def test_dpu(dod):
with open("check_debug.pkl", 'rb') as f:
clean_jp = pickle.load(f)
for mjp in clean_jp:
attrs_to_project = dpu.obtain_attributes_to_project(mjp)
# materialized_virtual_schema = dpu.materialize_join_path(mjp, self)
materialized_virtual_schema = dpu.materialize_join_graph(mjp, dod)
yield materialized_virtual_schema, attrs_to_project
def materialize_join_graphs(self, materializable_join_graphs):
to_return = []
for mjg, filters in materializable_join_graphs:
# if is_join_graph_valid:
attrs_to_project = dpu.obtain_attributes_to_project(filters)
# continue # test
materialized_virtual_schema = dpu.materialize_join_graph_sample(
mjg, self, sample_size=1000)
# materialized_virtual_schema = dpu.materialize_join_graph(mjg, self)
if materialized_virtual_schema is False:
continue # happens when the join was an outlier
# Create metadata to document this view
view_metadata = dict()
view_metadata["#join_graphs"] = len(materializable_join_graphs)
# view_metadata["join_graph"] = self.format_join_paths_pairhops(jpg)
view_metadata[
"join_graph"] = self.format_join_graph_into_nodes_edges(mjg)
to_return.append(
(materialized_virtual_schema, attrs_to_project, view_metadata))
# yield materialized_virtual_schema, attrs_to_project, view_metadata
return to_return
def virtual_schema_iterative_search(self,
list_attributes: [str],
list_samples: [str],
max_hops=2,
debug_enumerate_all_jps=False):
# Align schema definition and samples
assert len(list_attributes) == len(list_samples)
sch_def = {
attr: value
for attr, value in zip(list_attributes, list_samples)
}
sch_def = OrderedDict(
sorted(sch_def.items(), key=lambda x: x[0], reverse=True))
filter_drs = self.joint_filters(sch_def)
# We group now into groups that convey multiple filters.
# Obtain list of tables ordered from more to fewer filters.
table_fulfilled_filters = defaultdict(list)
table_nid = dict(
) # collect nids -- used later to obtain an access path to the tables
for filter, drs in filter_drs.items():
drs.set_table_mode()
# All these tables fulfill the filter above
for table in drs:
# table_fulfilled_filters[table].append(filter)
if filter[1] == FilterType.ATTR:
columns = [c for c in drs.data] # copy
for c in columns:
if c.source_name == table:
table_nid[table] = c.nid
# if filter not in table_fulfilled_filters[table]:
if filter[2] not in [
id for _, _, id in table_fulfilled_filters[table]
]:
table_fulfilled_filters[table].append(
((filter[0], None), FilterType.ATTR, filter[2]))
elif filter[1] == FilterType.CELL:
columns = [c for c in drs.data] # copy
for c in columns:
if c.source_name == table: # filter in this column
table_nid[table] = c.nid
# if filter not in table_fulfilled_filters[table]:
if filter[2] not in [
id for _, _, id in
table_fulfilled_filters[table]
]:
table_fulfilled_filters[table].append(
((filter[0], c.field_name),
FilterType.CELL, filter[2]))
table_path = obtain_table_paths(table_nid, self)
# sort by value len -> # fulfilling filters
table_fulfilled_filters = OrderedDict(
sorted(table_fulfilled_filters.items(),
key=lambda el:
(len({filter_id
for _, _, filter_id in el[1]}), el[0]),
reverse=True)) # len of unique filters, then lexico
# Ordering filters for more determinism
for k, v in table_fulfilled_filters.items():
v = sorted(v, key=lambda el: (el[2], el[0][0]),
reverse=True) # sort by id, then filter_name
table_fulfilled_filters[k] = v
def eager_candidate_exploration():
def covers_filters(candidate_filters, all_filters):
all_filters_set = set([id for _, _, id in filter_drs.keys()])
candidate_filters_set = set(
[id for _, _, id in candidate_filters])
if len(candidate_filters_set) == len(all_filters_set):
return True
return False
def compute_size_filter_ix(filters,
candidate_group_filters_covered):
new_fs_set = set([id for _, _, id in filters])
candidate_fs_set = set(
[id for _, _, id in candidate_group_filters_covered])
ix_size = len(
new_fs_set.union(candidate_fs_set)) - len(candidate_fs_set)
return ix_size
def clear_state():
candidate_group.clear()
candidate_group_filters_covered.clear()
# Eagerly obtain groups of tables that cover as many filters as possible
backup = []
go_on = True
while go_on:
candidate_group = []
candidate_group_filters_covered = set()
for i in range(len(list(table_fulfilled_filters.items()))):
table_pivot, filters_pivot = list(
table_fulfilled_filters.items())[i]
# Eagerly add pivot
candidate_group.append(table_pivot)
candidate_group_filters_covered.update(filters_pivot)
