def continue_left_join_tree(
iterator: PreemptableIterator,
query_vars: List[str],
bgp: List[Dict[str, str]],
dataset: Dataset,
default_graph: str,
as_of: Optional[datetime] = None
) -> Tuple[PreemptableIterator, List[str], Dict[str, str]]:
"""Build a Left-linear join tree from a Basic Graph pattern.
Args:
* bgp: Basic Graph pattern used to build the join tree.
* dataset: RDF dataset on which the BGPC is evaluated.
* default_graph: URI of the default graph used for BGP evaluation.
* as_of: A timestamp used to perform all reads against a consistent version of the dataset. If `None`, use the latest version of the dataset, which does not guarantee snapshot isolation.
Returns: A tuple (`iterator`, `query_vars`, `cardinalities`) where:
* `iterator` is the root of the Left-linear join tree.
* `query_vars` is the list of all SPARQL variables found in the BGP.
* `cardinalities` is the list of estimated cardinalities of all triple patterns in the BGP.
"""
# gather metadata about triple patterns
triples = []
cardinalities = []
# analyze each triple pattern in the BGP
for triple in bgp:
# select the graph used to evaluate the pattern
graph_uri = triple['graph'] if 'graph' in triple and len(
triple['graph']) > 0 else default_graph
triple['graph'] = graph_uri
# get iterator and statistics about the pattern
if dataset.has_graph(graph_uri):
it, c = dataset.get_graph(graph_uri).search(triple['subject'],
triple['predicate'],
triple['object'],
as_of=as_of)
else:
it, c = EmptyIterator(), 0
triples += [{'triple': triple, 'cardinality': c, 'iterator': it}]
cardinalities += [{'triple': triple, 'cardinality': c}]
# sort triples by ascending cardinality
triples = sorted(triples, key=lambda v: v['cardinality'])
pipeline = iterator
# build the left linear tree of joins
while len(triples) > 0:
pattern, pos, query_vars = find_connected_pattern(query_vars, triples)
# no connected pattern = disconnected BGP => pick the first remaining pattern in the BGP
if pattern is None:
pattern = triples[0]
query_vars = query_vars | get_vars(pattern['triple'])
pos = 0
graph_uri = pattern['triple']['graph']
pipeline = IndexJoinIterator(pipeline,
pattern['triple'],
dataset.get_graph(graph_uri),
as_of=as_of)
triples.pop(pos)
return pipeline, query_vars, cardinalities
def build_left_join_tree(
bgp: List[Dict[str, str]],
dataset: Dataset,
default_graph: str,
context: dict,
as_of: Optional[datetime] = None
) -> Tuple[PreemptableIterator, List[str], Dict[str, str]]:
"""Build a Left-linear join tree from a Basic Graph pattern.
Args:
* bgp: Basic Graph pattern used to build the join tree.
* dataset: RDF dataset on which the BGPC is evaluated.
* default_graph: URI of the default graph used for BGP evaluation.
* context: Information about the query execution.
* as_of: A timestamp used to perform all reads against a consistent version of the dataset. If `None`, use the latest version of the dataset, which does not guarantee snapshot isolation.
Returns: A tuple (`iterator`, `query_vars`, `cardinalities`) where:
* `iterator` is the root of the Left-linear join tree.
* `query_vars` is the list of all SPARQL variables found in the BGP.
* `cardinalities` is the list of estimated cardinalities of all triple patterns in the BGP.
