def query_obj(self):
ds = self.datasource
args = self.form_data
groupby = args.getlist("groupby") or []
metrics = args.getlist("metrics") or ["count"]
granularity = args.get("granularity", "1 day")
if granularity != "all":
granularity = utils.parse_human_timedelta(granularity).total_seconds() * 1000
limit = int(args.get("limit", config.ROW_LIMIT))
row_limit = int(args.get("row_limit", config.ROW_LIMIT))
since = args.get("since", "1 year ago")
from_dttm = utils.parse_human_datetime(since)
if from_dttm > datetime.now():
from_dttm = datetime.now() - (from_dttm - datetime.now())
until = args.get("until", "now")
to_dttm = utils.parse_human_datetime(until)
if from_dttm >= to_dttm:
flash("The date range doesn't seem right.", "danger")
from_dttm = to_dttm # Making them identicial to not raise
d = {
"granularity": granularity,
"from_dttm": from_dttm,
"to_dttm": to_dttm,
"groupby": groupby,
"metrics": metrics,
"row_limit": row_limit,
"filter": self.query_filters(),
"timeseries_limit": limit,
}
return d
def query_obj(self):
ds = self.datasource
args = self.form_data
groupby = args.getlist("groupby") or []
metrics = args.getlist("metrics") or ['count']
granularity = args.get("granularity", "1 day")
if granularity != "all":
granularity = utils.parse_human_timedelta(
granularity).total_seconds() * 1000
limit = int(args.get("limit", config.ROW_LIMIT))
row_limit = int(args.get("row_limit", config.ROW_LIMIT))
since = args.get("since", "1 year ago")
from_dttm = utils.parse_human_datetime(since)
if from_dttm > datetime.now():
from_dttm = datetime.now() - (from_dttm - datetime.now())
until = args.get("until", "now")
to_dttm = utils.parse_human_datetime(until)
if from_dttm >= to_dttm:
flash("The date range doesn't seem right.", "danger")
from_dttm = to_dttm # Making them identicial to not raise
d = {
'granularity': granularity,
'from_dttm': from_dttm,
'to_dttm': to_dttm,
'groupby': groupby,
'metrics': metrics,
'row_limit': row_limit,
'filter': self.query_filters(),
'timeseries_limit': limit,
}
return d
def query_obj(self):
ds = self.datasource
args = self.form_data
groupby = args.getlist("groupby") or []
granularity = args.get("granularity", "1 day")
granularity = utils.parse_human_timedelta(
granularity).total_seconds() * 1000
aggregations = {
m.metric_name: m.json_obj
for m in ds.metrics if m.metric_name in self.metrics
}
limit = int(args.get("limit", config.ROW_LIMIT)) or config.ROW_LIMIT
since = args.get("since", "1 year ago")
from_dttm = utils.parse_human_datetime(since)
if from_dttm > datetime.now():
from_dttm = datetime.now() - (from_dttm - datetime.now())
from_dttm = from_dttm.isoformat()
until = args.get("until", "now")
to_dttm = utils.parse_human_datetime(until).isoformat()
if from_dttm >= to_dttm:
flash("The date range doesn't seem right.", "danger")
from_dttm = to_dttm # Making them identicial to not raise
d = {
'datasource': ds.datasource_name,
'granularity': {
"type": "duration",
"duration": granularity
},
'intervals': from_dttm + '/' + to_dttm,
'dimensions': groupby,
'aggregations': aggregations,
'limit_spec': {
"type":
"default",
"limit":
limit,
"columns": [{
"dimension": self.metrics[0],
"direction": "descending",
}],
},
}
filters = self.query_filters()
if filters:
d['filter'] = filters
return d
def query_obj(self):
ds = self.datasource
args = self.form_data
groupby = args.getlist("groupby") or []
granularity = args.get("granularity", "1 day")
granularity = utils.parse_human_timedelta(granularity).total_seconds() * 1000
aggregations = {
m.metric_name: m.json_obj
for m in ds.metrics if m.metric_name in self.metrics
}
limit = int(
args.get("limit", config.ROW_LIMIT)) or config.ROW_LIMIT
since = args.get("since", "1 year ago")
from_dttm = utils.parse_human_datetime(since)
if from_dttm > datetime.now():
from_dttm = datetime.now() - (from_dttm-datetime.now())
from_dttm = from_dttm.isoformat()
until = args.get("until", "now")
to_dttm = utils.parse_human_datetime(until).isoformat()
if from_dttm >= to_dttm:
flash("The date range doesn't seem right.", "danger")
from_dttm = to_dttm # Making them identicial to not raise
d = {
'datasource': ds.datasource_name,
'granularity': {"type": "duration", "duration": granularity},
'intervals': from_dttm + '/' + to_dttm,
'dimensions': groupby,
'aggregations': aggregations,
'limit_spec': {
"type": "default",
"limit": limit,
"columns": [{
"dimension": self.metrics[0],
"direction": "descending",
}],
},
}
filters = self.query_filters()
if filters:
d['filter'] = filters
return d