# Did it cover all filters?
# if len(candidate_group_filters_covered) == len(filter_drs.items()):
if covers_filters(candidate_group_filters_covered,
filter_drs.items()):
candidate_group = sorted(candidate_group)
# print("1: " + str(table_pivot))
yield (candidate_group, candidate_group_filters_covered
) # early stop
# Cleaning
clear_state()
continue
for j in range(len(list(table_fulfilled_filters.items()))):
idx = i + j + 1
if idx == len(table_fulfilled_filters.items()):
break
table, filters = list(
table_fulfilled_filters.items())[idx]
# new_filters = len(set(filters).union(candidate_group_filters_covered)) - len(candidate_group_filters_covered)
new_filters = compute_size_filter_ix(
filters, candidate_group_filters_covered)
if new_filters > 0: # add table only if it adds new filters
candidate_group.append(table)
candidate_group_filters_covered.update(filters)
if covers_filters(candidate_group_filters_covered,
filter_drs.items()):
# if len(candidate_group_filters_covered) == len(filter_drs.items()):
candidate_group = sorted(candidate_group)
# print("2: " + str(table_pivot))
yield (candidate_group,
candidate_group_filters_covered)
clear_state()
# Re-add the current pivot, only necessary in this case
candidate_group.append(table_pivot)
candidate_group_filters_covered.update(
filters_pivot)
candidate_group = sorted(candidate_group)
# print("3: " + str(table_pivot))
if covers_filters(candidate_group_filters_covered,
filter_drs.items()):
yield (candidate_group,
candidate_group_filters_covered)
else:
backup.append(([
el for el in candidate_group
], set([el
for el in candidate_group_filters_covered])))
# Cleaning
clear_state()
# before exiting, return backup in case that may be useful
for candidate_group, candidate_group_filters_covered in backup:
yield (candidate_group, candidate_group_filters_covered)
go_on = False # finished exploring all groups
# Find ways of joining together each group
cache_unjoinable_pairs = defaultdict(int)
for candidate_group, candidate_group_filters_covered in eager_candidate_exploration(
):
print("")
print("Candidate group: " + str(candidate_group))
num_unique_filters = len(
{f_id
for _, _, f_id in candidate_group_filters_covered})
print("Covers #Filters: " + str(num_unique_filters))
if len(candidate_group) == 1:
table = candidate_group[0]
path = table_path[table]
materialized_virtual_schema = dpu.get_dataframe(path + "/" +
table)
attrs_to_project = dpu.obtain_attributes_to_project(
candidate_group_filters_covered)
# Create metadata to document this view
view_metadata = dict()
view_metadata["#join_graphs"] = 1
view_metadata["join_graph"] = {
"nodes": [{
"id": -101010,
"label": table
}],
"edges": []
}
yield materialized_virtual_schema, attrs_to_project, view_metadata
continue # to go to the next group
# Pre-check
# TODO: with a connected components index we can pre-filter many of those groups without checking
#group_with_all_relations, join_path_groups = self.joinable(candidate_group, cache_unjoinable_pairs)
max_hops = max_hops
# We find the different join graphs that would join the candidate_group
join_graphs = self.joinable(candidate_group,
cache_unjoinable_pairs,
max_hops=max_hops)
if debug_enumerate_all_jps:
for i, group in enumerate(join_graphs):
print("Group: " + str(i))
for el in group:
print(el)
continue # We are just interested in all JPs for all candidate groups
# if not graphs skip next
if len(join_graphs) == 0:
print("Group: " + str(candidate_group) +
" is Non-Joinable with max_hops=" + str(max_hops))
