def get_db():
if not hasattr(g, 'db'):
g.db = parser.connect_db()
return g.db
# regarding what were the most popular days, weeks, weekdays, hours, for an
# incident to occurr given a certain category (or no category). Since this
# query is nontrivial for a database to perform, and since it is unlikely for
# the statistics to change significantly given any time interval, this query
# was moved from the main API to its own separate process. This allows for the
# API to simply send a JSON file to the client instead of perfoming 10 seconds
# worth of calculations.
#
# In case there is doubt about my database optimization skills, one is free to
# waste a few hours on this subject. For those that value their time, the
# reason why this query will always be nontrivial is that because we are
# essentially doing 44 Group-bys, which involve sorting and filtering based on
# the type of incident -- a text based column
if __name__ == '__main__':
con = parser.connect_db()
top_categories = list(query.for_category_statistics(con))
top_categories = [category for category, count in top_categories[:10]]
top_categories.append(None)
print json.dumps(
{
'result': [{
'category': category,
'weekday': query.for_weekday_statistics(con, category),
'hour': query.for_hour_statistics(con, category),
'day': query.for_day_statistics(con, category),
'week': query.for_week_statistics(con, category)
} for category in top_categories]
},
separators=(',', ':'))
def get_db():
if not hasattr(g, 'db'):
g.db = parser.connect_db()
return g.db
# When invoked, this file will query the database for a bunch of information
# regarding what were the most popular days, weeks, weekdays, hours, for an
# incident to occurr given a certain category (or no category). Since this
# query is nontrivial for a database to perform, and since it is unlikely for
# the statistics to change significantly given any time interval, this query
# was moved from the main API to its own separate process. This allows for the
# API to simply send a JSON file to the client instead of perfoming 10 seconds
# worth of calculations.
#
# In case there is doubt about my database optimization skills, one is free to
# waste a few hours on this subject. For those that value their time, the
# reason why this query will always be nontrivial is that because we are
# essentially doing 44 Group-bys, which involve sorting and filtering based on
# the type of incident -- a text based column
if __name__ == '__main__':
con = parser.connect_db()
top_categories = list(query.for_category_statistics(con))
top_categories = [category for category, count in top_categories[:10]]
top_categories.append(None)
print json.dumps({'result': [{
'category': category,
'weekday': query.for_weekday_statistics(con, category),
'hour': query.for_hour_statistics(con, category),
'day': query.for_day_statistics(con, category),
'week': query.for_week_statistics(con, category)
} for category in top_categories ]}, separators=(',', ':'))
