Amazon Web Services propose un excellent package gratuit avec une bonne gamme de services et de généreux crédits de développement. J'étais sûr qu'il n'y aurait aucun problème à payer pour mon entourage, donc je ne m'inquiétais pas des dépenses. Mon application est 100% sans serveur et je rentre toujours dans le niveau gratuit, alors j'ai simplement ignoré le problème de paiement. À un moment donné, je me suis détendu et j'ai perdu ma garde.
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AWS Cost Explorer
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reports . AWS , .
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Cost Explorer API
– . , , AWS API. AWS Samples Github API Cost Explorer.
, Cost Explorer’a.
Lambda
import os
import sys
# Required to load modules from vendored subfolder (for clean development env)
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), "./vendored"))
import boto3
import datetime
import logging
import pandas as pd
#For date
from dateutil.relativedelta import relativedelta
#For email
from email.mime.application import MIMEApplication
from email.mime.multipart import MIMEMultipart
from email.mime.text import MIMEText
from email.utils import COMMASPACE, formatdate
SES_REGION="ap-south-1"
CURRENT_MONTH = True
#Default exclude support, as for Enterprise Support
#as support billing is finalised later in month so skews trends
INC_SUPPORT = os.environ.get('INC_SUPPORT')
if INC_SUPPORT == "true":
INC_SUPPORT = True
else:
INC_SUPPORT = False
TAG_VALUE_FILTER = os.environ.get('TAG_VALUE_FILTER') or '*'
TAG_KEY = os.environ.get('TAG_KEY')
class CostExplorer:
"""Retrieves BillingInfo checks from CostExplorer API
>>> costexplorer = CostExplorer()
>>> costexplorer.addReport(GroupBy=[{"Type": "DIMENSION","Key": "SERVICE"}])
>>> costexplorer.generateExcel()
"""
def __init__(self, CurrentMonth=False):
#Array of reports ready to be output to Excel.
self.reports = []
self.client = boto3.client('ce', region_name='us-east-1')
# self.end = datetime.date.today().replace(day=1)
self.riend = datetime.date.today()
self.end = self.riend
# Default is last 12 months
self.start = (datetime.date.today() - relativedelta(months=+12)).replace(day=1) #1st day of month 12 months ago
self.ristart = (datetime.date.today() - relativedelta(months=+11)).replace(day=1) #1st day of month 11 months ago
self.sixmonth = (datetime.date.today() - relativedelta(months=+6)).replace(day=1) #1st day of month 6 months ago, so RI util has savings values
self.accounts = {}
def addRiReport(self, Name='RICoverage', Savings=False, PaymentOption='PARTIAL_UPFRONT', Service='Amazon Elastic Compute Cloud - Compute'): #Call with Savings True to get Utilization report in dollar savings
type = 'chart' #other option table
if Name == "RICoverage":
results = []
response = self.client.get_reservation_coverage(
TimePeriod={
'Start': self.ristart.isoformat(),
'End': self.riend.isoformat()
},
Granularity='MONTHLY'
)
results.extend(response['CoveragesByTime'])
while 'nextToken' in response:
nextToken = response['nextToken']
response = self.client.get_reservation_coverage(
TimePeriod={
'Start': self.ristart.isoformat(),
'End': self.riend.isoformat()
},
Granularity='MONTHLY',
NextPageToken=nextToken
)
results.extend(response['CoveragesByTime'])
if 'nextToken' in response:
nextToken = response['nextToken']
else:
nextToken = False
rows = []
for v in results:
row = {'date':v['TimePeriod']['Start']}
row.update({'Coverage%':float(v['Total']['CoverageHours']['CoverageHoursPercentage'])})
rows.append(row)
df = pd.DataFrame(rows)
df.set_index("date", inplace= True)
df = df.fillna(0.0)
