def push_pop():
#push, pop: asc / dec / rand
min_num = 10
max_num = 200000
step = 5000
num_datas = list(range(min_num, max_num, step))
result_dics = list(
tqdm(map(lambda n: expr_result(n, 3), num_datas),
total=len(num_datas)))
exprs = F.join_with(list, result_dics)
#return num_datas, exprs
pprint(exprs)
print(num_datas)
y_keys = F.lremove(lambda key: 'merge' in key or 'pop' in key,
exprs.keys())
gradient_dic = {}
for key in sorted(y_keys):
plt.plot(num_datas,
exprs[key],
label=key,
marker='.' if 'bh' in key else 'x',
linestyle='-' if 'bh' in key else '--')
gradient_dic[key] = np.polyfit(num_datas, exprs[key], 1)[0]
pprint(gradient_dic)
plt.xlabel('number of keys')
plt.ylabel('milli seconds')
plt.legend()
plt.show()
y_keys = F.lremove(lambda key: 'merge' in key or 'push' in key,
exprs.keys())
gradient_dic = {}
for key in sorted(y_keys):
plt.plot(num_datas,
exprs[key],
label=key,
marker='.' if 'bh' in key else 'x',
linestyle='-' if 'bh' in key else '--')
gradient_dic[key] = np.polyfit(num_datas, exprs[key], 1)[0]
pprint(gradient_dic)
plt.xlabel('number of keys')
plt.ylabel('milli seconds')
plt.legend()
plt.show()
def merge():
min_num = 10
max_num = 5000
step = 100
num_datas = list(range(min_num, max_num, step))
result_dics = list(
tqdm(map(lambda n: expr_result(3, n), num_datas),
total=len(num_datas)))
def avrg_merge_result(dic):
def avrg(dics):
return sum(map(lambda d: d['time'], dics)) / len(dics)
dic['h.merge.2rand'] = avrg(dic['h.merge.2rand'])
dic['bh.merge.2rand'] = avrg(dic['bh.merge.2rand'])
return dic
result_dics = F.lmap(avrg_merge_result, result_dics)
print(result_dics)
exprs = F.join_with(list, result_dics)
#pprint(exprs)
y_keys = F.lremove(lambda key: 'pop' in key or 'push' in key, exprs.keys())
#print(y_keys)
gradient_dic = {}
for key in sorted(y_keys):
plt.plot(num_datas,
exprs[key],
label=key,
marker='.' if 'bh' in key else 'x',
linestyle='-' if 'bh' in key else '--')
gradient_dic[key] = np.polyfit(num_datas, exprs[key], 1)[0]
pprint(gradient_dic)
plt.xlabel('max number of keys')
plt.ylabel('milli seconds')
plt.legend()
plt.show()
def dnfs(qs):
"""
Converts query condition tree into a DNF of eq conds.
Separately for each alias.
Any negations, conditions with lookups other than __exact or __in,
conditions on joined models and subrequests are ignored.
__in is converted into = or = or = ...
"""
SOME = object()
SOME_TREE = [[(None, None, SOME, True)]]
def negate(term):
return (term[0], term[1], term[2], not term[3])
def _dnf(where):
"""
Constructs DNF of where tree consisting of terms in form:
(alias, attribute, value, negation)
meaning `alias.attribute = value`
or `not alias.attribute = value` if negation is False
Any conditions other then eq are dropped.