"""
# gather metadata about triple patterns
triples = []
cardinalities = []
# analyze each triple pattern in the BGP
for triple in bgp:
# select the graph used to evaluate the pattern
graph_uri = triple['graph'] if 'graph' in triple and len(
triple['graph']) > 0 else default_graph
triple['graph'] = graph_uri
# get iterator and statistics about the pattern
if dataset.has_graph(graph_uri):
it = ScanIterator(dataset.get_graph(graph_uri),
triple,
context,
as_of=as_of)
c = it.__len__()
else:
it, c = EmptyIterator(), 0
triples += [{'triple': triple, 'cardinality': c, 'iterator': it}]
cardinalities += [{'triple': triple, 'cardinality': c}]
# sort triples by ascending cardinality
triples = sorted(triples, key=lambda v: v['cardinality'])
# start the pipeline with the Scan with the most selective pattern
pattern = triples.pop(0)
query_vars = get_vars(pattern['triple'])
# add a equality filter if the pattern has several variables that binds to the same value
# example: ?s rdf:type ?s => Filter(Scan(?s rdf:type ?s_2), ?s == ?s_2)
# eq_expr, new_pattern = equality_variables(pattern['triple']['subject'], pattern['triple']['predicate'], pattern['triple']['object'])
# if eq_expr is not None:
# # copy pattern with rewritten values
# triple = pattern['triple'].copy()
# triple["subject"] = new_pattern[0]
# triple["predicate"] = new_pattern[1]
# triple["object"] = new_pattern[2]
# # build a pipline with Index Scan + Equality filter
# pipeline = ScanIterator(pattern['iterator'], triple, pattern['cardinality'])
# pipeline = FilterIterator(pipeline, eq_expr)
# # update query variables
# query_vars = query_vars | get_vars(triple)
# else:
# pipeline = ScanIterator(pattern['iterator'], pattern['triple'], pattern['cardinality'])
pipeline = pattern['iterator']
# build the left linear tree of joins
while len(triples) > 0:
pattern, pos, query_vars = find_connected_pattern(query_vars, triples)
# no connected pattern = disconnected BGP => pick the first remaining pattern in the BGP
if pattern is None:
pattern = triples[0]
query_vars = query_vars | get_vars(pattern['triple'])
pos = 0
graph_uri = pattern['triple']['graph']
pipeline = IndexJoinIterator(pipeline, pattern['iterator'], context)
triples.pop(pos)
return pipeline, query_vars, cardinalities
def build_left_plan(bgp, dataset, default_graph):
"""Build a Left-linear tree of joins from a BGP"""
# gather metadata about triple patterns
triples = []
cardinalities = []
# analyze each triple pattern in the BGP
for triple in bgp:
# select the graph used to evaluate the pattern
graph_uri = triple['graph'] if 'graph' in triple and len(
triple['graph']) > 0 else default_graph
triple['graph'] = graph_uri
# get iterator and statistics about the pattern
if dataset.has_graph(graph_uri):
it, c = dataset.get_graph(graph_uri).search(
triple['subject'], triple['predicate'], triple['object'])
else:
it, c = EmptyIterator(), 0
triples += [{'triple': triple, 'cardinality': c, 'iterator': it}]
cardinalities += [{'triple': triple, 'cardinality': c}]
# sort triples by ascending cardinality
triples = sorted(triples, key=lambda v: v['cardinality'])
# start the pipeline with the Scan with the most selective pattern
pattern = triples.pop(0)
query_vars = get_vars(pattern['triple'])
# add a equality filter if the pattern has several variables that binds to the same value
# example: ?s rdf:type ?s => Filter(Scan(?s rdf:type ?s_2), ?s == ?s_2)
eq_expr, new_pattern = equality_variables(pattern['triple']['subject'],
pattern['triple']['predicate'],
pattern['triple']['object'])
if eq_expr is not None:
# copy pattern with rewritten values
triple = pattern['triple'].copy()
triple["subject"] = new_pattern[0]
triple["predicate"] = new_pattern[1]
triple["object"] = new_pattern[2]
# build a pipline with Index Scan + Equality filter
pipeline = ScanIterator(pattern['iterator'], triple,
pattern['cardinality'])
pipeline = FilterIterator(pipeline, eq_expr)
# update query variables
query_vars = query_vars | get_vars(triple)
else:
pipeline = ScanIterator(pattern['iterator'], pattern['triple'],
pattern['cardinality'])
# build the left linear tree of joins
while len(triples) > 0:
pattern, pos, query_vars = find_connected_pattern(query_vars, triples)
# no connected pattern = disconnected BGP => pick the first remaining pattern in the BGP
if pattern is None:
pattern = triples[0]
query_vars = query_vars | get_vars(pattern['triple'])
pos = 0
graph_uri = pattern['triple']['graph']
pipeline = IndexJoinIterator(pipeline, pattern['triple'],
dataset.get_graph(graph_uri))
triples.pop(pos)
return pipeline, query_vars, cardinalities