Aaron VanSledright

Category: Cloud Architecting

  • 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.

  • 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!

  • Where Is It 5 O’Clock Pt: 2

    So I spend the evening deploying this web application to Amazon Web Services. In my test environment, everything appeared to be working great because every time I reloaded the page it reloaded the function as well.

    When I transferred this over to a live environment I realized the Python function only ran every time I committed a change and it was re-deployed to my Elastic Beanstalk environment.

    This poses a new problem. If the function doesn’t fire every time the page is refreshed the time won’t properly update and it will show incorrect areas of where it is 5 O’Clock. Ugh.

    So, over the next few weeks, in my spare time, I will be re-writing this entire application to function the way I intended it to.

    I think to do this I will write each function as an AWS Lambda function and then write a frontend that calls these functions on page load. Or, the entire thing will be one function and return the information and it will deploy in one API call.

    I also really want to display a map that shows the areas that it is 5PM or later but I think this will come in a later revision once the project is actually functioning correctly. Along with some more CSS to make it pretty and responsive so it works on all devices.

    The punch list is getting long…

    Follow along here: https://whereisitfiveoclock.net

  • Where Is It Five O’Clock Pt: 1

    I bought the domain whereisitfiveoclock.net a while back and have been sitting on it for quite some time. I had an idea to make a web application that would tell you where it is five o’clock. Yes, this is a drinking website.

    I saw this project as a way to learn more Python skills, as well as some more AWS skills, and boy, has it put me to the test. So I’m going to write this series of posts as a way to document my progress in building this application.

    Part One: Building The Application

    I know that I want to use Python because it is my language of choice. I then researched what libraries I could use to build the frontend with. I came across Flask as an option and decided to run with that. The next step I had to do was actually find out where it was 5PM.

    In my head, I came up with the process that if I could first get a list of all the timezone and identify the current time in them I could filter out which timezones it was 5PM. Once establishing where it was 5PM, I can then get that information to Flask and figure out a way to display it.

    Here is the function for identifying the current time in all timezones and then storing each key pair of {Timezone : Current_Time }

    def getTime():
    now_utc = datetime.now(timezone('UTC'))
    #print('UTC:', now_utc)
    timezones = pytz.all_timezones
    #get all current times and store them into a list
    tz_array = []
    for tz in timezones:
        current_time = now_utc.astimezone(timezone(tz))
        values = {tz: current_time.hour}
        tz_array.append(values)
        
    return tz_array

    Once everything was stored into tz_array I took that info and passed it through the following function to identify it was 5PM. I have another function that identifies everything that is NOT 5PM.

    def find5PM():
    its5pm = []
    for tz in tz_array:
        timezones = tz.items()
        for timezone, hour in timezones:
            if hour >= 17:
                its5pm.append(timezone)
    return its5pm

    I made a new list and stored just the timezone name into that list and return it.

    Once I had all these together I passed them through as variables to Flask. This is where I first started to struggle. In my original revisions of the functions, I was only returning one of the values rather than returning ALL of the values. This resulted in hours of struggling to identify the cause of the problem. Eventually, I had to start over and completely re-work the code until I ended up with what you see above.

    The code was finally functional and I was ready to deploy it to Amazon Web Services for public access. I will discuss my design and deployment in Part Two.

    http://whereisitfiveoclock.net

  • EC2 Action Slack Notification

    I took a brief break from my Lambda function creation journey to go on vacation but, now i’m back!

    This function will notify a Slack channel of your choosing when an EC2 instance enters “Starting, Stopping, Stopped, or Shutting-Down” status. I thought this might be useful for instances that reside under a load balancer. It would be useful to see when your load balancer is scaling up or down in real-time via Slack notification.

    In order to use this function, you will need to create a Slack Application with an OAuth key and set that key as an environment variable in your Lambda function. If you are unsure of how to do this I can walk you through it!

    Please review the function below

    import logging
import requests
import boto3
import os
from urllib.parse import unquote_plus
from slack import WebClient
from slack.errors import SlackApiError
logging.basicConfig(level=logging.DEBUG)

# Check EC2 Status
def lambda_handler(event, context):
    detail = event['detail']
    ids = detail['instance-id']
    eventname = detail['state']
    ec2 = boto3.resource('ec2')
# Slack Variables
    slack_token = os.environ["slackBot"]
    client = WebClient(token=slack_token)
    channel_string = "XXXXXXXXXXXXXXXXXXXX"

# Post to slack that the instance is running
    if eventname == 'running':
        try:
          instance = ids
          response_string = f"The instance: {instance} has started"
          response = client.chat_postMessage(
            channel= channel_string,
          	text="An Instance has started",
           	blocks = [{"type": "section", "text": {"type": "plain_text", "text": response_string}}]
	        	)
        except SlackApiError as e:
          assert e.response["error"]  