"""
if isinstance(where, Lookup):
# If where.lhs don't refer to a field then don't bother
if not hasattr(where.lhs, 'target'):
return SOME_TREE
# Don't bother with complex right hand side either
if isinstance(where.rhs, (QuerySet, Query, Subquery, RawSQL)):
return SOME_TREE
# Skip conditions on non-serialized fields
if isinstance(where.lhs.target, settings.CACHEOPS_SKIP_FIELDS):
return SOME_TREE
attname = where.lhs.target.attname
if isinstance(where, Exact):
return [[(where.lhs.alias, attname, where.rhs, True)]]
elif isinstance(where, IsNull):
return [[(where.lhs.alias, attname, None, where.rhs)]]
elif isinstance(where, In) and len(
where.rhs) < settings.CACHEOPS_LONG_DISJUNCTION:
return [[(where.lhs.alias, attname, v, True)]
for v in where.rhs]
else:
return SOME_TREE
elif isinstance(where, NothingNode):
return []
elif isinstance(where, (ExtraWhere, SubqueryConstraint)):
return SOME_TREE
elif len(where) == 0:
return [[]]
else:
chilren_dnfs = lmap(_dnf, where.children)
if len(chilren_dnfs) == 0:
return [[]]
elif len(chilren_dnfs) == 1:
result = chilren_dnfs[0]
else:
# Just unite children joined with OR
if where.connector == OR:
result = lcat(chilren_dnfs)
# Use Cartesian product to AND children
else:
result = lmap(lcat, product(*chilren_dnfs))
# Negating and expanding brackets
if where.negated:
result = [lmap(negate, p) for p in product(*result)]
return result
def clean_conj(conj, for_alias):
conds = {}
for alias, attname, value, negation in conj:
# "SOME" conds, negated conds and conds for other aliases should be stripped
if value is not SOME and negation and alias == for_alias:
# Conjs with fields eq 2 different values will never cause invalidation
if attname in conds and conds[attname] != value:
return None
conds[attname] = value
return conds
def clean_dnf(tree, aliases):
cleaned = [
clean_conj(conj, alias) for conj in tree for alias in aliases
]
# Remove deleted conjunctions
cleaned = [conj for conj in cleaned if conj is not None]
# Any empty conjunction eats up the rest
# NOTE: a more elaborate DNF reduction is not really needed,
# just keep your querysets sane.
if not all(cleaned):
return [{}]
return cleaned
def query_dnf(query):
def table_for(alias):
if alias == main_alias:
return alias
return query.alias_map[alias].table_name
dnf = _dnf(query.where)
# NOTE: we exclude content_type as it never changes and will hold dead invalidation info
main_alias = query.model._meta.db_table
aliases = {alias for alias, join in query.alias_map.items()
if query.alias_refcount[alias]} \
| {main_alias} - {'django_content_type'}
tables = group_by(table_for, aliases)
return {
table: clean_dnf(dnf, table_aliases)
for table, table_aliases in tables.items()
}
if qs.query.combined_queries:
return join_with(lcat,
(query_dnf(q) for q in qs.query.combined_queries))
else:
return query_dnf(qs.query)
def cached_as(*samples,
timeout=None,
extra=None,
lock=None,
keep_fresh=False,
key_func=func_cache_key):
"""
Caches results of a function and invalidates them same way as given queryset(s).
NOTE: Ignores queryset cached ops settings, always caches.
If keep_fresh is True, this will prevent caching if the given querysets are
invalidated during the function call. This prevents prolonged caching of
stale data.
"""
if not samples:
raise TypeError('Pass a queryset, a model or an object to cache like')
