These method recursively evaluate the SPARQL Algebra
evalQuery is the entry-point, it will setup context and
return the SPARQLResult object
evalPart is called on each level and will delegate to the right method
A rdflib.plugins.sparql.sparql.QueryContext is passed along, keeping
information needed for evaluation
A list of dicts (solution mappings) is returned, apart from GroupBy which may
also return a dict of list of dicts
Functions:
Source code in rdflib/plugins/sparql/evaluate.py
| def evalAggregateJoin(
ctx: QueryContext, agg: CompValue
) -> Generator[FrozenBindings, None, None]:
# import pdb ; pdb.set_trace()
p = evalPart(ctx, agg.p)
# p is always a Group, we always get a dict back
group_expr = agg.p.expr
res: Dict[Any, Any] = collections.defaultdict(
lambda: Aggregator(aggregations=agg.A)
)
if group_expr is None:
# no grouping, just COUNT in SELECT clause
# get 1 aggregator for counting
aggregator = res[True]
for row in p:
aggregator.update(row)
else:
for row in p:
# determine right group aggregator for row
k = tuple(_eval(e, row, False) for e in group_expr)
res[k].update(row)
# all rows are done; yield aggregated values
for aggregator in res.values():
yield FrozenBindings(ctx, aggregator.get_bindings())
# there were no matches
if len(res) == 0:
yield FrozenBindings(ctx)
|
Source code in rdflib/plugins/sparql/evaluate.py
| def evalAskQuery(ctx: QueryContext, query: CompValue) -> Mapping[str, Union[str, bool]]:
res: Dict[str, Union[bool, str]] = {}
res["type_"] = "ASK"
res["askAnswer"] = False
for x in evalPart(ctx, query.p):
res["askAnswer"] = True
break
return res
|
A basic graph pattern
Source code in rdflib/plugins/sparql/evaluate.py
| def evalBGP(
ctx: QueryContext, bgp: List[_Triple]
) -> Generator[FrozenBindings, None, None]:
"""
A basic graph pattern
"""
if not bgp:
yield ctx.solution()
return
s, p, o = bgp[0]
_s = ctx[s]
_p = ctx[p]
_o = ctx[o]
# type error: Item "None" of "Optional[Graph]" has no attribute "triples"
# type Argument 1 to "triples" of "Graph" has incompatible type "Tuple[Union[str, Path, None], Union[str, Path, None], Union[str, Path, None]]"; expected "Tuple[Optional[Node], Optional[Node], Optional[Node]]"
for ss, sp, so in ctx.graph.triples((_s, _p, _o)): # type: ignore[union-attr, arg-type]
if None in (_s, _p, _o):
c = ctx.push()
else:
c = ctx
if _s is None:
# type error: Incompatible types in assignment (expression has type "Union[Node, Any]", target has type "Identifier")
c[s] = ss # type: ignore[assignment]
try:
if _p is None:
# type error: Incompatible types in assignment (expression has type "Union[Node, Any]", target has type "Identifier")
c[p] = sp # type: ignore[assignment]
except AlreadyBound:
continue
try:
if _o is None:
# type error: Incompatible types in assignment (expression has type "Union[Node, Any]", target has type "Identifier")
c[o] = so # type: ignore[assignment]
except AlreadyBound:
continue
for x in evalBGP(c, bgp[1:]):
yield x
|
Source code in rdflib/plugins/sparql/evaluate.py
| def evalConstructQuery(
ctx: QueryContext, query: CompValue
) -> Mapping[str, Union[str, Graph]]:
template = query.template
if not template:
# a construct-where query
template = query.p.p.triples # query->project->bgp ...
