords']:
                if entry['Value'] != getWanIP():
                    print("The Records Do Not Match")
                    response = route53.change_resource_record_sets(
                        HostedZoneId=zoneID,
                        ChangeBatch={
                            'Changes': [
                                {
                                'Action': 'UPSERT',
                                'ResourceRecordSet': {
                                    'Name': pfsenseDNS,
                                    'Type': 'A',
                                    'ResourceRecords': [
                                        {
                                            'Value': getWanIP(),
                                        }
                                    ],
                                    'TTL': 300,
                                },
                                }
                            ]
                         }
                    )

What this code does is pretty simple. First we have a function that will get us the WAN IP through the ARP table of the PFSense box. We use this function later when we get and check our record sets against this IP address.

If the addresses do not match, the script will automatically change the entry in Route53 for you!

To test out the function modify your Route53 entry to some bogus IP address and then run the script. If everything goes as planned you should see your DNS entry changed!

If you found this helpful please share it with your friends. If you have questions feel free to comment or reach out to me via any method.

January 24, 2021

Setting the Starting Directory for Windows Subsystem for Linux
I use Windows Subsystem for Linux almost every day. I run Ubuntu 20.04 for almost all of my development work. I recently re-installed Windows because I upgraded my PC after many years. One thing that has always bothered me is that when you launch WSL for the first time it doesn’t put you into your user’s home directory. But rather your Windows home directory. The fix for this is really quite simple.
First, navigate to the settings for Microsoft Terminal:
I use Visual Studio Code to do editing. Find the section that contains your WSL installation:
Just below the “source” line, add the following:
```
"startingDirectory": "//wsl$/Ubuntu-20.04/home/<user>",
```
Replace “Ubuntu-20.04” with your distro name and “<user>” with your username.
Save and exit!
January 24, 2021
Pandas & NumPy with AWS Lambda
Fun fact: Pandas and NumPy don’t work out of the box with Lambda. The libraries that you might download from your development machine probably won’t work either.
The standard Lambda Python environment is very barebones by default. There is no point in loading in a bunch of libraries if they aren’t needed. This is why we package our Lambda functions into ZIP files to be deployed.
My first time attempting to use Pandas on AWS Lambda was in regards to concatenating Excel files. The point of this was to take a multi-sheet Excel file and combine it into one sheet for ingestion into a data lake. To accomplish this I used the Pandas library to build the new sheet. In order to automate the process I setup an S3 trigger on a Lambda function to execute the script every time a file was uploaded.
And then I ran into this error:
```
[ERROR] Runtime.ImportModuleError: Unable to import module 'your_module':
IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE!
Importing the numpy c-extensions failed.
```
I had clearly added the NumPy library into my ZIP file:
So what was the problem? Well, apparently, the version of NumPy that I downloaded on both my Macbook and my Windows desktop is not compatible with Amazon Linux.
To resolve this issue, I first attempted to download the package files manually from PyPi.org. I grabbed the latest “manylinux1_x86_x64.whl” file for both NumPy and Pandas. I put them back into my ZIP file and re-uploaded the file. This resulted in the same error.
THE FIX THAT WORKED:
The way to get this to work without failure is to spin up an Amazon Linux EC2 instance. Yes this seems excessive and it is. Not only did I have to spin up a new instance I had to install Python 3.8 because Amazon Linux ships with Python 2.7 by default. But, once installed you can use Pip to install the libraries to a directory by doing:
```
pip3 install -t . <package name>
```
This is useful for getting the libraries in the same location to ZIP back up for use. You can remove a lot of the files that are not needed by running:
```
rm -r *.dist-info __pycache__
```
After you have done the cleanup, you can ZIP up the files and move them back to your development machine, add your Lambda function and, upload to the Lambda console.
Run a test! It should work as you intended now!
If you need help with this please reach out to me on social media or leave a comment below.
January 8, 2021
Concatenating Multi-Sheet Excel Files with Python
I recently came across a data source that used multi-sheets within an Excel file. My dashboard cannot read a multi-sheet Excel file so I needed to combine them into one sheet.
The file is being uploaded into an S3 bucket and then needs to move through the data lake to be read into the dashboard. The final version of this script will be a Lambda function that is triggered on upload of the file, concatenate the sheets, and then place a new file into the next layer of the data lake.
Using Pandas you can easily accomplish this task. One issue I did run into is that Pandas no longer will read XLSX files so I did have to convert it down into an XLS file which is easily done through Excel. In the future this will also have to be done programmatically. Let’s get into the code.
```
import pandas as pd