# If we unexpectedly get list instead of queryset return identity decorator.
# Paginator could do this when page.object_list is empty.
if len(samples) == 1 and isinstance(samples[0], list):
return lambda func: func
def _get_queryset(sample):
if isinstance(sample, models.Model):
queryset = sample.__class__.objects.filter(pk=sample.pk)
elif isinstance(sample, type) and issubclass(sample, models.Model):
queryset = sample.objects.all()
else:
queryset = sample
queryset._require_cacheprofile()
return queryset
querysets = lmap(_get_queryset, samples)
dbs = list({qs.db for qs in querysets})
cond_dnfs = join_with(lcat, map(dnfs, querysets))
key_extra = [qs._cache_key(prefix=False) for qs in querysets]
key_extra.append(extra)
if timeout is None:
timeout = min(qs._cacheprofile['timeout'] for qs in querysets)
if lock is None:
lock = any(qs._cacheprofile['lock'] for qs in querysets)
def decorator(func):
@wraps(func)
def wrapper(*args, **kwargs):
if not settings.CACHEOPS_ENABLED or transaction_states.is_dirty(
dbs):
return func(*args, **kwargs)
prefix = get_prefix(func=func, _cond_dnfs=cond_dnfs, dbs=dbs)
cache_key = prefix + 'as:' + key_func(func, args, kwargs,
key_extra)
with redis_client.getting(cache_key, lock=lock) as cache_data:
cache_read.send(sender=None,
func=func,
hit=cache_data is not None)
if cache_data is not None:
return pickle.loads(cache_data)
else:
if keep_fresh:
# We call this "asp" for "as precall" because this key is
# cached before the actual function is called. We randomize
# the key to prevent falsely thinking the key was not
# invalidated when in fact it was invalidated and the
# function was called again in another process.
suffix = key_func(func, args, kwargs,
key_extra + [random()])
precall_key = prefix + 'asp:' + suffix
# Cache a precall_key to watch for invalidation during
# the function call. Its value does not matter. If and
# only if it remains valid before, during, and after the
# call, the result can be cached and returned.
cache_thing(prefix,
precall_key,
'PRECALL',
cond_dnfs,
timeout,
dbs=dbs)
else:
precall_key = ''
result = func(*args, **kwargs)
cache_thing(prefix,
cache_key,
result,
cond_dnfs,
timeout,
dbs=dbs,
precall_key=precall_key)
return result
return wrapper
return decorator
def handle(self, *args, **options):
series = {}
samples = {}
platform_created_on = join_with(
min, [{p: ceil_attrs_date(s)
for p in s.platforms} for s in Series.objects.all()])
platform_qs = Platform.objects.annotate(probes_count=Count('probes'))\
.values('gpl_name', 'probes_count')
platforms = {}
platforms_probes = {}
series_annotations = {}
sample_annotations = {}
concordant_series_annotations = {}
concordant_sample_annotations = {}
series_tags = {}
concordant_series_tags = {}
sample_tags = {}
concordant_sample_tags = {}
series_validations = {}
sample_validations = {}
concordant_series_validations = {}
concordant_sample_validations = {}
for specie in SPECIES.values():
series[specie] = accumulate(
count_by(ceil_attrs_date,
Series.objects.filter(specie=specie)))
qs = Sample.objects.filter(platform__specie=specie)
iterator = tqdm(queryset_iterator(qs, 30000),
total=qs.count(),
desc='{0} samples'.format(specie))
samples[specie] = accumulate(count_by(ceil_attrs_date, iterator))
platforms_data = [[
platform_created_on[item['gpl_name']], item['probes_count']
] for item in platform_qs.filter(specie=specie)]
platforms[specie] = accumulate(count_by(first, platforms_data))
group = group_values(platforms_data)
platforms_probes[specie] = accumulate(walk_values(sum, group))
qs = SeriesAnnotation.objects.filter(series__specie=specie)
series_annotations[specie], \
sample_annotations[specie] = distribute_series_and_sample_annotations(qs)
concordant_series_annotations[specie], \