df = df.T
elif Name in ['RIUtilization','RIUtilizationSavings']:
#Only Six month to support savings
results = []
response = self.client.get_reservation_utilization(
TimePeriod={
'Start': self.sixmonth.isoformat(),
'End': self.riend.isoformat()
},
Granularity='MONTHLY'
)
results.extend(response['UtilizationsByTime'])
while 'nextToken' in response:
nextToken = response['nextToken']
response = self.client.get_reservation_utilization(
TimePeriod={
'Start': self.sixmonth.isoformat(),
'End': self.riend.isoformat()
},
Granularity='MONTHLY',
NextPageToken=nextToken
)
results.extend(response['UtilizationsByTime'])
if 'nextToken' in response:
nextToken = response['nextToken']
else:
nextToken = False
rows = []
if results:
for v in results:
row = {'date':v['TimePeriod']['Start']}
if Savings:
row.update({'Savings$':float(v['Total']['NetRISavings'])})
else:
row.update({'Utilization%':float(v['Total']['UtilizationPercentage'])})
rows.append(row)
df = pd.DataFrame(rows)
df.set_index("date", inplace= True)
df = df.fillna(0.0)
df = df.T
type = 'chart'
else:
df = pd.DataFrame(rows)
type = 'table' #Dont try chart empty result
elif Name == 'RIRecommendation':
results = []
response = self.client.get_reservation_purchase_recommendation(
#AccountId='string', May use for Linked view
LookbackPeriodInDays='SIXTY_DAYS',
TermInYears='ONE_YEAR',
PaymentOption=PaymentOption,
Service=Service
)
results.extend(response['Recommendations'])
while 'nextToken' in response:
nextToken = response['nextToken']
response = self.client.get_reservation_purchase_recommendation(
#AccountId='string', May use for Linked view
LookbackPeriodInDays='SIXTY_DAYS',
TermInYears='ONE_YEAR',
PaymentOption=PaymentOption,
Service=Service,
NextPageToken=nextToken
)
results.extend(response['Recommendations'])
if 'nextToken' in response:
nextToken = response['nextToken']
else:
nextToken = False
rows = []
for i in results:
for v in i['RecommendationDetails']:
row = v['InstanceDetails'][list(v['InstanceDetails'].keys())[0]]
row['Recommended']=v['RecommendedNumberOfInstancesToPurchase']
row['Minimum']=v['MinimumNumberOfInstancesUsedPerHour']
row['Maximum']=v['MaximumNumberOfInstancesUsedPerHour']
row['Savings']=v['EstimatedMonthlySavingsAmount']
row['OnDemand']=v['EstimatedMonthlyOnDemandCost']
row['BreakEvenIn']=v['EstimatedBreakEvenInMonths']
row['UpfrontCost']=v['UpfrontCost']
row['MonthlyCost']=v['RecurringStandardMonthlyCost']
rows.append(row)
df = pd.DataFrame(rows)
df = df.fillna(0.0)
type = 'table' #Dont try chart this
self.reports.append({'Name':Name,'Data':df, 'Type':type})
def addReport(self, Name="Default",GroupBy=[{"Type": "DIMENSION","Key": "SERVICE"},],
Style='Total', NoCredits=True, CreditsOnly=False, RefundOnly=False, UpfrontOnly=False, IncSupport=False):
type = 'chart' #other option table
results = []
if not NoCredits:
response = self.client.get_cost_and_usage(
TimePeriod={
'Start': self.start.isoformat(),
'End': self.end.isoformat()
},
Granularity='MONTHLY',
Metrics=[
'UnblendedCost',
],
GroupBy=GroupBy
)
else:
Filter = {"And": []}
Dimensions={"Not": {"Dimensions": {"Key": "RECORD_TYPE","Values": ["Credit", "Refund", "Upfront", "Support"]}}}
if INC_SUPPORT or IncSupport: #If global set for including support, we dont exclude it
Dimensions={"Not": {"Dimensions": {"Key": "RECORD_TYPE","Values": ["Credit", "Refund", "Upfront"]}}}
if CreditsOnly:
Dimensions={"Dimensions": {"Key": "RECORD_TYPE","Values": ["Credit",]}}
if RefundOnly:
Dimensions={"Dimensions": {"Key": "RECORD_TYPE","Values": ["Refund",]}}
if UpfrontOnly:
Dimensions={"Dimensions": {"Key": "RECORD_TYPE","Values": ["Upfront",]}}