workbook = pd.ExcelFile('Yourfile.XLS')
sheets = ['create', 'a', 'list']
dataframe = []import pandas as pd

workbook = pd.ExcelFile('file.xls')
sheets = ['create', 'a', 'list']
dataframe = []

for sheet in sheets:
    df = pd.read_excel(workbook, sheet_name=sheet, skiprows=[list of rows to skip], skipfooter=number_of_rows_to_skip_from_bottom)
    df.columns = ['list', 'of', 'column', 'headers']
    dataframe.append(df)
df = pd.concat(dataframe)
df.to_excel("output.xls", index=False)
```
To start we are going to import the Pandas library and then read in our Excel file. In the future revision of this script I will be reading in the file from S3 through the Lambda event so this will need to change.
The “sheets” variable is a list of sheets that you want the script to look at. You can remove this if you want it to look at all the sheets. My file had a few sheets that could be ignored. We will also create an empty list called “dataframe”. This empty list will be used to store each of the sheets that we want to concatenate. In the production version of this script there is some modifications that need to be done on each sheet. I accomplished this by adding in “if/then” statements based on the sheet name.
At the end of the “for” loop we will append the data frame into our empty list. Once all the sheets have been added, we will use Pandas to concatenate the objects and output the file. You can specify your output file name. I also included the “index=false” which removes the first column of index numbers. This is not needed for my project.
So there you have it, a simple Python script to concatenate a multi-sheet Excel file. If this script helps you please share it with your network!
December 29, 2020
A File Management Architecture
This post is a continuation of my article: “A File Extraction Project”. This project has been a great learning experience for both frontend and backend application architecture and design. Below you will find a diagram and an explanation of all the pieces that make this work.
1. The entire architecture is powered by Flask on an EC2 instance. When I move this project to production I intend to put an application load balancer in front to manage traffic. The frontend is also secured by Google Authentication. This provides authentication against the users existing GSuite deployment so that only individuals within the organization can access the application.
2. The first Lambda function processes the upload functions. I am allowing for as many files as needed by the customer. The form also includes a single text field for specifying the value of the object tag. The function sends the objects into the first bucket which is object #4.
3. The second Lambda function is the search functionality. This function allows the user to provide a tag value. The function queries all objects in bucket #4 and creates a list of objects that match the query. It then moves the objects to bucket #5 where it packages them up and presents them to the user in the form of a ZIP file.
4. The first bucket is the storage for all of the objects. This is the bucket where all the objects are uploaded to from the first Lambda function. It is not publicly accessible.
5. The second bucket is a temporary storage for files requested by the user. Objects are moved into this bucket from the first bucket. This bucket has a deletion policy that only allows objects to live inside it for 24 hours.
Lambda Function for File Uploading:
```
def upload():
    if request.method == 'POST':
        tag = request.form['tag']
        files = request.files.getlist('file')
        print(files)
        for file in files:

            print(file)
            if file:
                    filename = secure_filename(file.filename)
                    file.save(filename)
                    s3.upload_file(
                        Bucket = BUCKET_NAME,
                        Filename=filename,
                        Key = filename
                    )
                    
                    s3.put_object_tagging(
                        Bucket=BUCKET_NAME,
                        Key=filename,
                        Tagging={
                            'TagSet': [
                                {
                                    'Key': 'Tag1',
                                    'Value': tag
                                },
                                {
                                    'Key': 'Tag2',
                                    'Value': 'Tag-value'
                                },
                            ]
                        },
                    )
        msg = "Upload Done ! "
```
The function lives within the Flask application. I have AWS permissions setup on my EC2 instance to allow the “put_object” function. You can assign tags as needed. The first tag references the $tag variable which is provided by the form submission.
For Google Authentication I utilized a project I found on Github here. In the “auth” route that is created I modified it to authenticate against the “hd” parameter passed by the processes. You can see how this works here:
```
@app.route('/auth')
def auth():
    token = oauth.google.authorize_access_token()
    user = oauth.google.parse_id_token(token)
    session['user'] = user
    if "hd" not in user:
        abort(403)
    elif user['hd'] != 'Your hosted domain':
        abort(403)
    else:
        return redirect('/')
```
If the “hd” parameter is not passed through the function it will abort with a “403” error.
If you are interested in this project and want more information feel free to reach out and I can provide more code examples or package up the project for you to deploy on your own!
If you found this article helpful please share it with your friends.
December 16, 2020

