def get_data(self):
df = self.get_df()
chart_data = self.to_series(df)
time_compare = self.form_data.get('time_compare')
if time_compare:
query_object = self.query_obj()
delta = utils.parse_human_timedelta(time_compare)
query_object['inner_from_dttm'] = query_object['from_dttm']
query_object['inner_to_dttm'] = query_object['to_dttm']
query_object['from_dttm'] -= delta
query_object['to_dttm'] -= delta
df2 = self.get_df(query_object)
df2.index += delta
chart_data += self.to_series(
df2, classed='dashed', title_suffix="---")
chart_data = sorted(chart_data, key=lambda x: x['key'])
return chart_data
def get_json_data(self):
df = self.get_df()
chart_data = self.to_series(df)
time_compare = self.form_data.get('time_compare')
if time_compare:
query_object = self.query_obj()
delta = utils.parse_human_timedelta(time_compare)
query_object['inner_from_dttm'] = query_object['from_dttm']
query_object['inner_to_dttm'] = query_object['to_dttm']
query_object['from_dttm'] -= delta
query_object['to_dttm'] -= delta
df2 = self.get_df(query_object)
df2.index += delta
chart_data += self.to_series(df2,
classed='dashed',
title_suffix="---")
chart_data = sorted(chart_data, key=lambda x: x['key'])
return dumps(chart_data)
def query(
self, groupby, metrics,
granularity,
from_dttm, to_dttm,
filter=None, # noqa
is_timeseries=True,
timeseries_limit=None,
row_limit=None,
inner_from_dttm=None, inner_to_dttm=None,
extras=None, # noqa
select=None,): # noqa
"""Runs a query against Druid and returns a dataframe.
This query interface is common to SqlAlchemy and Druid
"""
# TODO refactor into using a TBD Query object
qry_start_dttm = datetime.now()
inner_from_dttm = inner_from_dttm or from_dttm
inner_to_dttm = inner_to_dttm or to_dttm
# add tzinfo to native datetime with config
from_dttm = from_dttm.replace(tzinfo=config.get("DRUID_TZ"))
to_dttm = to_dttm.replace(tzinfo=config.get("DRUID_TZ"))
query_str = ""
aggregations = {
m.metric_name: m.json_obj
for m in self.metrics if m.metric_name in metrics
}
granularity = granularity or "all"
if granularity != "all":
granularity = utils.parse_human_timedelta(
granularity).total_seconds() * 1000
if not isinstance(granularity, string_types):
granularity = {"type": "duration", "duration": granularity}
qry = dict(
datasource=self.datasource_name,
dimensions=groupby,
aggregations=aggregations,
granularity=granularity,
intervals=from_dttm.isoformat() + '/' + to_dttm.isoformat(),
)
filters = None
for col, op, eq in filter:
cond = None
if op == '==':
cond = Dimension(col) == eq
elif op == '!=':
cond = ~(Dimension(col) == eq)
elif op in ('in', 'not in'):
fields = []
splitted = eq.split(',')
if len(splitted) > 1:
for s in eq.split(','):
s = s.strip()
fields.append(Filter.build_filter(Dimension(col) == s))
cond = Filter(type="or", fields=fields)
else:
cond = Dimension(col) == eq
if op == 'not in':
cond = ~cond
if filters:
filters = Filter(type="and", fields=[
Filter.build_filter(cond),
Filter.build_filter(filters)
])
else:
filters = cond
if filters:
qry['filter'] = filters
client = self.cluster.get_pydruid_client()
orig_filters = filters
if timeseries_limit and is_timeseries:
# Limit on the number of timeseries, doing a two-phases query
pre_qry = deepcopy(qry)
pre_qry['granularity'] = "all"
pre_qry['limit_spec'] = {
"type": "default",
"limit": timeseries_limit,
'intervals': (
inner_from_dttm.isoformat() + '/' +
inner_to_dttm.isoformat()),
"columns": [{
"dimension": metrics[0] if metrics else self.metrics[0],
"direction": "descending",
}],
}
client.groupby(**pre_qry)
query_str += "// Two phase query\n// Phase 1\n"
query_str += json.dumps(client.query_dict, indent=2) + "\n"
query_str += "//\nPhase 2 (built based on phase one's results)\n"
df = client.export_pandas()
if df is not None and not df.empty:
dims = qry['dimensions']
filters = []
for _, row in df.iterrows():
fields = []
for dim in dims:
f = Filter.build_filter(Dimension(dim) == row[dim])
fields.append(f)
if len(fields) > 1:
filt = Filter(type="and", fields=fields)
filters.append(Filter.build_filter(filt))
elif fields:
filters.append(fields[0])
if filters:
ff = Filter(type="or", fields=filters)
if not orig_filters:
qry['filter'] = ff
else:
qry['filter'] = Filter(type="and", fields=[
Filter.build_filter(ff),
Filter.build_filter(orig_filters)])
qry['limit_spec'] = None
if row_limit:
qry['limit_spec'] = {
"type": "default",
"limit": row_limit,
"columns": [{
"dimension": metrics[0] if metrics else self.metrics[0],
"direction": "descending",
}],
}
client.groupby(**qry)
query_str += json.dumps(client.query_dict, indent=2)
df = client.export_pandas()
if df is None or df.size == 0:
raise Exception("No data was returned.")