tagValues = None
if TAG_KEY:
tagValues = self.client.get_tags(
SearchString=TAG_VALUE_FILTER,
TimePeriod = {
'Start': self.start.isoformat(),
'End': datetime.date.today().isoformat()
},
TagKey=TAG_KEY
)
if tagValues:
Filter["And"].append(Dimensions)
if len(tagValues["Tags"]) > 0:
Tags = {"Tags": {"Key": TAG_KEY, "Values": tagValues["Tags"]}}
Filter["And"].append(Tags)
else:
Filter = Dimensions.copy()
response = self.client.get_cost_and_usage(
TimePeriod={
'Start': self.start.isoformat(),
'End': self.end.isoformat()
},
Granularity='MONTHLY',
Metrics=[
'UnblendedCost',
],
GroupBy=GroupBy,
Filter=Filter
)
if response:
results.extend(response['ResultsByTime'])
while 'nextToken' in response:
nextToken = response['nextToken']
response = self.client.get_cost_and_usage(
TimePeriod={
'Start': self.start.isoformat(),
'End': self.end.isoformat()
},
Granularity='MONTHLY',
Metrics=[
'UnblendedCost',
],
GroupBy=GroupBy,
NextPageToken=nextToken
)
results.extend(response['ResultsByTime'])
if 'nextToken' in response:
nextToken = response['nextToken']
else:
nextToken = False
rows = []
sort = ''
for v in results:
row = {'date':v['TimePeriod']['Start']}
sort = v['TimePeriod']['Start']
for i in v['Groups']:
key = i['Keys'][0]
if key in self.accounts:
key = self.accounts[key][ACCOUNT_LABEL]
row.update({key:float(i['Metrics']['UnblendedCost']['Amount'])})
if not v['Groups']:
row.update({'Total':float(v['Total']['UnblendedCost']['Amount'])})
rows.append(row)
df = pd.DataFrame(rows)
df.set_index("date", inplace= True)
df = df.fillna(0.0)
if Style == 'Change':
dfc = df.copy()
lastindex = None
for index, row in df.iterrows():
if lastindex:
for i in row.index:
try:
df.at[index,i] = dfc.at[index,i] - dfc.at[lastindex,i]
except:
logging.exception("Error")
df.at[index,i] = 0
lastindex = index
df = df.T
df = df.sort_values(sort, ascending=False)
self.reports.append({'Name':Name,'Data':df, 'Type':type})
def generateExcel(self):
# Create a Pandas Excel writer using XlsxWriter as the engine.\
os.chdir('/tmp')
writer = pd.ExcelWriter('cost_explorer_report.xlsx', engine='xlsxwriter')
workbook = writer.book
for report in self.reports:
print(report['Name'],report['Type'])
report['Data'].to_excel(writer, sheet_name=report['Name'])
worksheet = writer.sheets[report['Name']]
if report['Type'] == 'chart':
# Create a chart object.
chart = workbook.add_chart({'type': 'column', 'subtype': 'stacked'})
chartend=13
for row_num in range(1, len(report['Data']) + 1):
chart.add_series({
'name': [report['Name'], row_num, 0],
'categories': [report['Name'], 0, 1, 0, chartend],
'values': [report['Name'], row_num, 1, row_num, chartend],
})
chart.set_y_axis({'label_position': 'low'})
chart.set_x_axis({'label_position': 'low'})
worksheet.insert_chart('O2', chart, {'x_scale': 2.0, 'y_scale': 2.0})
writer.save()
#Time to deliver the file to S3
if os.environ.get('S3_BUCKET'):
s3 = boto3.client('s3')
s3.upload_file("cost_explorer_report.xlsx", os.environ.get('S3_BUCKET'), "cost_explorer_report.xlsx")
if os.environ.get('SES_SEND'):
#Email logic
msg = MIMEMultipart()
msg['From'] = os.environ.get('SES_FROM')
msg['To'] = COMMASPACE.join(os.environ.get('SES_SEND').split(","))
msg['Date'] = formatdate(localtime=True)
msg['Subject'] = "Cost Explorer Report"
text = "Find your Cost Explorer report attached\n\n"
msg.attach(MIMEText(text))
with open("cost_explorer_report.xlsx", "rb") as fil:
part = MIMEApplication(
fil.read(),
Name="cost_explorer_report.xlsx"
)
part['Content-Disposition'] = 'attachment; filename="%s"' % "cost_explorer_report.xlsx"
msg.attach(part)
#SES Sending
ses = boto3.client('ses', region_name=SES_REGION)