continue
# Now we need to check every join graph individually and see if it's materializable. Only once we've
# exhausted these join graphs we move on to the next candidate group. We know already that each of the
# join graphs covers all tables in candidate_group, so if they're materializable we're good.
# materializable_join_graphs = []
for jpg in join_graphs:
# Obtain filters that apply to this join graph
filters = set()
for l, r in jpg:
if l.source_name in table_fulfilled_filters:
filters.update(table_fulfilled_filters[l.source_name])
if r.source_name in table_fulfilled_filters:
filters.update(table_fulfilled_filters[r.source_name])
# TODO: obtain join_graph score for diff metrics. useful for ranking later
# rank_materializable_join_graphs(materializable_join_paths, table_path, dod)
is_join_graph_valid = self.is_join_graph_materializable(
jpg, table_fulfilled_filters)
if is_join_graph_valid:
attrs_to_project = dpu.obtain_attributes_to_project(
filters)
materialized_virtual_schema = dpu.materialize_join_graph(
jpg, self)
# Create metadata to document this view
view_metadata = dict()
view_metadata["#join_graphs"] = len(join_graphs)
# view_metadata["join_graph"] = self.format_join_paths_pairhops(jpg)
view_metadata[
"join_graph"] = self.format_join_graph_into_nodes_edges(
jpg)
yield materialized_virtual_schema, attrs_to_project, view_metadata
print("Finished enumerating groups")
cache_unjoinable_pairs = OrderedDict(
sorted(cache_unjoinable_pairs.items(),
key=lambda x: x[1],
reverse=True))
for k, v in cache_unjoinable_pairs.items():
print(str(k) + " => " + str(v))
def virtual_schema_iterative_search(self,
list_attributes: [str],
list_samples: [str],
debug_enumerate_all_jps=False):
# Align schema definition and samples
assert len(list_attributes) == len(list_samples)
sch_def = {
attr: value
for attr, value in zip(list_attributes, list_samples)
}
sch_def = OrderedDict(
sorted(sch_def.items(), key=lambda x: x[0], reverse=True))
filter_drs = self.joint_filters(sch_def)
# We group now into groups that convey multiple filters.
# Obtain list of tables ordered from more to fewer filters.
table_fulfilled_filters = defaultdict(list)
table_nid = dict(
) # collect nids -- used later to obtain an access path to the tables
for filter, drs in filter_drs.items():
drs.set_table_mode()
# All these tables fulfill the filter above
for table in drs:
# table_fulfilled_filters[table].append(filter)
if filter[1] == FilterType.ATTR:
columns = [c for c in drs.data] # copy
for c in columns:
if c.source_name == table:
table_nid[table] = c.nid
# if filter not in table_fulfilled_filters[table]:
if filter[2] not in [
id for _, _, id in table_fulfilled_filters[table]
]:
table_fulfilled_filters[table].append(
((filter[0], None), FilterType.ATTR, filter[2]))
elif filter[1] == FilterType.CELL:
columns = [c for c in drs.data] # copy
for c in columns:
if c.source_name == table: # filter in this column
table_nid[table] = c.nid
# if filter not in table_fulfilled_filters[table]:
if filter[2] not in [
id for _, _, id in
table_fulfilled_filters[table]
]:
table_fulfilled_filters[table].append(
((filter[0], c.field_name),
FilterType.CELL, filter[2]))
table_path = obtain_table_paths(table_nid, self)
# sort by value len -> # fulfilling filters
table_fulfilled_filters = OrderedDict(
sorted(table_fulfilled_filters.items(),
key=lambda el:
(len({filter_id
for _, _, filter_id in el[1]}), el[0]),
reverse=True)) # len of unique filters, then lexico
# Ordering filters for more determinism
for k, v in table_fulfilled_filters.items():
v = sorted(v, key=lambda el: (el[2], el[0][0]),
reverse=True) # sort by id, then filter_name
table_fulfilled_filters[k] = v
def eager_candidate_exploration():
def covers_filters(candidate_filters, all_filters):
all_filters_set = set([id for _, _, id in filter_drs.keys()])
candidate_filters_set = set(
[id for _, _, id in candidate_filters])
if len(candidate_filters_set) == len(all_filters_set):
return True
return False
def compute_size_filter_ix(filters,
candidate_group_filters_covered):
new_fs_set = set([id for _, _, id in filters])
candidate_fs_set = set(
[id for _, _, id in candidate_group_filters_covered])
ix_size = len(
new_fs_set.union(candidate_fs_set)) - len(candidate_fs_set)
return ix_size
def clear_state():
