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Fewer queries on stats/scales + Fix

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Scales:
- Fix #chunks when used with std_chunk=True (there was one too many at
  the beginning)
- Scale.end gives the end of the last chunk (instead of its start)
  So scale.begin -> scale.end gives the full range of the scale.

`kfet_day` now returns an aware datetime.

ScaleMixin:
- new method `get_by_chunks` which use only one query and ranks
  elements according to the scale. Elements are returned by a generator
  for each scale chunk (and all chunks are returned as a generator too).

ArticlesStatSales and AccountStatOperations use this new method to
avoid issuing #scale_chunks queries.

ArticleStat:
- fixed on Chrome
This commit is contained in:
Aurélien Delobelle 2017-04-12 18:03:31 +02:00
parent e97e0081d7
commit 3f4a1adbb9
4 changed files with 140 additions and 32 deletions
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4
kfet/static/kfet/js/statistic.js
View file

@ -61,7 +61,7 @@
var chart = charts[i];
// format the data
var chart_data = is_time_chart ? handleTimeChart(chart.values) : dictToArray(chart.values, 1);
var chart_data = is_time_chart ? handleTimeChart(chart.values) : dictToArray(chart.values, 0);
chart_datasets.push(
{
@ -132,7 +132,7 @@
type: 'line',
options: chart_options,
data: {
labels: (data.labels || []).slice(1),
labels: data.labels || [],
datasets: chart_datasets,
}
};

103
kfet/statistic.py
View file

@ -4,6 +4,7 @@ from datetime import date, datetime, time, timedelta
from dateutil.relativedelta import relativedelta
from dateutil.parser import parse as dateutil_parse
import pytz
from django.utils import timezone
from django.db.models import Sum
@ -13,7 +14,8 @@ KFET_WAKES_UP_AT = time(7, 0)
def kfet_day(year, month, day, start_at=KFET_WAKES_UP_AT):
"""datetime wrapper with time offset."""
return datetime.combine(date(year, month, day), start_at)
naive = datetime.combine(date(year, month, day), start_at)
return pytz.timezone('Europe/Paris').localize(naive, is_dst=None)
def to_kfet_day(dt, start_at=KFET_WAKES_UP_AT):
@ -32,16 +34,21 @@ class Scale(object):
self.std_chunk = std_chunk
if last:
end = timezone.now()
if std_chunk:
if begin is not None:
begin = self.get_chunk_start(begin)
if end is not None:
end = self.do_step(self.get_chunk_start(end))
if begin is not None and n_steps != 0:
self.begin = self.get_from(begin)
self.begin = begin
self.end = self.do_step(self.begin, n_steps=n_steps)
elif end is not None and n_steps != 0:
self.end = self.get_from(end)
self.end = end
self.begin = self.do_step(self.end, n_steps=-n_steps)
elif begin is not None and end is not None:
self.begin = self.get_from(begin)
self.end = self.get_from(end)
self.begin = begin
self.end = end
else:
raise Exception('Two of these args must be specified: '
'n_steps, begin, end; '
@ -71,7 +78,7 @@ class Scale(object):
def get_datetimes(self):
datetimes = [self.begin]
tmp = self.begin
while tmp <= self.end:
while tmp < self.end:
tmp = self.do_step(tmp)
datetimes.append(tmp)
return datetimes
@ -232,3 +239,87 @@ class ScaleMixin(object):
qs.filter(**{begin_f: begin, end_f: end})
for begin, end in scale
]
def get_by_chunks(self, qs, scale, field_callback=None, field_db='at'):
"""Objects of queryset ranked according to a given scale.
Returns a generator whose each item, corresponding to a scale chunk,
is a generator of objects from qs for this chunk.
Args:
qs: Queryset of source objects, must be ordered *first* on the
same field returned by `field_callback`.
scale: Used to rank objects.
field_callback: Callable which gives value from an object used
to compare against limits of the scale chunks.
Default to: lambda obj: getattr(obj, field_db)
field_db: Used to filter against `scale` limits.
Default to 'at'.
Examples:
If queryset `qs` use `values()`, `field_callback` must be set and
could be: `lambda d: d['at']`
If `field_db` use foreign attributes (eg with `__`), it should be
something like: `lambda obj: obj.group.at`.
"""
if field_callback is None:
def field_callback(obj):
return getattr(obj, field_db)
begin_f = '{}__gte'.format(field_db)
end_f = '{}__lte'.format(field_db)
qs = (
qs
.filter(**{begin_f: scale.begin, end_f: scale.end})
)
obj_iter = iter(qs)
last_obj = None
def _objects_until(obj_iter, field_callback, end):
"""Generator of objects until `end`.
Ends if objects source is empty or when an object not verifying
field_callback(obj) <= end is met.
If this object exists, it is stored in `last_obj` which is found
from outer scope.
Also, if this same variable is non-empty when the function is
called, it first yields its content.
Args:
obj_iter: Source used to get objects.
field_callback: Returned value, when it is called on an object
will be used to test ordering against `end`.
end
"""
nonlocal last_obj
if last_obj is not None:
yield last_obj
last_obj = None
for obj in obj_iter:
if field_callback(obj) <= end:
yield obj
else:
last_obj = obj
return
for begin, end in scale:
# forward last seen object, if it exists, to the right chunk,
# and fill with empty generators for intermediate chunks of scale
if last_obj is not None:
if field_callback(last_obj) > end:
yield iter(())
continue
# yields generator for this chunk
# this set last_obj to None if obj_iter reach its end, otherwise
# it's set to the first met object from obj_iter which doesn't
# belong to this chunk
yield _objects_until(obj_iter, field_callback, end)

