def time_range(client, index, path_log):
### Cliente Sv
try:
s = Search(using=client, index=index) \
.query('range' , **{'Date': {'gte': "now-7d/d"}}) \
.sort({"Date" : {"order" : "desc"}}) \
.query("match_all")
for hit in s[0]:
print(hit.Date)
last_update = hit.Date
start = datetime.datetime.strptime(last_update.strip(),
"%Y-%m-%dT%H:%M:%S.%fZ")
start = start + datetime.timedelta(days=1)
end = start + datetime.timedelta(days=1)
start = start.strftime("%Y-%m-%d")
end = end.strftime("%Y-%m-%d")
time_dict = {"start": start, "end": end}
except:
error = sys.exc_info()
simple_log(path_log, index, error[0], error[1])
time_dict = None
return time_dict
def pandasnorm(response_data):
name = __name__ + '.pandasnorm'
list_df = []
fileds = [
'account_id', 'resource_instance_id', 'resource_group_id', 'month',
'pricing_country', 'billing_country', 'currency_code', 'plan_id',
'resource_id', 'billable', 'pricing_plan_id', 'region', 'usage',
'plan_name', 'resource_name', 'resource_instance_name',
'resource_group_name'
]
try:
for i in response_data:
df = pd.json_normalize(i, 'usage', fileds)
for k in ['price', 'break_down', 'discounts', 'usage']:
try:
df = df.drop(k, axis=1)
except:
pass
list_df.append(df)
result = pd.concat(list_df)
except:
error = sys.exc_info()
simple_log(path_log, index, name, error[0], error[1])
result = None
return result
def DownloadReport(account, apikey, date, path, batch, index, path_log):
name = __name__ + '.DownloadReport'
token = get_token(apikey)
billMonth = date
offset = ''
data = concatdata(account, billMonth, token, offset)
df = pandasnorm(data)
try:
# df['BatchID'] = batch
dic = df.to_dict(orient='records')
now = datetime.datetime.now().strftime('%Yy%mm%dd%Hh%Mm')
name_file = path + 'DataIBM_' + now + '.json'
jsonfile = open(name_file, 'w')
for row in dic:
json.dump(row, jsonfile)
jsonfile.write('\n')
jsonfile.close()
except:
error = sys.exc_info()
simple_log(path_log, index, batch, name, error[0], error[1])
return None
def get_token(apikey):
name = __name__ + '.get_token'
try:
url = "https://iam.cloud.ibm.com/identity/token?grant_type=urn:ibm:params:oauth:grant-type:apikey&apikey="
payload = {}
headers = {
'Content-Type': 'application/x-www-form-urlencoded',
'Accept': 'application/json',
}
response = requests.request("POST",
url + apikey,
headers=headers,
data=payload)
data = json.loads(response.text)
token = data['access_token']
except:
error = sys.exc_info()
simple_log(path_log, index, name, error[0], error[1])
token = None
return token
def delete_rows(client, index, date, path_log):
try:
s = Search(using=client, index=index) \
.filter('range' , **{'Date': {'gte': date, "lte": date}})
response = s.delete()
except:
error = sys.exc_info()
simple_log(path_log, index, error[0], error[1])
return None
def get_id_batch(client, index, path_log):
name = __name__ + '.get_id_batch'
try:
s = Search(using=client, index=index) \
.query('range' , **{'Date': {'gte': "now-7d/d"}}) \
.sort({"BatchID" : {"order" : "desc"}}) \
.query("match_all")
for hit in s[0]:
last_batch = hit.BatchID
except:
last_batch = None
error = sys.exc_info()
simple_log(path_log, index, last_batch, name, error[0], error[1])
return last_batch
def processResourceInstanceUsage(account_id, billMonth, iam_token, offset):
name = __name__ + '.processResourceInstanceUsage'
try:
METERING_HOST = "https://billing.cloud.ibm.com"
USAGE_URL = "/v4/accounts/" + account_id + "/resource_instances/usage/" + billMonth + "?_limit=200&_names=true&_start=" + offset
url = METERING_HOST + USAGE_URL
headers = {
"Authorization": "{}".format(iam_token),
"Accept": "application/json",
"Content-Type": "application/json"
}
response = requests.get(url, headers=headers)
response = response.json()
except:
error = sys.exc_info()
simple_log(path_log, index, name, error[0], error[1])
response = None
return response
def donwload(client, start, end, folder_download, path_log, index):
metric = 'BlendedCost'
cost = [{
'Type': 'DIMENSION',
'Key': 'SERVICE'}]
try:
response = client.get_cost_and_usage(
TimePeriod={
'Start': start,
'End': end
},
Granularity='DAILY',
Metrics=['BlendedCost'],
GroupBy=cost
)
response = response['ResultsByTime']
except:
error = sys.exc_info()
simple_log(path_log, index, error[0], error[1])
response = None
i = 0
list_df = []
while True:
try:
df = pd.json_normalize(response[i]['Groups'])
timestart = response[i]['TimePeriod']['Start']
timeend = response[i]['TimePeriod']['End']
df["Period_start"] = timestart
df["Period_end"] = timeend
df = df.rename(columns={'Keys': 'Service',
'Metrics.BlendedCost.Amount': 'Amount',
'Metrics.BlendedCost.Unit': 'CurrencyCode',})
df['Amount'] = df['Amount'].astype(float)
i = i + 1
list_df.append(df)
except IndexError:
break
try:
result = pd.concat(list_df)
dic = result.to_dict(orient='records')
except:
error = sys.exc_info()
simple_log(path_log, index, error[0], error[1])
dic = None
now = datetime.datetime.now()
now = now.strftime('%Ya%mm%dd%Hh%Mm')
namefile = start + '_' + now +'.json'
jsonfile = open(folder_download + namefile, 'w')
for row in dic:
json.dump(row, jsonfile)
jsonfile.write('\n')
jsonfile.close()
return None