concordant_sample_annotations[specie] = distribute_series_and_sample_annotations(
qs.filter(best_cohens_kappa=1))
qs = SeriesTag.objects.filter(platform__specie=specie,
is_active=True)
series_tags[specie] = distribute_by_created_on(qs)
concordant_series_tags[specie] = distribute_by_created_on(
qs.exclude(agreed=None))
qs = SampleTag.objects.filter(sample__platform__specie=specie,
is_active=True)
sample_tags[specie] = distribute_by_created_on(qs)
concordant_sample_tags[specie] = distribute_by_created_on(
qs.exclude(series_tag__agreed=None))
qs = SerieValidation.objects.filter(platform__specie=specie,
ignored=False,
by_incompetent=False)
series_validations[specie] = distribute_by_created_on(qs)
concordant_series_validations[specie] = distribute_by_created_on(
qs.filter(best_kappa=1))
qs = SampleValidation\
.objects\
.filter(sample__platform__specie=specie,
serie_validation__ignored=False,
serie_validation__by_incompetent=False)
sample_validations[specie] = distribute_by_created_on(qs)
concordant_sample_validations[specie] = distribute_by_created_on(
qs.filter(
Q(serie_validation__best_kappa=1) | Q(concordant=True)))
users = accumulate(
count_by(ceil_date,
User.objects.values_list('date_joined', flat=True)))
tags = accumulate(
count_by(ceil_date, Tag.objects.values_list('created_on',
flat=True)))
delta = CURRENT_DATE - START_DATE
keys = sorted(
set(
ceil_date(START_DATE + timedelta(days=index * 20))
for index in range(delta.days / 20 + 1)))
specie_data = {
'series':
series,
'samples':
samples,
'platforms':
platforms,
'platforms_probes':
platforms_probes,
'series_annotations':
series_annotations,
'sample_annotations':
sample_annotations,
'concordant_series_annotations':
concordant_series_annotations,
'concordant_sample_annotations':
concordant_sample_annotations,
'series_tags':
series_tags,
'sample_tags':
sample_tags,
'concordant_series_tags':
concordant_series_tags,
'concordant_sample_tags':
concordant_sample_tags,
'series_validations':
series_validations,
'sample_validations':
sample_validations,
'concordant_series_validations':
concordant_series_validations,
'concordant_sample_validations':
concordant_sample_validations,
'series_tags_by_users':
distribute_by_user_id(SeriesTag.objects.filter(is_active=True)),
'sample_tags_by_users':
distribute_by_user_id(SampleTag.objects.filter(is_active=True)),
'series_validations_by_users':
distribute_by_user_id(
SerieValidation.objects.filter(ignored=False,
by_incompetent=False)),
'sample_validations_by_users':
distribute_by_user_id(
SampleValidation.objects.filter(
serie_validation__ignored=False,
serie_validation__by_incompetent=False)),
'series_tag_history':
get_series_tag_history(),
}
data = {
'users': users,
'tags': tags,
}
with transaction.atomic():
HistoricalCounter.objects.filter(
created_on__lte=CURRENT_DATE).delete()
HistoricalCounter.objects.bulk_create([
HistoricalCounter(created_on=key,
counters=merge(
walk_values(get_value(keys, index),
data),
walk_values(
lambda value: walk_values(
get_value(keys, index), value),
specie_data)))
for index, key in enumerate(keys)
])
def dnfs(qs):
"""
Converts query condition tree into a DNF of eq conds.
Separately for each alias.
Any negations, conditions with lookups other than __exact or __in,
conditions on joined models and subrequests are ignored.
__in is converted into = or = or = ...
"""
SOME = object()
SOME_TREE = [[(None, None, SOME, True)]]
def negate(term):
return (term[0], term[1], term[2], not term[3])
def _dnf(where):
"""
Constructs DNF of where tree consisting of terms in form:
(alias, attribute, value, negation)
meaning `alias.attribute = value`
or `not alias.attribute = value` if negation is False
Any conditions other then eq are dropped.