candidate_group.clear()
candidate_group_filters_covered.clear()
# Eagerly obtain groups of tables that cover as many filters as possible
go_on = True
while go_on:
candidate_group = []
candidate_group_filters_covered = set()
for i in range(len(list(table_fulfilled_filters.items()))):
table_pivot, filters_pivot = list(
table_fulfilled_filters.items())[i]
# Eagerly add pivot
candidate_group.append(table_pivot)
candidate_group_filters_covered.update(filters_pivot)
# Did it cover all filters?
# if len(candidate_group_filters_covered) == len(filter_drs.items()):
if covers_filters(candidate_group_filters_covered,
filter_drs.items()):
candidate_group = sorted(candidate_group)
# print("1: " + str(table_pivot))
yield (candidate_group, candidate_group_filters_covered
) # early stop
# Cleaning
clear_state()
continue
for j in range(len(list(table_fulfilled_filters.items()))):
idx = i + j + 1
if idx == len(table_fulfilled_filters.items()):
break
table, filters = list(
table_fulfilled_filters.items())[idx]
# new_filters = len(set(filters).union(candidate_group_filters_covered)) - len(candidate_group_filters_covered)
new_filters = compute_size_filter_ix(
filters, candidate_group_filters_covered)
if new_filters > 0: # add table only if it adds new filters
candidate_group.append(table)
candidate_group_filters_covered.update(filters)
if covers_filters(candidate_group_filters_covered,
filter_drs.items()):
# if len(candidate_group_filters_covered) == len(filter_drs.items()):
candidate_group = sorted(candidate_group)
# print("2: " + str(table_pivot))
yield (candidate_group,
candidate_group_filters_covered)
clear_state()
# Re-add the current pivot, only necessary in this case
candidate_group.append(table_pivot)
candidate_group_filters_covered.update(
filters_pivot)
candidate_group = sorted(candidate_group)
# print("3: " + str(table_pivot))
yield (candidate_group, candidate_group_filters_covered)
# Cleaning
clear_state()
go_on = False # finished exploring all groups
# Find ways of joining together each group
cache_unjoinable_pairs = defaultdict(int)
for candidate_group, candidate_group_filters_covered in eager_candidate_exploration(
):
print("")
print("Candidate group: " + str(candidate_group))
num_unique_filters = len(
{f_id
for _, _, f_id in candidate_group_filters_covered})
print("Covers #Filters: " + str(num_unique_filters))
if len(candidate_group) == 1:
table = candidate_group[0]
path = table_path[table]
materialized_virtual_schema = dpu.get_dataframe(path + "/" +
table)
attrs_to_project = dpu.obtain_attributes_to_project(
(candidate_group_filters_covered, None))
yield materialized_virtual_schema, attrs_to_project
continue # to go to the next group
# Pre-check
# TODO: with a connected components index we can pre-filter many of those groups without checking
group_with_all_relations, join_path_groups = self.joinable(
candidate_group, cache_unjoinable_pairs)
if debug_enumerate_all_jps:
print("Join paths which cover candidate group:")
for jp in group_with_all_relations:
print(jp)
print("Join graphs which cover candidate group: ")
for i, group in enumerate(join_path_groups):
print("Group: " + str(i))
for el in group:
print(el)
continue # We are just interested in all JPs for all candidate groups
# if not paths or graphs skip next
if len(join_path_groups) == 0 and len(
group_with_all_relations) == 0:
print("Group: " + str(candidate_group) + " is Non-Joinable")
continue
# We first check if the group_with_all_relations is materializable
materializable_join_paths = []
if len(group_with_all_relations) > 0:
join_paths = self.tx_join_paths_to_pair_hops(
group_with_all_relations)
annotated_join_paths = self.annotate_join_paths_with_filter(
join_paths, table_fulfilled_filters, candidate_group)
# Check JP materialization
print("Found " + str(len(annotated_join_paths)) +
" candidate join paths")
valid_join_paths = self.verify_candidate_join_paths(
annotated_join_paths)
print("Found " + str(len(valid_join_paths)) +
" materializable join paths")
materializable_join_paths.extend(valid_join_paths)
# We need that at least one JP from each group is materializable
if len(materializable_join_paths) == 0 and len(
join_path_groups) == 0:
print("No join graphs for this candidate group")
continue
print("Processing join graphs...")
