def search(self, query=None, offset=None, limit=None, order_by=None):
if query:
q = {'query': compile_condition('elasticsearch', query)()}
else:
q = {'query': {'match_all': {}}}
if limit is not None:
q['from'] = 0
q['size'] = limit
self.create_index()
params = {}
if offset is not None:
params['from_'] = offset
if limit:
params['size'] = limit
if order_by:
params['sort'] = [':'.join(order_by)]
res = self.client.search(index=self.index_name,
doc_type=self.doc_type,
body=q,
**params)
data = [
self.model(self.request, self, o['_source'])
for o in res['hits']['hits']
]
return list(data)
def search(self, query=None, offset=None, limit=None, order_by=None):
if query:
f = compile_condition('sqlalchemy', query)
filterquery = f(self.orm_model)
q = self.session.query(self.orm_model).filter(
sa.and_(self.orm_model.deleted.is_(None), filterquery))
else:
q = self.session.query(self.orm_model).filter(
self.orm_model.deleted.is_(None))
if order_by is not None:
col = order_by[0]
d = order_by[1]
if d not in ['asc', 'desc']:
raise KeyError(d)
colattr = getattr(self.orm_model, col)
if d == 'desc':
q = q.order_by(colattr.desc())
else:
q = q.order_by(colattr)
if offset is not None:
q = q.offset(offset)
if limit is not None:
q = q.limit(limit)
return [self.model(self.request, self, o) for o in q.all()]
def search(self, query=None, offset=None, limit=None, order_by=None):
res = []
if query:
f = compile_condition('native', query)
for o in DATA[self.typekey].values():
if f(o.data):
res.append(o)
else:
res = list(DATA[self.typekey].values())
for r in res:
r.request = self.request
if offset is not None:
res = res[offset:]
if limit is not None:
res = res[:limit]
if order_by is not None:
col, d = order_by
res = list(sorted(res, key=lambda x: x.data[col]))
if d == 'desc':
res = list(reversed(res))
return res
def aggregate(self, query=None, group=None, order_by=None):
group_bys = []
group_bys_map = {}
if group:
fields = []
for k, v in group.items():
if isinstance(v, str):
c = getattr(self.orm_model, v)
fields.append(c)
group_bys.append(c)
elif isinstance(v, dict):
ff = v['function']
f = v['field']
c = getattr(self.orm_model, f)
if ff == 'count':
op = func.count(c).label(k)
fields.append(op)
group_bys_map[k] = op
elif ff == 'sum':
op = func.sum(c).label(k)
fields.append(op)
group_bys_map[k] = op
elif ff == 'avg':
op = func.avg(c).label(k)
fields.append(op)
group_bys_map[k] = op
elif ff == 'min':
op = func.min(c).label(k)
fields.append(op)
group_bys_map[k] = op
elif ff == 'max':
op = func.max(c).label(k)
fields.append(op)
group_bys_map[k] = op
elif ff == 'year':
op = func.date_part('YEAR', c).label(k)
fields.append(op)
group_bys.append(op)
group_bys_map[k] = op
elif ff == 'month':
op = func.date_part('MONTH', c).label(k)
fields.append(op)
group_bys.append(op)
group_bys_map[k] = op
elif ff == 'day':
op = func.date_part('DAY', c).label(k)
fields.append(op)
group_bys.append(op)
group_bys_map[k] = op
elif ff == 'date':
op = func.to_char(c, 'YYYY-MM-DD').label(k)
fields.append(op)
group_bys.append(op)
group_bys_map[k] = op
else:
raise ValueError('Unknown function %s' % ff)
else:
fields = [self.orm_model]
if query:
f = compile_condition('sqlalchemy', query)
filterquery = f(self.orm_model)
filterquery = sa.and_(self.orm_model.deleted.is_(None),
filterquery)
q = self.session.query(*fields).filter(filterquery)
else:
q = self.session.query(*fields).filter(
self.orm_model.deleted.is_(None))
if order_by is not None:
col = order_by[0]
d = order_by[1]
if d not in ['asc', 'desc']:
raise KeyError(d)
if col in group_bys_map:
colattr = group_bys_map[col]
else:
colattr = getattr(self.orm_model, col)
if d == 'desc':
q = q.order_by(colattr.desc())
else:
q = q.order_by(colattr)
if group_bys:
q = q.group_by(*group_bys)
results = []
for o in q.all():
d = o._asdict()
for k, v in d.items():
if isinstance(v, datetime):
d[k] = v.isoformat()
elif isinstance(v, Decimal):
d[k] = float(v)
elif isinstance(v, uuid.UUID):
d[k] = v.hex
results.append(d)
return results
def aggregate(self, query=None, group=None, order_by=None):
if query:
q = {'query': compile_condition('elasticsearch', query)()}
else:
q = {'query': {'match_all': {}}}
q['size'] = 0
self.create_index()
params = {}
if order_by:
params['sort'] = [':'.join(order_by)]
if group:
aggs = Aggregate()
for k, v in group.items():
if isinstance(v, str):
aggs.add_group(k, v)
elif isinstance(v, dict):
ff = v['function']
f = v['field']
if ff == 'count':
aggs.add(k, 'count', f)
elif ff == 'sum':
aggs.add(k, 'sum', f)
elif ff == 'avg':
aggs.add(k, 'avg', f)
elif ff == 'year':
aggs.add_group(k,
f,
type='date_histogram',
opts={
'interval': 'year',
'format': 'yyyy'
})
elif ff == 'month':
aggs.add_group(k,
f,
type='date_histogram',
opts={
'interval': 'month',
'format': 'MM'
})
elif ff == 'day':
aggs.add_group(k,
f,
type='date_histogram',
opts={
'interval': 'day',
'format': 'dd'
})
elif ff == 'interval-1m':
aggs.add_group(k,
f,
type='date_histogram',
opts={
'interval': '1m',
'format': "yyyy-MM-dd'T'HH:mm"
})
elif ff == 'interval-15m':
aggs.add_group(k,
f,
type='date_histogram',
opts={
'interval': '15m',
'format': "yyyy-MM-dd'T'HH:mm"
})
elif ff == 'interval-30m':
aggs.add_group(k,
f,
type='date_histogram',
opts={
'interval': '30m',
'format': "yyyy-MM-dd'T'HH:mm"
})
elif ff == 'interval-1h':
aggs.add_group(k,
f,
type='date_histogram',
opts={
'interval': '1h',
'format': "yyyy-MM-dd'T'HH:mm"
})
else:
raise ValueError('Unknown function %s' % ff)
aggs.finalize()
q['aggs'] = aggs.json()
res = self.client.search(index=self.index_name,
doc_type=self.doc_type,
body=q,
**params)
data = aggs.parse(res['aggregations'])
return list(data)