A File Extraction Project

I had a client approach me regarding a set of files they had. The files were a set of certificates to support their products. They deliver these files to customers in the sales process.

The workflow currently involves manually packaging the files up into a deliverable format. The client asked me to automate this process across their thousands of documents.

As I started thinking through how this would work, I decided to create a serverless approach utilizing Amazon S3 for document storage and Lambda to do the processing and Amazon S3 and Cloudfront to generate a front end for the application.

My current architecture involves two S3 buckets. One bucket to store the original PDF documents and one to pull in the documents that we are going to package up for the client before sending.

The idea is that we can tag each PDF file with its appropriate lot number supplied by the client. I will then use a simple form submission process to supply input into the function that will collect the required documents.

Here is the code for the web frontend:

<!DOCTYPE html>
<html>
<head>
    <script src="https://ajax.googleapis.com/ajax/libs/jquery/2.2.4/jquery.min.js"></script>
    <script type="text/javascript">
        $(document).ready(function() {

            $("#submit").click(function(e) {
                e.preventDefault();

                var lot = $("#lot").val();

                $.ajax({
                    type: "POST",
                    url: 'API_URLHERE',
                    contentType: 'application/json',
                    data: JSON.stringify({
                        'body': lot,
                    }),
                    success: function(res){
                        $('#form-response').text('Query Was processed.');
                    },
                    error: function(){
                        $('#form-response').text('Error.');
                    }
                });

            })

        });
    </script>
</head>
<body>
<form>
    <label for="lot">Lot</label>
    <input id="lot">
    <button id="submit">Submit</button>
</form>
<div id="form-response"></div>
</body>
</html>

This is a single field input form that sends a string to my Lambda function. Once the string is received we will convert it into a JSON object and then use that to find our objects within Amazon S3.

Here is the function:

import boto3
import json


def lambda_handler(event, context):
    form_response = event['body']
    tag_list = json.loads(form_response)
    print(tag_list)
    tag_we_want = tag_list['body']
    
    
    
    s3 = boto3.client('s3')
    bucket = "source_bucket"
    destBucket = "destination_bucket"
    download_list = []
    #get all the objects in a bucket
    get_objects = s3.list_objects(
        Bucket= bucket,
    )

    object_list = get_objects['Contents']

    object_keys = []
    for object in object_list:
        object_keys.append(object['Key'])

    object_tags = []
    for key in object_keys:
        object_key = s3.get_object_tagging(
            Bucket= bucket,
            Key=key,
        )

        object_tags.append(
            {
            'Key': key,
            'tags': object_key['TagSet'][0]['Value']
            }
        )

    for tag in object_tags:

        if tag['tags'] == tag_we_want:
            object_name = tag['Key']
            s3.copy_object(
                Bucket= destBucket,
                CopySource= {
                    'Bucket': bucket,
                    'Key': object_name,
                },
                Key= object_name,
            )
            download_list.append(object_name)

    return download_list, tag_we_want

In this code, we define our source and destination buckets first. With the string from the form submission, we first gather all the objects within the bucket and then iterate over each object to find matching tags.

Once we gather the files we want for our customers we then transfer these files to a new bucket. I return the list of files out of the function as well as the tag name.

My next step is to package all the files required into a ZIP file for downloading. I first attempted to do this in Lambda but quickly realized you cannot use Lambda to generate files as the file system is read only.

Right now, I am thinking of utilizing Docker to spawn a worker which will generate the ZIP file, place it back into the bucket and provide a time-sensitive download link to the client.

Stay tuned for more updates on this project.

December 7, 2020

A Self Hosted Server Health Check

I’m not big on creating dashboards. I find that I don’t look at them enough to warrant hosting the software on an instance and having to have the browser open to the page all the time.