2
kfet/templates/kfet/article_read.html
View file

@ -104,7 +104,7 @@
$(document).ready(function() {
var stat_last = new StatsGroup(
"{% url 'kfet.article.stat.sales.list' article.id %}",
$("#stat_last"),
$("#stat_last")
);
});
</script>

63
kfet/views.py
View file

@ -2369,13 +2369,19 @@ class AccountStatOperation(ScaleMixin, PkUrlMixin, JSONDetailView):
# à l'article en question et qui ne sont pas annulées
# puis on choisi pour chaques intervalle les opérations
# effectuées dans ces intervalles de temps
all_operations = (Operation.objects
.filter(group__on_acc=self.object)
.filter(canceled_at=None)
)
all_operations = (
Operation.objects
.filter(group__on_acc=self.object,
canceled_at=None)
.values('article_nb', 'group__at')
.order_by('group__at')
)
if types is not None:
all_operations = all_operations.filter(type__in=types)
chunks = self.chunkify_qs(all_operations, scale, field='group__at')
chunks = self.get_by_chunks(
all_operations, scale, field_db='group__at',
field_callback=(lambda d: d['group__at']),
)
return chunks
def get_context_data(self, *args, **kwargs):
@ -2391,7 +2397,8 @@ class AccountStatOperation(ScaleMixin, PkUrlMixin, JSONDetailView):
# On compte les opérations
nb_ventes = []
for chunk in operations:
nb_ventes.append(tot_ventes(chunk))
ventes = sum(ope['article_nb'] for ope in chunk)
nb_ventes.append(ventes)
context['charts'] = [{"color": "rgb(255, 99, 132)",
"label": "NB items achetés",
@ -2442,29 +2449,39 @@ class ArticleStatSales(ScaleMixin, JSONDetailView):
context = {'labels': old_ctx['labels']}
scale = self.scale
# On selectionne les opérations qui correspondent
# à l'article en question et qui ne sont pas annulées
# puis on choisi pour chaques intervalle les opérations
# effectuées dans ces intervalles de temps
all_operations = (
all_purchases = (
Operation.objects
.filter(type=Operation.PURCHASE,
article=self.object,
canceled_at=None,
)
.filter(
type=Operation.PURCHASE,
article=self.object,
canceled_at=None,
)
.values('group__at', 'article_nb')
.order_by('group__at')
)
chunks = self.chunkify_qs(all_operations, scale, field='group__at')
liq_only = all_purchases.filter(group__on_acc__trigramme='LIQ')
liq_exclude = all_purchases.exclude(group__on_acc__trigramme='LIQ')
chunks_liq = self.get_by_chunks(
liq_only, scale, field_db='group__at',
field_callback=lambda d: d['group__at'],
)
chunks_no_liq = self.get_by_chunks(
liq_exclude, scale, field_db='group__at',
field_callback=lambda d: d['group__at'],
)
# On compte les opérations
nb_ventes = []
nb_accounts = []
nb_liq = []
for qs in chunks:
nb_ventes.append(
tot_ventes(qs))
nb_liq.append(
tot_ventes(qs.filter(group__on_acc__trigramme='LIQ')))
nb_accounts.append(
tot_ventes(qs.exclude(group__on_acc__trigramme='LIQ')))
for chunk_liq, chunk_no_liq in zip(chunks_liq, chunks_no_liq):
sum_accounts = sum(ope['article_nb'] for ope in chunk_no_liq)
sum_liq = sum(ope['article_nb'] for ope in chunk_liq)
nb_ventes.append(sum_accounts + sum_liq)
nb_accounts.append(sum_accounts)
nb_liq.append(sum_liq)
context['charts'] = [{"color": "rgb(255, 99, 132)",
"label": "Toutes consommations",
"values": nb_ventes},