"""
if isinstance(where, Lookup):
# If where.lhs don't refer to a field then don't bother
if not hasattr(where.lhs, 'target'):
return SOME_TREE
# Don't bother with complex right hand side either
if isinstance(where.rhs, (QuerySet, Query)):
return SOME_TREE
# Skip conditions on non-serialized fields
if isinstance(where.lhs.target, NOT_SERIALIZED_FIELDS):
return SOME_TREE
attname = where.lhs.target.attname
if isinstance(where, Exact):
return [[(where.lhs.alias, attname, where.rhs, True)]]
elif isinstance(where, IsNull):
return [[(where.lhs.alias, attname, None, where.rhs)]]
elif isinstance(where, In) and len(where.rhs) < LONG_DISJUNCTION:
return [[(where.lhs.alias, attname, v, True)] for v in where.rhs]
else:
return SOME_TREE
elif isinstance(where, EverythingNode):
return [[]]
elif isinstance(where, NothingNode):
return []
elif isinstance(where, (ExtraWhere, SubqueryConstraint)):
return SOME_TREE
elif len(where) == 0:
return [[]]
else:
chilren_dnfs = lmap(_dnf, where.children)
if len(chilren_dnfs) == 0:
return [[]]
elif len(chilren_dnfs) == 1:
result = chilren_dnfs[0]
else:
# Just unite children joined with OR
if where.connector == OR:
result = lcat(chilren_dnfs)
# Use Cartesian product to AND children
else:
result = lmap(lcat, product(*chilren_dnfs))
# Negating and expanding brackets
if where.negated:
result = [lmap(negate, p) for p in product(*result)]
return result
def clean_conj(conj, for_alias):
conds = {}
for alias, attname, value, negation in conj:
# "SOME" conds, negated conds and conds for other aliases should be stripped
if value is not SOME and negation and alias == for_alias:
# Conjs with fields eq 2 different values will never cause invalidation
if attname in conds and conds[attname] != value:
return None
conds[attname] = value
return conds
def clean_dnf(tree, aliases):
cleaned = [clean_conj(conj, alias) for conj in tree for alias in aliases]
# Remove deleted conjunctions
cleaned = [conj for conj in cleaned if conj is not None]
# Any empty conjunction eats up the rest
# NOTE: a more elaborate DNF reduction is not really needed,
# just keep your querysets sane.
if not all(cleaned):
return [[]]
return cleaned
def query_dnf(query):
def table_for(alias):
if alias == main_alias:
return alias
return query.alias_map[alias].table_name
dnf = _dnf(query.where)
# NOTE: we exclude content_type as it never changes and will hold dead invalidation info
main_alias = query.model._meta.db_table
aliases = {alias for alias, (join, cnt) in zip_dicts(query.alias_map, query.alias_refcount)
if cnt and family_has_profile(table_to_model(join.table_name))} \
| {main_alias} - {'django_content_type'}
tables = group_by(table_for, aliases)
return {table: clean_dnf(dnf, table_aliases) for table, table_aliases in tables.items()}
if django.VERSION >= (1, 11) and qs.query.combined_queries:
return join_with(lcat, (query_dnf(q) for q in qs.query.combined_queries))
else:
return query_dnf(qs.query)
universal_newlines=True))
#min_n = 3 if len(sys.argv) != 3 else int(sys.argv[1])
#max_n = 10 if len(sys.argv) != 3 else int(sys.argv[2])
#num_expr = 3 if len(sys.argv) != 2 else int(sys.argv[1])
num_expr = 10 if len(sys.argv) != 2 else int(sys.argv[1])
#block = 2**29 // num_expr
block = 2**10 // num_expr
exprs = list(tqdm(
map(expr_result, [n * block for n in range(1,num_expr)]),
total = num_expr
))
with open('result_%d.yml' % (num_expr,), 'w') as f:
yaml.dump(F.join_with(list, exprs), f)
with open('result_%d.yml' % (num_expr,)) as f:
result_dic = yaml.safe_load(f)
y_keys = F.lremove('data.num', result_dic.keys())
#print(F.lmap(result_dic, y_keys))
import numpy as np
xs = result_dic['data.num']
gradient_dic = {}
for key in y_keys:
plt.plot(xs, result_dic[key], label=key,
marker='x' if 'q' in key else 'o',
linestyle='--' if 'q' in key else '-',)
gradient_dic[key] = np.polyfit(xs, result_dic[key], 1)[0]