materializable_join_graphs = dict()
for k, v in join_path_groups.items():
print("Pair: " + str(k))
join_paths = self.tx_join_paths_to_pair_hops(v)
annotated_join_paths = self.annotate_join_paths_with_filter(
join_paths, table_fulfilled_filters, candidate_group)
# Check JP materialization
print("Found " + str(len(annotated_join_paths)) +
" candidate join paths for join graph")
# For each candidate join_path, check whether it can be materialized or not,
# then show to user (or the other way around)
valid_join_paths = self.verify_candidate_join_paths(
annotated_join_paths)
print("Found " + str(len(valid_join_paths)) +
" materializable join paths for join graph")
if len(valid_join_paths) > 0:
materializable_join_graphs[k] = valid_join_paths
else:
# This pair is non-materializable, but there may be other groups of pairs that cover
# the same tables, therefore we can only continue, we cannot determine at this point that
# the group is non-materializable, not yet.
continue
# Verify whether the join_graphs cover the group or not
covered_tables = set(candidate_group)
for k, _ in materializable_join_graphs.items():
(t1, t2) = k
if t1 in covered_tables:
covered_tables.remove(t1)
if t2 in covered_tables:
covered_tables.remove(t2)
if len(covered_tables) > 0:
# now we know there are not join graphs in this group, so we explicitly mark it as such
materializable_join_graphs.clear()
materializable_join_graphs = list(
) # next block of processing expects a list
else:
# 1) find key-groups
keygroups = defaultdict(list)
current_id = 0
for keygroup in itertools.combinations(
list(materializable_join_graphs.keys()),
len(candidate_group) - 1):
for key in keygroup:
keygroups[current_id].append(
materializable_join_graphs[key])
current_id += 1
# 2) for each key-group, enumerate all paths
unit_jp = []
for _, keygroup in keygroups.items():
# def unpack(packed_list):
# for el in packed_list:
# yield [v[0] for v in el]
args = keygroup
for comb in itertools.product(*args):
unit_jp.append(comb)
# pack units into more compact format
materializable_join_graphs = [
] # TODO: note we are rewriting the type of a var in scope
for unit in unit_jp:
packed_unit = []
for el in unit:
packed_unit.append(el[0])
materializable_join_graphs.append(packed_unit)
print("Processing join graphs...OK")
# Merge join paths and join graphs, at this point the difference is meaningless
# TODO: are paths necessarily contained in graphs? if so, simplify code above
all_jgs = materializable_join_graphs + materializable_join_paths
print("Processing materializable join paths...")
