def delete(self, path, subscription):
url = self.endpoint + path
headers = {
'Content-Type': 'application/json',
'apim-subscription-id': subscription
}
if self.username and self.password:
auth = (self.username, self.password)
else:
auth = None
retryrequests = RetryRequests(self.retrycount, self.retryinterval)
try:
r = retryrequests.delete(url=url,
headers=headers,
auth=auth,
timeout=REQUEST_TIMEOUT_SECONDS,
verify=False)
return r.json()
except Exception as e:
raise Exception('MAGA service api "{}" failed, {}'.format(
path, str(e)))
def post(self, path, data, subscription):
url = self.endpoint + path
headers = {
'Content-Type': 'application/json',
'Ocp-Apim-Subscription-Key': subscription
}
if self.username and self.password:
auth = (self.username, self.password)
else:
auth = None
retryrequests = RetryRequests(self.retrycount, self.retryinterval)
try:
r = retryrequests.post(url=url,
headers=headers,
auth=auth,
data=json.dumps(data),
timeout=REQUEST_TIMEOUT_SECONDS,
verify=False)
if r.status_code != 204:
return r.json()
except Exception as e:
raise Exception(
'MAGA service api "{}" failed, request:{}, {}'.format(
path, data, str(e)))
def __init__(self, endpoint, series_limit, username=None, password=None, retrycount=3, retryinterval=1000):
self.endpoint = endpoint
self.series_limit = series_limit
self.username = username
self.password = password
self.retryrequests = RetryRequests(retrycount, retryinterval)
class TSANAClient(object):
def __init__(self, endpoint, series_limit, username=None, password=None, retrycount=3, retryinterval=1000):
self.endpoint = endpoint
self.series_limit = series_limit
self.username = username
self.password = password
self.retryrequests = RetryRequests(retrycount, retryinterval)
def post(self, api_key, path, data):
url = self.endpoint + path
headers = {
"x-api-key": api_key,
"Content-Type": "application/json"
}
if self.username and self.password:
auth = (self.username, self.password)
else:
auth = None
try:
r = self.retryrequests.post(url=url, headers=headers, auth=auth, data=json.dumps(data),
timeout=REQUEST_TIMEOUT_SECONDS, verify=False)
if r.status_code != 204:
return r.json()
except Exception as e:
raise Exception('TSANA service api "{}" failed, request:{}, {}'.format(path, data, str(e)))
def put(self, api_key, path, data):
url = self.endpoint + path
headers = {
"x-api-key": api_key,
"Content-Type": "application/json"
}
if self.username and self.password:
auth = (self.username, self.password)
else:
auth = None
try:
r = self.retryrequests.put(url=url, headers=headers, auth=auth, data=json.dumps(data),
timeout=REQUEST_TIMEOUT_SECONDS, verify=False)
if r.status_code != 204:
return r.json()
except Exception as e:
raise Exception('TSANA service api "{}" failed, request:{}, {}'.format(path, data, str(e)))
def get(self, api_key, path):
url = self.endpoint + path
headers = {
"x-api-key": api_key,
"Content-Type": "application/json"
}
if self.username and self.password:
auth = (self.username, self.password)
else:
auth = None
try:
r = self.retryrequests.get(url=url, headers=headers, auth=auth, timeout=REQUEST_TIMEOUT_SECONDS,
verify=False)
return r.json()
except Exception as e:
raise Exception('TSANA service api "{}" failed, {}'.format(path, str(e)))
