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Monitoring Wrocław weather with AWS

lukaszkuczynski profile image lukaszkuczynski Originally published at lukaszkuczynski.github.io on ・4 min read

What if I wanted to compare AWS Lambdas with Azure Function apps? The previous weeks I did some Azure sample app. There is some article on that. But I always felt AWS compared to Azure is smarter, GUI is lighter and I love Python. So why not to copy my project to AWS?

Plan

Create a notification mechanism telling me about changes of weather in Wrocław. This will be done using Lambda architecture in AWS mechanism.

Implementation

Check the weather

Lambda function can be fired by CloudWatch Events, using which I can set up some cron expression. Thus I will check weather exposed by openweather API and put it on a queue. Queues in Amazon are part of so-called SQS. Handling SQS in Amazon is pretty easy using Python boto clients. Following there is my function to fetch data. In my case every 10 minutes I will call openweather API to query the current weather and I will put its description in a queue.

import os
from datetime import datetime
from urllib.request import Request, urlopen
import json
import boto3

SITE = os.environ['site'] # URL of the site to check, stored in the site environment variable

def validate(res):
    return EXPECTED in res

def lambda_handler(event, context):
    print('Checking {} at {}...'.format(SITE, event['time']))
    try:
        req = Request(SITE, headers={'User-Agent': 'AWS Lambda'})
        response = urlopen(req).read()
        print(response)
        data = json.loads(response.decode("utf-8"))
        weather = data['weather'][0]['main']
        dt = data['dt']
        dt_iso = datetime.isoformat(datetime.fromtimestamp(dt))
        print(f"weather is {weather} with UTC time {dt_iso}")

        queueUrl = os.environ['SQS_NEW_WEATHER']
        client = boto3.client('sqs')
        response = client.send_message(
            QueueUrl=queueUrl,
            MessageBody=weather
        )

    except:
        print('Check failed!')
        raise
    else:
        print('Check passed!')
        return weather
    finally:
        print('Check complete at {}'.format(str(datetime.now())))

Write to persistence

When received the value I would like to compare it against the previous one to decide whether there was a change. I have to put state somewhere. I will use default database for AWS: DynamoDB.

The function is triggered for a new message on SQS, and it is easily configurable with AWS lambda GUI. The message received on SQS is available as event parameter of lambda.

import boto3
from boto3.dynamodb.conditions import Key
from datetime import datetime

def lambda_handler(event, context):
    print("received!")
    print(event)
    new_value = event['Records'][0]['body']
    dynamodb = boto3.resource('dynamodb')
    table = dynamodb.Table('weather')
    table.put_item(
       Item={
            'created_at': datetime.utcnow().strftime('%Y%m%d_%H%M%S'),
            'type': 'wroclawWeather',
            'text': new_value
        }
    )
    return "time created with value "+new_value

So the above function will create a new document in DynamoDB instance. As you can see boto3 library helps to handle DynamoDB connections. All you need is to call resource and table and you are ready to put_item into the table. This will enable the comparison in the near future. Behold!

Compare time!

Having the new document inserted into the table, I can compare it against the previous one of the same type. I will use again the appropriate trigger, this time it will be “New row” trigger for a DynamoDB. The last 2 items will be fetched using this complicated query with KeyConditionExpression because I want to get only two previous values of the exact type (like a WHERE clause in SQL).

import os
import json
import boto3
from boto3.dynamodb.conditions import Key

def lambda_handler(event, context):
    # print("Received event: " + json.dumps(event, indent=2))
    records = event['Records']
    if len(records) != 1:
        print(f"len records != 1, len = {len(records)}")
        print(records)
        return "len(REC) <> 0"
    record = records[0]
    current_type = record['dynamodb']['NewImage']['type']['S']
    print(f"current_type = {current_type}")
    dynamodb = boto3.resource('dynamodb')
    table = dynamodb.Table('weather')
    response = table.query(
        KeyConditionExpression=Key('type').eq(current_type),
        ScanIndexForward=False, 
        Limit=2
    )
    new_value = record['dynamodb']['NewImage']['text']['S']
    old_value = response['Items'][1]['text']

    if old_value != new_value:
        change_text = "changed IN DYNAMO trigger! "+old_value+"->"+new_value
        print(change_text)
        queueUrl = os.environ['SQS_CHANGED_WEATHER']
        client = boto3.client('sqs')
        response = client.send_message(
            QueueUrl=queueUrl,
            MessageBody=change_text
        )
    print(f"Current FROM DYNAMO {new_value}, previous {old_value}")

If the value was changed the message will wander to another SQS topic, and this one I will use to notify the user about the changes.

Notification with SNS

When the weather is changed, it will go to Simple Notification Service , that can be used to… apply notification rules. In my case, I will simply use an email to be sent with the text of a change.

And once again, thanks to boto3 library enables I create a SNS client so I can send SNS message super-easily from within the lambda.

import os
import json
import boto3

def lambda_handler(event, context):
    print("called weatherChanged")
    print(event)
    change_text_value = event['Records'][0]['body']
    print("I received a change: "+change_text_value)
    arn = os.environ['SNS_TOPIC']
    message = change_text_value
    client = boto3.client('sns')
    response = client.publish(
        TargetArn=arn,
        Message=message
    )
    return change_text_value

After that one I just needed to configure my SNS connection.

Configuring SNS

To receive emails I had to create a topic. This topic has SNS address that I used in lambda function code. Then there is a need to create a subscription. You can use subscription of “Email” type, so then AWS will push the messages to your email inbox.

AWS or Azure?

I configure the same business logic in both AWS and Azure. Time for a comparison.

Azure pros:

security made easier

I wasn’t forced to care about securty too much, all the functions were just connected to each other out-of-the-box.

functions bundled together

For a reason Function App in Azure is a root for all the functions inside, I could nicely put all the related functions in one resource.

AWS pros:

python

The language is waaay better for me. Have no experience in C# and Python is just beautiful for playing with functions.

no hassle with param bindings

Binding in Azure is not so straighforward for me as AWS triggers and handling params for AWS Lambdas. Just better.

boto3

I had to make some strange returning and AsyncCollectors in Azure while AWS gives boto3 that makes everything just simple.

My choice? AWS.

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lukaszkuczynski

@lukaszkuczynski

Developer. Integrations. Searching and finding. Analysis.

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