# Sort materializable_join_paths by likely joining on key
all_jgs_scores = rank_materializable_join_graphs(
all_jgs, table_path, self)
clean_jp = []
for annotated_jp, aggr_score, mul_score in all_jgs_scores:
jp = []
filters = set()
for filter, l, r in annotated_jp:
# To drag filters along, there's a leaf special tuple where r may be None
# since we don't need it at this point anymore, we check for its existence and do not include it
if r is not None:
jp.append((l, r))
if filter is not None:
filters.update(filter)
clean_jp.append((filters, jp))
import pickle
with open("check_debug.pkl", 'wb') as f:
pickle.dump(clean_jp, f)
for mjp in clean_jp:
attrs_to_project = dpu.obtain_attributes_to_project(mjp)
# materialized_virtual_schema = dpu.materialize_join_path(mjp, self)
materialized_virtual_schema = dpu.materialize_join_graph(
mjp, self)
yield materialized_virtual_schema, attrs_to_project
print("Finished enumerating groups")
cache_unjoinable_pairs = OrderedDict(
sorted(cache_unjoinable_pairs.items(),
key=lambda x: x[1],
reverse=True))
for k, v in cache_unjoinable_pairs.items():
print(str(k) + " => " + str(v))
def virtual_schema_iterative_search(self,
list_attributes: [str],
list_samples: [str],
perf_stats,
max_hops=2,
debug_enumerate_all_jps=False):
# Align schema definition and samples
st_stage1 = time.time()
assert len(list_attributes) == len(list_samples)
sch_def = {
attr: value
for attr, value in zip(list_attributes, list_samples)
}
sch_def = OrderedDict(
sorted(sch_def.items(), key=lambda x: x[0], reverse=True))
filter_drs = self.joint_filters(sch_def)
et_stage1 = time.time()
perf_stats['t_stage1'] = (et_stage1 - st_stage1)
st_stage2 = time.time()
# We group now into groups that convey multiple filters.
# Obtain list of tables ordered from more to fewer filters.
table_fulfilled_filters = defaultdict(list)
table_nid = dict(
) # collect nids -- used later to obtain an access path to the tables
for filter, drs in filter_drs.items():
drs.set_table_mode()
# All these tables fulfill the filter above
for table in drs:
# table_fulfilled_filters[table].append(filter)
if filter[1] == FilterType.ATTR:
columns = [c for c in drs.data] # copy
for c in columns:
if c.source_name == table:
table_nid[table] = c.nid
# if filter not in table_fulfilled_filters[table]:
if filter[2] not in [
id for _, _, id in table_fulfilled_filters[table]
]:
table_fulfilled_filters[table].append(
((filter[0], None), FilterType.ATTR, filter[2]))
elif filter[1] == FilterType.CELL:
columns = [c for c in drs.data] # copy
for c in columns:
if c.source_name == table: # filter in this column
table_nid[table] = c.nid
# if filter not in table_fulfilled_filters[table]:
if filter[2] not in [
id for _, _, id in
table_fulfilled_filters[table]
]:
table_fulfilled_filters[table].append(
((filter[0], c.field_name),
FilterType.CELL, filter[2]))
table_path = obtain_table_paths(table_nid, self)
# sort by value len -> # fulfilling filters
table_fulfilled_filters = OrderedDict(
sorted(table_fulfilled_filters.items(),
key=lambda el:
(len({filter_id
for _, _, filter_id in el[1]}), el[0]),
reverse=True)) # len of unique filters, then lexico
# Ordering filters for more determinism
for k, v in table_fulfilled_filters.items():
v = sorted(v, key=lambda el: (el[2], el[0][0]),
reverse=True) # sort by id, then filter_name
table_fulfilled_filters[k] = v
def eager_candidate_exploration():
def covers_filters(candidate_filters, all_filters):
all_filters_set = set([id for _, _, id in filter_drs.keys()])
candidate_filters_set = set(
[id for _, _, id in candidate_filters])
if len(candidate_filters_set) == len(all_filters_set):
return True
return False
def compute_size_filter_ix(filters,
candidate_group_filters_covered):
new_fs_set = set([id for _, _, id in filters])
candidate_fs_set = set(
[id for _, _, id in candidate_group_filters_covered])
ix_size = len(
new_fs_set.union(candidate_fs_set)) - len(candidate_fs_set)
return ix_size
def clear_state():
candidate_group.clear()
candidate_group_filters_covered.clear()
# Eagerly obtain groups of tables that cover as many filters as possible
backup = []
go_on = True
while go_on:
candidate_group = []
candidate_group_filters_covered = set()
for i in range(len(list(table_fulfilled_filters.items()))):
table_pivot, filters_pivot = list(
table_fulfilled_filters.items())[i]
# Eagerly add pivot
candidate_group.append(table_pivot)
candidate_group_filters_covered.update(filters_pivot)