graph = Graph()
for c in evalPart(ctx, query.p):
graph += _fillTemplate(template, c)
res: Dict[str, Union[str, Graph]] = {}
res["type_"] = "CONSTRUCT"
res["graph"] = graph
return res
|
Source code in rdflib/plugins/sparql/evaluate.py
| def evalDescribeQuery(ctx: QueryContext, query) -> Dict[str, Union[str, Graph]]:
# Create a result graph and bind namespaces from the graph being queried
graph = Graph()
# type error: Item "None" of "Optional[Graph]" has no attribute "namespaces"
for pfx, ns in ctx.graph.namespaces(): # type: ignore[union-attr]
graph.bind(pfx, ns)
to_describe = set()
# Explicit IRIs may be provided to a DESCRIBE query.
# If there is a WHERE clause, explicit IRIs may be provided in
# addition to projected variables. Find those explicit IRIs and
# prepare to describe them.
for iri in query.PV:
if isinstance(iri, URIRef):
to_describe.add(iri)
# If there is a WHERE clause, evaluate it then find the unique set of
# resources to describe across all bindings and projected variables
if query.p is not None:
bindings = evalPart(ctx, query.p)
to_describe.update(*(set(binding.values()) for binding in bindings))
# Get a CBD for all resources identified to describe
for resource in to_describe:
# type error: Item "None" of "Optional[Graph]" has no attribute "cbd"
ctx.graph.cbd(resource, target_graph=graph) # type: ignore[union-attr]
res: Dict[str, Union[str, Graph]] = {}
res["type_"] = "DESCRIBE"
res["graph"] = graph
return res
|
Source code in rdflib/plugins/sparql/evaluate.py
| def evalDistinct(
ctx: QueryContext, part: CompValue
) -> Generator[FrozenBindings, None, None]:
res = evalPart(ctx, part.p)
done = set()
for x in res:
if x not in done:
yield x
done.add(x)
|
Source code in rdflib/plugins/sparql/evaluate.py
| def evalExtend(
ctx: QueryContext, extend: CompValue
) -> Generator[FrozenBindings, None, None]:
# TODO: Deal with dict returned from evalPart from GROUP BY
for c in evalPart(ctx, extend.p):
try:
e = _eval(extend.expr, c.forget(ctx, _except=extend._vars))
if isinstance(e, SPARQLError):
raise e
yield c.merge({extend.var: e})
except SPARQLError:
yield c
|
Source code in rdflib/plugins/sparql/evaluate.py
| def evalFilter(
ctx: QueryContext, part: CompValue
) -> Generator[FrozenBindings, None, None]:
# TODO: Deal with dict returned from evalPart!
for c in evalPart(ctx, part.p):
if _ebv(
part.expr,
c.forget(ctx, _except=part._vars) if not part.no_isolated_scope else c,
):
yield c
|
Source code in rdflib/plugins/sparql/evaluate.py
| def evalGraph(
ctx: QueryContext, part: CompValue
) -> Generator[FrozenBindings, None, None]:
if ctx.dataset is None:
raise Exception(
"Non-conjunctive-graph doesn't know about "
+ "graphs. Try a query without GRAPH."
