app/like-service/service/mysfitsTableClient.py [13:105]:
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if (os.environ['DDB_TABLE_NAME'] != ''):
    table = os.environ['DDB_TABLE_NAME']
else: 
    table = 'MysfitsTable'

if (os.environ['AWS_REGION'] != ''):
    region = os.environ['AWS_REGION']
else:
    r = requests.get("http://169.254.169.254/latest/dynamic/instance-identity/document")
    region = r.json()['region']    

client = boto3.client('dynamodb', region_name=region)

def getMysfitsJson(items):
    # loop through the returned mysfits and add their attributes to a new dict
    # that matches the JSON response structure expected by the frontend.
    mysfitList = defaultdict(list)

    for item in items:
        mysfit = {}

        mysfit["mysfitId"] = item["MysfitId"]["S"]
        mysfit["name"] = item["Name"]["S"]
        mysfit["species"] = item["Species"]["S"]
        mysfit["description"] = item["Description"]["S"]
        mysfit["age"] = int(item["Age"]["N"])
        mysfit["goodevil"] = item["GoodEvil"]["S"]
        mysfit["lawchaos"] = item["LawChaos"]["S"]
        mysfit["thumbImageUri"] = item["ThumbImageUri"]["S"]
        mysfit["profileImageUri"] = item["ProfileImageUri"]["S"]
        mysfit["likes"] = item["Likes"]["N"]
        mysfit["adopted"] = item["Adopted"]["BOOL"]

        mysfitList["mysfits"].append(mysfit)

    return mysfitList

def getAllMysfits():
    # Retrieve all Mysfits from DynamoDB using the DynamoDB scan operation.
    # Note: The scan API can be expensive in terms of latency when a DynamoDB
    # table contains a high number of records and filters are applied to the
    # operation that require a large amount of data to be scanned in the table
    # before a response is returned by DynamoDB. For high-volume tables that
    # receive many requests, it is common to store the result of frequent/common
    # scan operations in an in-memory cache. DynamoDB Accelerator (DAX) or
    # use of ElastiCache can provide these benefits. But, because out Mythical
    # Mysfits API is low traffic and the table is very small, the scan operation
    # will suit our needs for this workshop.
    response = client.scan(
        TableName=table
    )

    logging.info(response["Items"])

    # loop through the returned mysfits and add their attributes to a new dict
    # that matches the JSON response structure expected by the frontend.
    mysfitList = getMysfitsJson(response["Items"])

    return json.dumps(mysfitList)

def queryMysfitItems(filter, value):
    # Use the DynamoDB API Query to retrieve mysfits from the table that are
    # equal to the selected filter values.
    response = client.query(
        TableName=table,
        IndexName=filter+'Index',
        KeyConditions={
            filter: {
                'AttributeValueList': [
                    {
                        'S': value
                    }
                ],
                'ComparisonOperator': "EQ"
            }
        }
    )

    # loop through the returned mysfits and add their attributes to a new dict
    # that matches the JSON response structure expected by the frontend.
    mysfitList = getMysfitsJson(response["Items"])

    # convert the create list of dicts in to JSON
    return json.dumps(mysfitList)

def queryMysfits(queryParam):

    logging.info(json.dumps(queryParam))

    filter = queryParam['filter']
    value = queryParam['value']

    return queryMysfitItems(filter, value)
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app/mysfits-service/service/mysfitsTableClient.py [13:105]:
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if (os.environ['DDB_TABLE_NAME'] != ''):
    table = os.environ['DDB_TABLE_NAME']
else: 
    table = 'MysfitsTable'

if (os.environ['AWS_REGION'] != ''):
    region = os.environ['AWS_REGION']
else:
    r = requests.get("http://169.254.169.254/latest/dynamic/instance-identity/document")
    region = r.json()['region']    

client = boto3.client('dynamodb', region_name=region)

def getMysfitsJson(items):
    # loop through the returned mysfits and add their attributes to a new dict
    # that matches the JSON response structure expected by the frontend.
    mysfitList = defaultdict(list)

    for item in items:
        mysfit = {}

        mysfit["mysfitId"] = item["MysfitId"]["S"]
        mysfit["name"] = item["Name"]["S"]
        mysfit["species"] = item["Species"]["S"]
        mysfit["description"] = item["Description"]["S"]
        mysfit["age"] = int(item["Age"]["N"])
        mysfit["goodevil"] = item["GoodEvil"]["S"]
        mysfit["lawchaos"] = item["LawChaos"]["S"]
        mysfit["thumbImageUri"] = item["ThumbImageUri"]["S"]
        mysfit["profileImageUri"] = item["ProfileImageUri"]["S"]
        mysfit["likes"] = item["Likes"]["N"]
        mysfit["adopted"] = item["Adopted"]["BOOL"]

        mysfitList["mysfits"].append(mysfit)

    return mysfitList

def getAllMysfits():
    # Retrieve all Mysfits from DynamoDB using the DynamoDB scan operation.
    # Note: The scan API can be expensive in terms of latency when a DynamoDB
    # table contains a high number of records and filters are applied to the
    # operation that require a large amount of data to be scanned in the table
    # before a response is returned by DynamoDB. For high-volume tables that
    # receive many requests, it is common to store the result of frequent/common
    # scan operations in an in-memory cache. DynamoDB Accelerator (DAX) or
    # use of ElastiCache can provide these benefits. But, because out Mythical
    # Mysfits API is low traffic and the table is very small, the scan operation
    # will suit our needs for this workshop.
    response = client.scan(
        TableName=table
    )

    logging.info(response["Items"])

    # loop through the returned mysfits and add their attributes to a new dict
    # that matches the JSON response structure expected by the frontend.
    mysfitList = getMysfitsJson(response["Items"])

    return json.dumps(mysfitList)

def queryMysfitItems(filter, value):
    # Use the DynamoDB API Query to retrieve mysfits from the table that are
    # equal to the selected filter values.
    response = client.query(
        TableName=table,
        IndexName=filter+'Index',
        KeyConditions={
            filter: {
                'AttributeValueList': [
                    {
                        'S': value
                    }
                ],
                'ComparisonOperator': "EQ"
            }
        }
    )

    # loop through the returned mysfits and add their attributes to a new dict
    # that matches the JSON response structure expected by the frontend.
    mysfitList = getMysfitsJson(response["Items"])

    # convert the create list of dicts in to JSON
    return json.dumps(mysfitList)

def queryMysfits(queryParam):

    logging.info(json.dumps(queryParam))

    filter = queryParam['filter']
    value = queryParam['value']

    return queryMysfitItems(filter, value)
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