# Did it cover all filters?
# if len(candidate_group_filters_covered) == len(filter_drs.items()):
if covers_filters(candidate_group_filters_covered,
filter_drs.items()):
candidate_group = sorted(candidate_group)
# print("1: " + str(table_pivot))
yield (candidate_group, candidate_group_filters_covered
) # early stop
# Cleaning
clear_state()
continue
for j in range(len(list(table_fulfilled_filters.items()))):
idx = i + j + 1
if idx == len(table_fulfilled_filters.items()):
break
table, filters = list(
table_fulfilled_filters.items())[idx]
# new_filters = len(set(filters).union(candidate_group_filters_covered)) - len(candidate_group_filters_covered)
new_filters = compute_size_filter_ix(
filters, candidate_group_filters_covered)
if new_filters > 0: # add table only if it adds new filters
candidate_group.append(table)
candidate_group_filters_covered.update(filters)
if covers_filters(candidate_group_filters_covered,
filter_drs.items()):
# if len(candidate_group_filters_covered) == len(filter_drs.items()):
candidate_group = sorted(candidate_group)
# print("2: " + str(table_pivot))
yield (candidate_group,
candidate_group_filters_covered)
clear_state()
# Re-add the current pivot, only necessary in this case
candidate_group.append(table_pivot)
candidate_group_filters_covered.update(
filters_pivot)
candidate_group = sorted(candidate_group)
# print("3: " + str(table_pivot))
if covers_filters(candidate_group_filters_covered,
filter_drs.items()):
yield (candidate_group,
candidate_group_filters_covered)
else:
backup.append(([
el for el in candidate_group
], set([el
for el in candidate_group_filters_covered])))
# Cleaning
clear_state()
# before exiting, return backup in case that may be useful
for candidate_group, candidate_group_filters_covered in backup:
yield (candidate_group, candidate_group_filters_covered)
go_on = False # finished exploring all groups
# """
# # FIXME: obtaining pairs of tables to join?
# """
# all_pairs = 0
# candidate_groups = 0
# for cg, _ in eager_candidate_exploration():
# candidate_groups += 1
# all_pairs += len([el for el in list(itertools.combinations(cg, 2))])
# # all_pairs_to_join = [len([el for el in list(itertools.combinations(group_tables, 2))])
# # for group_tables in all_candidate_groups]
# # all_pairs += all_pairs_to_join[0]
# # print([el for el in all_candidate_groups])
# # print("all pairs to join: " + str(all_pairs))
# print("CG: " + str(candidate_groups))
# print("TOTAL: " + str(all_pairs))
# exit()
# """
# # FIXME
# """
et_stage2 = time.time()
perf_stats['t_stage2'] = (et_stage2 - st_stage2)
# Find ways of joining together each group
cache_unjoinable_pairs = defaultdict(int)
perf_stats['time_joinable'] = 0
perf_stats['time_is_materializable'] = 0
perf_stats['time_materialize'] = 0
num_candidate_groups = 0
for candidate_group, candidate_group_filters_covered in eager_candidate_exploration(
):
num_candidate_groups += 1
print("")
print("Candidate group: " + str(candidate_group))
num_unique_filters = len(
{f_id
for _, _, f_id in candidate_group_filters_covered})
print("Covers #Filters: " + str(num_unique_filters))
if len(candidate_group) == 1:
table = candidate_group[0]
path = table_path[table]
# materialized_virtual_schema = dpu.get_dataframe(path + "/" + table)
materialized_virtual_schema = dpu.read_relation(path + "/" +
table)
attrs_to_project = dpu.obtain_attributes_to_project(
candidate_group_filters_covered)
# Create metadata to document this view
view_metadata = dict()
view_metadata["#join_graphs"] = 1
view_metadata["join_graph"] = {
"nodes": [{
"id": -101010,
"label": table
}],
"edges": []
}
if 'single_relation_group' not in perf_stats:
perf_stats['single_relation_group'] = 0
perf_stats['single_relation_group'] += 1
yield materialized_virtual_schema, attrs_to_project, view_metadata
continue # to go to the next group