)
ctx = ctx.clone()
graph: Union[str, Path, None, Graph] = ctx[part.term]
prev_graph = ctx.graph
if graph is None:
for graph in ctx.dataset.contexts():
# in SPARQL the default graph is NOT a named graph
if graph == ctx.dataset.default_context:
continue
c = ctx.pushGraph(graph)
c = c.push()
graphSolution = [{part.term: graph.identifier}]
for x in _join(evalPart(c, part.p), graphSolution):
x.ctx.graph = prev_graph
yield x
else:
if TYPE_CHECKING:
assert not isinstance(graph, Graph)
# type error: Argument 1 to "get_context" of "ConjunctiveGraph" has incompatible type "Union[str, Path]"; expected "Union[Node, str, None]"
c = ctx.pushGraph(ctx.dataset.get_context(graph)) # type: ignore[arg-type]
for x in evalPart(c, part.p):
x.ctx.graph = prev_graph
yield x
|
http://www.w3.org/TR/sparql11-query/#defn_algGroup
Source code in rdflib/plugins/sparql/evaluate.py
| def evalGroup(ctx: QueryContext, group: CompValue):
"""
http://www.w3.org/TR/sparql11-query/#defn_algGroup
"""
# grouping should be implemented by evalAggregateJoin
return evalPart(ctx, group.p)
|
Source code in rdflib/plugins/sparql/evaluate.py
| def evalJoin(ctx: QueryContext, join: CompValue) -> Generator[FrozenDict, None, None]:
# TODO: Deal with dict returned from evalPart from GROUP BY
# only ever for join.p1
if join.lazy:
return evalLazyJoin(ctx, join)
else:
a = evalPart(ctx, join.p1)
b = set(evalPart(ctx, join.p2))
return _join(a, b)
|
A lazy join will push the variables bound
in the first part to the second part,
essentially doing the join implicitly
hopefully evaluating much fewer triples
Source code in rdflib/plugins/sparql/evaluate.py
| def evalLazyJoin(
ctx: QueryContext, join: CompValue
) -> Generator[FrozenBindings, None, None]:
"""
A lazy join will push the variables bound
in the first part to the second part,
essentially doing the join implicitly
hopefully evaluating much fewer triples
"""
for a in evalPart(ctx, join.p1):
c = ctx.thaw(a)
for b in evalPart(c, join.p2):
yield b.merge(a) # merge, as some bindings may have been forgotten
|
Source code in rdflib/plugins/sparql/evaluate.py
| def evalLeftJoin(
ctx: QueryContext, join: CompValue
) -> Generator[FrozenBindings, None, None]:
# import pdb; pdb.set_trace()
for a in evalPart(ctx, join.p1):
ok = False
c = ctx.thaw(a)
for b in evalPart(c, join.p2):
if _ebv(join.expr, b.forget(ctx)):
ok = True
yield b.merge(a)
if not ok:
# we've cheated, the ctx above may contain
# vars bound outside our scope
# before we yield a solution without the OPTIONAL part
# check that we would have had no OPTIONAL matches
# even without prior bindings...
p1_vars = join.p1._vars
if p1_vars is None or not any(
_ebv(join.expr, b)
for b in evalPart(ctx.thaw(a.remember(p1_vars)), join.p2)
):
yield a
|
Source code in rdflib/plugins/sparql/evaluate.py
| def evalMinus(ctx: QueryContext, minus: CompValue) -> Generator[FrozenDict, None, None]:
a = evalPart(ctx, minus.p1)
b = set(evalPart(ctx, minus.p2))
return _minus(a, b)
|
Source code in rdflib/plugins/sparql/evaluate.py
| def evalMultiset(ctx: QueryContext, part: CompValue):
if part.p.name == "values":
return evalValues(ctx, part)
return evalPart(ctx, part.p)
|
Source code in rdflib/plugins/sparql/evaluate.py
| def evalOrderBy(
ctx: QueryContext, part: CompValue
) -> Generator[FrozenBindings, None, None]:
res = evalPart(ctx, part.p)
for e in reversed(part.expr):
reverse = bool(e.order and e.order == "DESC")
res = sorted(
res, key=lambda x: _val(value(x, e.expr, variables=True)), reverse=reverse
)
return res
|
Source code in rdflib/plugins/sparql/evaluate.py
| def evalPart(ctx: QueryContext, part: CompValue) -> Any:
# try custom evaluation functions
for name, c in CUSTOM_EVALS.items():
try:
return c(ctx, part)
except NotImplementedError:
pass # the given custome-function did not handle this part
