def reqTimelyData(request, wi_id):
'''
获取及时的新数据
'''
hk = request.session.get('hk')
st = SqlExecutorMgr.stRestore(hk)
widget_model = WidgetModel.objects.get(pk = wi_id)
if not widget_model.m_if_update:
return MyHttpJsonResponse({'succ': False})
req_data = widget_model.restoreReqDataDict()
factors_lists_dict = classifyFactors(req_data)
# 看看用户选择的轴上面的项，有没有涉及到DateTime类型
# 如果没有，那么就是整体刷新; 如果有，那么来逐步更新
time_factor = filterDateTimeColumn(factors_lists_dict['msn'], st)
if time_factor:
update_way = 'add'
origin_sql_obj = transReqDataToSqlObj(req_data, st)
time_column_obj = st.sql_relation.getColumnObj(time_factor)
# 分查询条件里面，有没有聚合运算
#if hasAggreate():
if False:
sql_obj = select([]).over(f()).order_by()
origin_sql_obj.over(f()).order_by(time_column_obj)
else:
sql_obj = origin_sql_obj.order_by(time_column_obj)
factor_dict = dict(zip(EXPRESS_FACTOR_KEYS_TUPLE, ( \
time_factor.getProperty(Protocol.Table)
, time_factor.getProperty(Protocol.Attr)
, time_factor.getProperty(Protocol.Kind)
, 'max'
)))
latest_time_obj = st.makeSelectSql([FactorFactory.make(**factor_dict)])
sql_obj = origin_sql_obj.where(time_column_obj == latest_time_obj)
data_from_db = st.execute(sql_obj).fetchall()
str_data = strfDataAfterFetchDb(data_from_db)
data = EChartManager().get_echart(widget_model.m_graph) \
.makeAddData( \
str_data \
, len(factors_lists_dict['msu']) \
, len(factors_lists_dict['msn']) \
, len(factors_lists_dict['group']) \
)
else:
update_way = 'all'
data = genWidgetImageData(req_data, hk)
return MyHttpJsonResponse({'succ': True, 'way': update_way, 'data': data})
def extractFactor(req_data):
'''
解析获得各维度上的Factor对象
'''
[col_kind_attr_list, row_kind_attr_list] = \
map( lambda i: req_data.get(i, []), \
(u'x', u'y') \
)
# 获取轴上属性Factor对象
axis_factor_list = []
for idx, col_element in enumerate(row_kind_attr_list + col_kind_attr_list):
element_dict = {key:col_element[key] for key in EXPRESS_FACTOR_KEYS_TUPLE}
factor = FactorFactory.make(element_dict)
if idx < len(row_kind_attr_list):
factor.setBelongToAxis('row')
else:
factor.setBelongToAxis('col')
axis_factor_list.append(factor)
# 获取选择器上属性Factor对象
group_factor_list = []
color_dict = req_data.get(Protocol.Color)
if color_dict:
color_attr_table = color_dict.get(u'table', u'')
color_attr_column = color_dict.get('column', u'')
color_dict = dict(zip(EXPRESS_FACTOR_KEYS_TUPLE, \
(color_attr_table, color_attr_column, -1, u'')))
factor = FactorFactory.make(color_dict)
factor.setBelongToAxis('group')
group_factor_list.append(factor)
return axis_factor_list, group_factor_list
def handleDistinct(request):
req = json.loads(request.GET.get(Protocol.FilterColumn))
info = map(lambda x: (x, req.get(x)), EXPRESS_FACTOR_KEYS_TUPLE)
factor = FactorFactory.make(dict(info))
try:
hk = request.session.get('hk')
st = SqlExecutorMgr.stRestore(hk)
sql_obj = st.getSwither().makeSelectSql( \
[factor], [], [], distinct = True \
)
data = st.execute(sql_obj).fetchall()
result = zip(*data)[0]
except ExternalDbModel.DoesNotExist, e:
logExcInfo()
resp = {'succ': False, 'msg': 'yyyyyyyyy'}
def extract(self, req):
'''
解析获得各维度上的Factor对象
'''
[col_kind_attr_list, row_kind_attr_list] = \
map( lambda i: req.get(i, []),
(Protocol.Xaxis, Protocol.Yaxis)
)
# 获取轴上属性Factor对象
row_factors, col_factors = [], []
for idx, col_element in enumerate(row_kind_attr_list + col_kind_attr_list):
element_dict = {key:col_element[key] for key in EXPRESS_FACTOR_KEYS_TUPLE}
factor = FactorFactory.make(element_dict)
if idx < len(row_kind_attr_list):
factor.location = Protocol.Row
row_factors.append(factor)
else:
factor.location = Protocol.Column
col_factors.append(factor)
# 过滤条件部分
filter_factors = []
filters = req.get('filters', [])
for item in filters:
[left, op, right] = map(lambda x: item.get(x), ( \
'field', 'operator', 'value' \
))
lfactor = FactorFactory.make(left)
rfactor = FactorFactory.make(right)
clause = Clause(lfactor, rfactor, op, None)
filter_factors.append(clause)
# 获取group之类的属性，按照颜色，大小的顺序
group_factors = []
color_dict = req.get(Protocol.Mapping).get(Protocol.Color) \
if Protocol.Mapping in req else None
if color_dict:
color_attr_table = color_dict.get(Protocol.Table, '')
color_attr_column = color_dict.get(Protocol.Attr, '')
color_dict = dict(zip(EXPRESS_FACTOR_KEYS_TUPLE, \
(color_attr_table, color_attr_column, -1, '')))
factor = FactorFactory.make(color_dict)
factor.location = Protocol.Color
group_factors.append(factor)
# 获取排序Factor对象
order_factors = []
orders = req.get(Protocol.Order, [])
for item in orders:
order_attr_table = item.get(Protocol.Table, '')
order_attr_column = item.get(Protocol.Attr, '')
item = dict(zip(EXPRESS_FACTOR_KEYS_TUPLE, \
(order_attr_table, order_attr_column, -1, '')))
factor = FactorFactory.make(item)
factor.location = Protocol.Order
order_factors.append(factor)
self.rows = row_factors
self.cols = col_factors
self.filters = filter_factors
self.groups = group_factors
self.orders = order_factors
self.extracted = True
return