# Data points with a period of less than 60 seconds are available for 3 hours. These data points are high-resolution metrics and are available only for custom metrics that have been defined with a StorageResolution of 1.
# Data points with a period of 60 seconds (1-minute) are available for 15 days.
# Data points with a period of 300 seconds (5-minute) are available for 63 days.
# Data points with a period of 3600 seconds (1 hour) are available for 455 days (15 months)

# Period
# If the StartTime parameter specifies a time stamp that is greater than 3 hours ago, you must specify the period as follows or no data points in that time range is returned:
# Start time between 3 hours and 15 days ago - Use a multiple of 60 seconds (1 minute).
# Start time between 15 and 63 days ago - Use a multiple of 300 seconds (5 minutes).
# Start time greater than 63 days ago - Use a multiple of 3600 seconds (1 hour).

# Write request units	$1.25 per million write request units
# Read request units	$0.25 per million read request units


# Provisioned Throughput Type	Price per hour
# Write capacity unit (WCU)	$0.00065 per WCU
# Read capacity unit (RCU)	$0.00013 per RCU

ONDEMAND_WPM = 1.25
ONDEMAND_RPM = 0.25

PROVISIONED_WCU = 0.00065
PROVISIONED_RCU = 0.00013

import boto3
import datetime

savings_total = 0
month_days = 31
endTime = datetime.datetime(2020, 8, 20)
startTime = endTime - datetime.timedelta(28)

table = 'prod-masters_audit_new'

cloudwatch = boto3.resource('cloudwatch', region_name='us-east-1')
dynamodb = boto3.resource('dynamodb', region_name='us-east-1')

savings = dict()

#cloudwatch = boto3.client('cloudwatch', region_name='us-east-1')

dynamo_client = boto3.client('dynamodb', region_name='us-east-1')
dynamo_table_list = dynamo_client.list_tables()

import pprint
tables = []
tables.extend(dynamo_table_list['TableNames'])

while dynamo_table_list.get('LastEvaluatedTableName') is not None:
    dynamo_table_list = dynamo_client.list_tables(ExclusiveStartTableName=dynamo_table_list['LastEvaluatedTableName'])
    tables.extend(dynamo_table_list['TableNames'])

for table in tables:
    dynamo_table = dynamodb.Table(table)
    if dynamo_table.billing_mode_summary is not None:
        continue

    dimensions = [{'Name': 'TableName', 'Value': table} ]
    metric = cloudwatch.Metric("AWS/DynamoDB", "ConsumedReadCapacityUnits")
    all_the_metrics = metric.get_statistics(
        Dimensions=dimensions,
        StartTime=startTime,
        EndTime=endTime,
        Period=7200,
        Statistics=[
            'Sum',
        ]
    )
    consumed_read = 0
    for metrics in all_the_metrics['Datapoints']:
        consumed_read+=metrics['Sum']


    metric = cloudwatch.Metric("AWS/DynamoDB", "ConsumedWriteCapacityUnits")
    all_the_metrics = metric.get_statistics(
        Dimensions=dimensions,
        StartTime=startTime,
        EndTime=endTime,
        Period=7200,
        Statistics=[
            'Sum',
        ]
    )
    consumed_write = 0
    for metrics in all_the_metrics['Datapoints']:
        consumed_write+=metrics['Sum']
    # print(consumed_write)

    # print('consumed {} read and {} write'.format(consumed_read, consumed_write))
    ondemand_pricing = (consumed_read/1000000 * ONDEMAND_RPM + consumed_write/1000000 * ONDEMAND_WPM)
    # print('ondemand pricing {}'.format(ondemand_pricing))

    provisioned_read_capacity = dynamo_table.provisioned_throughput['ReadCapacityUnits']
    provisioned_write_capacity = dynamo_table.provisioned_throughput['WriteCapacityUnits']

    provisioned_pricing = (provisioned_read_capacity * PROVISIONED_RCU + provisioned_write_capacity * PROVISIONED_WCU) * 24 * month_days
    # print('provisioned pricing {}'.format(provisioned_pricing))

    price_diff = provisioned_pricing - ondemand_pricing
    # print('Savings for table {}: {}'.format(table, price_diff))

    print('{} {}'.format(price_diff, table))

    savings[table] = price_diff
    savings_total+=price_diff

pprint.pprint(savings)
print(savings_total)