AWS/LambdaExamples

• 2021 - Coursera course

• Bash script to upload python lambda in zip file

     1 import boto3
     2 import subprocess
     3 
     4 client = boto3.client('lambda')
     5 ROLE = 'arn:' + subprocess.getoutput('aws iam list-roles | grep role/lab4 | cut -f3- -d : | cut  --complement -c 44,45')
     6 BUCKET = subprocess.getoutput('aws s3api list-buckets --query "Buckets[].Name" | grep s3bucket | tr -d "," | xargs')
     7 
     8 
     9 response = client.create_function(
    10     FunctionName='get_reviews',
    11     Runtime='python3.8',
    12     Role=ROLE,
    13     Handler='get_reviews_code.lambda_handler',
    14     Code={
    15         'S3Bucket': BUCKET,
    16         'S3Key': 'get_reviews.zip'
    17     }
    18    
    19 )
    20 
    21 print ("DONE")
  

• Python lambda example from course

  • APIGW setup, Integration mapping template, Content-Type application/json

    {
    
        "product_id_str": "$input.params().querystring.get('product_id')"
    
    }

  • Test ApiGW Method Execution, "Query Strings {get_reviews}"

    product_id=TESTID

    • Python lambda code

         1 import boto3
         2 import json
         3 import re 
         4 
         5 def lambda_handler(event, context):
         6     product_id_str = event['product_id_str']
         7     S3_BUCKET = 'aws-tc-largeobjects'
         8     S3_FILE = 'DEV-AWS-MO-Building_2.0/my_json_lines.jsonl'
         9 
        10     s3 = boto3.client('s3')
        11 
        12     r = s3.select_object_content(
        13             Bucket=S3_BUCKET,
        14             Key=S3_FILE,
        15             ExpressionType='SQL',
        16             Expression="select s.review_headline, s.review_body, s.star_rating from s3object[*] s where s.product_id = '" + product_id_str + "'",
        17             InputSerialization={'JSON': {"Type": "Lines"}},
        18             OutputSerialization={'JSON': {}}
        19     )
        20     
        21     all_reviews_list = []
        22     helper_format_str = ""
        23     for event in r['Payload']:
        24         if 'Records' in event:
        25             helper_format_str = event['Records']['Payload'].decode('utf-8')
        26             helper_format_str = helper_format_str.replace("review_headline", "review_headline_str").replace("review_body", "review_body_str")
        27             all_reviews_list = helper_format_str.splitlines()
        28             
        29     result_list = []
        30     for review_chunk in all_reviews_list:
        31         result_list.append(json.loads(review_chunk))
        32     
        33     return {
        34         "product_id_str" : product_id_str,
        35         "reviews_arr": result_list
        36     }
      

• lambda python, checking and extracting info from Bearer token

     1 import json
     2 import base64
     3 
     4 def lambda_handler(event, context):
     5     token_str = ""
     6     ipv4_str = ""
     7     decoded = {}
     8     return_me = {}
     9     name_str = ""
    10     extract_str = ""
    11     cell_str = ""
    12     return_me["message_str"] =  "Report processing, check your phone shortly"
    13     if "Authorization" in json.dumps(event):
    14         #i.e we are at the website and have a valid Bearer token passed
    15         token_str = event["params"]["header"]["Authorization"]
    16         extract_str = token_str.replace("Bearer ", "").strip().split(".")[1]
    17         extract_str += '=' * (-len(extract_str) % 4)
    18         decoded = json.loads(base64.b64decode(extract_str))
    19         cell_str = decoded["phone_number"]
    20         name_str = decoded["cognito:username"]
    21         ipv4_str = event["params"]["header"]["X-Forwarded-For"]
    22         return_me["cell_str"] = cell_str
    23         return_me["name_str"] = name_str
    24         return_me["name_str"] = decoded["cognito:username"]
    25         return_me["ipv4_str"] = ipv4_str
    26         return_me["message_str"] = "Report Processing"
    27     return return_me