# Pre-check
# TODO: with a connected components index we can pre-filter many of those groups without checking
#group_with_all_relations, join_path_groups = self.joinable(candidate_group, cache_unjoinable_pairs)
max_hops = max_hops
# We find the different join graphs that would join the candidate_group
st_joinable = time.time()
join_graphs = self.joinable(candidate_group,
cache_unjoinable_pairs,
max_hops=max_hops)
et_joinable = time.time()
perf_stats['time_joinable'] += (et_joinable - st_joinable)
if debug_enumerate_all_jps:
for i, group in enumerate(join_graphs):
print("Group: " + str(i))
for el in group:
print(el)
continue # We are just interested in all JPs for all candidate groups
# if not graphs skip next
if len(join_graphs) == 0:
if 'unjoinable_candidate_group' not in perf_stats:
perf_stats['unjoinable_candidate_group'] = 0
perf_stats['unjoinable_candidate_group'] += 1
print("Group: " + str(candidate_group) +
" is Non-Joinable with max_hops=" + str(max_hops))
continue
if 'joinable_candidate_group' not in perf_stats:
perf_stats['joinable_candidate_group'] = 0
perf_stats['joinable_candidate_group'] += 1
if 'num_join_graphs_per_candidate_group' not in perf_stats:
perf_stats['num_join_graphs_per_candidate_group'] = []
perf_stats['num_join_graphs_per_candidate_group'].append(
len(join_graphs))
# Now we need to check every join graph individually and see if it's materializable. Only once we've
# exhausted these join graphs we move on to the next candidate group. We know already that each of the
# join graphs covers all tables in candidate_group, so if they're materializable we're good.
total_materializable_join_graphs = 0
materializable_join_graphs = []
for jpg in join_graphs:
# Obtain filters that apply to this join graph
filters = set()
for l, r in jpg:
if l.source_name in table_fulfilled_filters:
filters.update(table_fulfilled_filters[l.source_name])
if r.source_name in table_fulfilled_filters:
filters.update(table_fulfilled_filters[r.source_name])
# TODO: obtain join_graph score for diff metrics. useful for ranking later
# rank_materializable_join_graphs(materializable_join_paths, table_path, dod)
st_is_materializable = time.time()
# if query view is all attributes, then it's always materializable or we could
# join on a small sample and see -- we can have 2 different impls.
if sum([0] + [1 for el in list_samples if el != '']) > 0:
is_join_graph_valid = self.is_join_graph_materializable(
jpg, table_fulfilled_filters)
else:
is_join_graph_valid = True
et_is_materializable = time.time()
perf_stats['time_is_materializable'] += (et_is_materializable -
st_is_materializable)
# Obtain all materializable graphs, then materialize
if is_join_graph_valid:
total_materializable_join_graphs += 1
materializable_join_graphs.append((jpg, filters))
# At this point we can empty is-join-graph-materializable cache and create a new one
# dpu.empty_relation_cache() # TODO: If df.copy() works, then this is a nice reuse
st_materialize = time.time()
to_return = self.materialize_join_graphs(
materializable_join_graphs)
et_materialize = time.time()
perf_stats['time_materialize'] += (et_materialize - st_materialize)
# yield to_return
for el in to_return:
if 'actually_materialized' not in perf_stats:
perf_stats['actually_materialized'] = 0
perf_stats['actually_materialized'] += 1
yield el
if 'materializable_join_graphs' not in perf_stats:
perf_stats['materializable_join_graphs'] = []
perf_stats['materializable_join_graphs'].append(
total_materializable_join_graphs)
perf_stats["num_candidate_groups"] = num_candidate_groups
print("Finished enumerating groups")
cache_unjoinable_pairs = OrderedDict(
sorted(cache_unjoinable_pairs.items(),
key=lambda x: x[1],
reverse=True))
for k, v in cache_unjoinable_pairs.items():
print(str(k) + " => " + str(v))