def format_response(request):
"""
Populates data for response to metrics requests.
Parameters
~~~~~~~~~~
request : RequestMeta
RequestMeta object that stores request data.
"""
args = ParameterMapping.map(request)
metric_class = get_metric_type(request.metric)
metric_obj = metric_class(**args)
# Prepare metrics output for json response
response = OrderedDict()
response['type'] = get_request_type(request)
response['header'] = metric_obj.header()
# Get metric object params
for key in metric_obj.__dict__:
if not search(r'^_.*', key) and str(key) not in response:
response[str(key)] = metric_obj.__dict__[key]
response['cohort'] = str(request.cohort_expr)
response['cohort_last_generated'] = str(request.cohort_gen_timestamp)
response['time_of_response'] = datetime.now().strftime(DATETIME_STR_FORMAT)
response['aggregator'] = str(request.aggregator)
response['metric'] = str(request.metric)
response['interval_hours'] = request.interval
if request.group:
response['group'] = REVERSE_GROUP_MAP[int(request.group)]
else:
# @TODO get access to the metric default for this attribute
response['group'] = 'default'
response['datetime_start'] = date_parse(metric_obj.datetime_start).\
strftime(DATETIME_STR_FORMAT)
response['datetime_end'] = date_parse(metric_obj.datetime_end).\
strftime(DATETIME_STR_FORMAT)
response['data'] = OrderedDict()
return response, metric_class, metric_obj
def format_response(request):
"""
Populates data for response to metrics requests.
Parameters
~~~~~~~~~~
request : RequestMeta
RequestMeta object that stores request data.
"""
args = ParameterMapping.map(request)
metric_class = get_metric_type(request.metric)
metric_obj = metric_class(**args)
# Prepare metrics output for json response
response = OrderedDict()
response["type"] = get_request_type(request)
response["header"] = metric_obj.header()
# Get metric object params
for key in metric_obj.__dict__:
if not search(r"^_.*", key) and str(key) not in response:
response[str(key)] = metric_obj.__dict__[key]
response["cohort"] = str(request.cohort_expr)
response["cohort_last_generated"] = str(request.cohort_gen_timestamp)
response["time_of_response"] = datetime.now().strftime(DATETIME_STR_FORMAT)
response["aggregator"] = str(request.aggregator)
response["metric"] = str(request.metric)
response["slice_size"] = request.slice
response["group"] = request.group
response["datetime_start"] = date_parse(metric_obj.datetime_start).strftime(DATETIME_STR_FORMAT)
response["datetime_end"] = date_parse(metric_obj.datetime_end).strftime(DATETIME_STR_FORMAT)
response["data"] = OrderedDict()
return response, metric_class, metric_obj
def process_data_request(request_meta, users):
"""
Main entry point of the module, prepares results for a given request.
Coordinates a request based on the following parameters::
metric_handle (string) - determines the type of metric object to
build. Keys metric_dict.
users (list) - list of user IDs.
**kwargs - Keyword arguments may contain a variety of variables.
Most notably, "aggregator" if the request requires aggregation,
"time_series" flag indicating a time series request. The
remaining kwargs specify metric object parameters.
"""
# Set interval length in hours if not present
if not request_meta.slice:
request_meta.slice = DEFAULT_INERVAL_LENGTH
else:
request_meta.slice = float(request_meta.slice)
# Get the aggregator key
agg_key = get_agg_key(request_meta.aggregator, request_meta.metric) if \
request_meta.aggregator else None
args = ParameterMapping.map(request_meta)
# Initialize the results
results, metric_class, metric_obj = format_response(request_meta)
start = metric_obj.datetime_start
end = metric_obj.datetime_end
if results['type'] == request_types.time_series:
# Get aggregator
try:
aggregator_func = get_aggregator_type(agg_key)
except MetricsAPIError as e:
results['data'] = 'Request failed. ' + e.message
return results
# Determine intervals and thread allocation
total_intervals = (date_parse(end) - date_parse(start)).\
total_seconds() / (3600 * request_meta.slice)
time_threads = max(1, int(total_intervals / INTERVALS_PER_THREAD))
time_threads = min(MAX_THREADS, time_threads)
logging.info(__name__ + ' :: Initiating time series for %(metric)s\n'
'\tAGGREGATOR = %(agg)s\n'
'\tFROM: %(start)s,\tTO: %(end)s.' %
{
'metric': metric_class.__name__,
'agg': request_meta.aggregator,
'start': str(start),
'end': str(end),
})
metric_threads = '"k_" : {0}, "kr_" : {1}'.format(USER_THREADS,
REVISION_THREADS)
metric_threads = '{' + metric_threads + '}'
new_kwargs = deepcopy(args)
del new_kwargs['slice']
del new_kwargs['aggregator']
del new_kwargs['datetime_start']
del new_kwargs['datetime_end']
out = tspm.build_time_series(start,
end,
request_meta.slice,
metric_class,
aggregator_func,
users,
kt_=time_threads,
metric_threads=metric_threads,
log=True,
**new_kwargs)
results['header'] = ['timestamp'] + \
getattr(aggregator_func,
um.METRIC_AGG_METHOD_HEAD)
for row in out:
timestamp = date_parse(row[0][:19]).strftime(
DATETIME_STR_FORMAT)
results['data'][timestamp] = row[3:]
elif results['type'] == request_types.aggregator:
# Get aggregator
try:
aggregator_func = get_aggregator_type(agg_key)
except MetricsAPIError as e:
results['data'] = 'Request failed. ' + e.message
return results
logging.info(__name__ + ' :: Initiating aggregator for %(metric)s\n'
'\AGGREGATOR = %(agg)s\n'
'\tFROM: %(start)s,\tTO: %(end)s.' %
{
'metric': metric_class.__name__,
'agg': request_meta.aggregator,
'start': str(start),
'end': str(end),
})
try:
metric_obj.process(users,
k_=USER_THREADS,
kr_=REVISION_THREADS,
log_=True,
**args)
except UserMetricError as e:
logging.error(__name__ + ' :: Metrics call failed: ' + str(e))
results['data'] = str(e)
return results
r = um.aggregator(aggregator_func, metric_obj, metric_obj.header())
results['header'] = to_string(r.header)
results['data'] = r.data[1:]
elif results['type'] == request_types.raw:
logging.info(__name__ + ':: Initiating raw request for %(metric)s\n'
'\tFROM: %(start)s,\tTO: %(end)s.' %
{
'metric': metric_class.__name__,
'start': str(start),
'end': str(end),
})
try:
metric_obj.process(users,
k_=USER_THREADS,
kr_=REVISION_THREADS,
log_=True,
**args)
except UserMetricError as e:
logging.error(__name__ + ' :: Metrics call failed: ' + str(e))
results['data'] = str(e)
return results
for m in metric_obj.__iter__():
results['data'][m[0]] = m[1:]
return results