Instead, I prefer to be alerted via Slack as much as possible. I wrote scripts to collect DNS records from Route53. I decided that I should expand on the idea and create a scheduled job that would execute at a time interval. This way my health checks are fully automated.

Before we get into the script, you might ask me why I don’t just use Route53 health checks! The answer is fairly simple. First, the cost of health checks for HTTPS doesn’t make sense for the number of web servers that I am testing. Second, I don’t want to test Route53 or any AWS resource from within AWS. Rather, I would like to use my own network to test as it is not connected to AWS.

You can find the code and the Lambda function hosted on GitHub. The overall program utilizes a few different AWS products:

Lambda
SNS
CloudWatch Logs

It also uses Slack but that is an optional piece that I will explain. The main functions reside in “main.py”. This piece of code follows the process of:

Iterating over Route53 Records
Filtering out “A” records and compiling a list of domains
Testing each domain and processing the response code
Logging all of the results to CloudWatch Logs
Sending errors to the SNS topic

I have the script running on a CRON job every hour.

The second piece of this is the Lambda function. The function is all packaged in the “lambda_function.zip” but, I also added the function outside of the ZIP file for editing. You can modify this function to utilize your Slack credentials.

The Lambda function is subscribed to your SNS topic so that whenever a new message appears, that message is sent to your specified Slack channel.

I have plans to test my Terraform skills to automate the deployment of the Lambda function, SNS topic, CloudWatch Logs, and the primary script in some form.

If you have any comments on how I could improve this function please post a comment here or raise an issue on GitHub. If you find this script helpful in anyway feel free to share it with your friends!

Links:
Server Health Check – GitHub

Code – Main Function (main.py)

import boto3
import requests
import os
import time


#aws variables
sns = boto3.client('sns')
aws = boto3.client('route53')
cw = boto3.client('logs')
paginator = aws.get_paginator('list_resource_record_sets')
response = aws.list_hosted_zones()
hosted_zones = response['HostedZones']
time_now = int(round(time.time() * 1000))

#create empty lists
zone_id_to_test = []
dns_entries = []
zones_with_a_record = []
#Create list of ZoneID's to get record sets from       
for key in hosted_zones:
    zoneid = key['Id']
    final_zone_id = zoneid[12:]
    zone_id_to_test.append(final_zone_id)

#Create ZoneID List    
def getARecord(zoneid):
    for zone in zoneid:
        try:
            response = paginator.paginate(HostedZoneId=zone)
            for record_set in response:
                dns = record_set['ResourceRecordSets']
                dns_entries.append(dns)

        except Exception as error:
            print('An Error')
            print(str(error))
            raise
#Get Records to test
def getCNAME(entry):
    for dns_entry in entry:
        for record in dns_entry:
            if record['Type'] == 'A':
                url = (record['Name'])
                final_url = url[:-1]
                zones_with_a_record.append(f"https://{final_url}")
#Send Result to SNS                
def sendToSNS(messages):
    message = messages
    try:
        send_message = sns.publish(
            TargetArn='YOUR_SNS_TOPIC_ARN_HERE',
            Message=message,
            )
    except:
        print("something didn't work")
def tester(urls):
    for url in urls:
        try:
            user_agent = {'User-agent': 'Mozilla/5.0'}
            status = requests.get(url, headers = user_agent, allow_redirects=True)
            code = (status.status_code)
            if code == 401:
                response = f"The site {url} reports status code: {code}"
                writeLog(response)
            elif code == 301:
                response = f"The site {url} reports status code: {code}"
                writeLog(response)
            elif code == 302:
                response = f"The site {url} reports status code: {code}"
                writeLog(response)
            elif code == 403:
                response = f"The site {url} reports status code: {code}"
                writeLog(response)
            elif code !=200:
                sendToSNS(f"The site {url} reports: {code}")
                response = f"The site {url} reports status code: {code}"
                writeLog(response)
            else:
                response = f"The site {url} reports status code: {code}"
                writeLog(response)
        except:
            sendToSNS(f"The site {url} failed testing")
            response = f"The site {url} reports status code: {code}"
            writeLog(response)

def writeLog(message):
    getToken = cw.describe_log_streams(
        logGroupName='healthchecks',   
        )
    logInfo = (getToken['logStreams'])
    nextToken = logInfo[0]['uploadSequenceToken']
    response = cw.put_log_events(
        logGroupName='YOUR_LOG_GROUP_NAME',
        logStreamName='YOUR_LOG_STREAM_NAME',
        logEvents=[
            {
                'timestamp': time_now,
                'message': message
            },
        ],
        sequenceToken=nextToken
    )
#Execute            
getARecord(zone_id_to_test)
getCNAME(dns_entries)
tester(zones_with_a_record)