# To get the meta of a specific metric from TSANA
# Parameters:
# apiKey: api key for specific user
# metric_id: a UUID string
# Return:
# meta: the meta of the specified metric, or None if there is something wrong.
def get_metric_meta(self, api_key, metric_id):
return self.get(api_key, '/metrics/' + metric_id + '/meta')
def get_dimesion_values(self, api_key, metric_id, dimension_name):
dims = self.get(api_key, '/metrics/' + metric_id + '/dimensions')
if 'dimensions' in dims and dimension_name in dims['dimensions']:
return dims['dimensions'][dimension_name]
else:
return None
# Query time series from TSANA
# Parameters:
# apiKey: api key for specific user
# series_sets: Array of series set
# start_time: inclusive, the first timestamp to be query
# end_time: exclusive
# offset: a number will be added to each timestamp of each time-series. The unit is defined by granularity
# granularityName: if Offset > 0, the granularityName is Monthly / Weekly / Daily / Hourly / Minutely / Secondly / Custom
# granularityAmount: if granularityName is Custom, granularityAmount is the seconds of the exact granularity
# Return:
# A array of Series object
def get_timeseries(self, api_key, series_sets, start_time, end_time, offset=0, granularityName=None, granularityAmount=0,
top=1):
if offset != 0 and granularityName is None:
offset = 0
end_str = dt_to_str(end_time)
start_str = dt_to_str(start_time)
dedup = {}
series = []
# Query each series's tag
for data in series_sets:
dim = {}
if 'dimensionFilter' not in data:
data['dimensionFilter'] = data['filters']
for dimkey in data['dimensionFilter']:
dim[dimkey] = [data['dimensionFilter'][dimkey]]
para = dict(metricId=data['metricId'], dimensions=dim, count=top, startTime=start_str)
ret = self.post(api_key, '/metrics/' + data['metricId'] + '/rank-series', data=para)
for s in ret['value']:
if s['seriesId'] not in dedup:
s['seriesSetId'] = data['seriesSetId']
s['startTime'] = start_str
s['endTime'] = end_str
s['dimension'] = s['dimensions']
del s['dimensions']
series.append(s)
dedup[s['seriesId']] = True
# Query the data
multi_series_data = None
if len(series) > 0:
ret = self.post(api_key, '/metrics/series/data', data=dict(value=series))
if granularityName is not None:
multi_series_data = [
Series(factor['id']['metricId'], series[idx]['seriesSetId'], factor['id']['dimension'],
[dict(timestamp=get_time_offset(str_to_dt(y[0]), (granularityName, granularityAmount),
offset)
, value=y[1])
for y in factor['values']])
for idx, factor in enumerate(ret['value'])
]
else:
multi_series_data = [
Series(factor['id']['metricId'], series[idx]['seriesSetId'], factor['id']['dimension'],
value=[dict(timestamp=y[0]
, value=y[1])
for y in factor['values']])
for idx, factor in enumerate(ret['value'])
]
else:
log.info("Series is empty")
return multi_series_data
# Save a training result back to TSANA
# Parameters:
# parameters: a dict object which should includes
# apiKey: api key for specific user
# groupId: groupId in TSANA, which is copied from inference request, or from the entity
# instance: instance object, which is copied from the inference request, or from the entity
# model_id: model id
# model_state: model state(TRAINING,READY,FAILED,DELETED)
# message: detail message
# Return:
# result: STATE_SUCCESS / STATE_FAIL
# message: description for the result
def save_training_result(self, parameters, model_id, model_state:str, message:str):
try:
body = {
'modelId': model_id,
'state': model_state,
'message': message
}
self.put(parameters['apiKey'], '/timeSeriesGroups/' + parameters['groupId'] + '/appInstances/' + parameters['instance']['instanceId'] + '/modelKey', body)
return STATUS_SUCCESS, ''
except Exception as e:
traceback.print_exc(file=sys.stdout)
return STATUS_FAIL, str(e)
# Save a inference result back to TSANA
# Parameters:
# parameters: a dict object which should includes
# apiKey: api key for specific user
# groupId: groupId in TSANA, which is copied from inference request, or from the entity
# instance: instance object, which is copied from the inference request, or from the entity
# result: an array of inference result.
# Return:
# result: STATE_SUCCESS / STATE_FAIL
# messagee: description for the result
def save_inference_result(self, parameters, result):
try:
if len(result) <= 0:
return STATUS_SUCCESS, ''
body = {
'groupId': parameters['groupId'],
'instanceId': parameters['instance']['instanceId'],
'results': []
}
for item in result:
item['timestamp'] = dt_to_str(str_to_dt(item['timestamp']))
body['results'].append({
'params': parameters['instance']['params'],
'timestamp': item['timestamp'],
'result': item,
'status': InferenceState.Ready.name
})
self.post(parameters['apiKey'], '/timeSeriesGroups/' + parameters['groupId'] + '/appInstances/' + parameters['instance']['instanceId'] + '/saveResult', body)
return STATUS_SUCCESS, ''
except Exception as e:
traceback.print_exc(file=sys.stdout)
return STATUS_FAIL, str(e)
# Save a inference result back to TSANA
# Parameters:
# parameters: a dict object which should includes
# apiKey: api key for specific user
# groupId: groupId in TSANA, which is copied from inference request, or from the entity
# instance: instance object, which is copied from the inference request, or from the entity
# metric_id: a UUID string
# dimensions: a dict includes dimension name and value
# timestamps: an array of timestamps
# values: an array of inference result values
# Return:
# result: STATE_SUCCESS / STATE_FAIL
# messagee: description for the result
def save_data_points(self, parameters, metricId, dimensions, timestamps, values):
try:
if len(values) <= 0:
return STATUS_SUCCESS, ''
body = {
"metricId": metricId,
"dimensions": dimensions,
"timestamps": timestamps,
"values": values
}
print(json.dumps(body))
self.post(parameters['apiKey'], '/pushData', body)
return STATUS_SUCCESS, ''
except Exception as e:
traceback.print_exc(file=sys.stdout)
return STATUS_FAIL, str(e)
def get_inference_result(self, parameters, start_time, end_time):
try:
ret = self.get(parameters['apiKey'], '/timeSeriesGroups/'
+ parameters['groupId']
+ '/appInstances/'
+ parameters['instance']['instanceId']
+ '/history?startTime='
+ dt_to_str(start_time)
+ '&endTime='
+ dt_to_str(end_time))
return STATUS_SUCCESS, '', ret
except Exception as e:
traceback.print_exc(file=sys.stdout)
return STATUS_FAIL, str(e), None