result = ses.send_raw_email(
Source=msg['From'],
Destinations=os.environ.get('SES_SEND').split(","),
RawMessage={'Data': msg.as_string()}
)
def lambda_handler(event, context):
costexplorer = CostExplorer(CurrentMonth=False)
#Default addReport has filter to remove Support / Credits / Refunds / UpfrontRI
#Overall Billing Reports
costexplorer.addReport(Name="Total", GroupBy=[],Style='Total',IncSupport=True)
costexplorer.addReport(Name="TotalChange", GroupBy=[],Style='Change')
costexplorer.addReport(Name="TotalInclCredits", GroupBy=[],Style='Total',NoCredits=False,IncSupport=True)
costexplorer.addReport(Name="TotalInclCreditsChange", GroupBy=[],Style='Change',NoCredits=False)
costexplorer.addReport(Name="Credits", GroupBy=[],Style='Total',CreditsOnly=True)
costexplorer.addReport(Name="Refunds", GroupBy=[],Style='Total',RefundOnly=True)
costexplorer.addReport(Name="RIUpfront", GroupBy=[],Style='Total',UpfrontOnly=True)
#GroupBy Reports
costexplorer.addReport(Name="Services", GroupBy=[{"Type": "DIMENSION","Key": "SERVICE"}],Style='Total',IncSupport=True)
costexplorer.addReport(Name="ServicesChange", GroupBy=[{"Type": "DIMENSION","Key": "SERVICE"}],Style='Change')
costexplorer.addReport(Name="Accounts", GroupBy=[{"Type": "DIMENSION","Key": "LINKED_ACCOUNT"}],Style='Total')
costexplorer.addReport(Name="AccountsChange", GroupBy=[{"Type": "DIMENSION","Key": "LINKED_ACCOUNT"}],Style='Change')
costexplorer.addReport(Name="Regions", GroupBy=[{"Type": "DIMENSION","Key": "REGION"}],Style='Total')
costexplorer.addReport(Name="RegionsChange", GroupBy=[{"Type": "DIMENSION","Key": "REGION"}],Style='Change')
if os.environ.get('COST_TAGS'): #Support for multiple/different Cost Allocation tags
for tagkey in os.environ.get('COST_TAGS').split(','):
tabname = tagkey.replace(":",".") #Remove special chars from Excel tabname
costexplorer.addReport(Name="{}".format(tabname)[:31], GroupBy=[{"Type": "TAG","Key": tagkey}],Style='Total')
costexplorer.addReport(Name="Change-{}".format(tabname)[:31], GroupBy=[{"Type": "TAG","Key": tagkey}],Style='Change')
#RI Reports
costexplorer.addRiReport(Name="RICoverage")
costexplorer.addRiReport(Name="RIUtilization")
costexplorer.addRiReport(Name="RIUtilizationSavings", Savings=True)
costexplorer.addRiReport(Name="RIRecommendation") #Service supported value(s): Amazon Elastic Compute Cloud - Compute, Amazon Relational Database Service
costexplorer.generateExcel()
return "Report Generated"
IAM Role
, Lambda :
Lambda
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": [
"logs:CreateLogGroup",
"logs:CreateLogStream",
"logs:PutLogEvents"
],
"Resource": "*"
}
]
}
Autorisation d'écrire des rapports dans le compartiment S3
{
"Version": "2012-10-17",
"Statement": [
{
"Sid": "VisualEditor0",
"Effect": "Allow",
"Action": [
"s3:PutObject",
"s3:GetObject"
],
"Resource": "arn:aws:s3:::account.admin/*"
}
]
}
Service de messagerie simple
{
"Version": "2012-10-17",
"Statement": [
{
"Sid": "VisualEditor0",
"Effect": "Allow",
"Action": [
"ses:SendEmail",
"ses:SendRawEmail"
],
"Resource": "*"
}
]
}
Explorateur de coûts
{
"Version": "2012-10-17",
"Statement": [
{
"Sid": "VisualEditor0",
"Effect": "Allow",
"Action": "ce:*",
"Resource": "*"
}
]
}
Lancer sur Event Bridge
Enfin, nous avons configuré notre fonction Lambda pour qu'elle s'exécute régulièrement sur Event Bridge, par exemple, le 5 de chaque mois. À la suite du fonctionnement de tous les paramètres, je recevrai un e-mail avec un rapport XLS en pièce jointe. Il est également possible de configurer le déclenchement sur une base hebdomadaire et même certains jours de la semaine, si nécessaire.