Code: Lambda Function (lambda_function.py)

import logging
logging.basicConfig(level=logging.DEBUG)

import os
from slack import WebClient
from slack.errors import SlackApiError


slack_token = os.environ["slackBot"]
client = WebClient(token=slack_token)

def lambda_handler(event, context):
    detail = event['Records'][0]['Sns']['Message']
    response_string = f"{detail}"
    try:
        response = client.chat_postMessage(
            channel="YOUR CHANNEL HERE",
            text="SERVER DOWN",
            blocks = [{"type": "section", "text": {"type": "plain_text", "text": response_string}}]
        )   

    except SlackApiError as e:
        assert e.response["error"]
    return

December 3, 2020

Collecting DNS Records from Route53

I had an idea the other day to query my web servers to get a response code and potentially alert me based on the result. Ideally, I would get a Slack notification on any response that is not “200”. I was thinking that I could run this either on a CRON so that I have real-time monitoring from within my own network rather than relying on an outside service.

I broke this down into a few stages based on how the information needed to be pulled together to generate the result.

Gather all the ZoneId values
Pass the ZoneId’s to get all the DNS records for each ZoneId.
Filter out just the “A” records
Use the Requests library to get a response from the server
Send a message to Slack based on the response

To start off we need to add in all our libraries and get our list of ZoneIds. You can see the code here:

import boto3
import requests
import sys
import os
from slack import WebClient
from slack.errors import SlackApiError
aws = boto3.client('route53')
paginator = aws.get_paginator('list_resource_record_sets')

#Create list of ZoneID's to get record sets from
response = aws.list_hosted_zones()
hosted_zones = response['HostedZones']
zone_id_to_test = []
dns_entries = []
zones_with_a_record = []        
for key in hosted_zones:
    zoneid = key['Id']
    final_zone_id = zoneid[12:]
    zone_id_to_test.append(final_zone_id)

You’ll notice that for the $final_zone_id we have to modify the string to get just the ID portion. By default, it includes a path such as “/hostedzone/ZXXXXXX”.

Once we have our list of zones we want to test we can pass it through a function to get all the records. I took the results and stored them into a list.

def getAllRecords(zoneid):
    for zone in zoneid:
        try:
            response = paginator.paginate(HostedZoneId=zone)
            for record_set in response:
                dns = record_set['ResourceRecordSets']
                dns_entries.append(dns)

        except Exception as error:
            print('An Error')
            print(str(error))
            raise

This code will iterate over all the DNS records for each zone and append them to the list $dns_entries. From there we can utilize another function to filter out just our “A” records that we need to test on.

def getARecords(entry):
    for dns_entry in entry:
        for record in dns_entry:
            if record['Type'] == 'A':
                url = (record['Name'])
                final_url = url[:-1]
               zones_with_a_record.append(f"https://{final_url}")

You’ll notice we are appending the “A” record to the list $zones_with_a_record. We are actually appending the name of the record which is modified to be a URL string. This is so that in our final function we can send the URL to the Requests library for testing.

This final section is two functions. One that will notify Slack on anything that is not a response of “200” and, one that will notify Slack on failure to retrieve a response from the server. This is important in the event that a server does not respond or the request times out.