if (
not is_timeseries and
granularity == "all" and
'timestamp' in df.columns):
del df['timestamp']
# Reordering columns
cols = []
if 'timestamp' in df.columns:
cols += ['timestamp']
cols += [col for col in groupby if col in df.columns]
cols += [col for col in metrics if col in df.columns]
cols += [col for col in df.columns if col not in cols]
df = df[cols]
return QueryResult(
df=df,
query=query_str,
duration=datetime.now() - qry_start_dttm)
def query(
self,
groupby,
metrics,
granularity,
from_dttm,
to_dttm,
filter=None, # noqa
is_timeseries=True,
timeseries_limit=None,
row_limit=None,
inner_from_dttm=None,
inner_to_dttm=None,
extras=None, # noqa
select=None):
"""Runs a query against Druid and returns a dataframe.
This query interface is common to SqlAlchemy and Druid
"""
# TODO refactor into using a TBD Query object
qry_start_dttm = datetime.now()
inner_from_dttm = inner_from_dttm or from_dttm
inner_to_dttm = inner_to_dttm or to_dttm
# add tzinfo to native datetime with config
from_dttm = from_dttm.replace(tzinfo=config.get("DRUID_TZ"))
to_dttm = to_dttm.replace(tzinfo=config.get("DRUID_TZ"))
query_str = ""
aggregations = {
m.metric_name: m.json_obj
for m in self.metrics if m.metric_name in metrics
}
granularity = granularity or "all"
if granularity != "all":
granularity = utils.parse_human_timedelta(
granularity).total_seconds() * 1000
if not isinstance(granularity, string_types):
granularity = {"type": "duration", "duration": granularity}
qry = dict(
datasource=self.datasource_name,
dimensions=groupby,
aggregations=aggregations,
granularity=granularity,
intervals=from_dttm.isoformat() + '/' + to_dttm.isoformat(),
)
filters = None
for col, op, eq in filter:
cond = None
if op == '==':
cond = Dimension(col) == eq
elif op == '!=':
cond = ~(Dimension(col) == eq)
elif op in ('in', 'not in'):
fields = []
splitted = eq.split(',')
if len(splitted) > 1:
for s in eq.split(','):
s = s.strip()
fields.append(Filter.build_filter(Dimension(col) == s))
cond = Filter(type="or", fields=fields)
else:
cond = Dimension(col) == eq
if op == 'not in':
cond = ~cond
if filters:
filters = Filter(type="and",
fields=[
Filter.build_filter(cond),
Filter.build_filter(filters)
])
else:
filters = cond
if filters:
qry['filter'] = filters
client = self.cluster.get_pydruid_client()
orig_filters = filters
if timeseries_limit and is_timeseries:
# Limit on the number of timeseries, doing a two-phases query
pre_qry = deepcopy(qry)
pre_qry['granularity'] = "all"
pre_qry['limit_spec'] = {
"type":
"default",
"limit":
timeseries_limit,
'intervals': (inner_from_dttm.isoformat() + '/' +
inner_to_dttm.isoformat()),
"columns": [{
"dimension":
metrics[0] if metrics else self.metrics[0],
"direction":
"descending",
}],
}
client.groupby(**pre_qry)
query_str += "// Two phase query\n// Phase 1\n"
query_str += json.dumps(client.query_dict, indent=2) + "\n"
query_str += "//\nPhase 2 (built based on phase one's results)\n"
df = client.export_pandas()
if df is not None and not df.empty:
dims = qry['dimensions']
filters = []
for _, row in df.iterrows():
fields = []
for dim in dims:
f = Filter.build_filter(Dimension(dim) == row[dim])
fields.append(f)
if len(fields) > 1:
filt = Filter(type="and", fields=fields)
filters.append(Filter.build_filter(filt))
elif fields:
filters.append(fields[0])
if filters:
ff = Filter(type="or", fields=filters)
if not orig_filters:
qry['filter'] = ff
else:
qry['filter'] = Filter(
type="and",
fields=[
Filter.build_filter(ff),
Filter.build_filter(orig_filters)
])
qry['limit_spec'] = None
if row_limit:
qry['limit_spec'] = {
"type":
"default",
"limit":
row_limit,
"columns": [{
"dimension":
metrics[0] if metrics else self.metrics[0],
"direction":
"descending",
}],
}
client.groupby(**qry)
query_str += json.dumps(client.query_dict, indent=2)
df = client.export_pandas()
if df is None or df.size == 0:
raise Exception("No data was returned.")
if (not is_timeseries and granularity == "all"
and 'timestamp' in df.columns):
del df['timestamp']
# Reordering columns
cols = []
if 'timestamp' in df.columns:
cols += ['timestamp']
cols += [col for col in groupby if col in df.columns]
cols += [col for col in metrics if col in df.columns]
cols += [col for col in df.columns if col not in cols]
df = df[cols]
return QueryResult(df=df,
query=query_str,
duration=datetime.now() - qry_start_dttm)