		#Post to slack that instance is shutting down
    elif eventname == 'shutting-down':
    	try:
	        instance = ids
	        response_string = f"The instance: {instance} is shutting down"
	        response = client.chat_postMessage(
	        	channel= channel_string,
	        	text="An Instance is Shutting Down",
	        	blocks = [{"type": "section", "text": {"type": "plain_text", "text": response_string}}]
	        	)
    	except SlackApiError as e:
           assert e.response["error"]
	      	
    elif eventname == 'stopped':
    	try:
	        instance = ids
	        response_string = f"The instance: {instance} has stopped"
	        response = client.chat_postMessage(
	        	channel= channel_string,
	        	text="An Instance has stopped",
	        	blocks = [{"type": "section", "text": {"type": "plain_text", "text": response_string}}]
	        	)
    	except SlackApiError as e:
    		assert e.response["error"]
	      	
    elif eventname == 'stopping':
    	try:
	        instance = ids
	        response_string = f"The instance: {instance} is stopping"
	        response = client.chat_postMessage(
	        	channel= channel_string,
	        	text="An Instance is stopping",
	        	blocks = [{"type": "section", "text": {"type": "plain_text", "text": response_string}}]
	        	)
    	except SlackApiError as e:
    		assert e.response["error"]

    As always the function is available on GitHub as well:
    https://github.com/avansledright/ec2ActionPostToSlack

    If you find this function helpful please share it with your friends or repost it on your favorite social media platform!

  • Check EC2 Instance Tags on Launch

    In my ever-growing quest to automate my AWS infrastructure deployments, I realized that just checking my tags wasn’t good enough. I should force myself to put tags in otherwise my instances won’t launch at all.

    I find this particularly useful because I utilize AWS Backup to do automated snapshots nightly of all of my instances. If I don’t put the “Backup” tag onto my instance it will not be included in the rule. This concept of forced tagging could be utilized across many different applications including tagging for development, production, or testing environments.

    To do this I created the Lambda function below. Utilizing EventBridge I have this function every time there is an EC2 instance that enters the “running” state. 

    import json
import boto3

def lambda_handler(event, context):
    detail = event['detail']
    ids = detail['instance-id']
    eventname = detail['state']
    ec2 = boto3.resource('ec2')
    
    while eventname == 'Running':
        print(ids)       
    #Check to see if backup tag is added to the instance
        tag_to_check = 'Backup'
        instance = ec2.Instance(ids)
        for tag in instance.tags:
            if tag_to_check not in [t['Key'] for t in instance.tags]:
                instance.stop()
                print("Stopping Instance: ", instance)
    #Get instance state to break the infinite loop
                state = instance.state['Name']          
                if state == "shutting-down":
                    print("instance is shutting-down")
                    break
                elif state == "stopped":
                    print("Instance is already stopped")
                    break
                elif state == "stopping":
                    print("instance is stopping")
                    break
        break

    The function then will check the status of the instance to ensure that it is stopped and then break the loop.

    You can clone the repository from GitHub here:
    https://github.com/avansledright/aws-force-ec2-launch-tags

    If you utilize the script please share it with your friends. Feel free to modify it as you please and let me know how it works for you! As always, if you have any questions feel free to reach out here or on any other platform!

  • AWS Tag Checker

    I wrote this script this morning as I was creating a new web server. I realized that I had been forgetting to add my “Backup” tag to my instances so that they would automatically be backed up via AWS Backup.

    This one is pretty straight forward. Utilizing Boto3 this script will iterate over all of your instances and check them for the tag specified on line 8. If the tag is not present it will then add the tag that is defined by JSON in $response.

    After that is all done it will iterate over the instances again to check that the tag has been added. If a new instance has been added or it failed to add the tag it will print out a list of instance ID’s that do not have the tag.

    Here is the script:

    import boto3


ec2 = boto3.resource('ec2')
inst_describe = ec2.instances.all()

for instance in inst_describe:
    tag_to_check = 'Backup'
    if tag_to_check not in [t['Key'] for t in instance.tags]:
        print("This instance is not tagged: ", instance.instance_id)
        response = ec2.create_tags(
            Resources= [instance.instance_id],
            Tags = [
                {
                    'Key': 'Backup',
                    'Value': 'Yes'
                }
            ]
        )
# Double check that there are no other instances without tags
for instance in inst_describe:
    if tag_to_check not in [t['Key'] for t in instance.tags]:
        print("Failed to assign tag, or new instance: ", instance.instance_id)

    The script is also available on GitHub here:
    https://github.com/avansledright/awsTagCheck

    If you find this script helpful feel free to share it with your friends and let me know in the comments!