if part.name == "BGP":
# Reorder triples patterns by number of bound nodes in the current ctx
# Do patterns with more bound nodes first
triples = sorted(
part.triples, key=lambda t: len([n for n in t if ctx[n] is None])
)
return evalBGP(ctx, triples)
elif part.name == "Filter":
return evalFilter(ctx, part)
elif part.name == "Join":
return evalJoin(ctx, part)
elif part.name == "LeftJoin":
return evalLeftJoin(ctx, part)
elif part.name == "Graph":
return evalGraph(ctx, part)
elif part.name == "Union":
return evalUnion(ctx, part)
elif part.name == "ToMultiSet":
return evalMultiset(ctx, part)
elif part.name == "Extend":
return evalExtend(ctx, part)
elif part.name == "Minus":
return evalMinus(ctx, part)
elif part.name == "Project":
return evalProject(ctx, part)
elif part.name == "Slice":
return evalSlice(ctx, part)
elif part.name == "Distinct":
return evalDistinct(ctx, part)
elif part.name == "Reduced":
return evalReduced(ctx, part)
elif part.name == "OrderBy":
return evalOrderBy(ctx, part)
elif part.name == "Group":
return evalGroup(ctx, part)
elif part.name == "AggregateJoin":
return evalAggregateJoin(ctx, part)
elif part.name == "SelectQuery":
return evalSelectQuery(ctx, part)
elif part.name == "AskQuery":
return evalAskQuery(ctx, part)
elif part.name == "ConstructQuery":
return evalConstructQuery(ctx, part)
elif part.name == "ServiceGraphPattern":
return evalServiceQuery(ctx, part)
elif part.name == "DescribeQuery":
return evalDescribeQuery(ctx, part)
else:
raise Exception("I dont know: %s" % part.name)
|
Source code in rdflib/plugins/sparql/evaluate.py
| def evalProject(ctx: QueryContext, project: CompValue):
res = evalPart(ctx, project.p)
return (row.project(project.PV) for row in res)
|
evalQuery(graph: Graph, query: Query, initBindings: Optional[Mapping[str, Identifier]] = None, base: Optional[str] = None) -> Mapping[Any, Any]
Evaluate a SPARQL query against a graph.
Caution
This method can access indirectly requested network endpoints, for
example, query processing will attempt to access network endpoints
specified in SERVICE directives.
When processing untrusted or potentially malicious queries, measures
should be taken to restrict network and file access.
For information on available security measures, see the RDFLib
Security Considerations
documentation.
Source code in rdflib/plugins/sparql/evaluate.py
| def evalQuery(
graph: Graph,
query: Query,
initBindings: Optional[Mapping[str, Identifier]] = None,
base: Optional[str] = None,
) -> Mapping[Any, Any]:
"""Evaluate a SPARQL query against a graph.
!!! warning "Caution"
This method can access indirectly requested network endpoints, for
example, query processing will attempt to access network endpoints
specified in `SERVICE` directives.
When processing untrusted or potentially malicious queries, measures
should be taken to restrict network and file access.
For information on available security measures, see the RDFLib
[Security Considerations](../security_considerations.md)
documentation.
"""
main = query.algebra
initBindings = dict((Variable(k), v) for k, v in (initBindings or {}).items())
ctx = QueryContext(
graph, initBindings=initBindings, datasetClause=main.datasetClause
)
ctx.prologue = query.prologue
return evalPart(ctx, main)
|
apply REDUCED to result
REDUCED is not as strict as DISTINCT, but if the incoming rows were sorted
it should produce the same result with limited extra memory and time per
incoming row.
Source code in rdflib/plugins/sparql/evaluate.py
| def evalReduced(
ctx: QueryContext, part: CompValue
) -> Generator[FrozenBindings, None, None]:
"""apply REDUCED to result
REDUCED is not as strict as DISTINCT, but if the incoming rows were sorted
it should produce the same result with limited extra memory and time per
incoming row.