def status_collector(urls):
    for url in urls:
        try:
            slack_token = "XXXXXXXXXXXXXX"
            client = WebClient(token=slack_token)   
            user_agent = {'User-agent': 'Mozilla/5.0'}
            status = requests.get(url, headers = user_agent, allow_redirects=True)
            code = (status.status_code)
            if code != 200:
                try:
                    response_string = f"The site {url} is down. Status code: {code}"
                    response = client.chat_postMessage(
                        channel="XXXXXX",
                        text="SERVER DOWN",
                        blocks = [{"type": "section", "text": {"type": "plain_text", "text": response_string}}]
                            )

                except SlackApiError as e:
                    assert e.response["error"]

            else:
                print(f"Server: {url} reports: {code}")
        except:
            slackFailure(url)
            
def slackFailure(url):
    try:
        slack_token = "XXXXXXXXXXX"
        client = WebClient(token=slack_token)
        response_string = f"The site {url} testing failed"
        response = client.chat_postMessage(
            channel="XXXXXXXXX",
            text="SERVER DOWN",
            blocks = [{"type": "section", "text": {"type": "plain_text", "text": response_string}}]
                )
    except SlackApiError as e:
        print(e.response["error"])

If you re-use this code you will need to add in your own Slack Token and channel string. It is also important to note the requirement of establishing a User-Agent for your requests. Without this, your server will likely reject the request and you will have repeated errors.

If you found this code helpful please share it with your friends. If you have suggestions on how to make it better please shoot me a message or a comment!

Boto3 Library
Requests Library

December 1, 2020

Where Is It 5 O’Clock Pt: 4
As much as I’ve scratched my head working on this project it has been fun to learn some new things and build something that isn’t infrastructure automation. I’ve learned some frontend web development some backend development and utilized some new Amazon Web Services products.
With all that nice stuff said I’m proud to announce that I have built a fully functioning project that is finally working the way I intended it. You can visit the website here:
www.whereisitfiveoclock.net
To recap, I bought this domain one night as a joke and thought “Hey, maybe one day I’ll build something”. I started off building a fully Python application backed by Flask. You can read about that in Part 1.This did not work out the way I intended as it did not refresh the timezones on page load. In part 3 I discussed how I was rearchitecting the project to include an API that would be called upon page load.
The API worked great and delivered two JSON objects into my frontend. I then parsed the two JSON objects into two separate tables that display where you can be drinking and where you probably shouldn’t be drinking.
This is a snippet of the JavaScript I wrote to iterate over the JSON objects while adding them into the appropriate table:
```
function buildTable(someinfo){
                var table1 = document.getElementById('its5pmsomewhere')
                var table2 = document.getElementById('itsnot5here')
                var its5_json = JSON.parse(someinfo[0]);
                var not5_json = JSON.parse(someinfo[1]);
                var its5_array = []
                var not5_array = []
                its5_json['its5'].forEach((value, index) => {

                    var row = `<tr>
                                <td>${value}</td>
                                <td></td>
                                </tr>`
                
                    table1.innerHTML += row
                })  
                not5_json['not5'].forEach((value, index) => {

                        var row = `<tr>
                                <td></td>
                                <td>${value}</td>
                                </tr>`
                
                    table2.innerHTML += row
                })  
```
First I reference two different HTML tables. I then parse the JSON from the API. I take both JSON objects and iterate over them adding the timezones into the table and then returning them into the HTML table.
If you want more information on how I did this feel free to reach out.
I want to continue iterating over this application to add new features. I need to do some standard things like adding Google Analytics so I can track traffic. I also want to add a search feature and a map that displays the different areas of drinking acceptability.
I also am open to requests. One of my friends suggested that I add a countdown timer to each location that it is not yet acceptable to be drinking.
Feel free to reach out in the comments or on your favorite social media platform! And as always, if you liked this project please share it with your friends.
September 23, 2020
Where Is It Five O’Clock Pt: 3

So I left this project at a point where I felt it needed to be re-architected based on the fact that Flask only executes the function once and not every time the page loads.
I re-architected the application in my head to include an API that calls the Lambda function and returns a list of places where it is and is not acceptable to be drinking based on the 5 O’Clock rules. These two lists will be JSON objects that have a single key with multiple values. The values will be the timezones appropriate to be drinking in.
After the JSON objects are generated I can reference them through the web frontend and display them in an appropriate way.
At this point I have the API built out and fully funcitoning the way I think I want it. You can use it by executing the following:
curl https://5xztnem7v4.execute-api.us-west-2.amazonaws.com/whereisit5
I will probably only have this publically accessible for a few days before locking it back down.
Hopefully, in part 4 of this series, I will have a frontend demo to show!

September 22, 2020