  • Lambda Function Post to Slack

    I wrote this script out of a need to practice my Python skills. The idea is that if a file gets uploaded to an S3 bucket then the function will trigger and a message with that file name will be posted to a Slack channel of your choosing.

    To utilize this you will need to include the Slack pip package as well as the slackclient pip package when you upload the function to the AWS Console.

    You will also need to create an OAuth key for a Slack application. If you are unfamiliar with this process feel free to drop a comment below and or shoot me a message and I can walk you through the process or write a second part of the guide.

    Here is a link to the project:
    https://github.com/avansledright/posttoSlackLambda

    If this helps you please share this post on your favorite social media platform!

  • Automatically Transcribing Audio Files with Amazon Web Services

    Automatically Transcribing Audio Files with Amazon Web Services

    I wrote this Lambda function to automatically transcribe audio files that are uploaded to an S3 bucket. This is written in Python3 and utilizes the Boto3 library.

    You will need to give your Lambda function permissions to access S3, Transcribe and CloudWatch.

    The script will create an AWS Transcribe job with the format: 'filetranscription'+YYYYMMDD-HHMMSS

    I will be iterating over the script to hopefully add in a web front end as well as potentially branching to do voice call transcriptions for phone calls and Amazon Connect.

    You can view the code here

    If you have questions or comments feel free to reach out to me here or on any Social Media.

  • Monitoring Disk Space with CloudWatch

    I recently had a request to monitor disk space. Being that I don’t use a traditional monitoring platform but rather send all of my alerting to Slack I wondered how this would work.

    There is not a direct metric in CloudWatch so we will utilize the scripts available in this guide.

    You can follow along on the Amazon guide or follow some simple steps here geared towards Ubuntu based Linux distributions.

    First, let’s install some dependancies:

    sudo apt-get install unzip

    sudo apt-get install libwww-perl libdatetime-perl

    Next, we will download the scripts from Amazon:

    curl https://aws-cloudwatch.s3.amazonaws.com/downloads/CloudWatchMonitoringScripts-1.2.2.zip -O

    Once downloaded you can unpack the ZIP file:

    unzip CloudWatchMonitoringScripts-1.2.2.zip && \ rm CloudWatchMonitoringScripts-1.2.2.zip && \ cd aws-scripts-mon

    This will put the scripts into a directory called aws-scripts-mon inside of whatever directory you are currently in. I recommend doing this inside of /home/your-user.

    There are a few ways to allow your scripts to have permissions to CloudWatch. I preferred to create the awscreds.conf method but you can also give your instance an IAM role or specify the credentials inline. If you are unsure of how to create IAM policies or roles feel free to message me and we can chat more about that.

    Inside the directory there is a template file that you can utilize to generate your awscreds.conf file.

    cp awscreds.template awscreds.conf && vi awscreds.conf

    Modify the file as needed and save and close it.

    Now let’s test the scripts to ensure functionality:

    ./mon-put-instance-data.pl --disk-space-util --disk-path=/ --verify --verbose

    You should see a “Completed Successfully” message. If not, troubleshoot as needed.

    The scripts have a lot of functionality but we are specifically looking at disk usage. I added the following line as a Cron Job:

    0 * * * * /home/ubuntu/aws-scripts-mon/mon-put-instance-data.pl --disk-space-util --disk-path=/

    This runs the script every hour on the hour and reports the data to CloudWatch.

    Now that our data is being put into CloudWatch we need to alert on any issues. For the purpose of testing I created an alarm that was below my threshold so I could verify the alerting worked. You can adjust as you need to.

    Login to your AWS Management Console and navigate to the CloudWatch Console. Your data will be placed into the “Metrics” tab. Once the Metrics tab is open you will see a section called “Linux Systems”. Navigate to this and you should metrics called “Filesystem, InstanceId, Mountpath”. This is where your metrics live. You can navigate around here and view your metrics in the graphing utility. Once you have verified that the data is accurate you can create an alarm based on this metric.

    Navigate to the Alarms section of CloudWatch. Click “Create alarm” in the top right corner. Follow the steps to create your Alarm. For Metric navigate to the metric we found in the previous step. For Conditions, I chose the following:

    Threshold Type: Static
    Whenever DiskSpaceUtilization is…: Greater than the threshold
    Than…: 45% (Note this value will change based on your actually usage. For testing I recommend setting this to a value lower than your actual usage percentage so that your alarm will fire.)

    Click Next to continue. On the following page you can setup your notifications. I covered creating an AWS Chatbot here I have all of my CloudWatch Alarms sent to an SNS topic called aws-alerts. You can create something similar and have your AWS Chatbot monitor that topic as well. Once the alarm fires you should be getting an alert in your specified Slack Channel that looks something like this:

    Once your alarm is firing you can fine tune your thresholds to notify you as you need!