"""
# This implementation uses a most recently used strategy and a limited
# buffer size. It relates to a LRU caching algorithm:
# https://en.wikipedia.org/wiki/Cache_algorithms#Least_Recently_Used_.28LRU.29
MAX = 1
# TODO: add configuration or determine "best" size for most use cases
# 0: No reduction
# 1: compare only with the last row, almost no reduction with
# unordered incoming rows
# N: The greater the buffer size the greater the reduction but more
# memory and time are needed
# mixed data structure: set for lookup, deque for append/pop/remove
mru_set = set()
mru_queue: Deque[Any] = collections.deque()
for row in evalPart(ctx, part.p):
if row in mru_set:
# forget last position of row
mru_queue.remove(row)
else:
# row seems to be new
yield row
mru_set.add(row)
if len(mru_set) > MAX:
# drop the least recently used row from buffer
mru_set.remove(mru_queue.pop())
# put row to the front
mru_queue.appendleft(row)
|
Source code in rdflib/plugins/sparql/evaluate.py
| def evalSelectQuery(
ctx: QueryContext, query: CompValue
) -> Mapping[str, Union[str, List[Variable], Iterable[FrozenDict]]]:
res: Dict[str, Union[str, List[Variable], Iterable[FrozenDict]]] = {}
res["type_"] = "SELECT"
res["bindings"] = evalPart(ctx, query.p)
res["vars_"] = query.PV
return res
|
Source code in rdflib/plugins/sparql/evaluate.py
| def evalServiceQuery(ctx: QueryContext, part: CompValue):
res = {}
match = re.match(
"^service <(.*)>[ \n]*{(.*)}[ \n]*$",
# type error: Argument 2 to "get" of "CompValue" has incompatible type "str"; expected "bool" [arg-type]
part.get("service_string", ""), # type: ignore[arg-type]
re.DOTALL | re.I,
)
if match:
service_url = match.group(1)
service_query = _buildQueryStringForServiceCall(ctx, match.group(2))
query_settings = {"query": service_query, "output": "json"}
headers = {
"accept": "application/sparql-results+json",
"user-agent": "rdflibForAnUser",
}
# GET is easier to cache so prefer that if the query is not to long
if len(service_query) < 600:
response = urlopen(
Request(service_url + "?" + urlencode(query_settings), headers=headers)
)
else:
response = urlopen(
Request(
service_url,
data=urlencode(query_settings).encode(),
headers=headers,
)
)
if response.status == 200:
if _HAS_ORJSON:
json_dict = orjson.loads(response.read())
else:
json_dict = json.loads(response.read())
variables = res["vars_"] = json_dict["head"]["vars"]
# or just return the bindings?
res = json_dict["results"]["bindings"]
if len(res) > 0:
for r in res:
# type error: Argument 2 to "_yieldBindingsFromServiceCallResult" has incompatible type "str"; expected "Dict[str, Dict[str, str]]"
for bound in _yieldBindingsFromServiceCallResult(ctx, r, variables): # type: ignore[arg-type]
yield bound
else:
raise Exception(
"Service: %s responded with code: %s", service_url, response.status
)
|
Source code in rdflib/plugins/sparql/evaluate.py
| def evalSlice(ctx: QueryContext, slice: CompValue):
res = evalPart(ctx, slice.p)
return itertools.islice(
res,
slice.start,
slice.start + slice.length if slice.length is not None else None,
)
|
Source code in rdflib/plugins/sparql/evaluate.py
| def evalUnion(ctx: QueryContext, union: CompValue) -> List[Any]:
branch1_branch2 = []
for x in evalPart(ctx, union.p1):
branch1_branch2.append(x)
for x in evalPart(ctx, union.p2):
branch1_branch2.append(x)
return branch1_branch2
|
Source code in rdflib/plugins/sparql/evaluate.py
| def evalValues(
ctx: QueryContext, part: CompValue
) -> Generator[FrozenBindings, None, None]:
for r in part.p.res:
c = ctx.push()
try:
for k, v in r.items():
if v != "UNDEF":
c[k] = v
except AlreadyBound:
continue
yield c.solution()
|