Source code for rdflib.plugins.sparql.evaluate

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


import collections

from rdflib import Variable, Graph, BNode, URIRef, Literal

from rdflib.plugins.sparql import CUSTOM_EVALS
from rdflib.plugins.sparql.parserutils import value
from rdflib.plugins.sparql.sparql import (
    QueryContext, AlreadyBound, FrozenBindings, SPARQLError)
from rdflib.plugins.sparql.evalutils import (
    _filter, _eval, _join, _diff, _minus, _fillTemplate, _ebv, _val)

from rdflib.plugins.sparql.aggregates import Aggregator
from rdflib.plugins.sparql.algebra import Join, ToMultiSet, Values

[docs]def evalBGP(ctx, bgp): """ 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] for ss, sp, so in ctx.graph.triples((_s, _p, _o)): if None in (_s, _p, _o): c = ctx.push() else: c = ctx if _s is None: c[s] = ss try: if _p is None: c[p] = sp except AlreadyBound: continue try: if _o is None: c[o] = so except AlreadyBound: continue for x in evalBGP(c, bgp[1:]): yield x
[docs]def evalExtend(ctx, extend): # 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
[docs]def evalLazyJoin(ctx, join): """ 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
[docs]def evalJoin(ctx, join): # 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)
[docs]def evalUnion(ctx, union): res = set() for x in evalPart(ctx, union.p1): res.add(x) yield x for x in evalPart(ctx, union.p2): if x not in res: yield x
[docs]def evalMinus(ctx, minus): a = evalPart(ctx, minus.p1) b = set(evalPart(ctx, minus.p2)) return _minus(a, b)
[docs]def evalLeftJoin(ctx, join): # 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 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
[docs]def evalFilter(ctx, part): # 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
[docs]def evalGraph(ctx, part): if ctx.dataset is None: raise Exception( "Non-conjunctive-graph doesn't know about " + "graphs. Try a query without GRAPH.") ctx = ctx.clone() graph = ctx[part.term] 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): yield x else: c = ctx.pushGraph(ctx.dataset.get_context(graph)) for x in evalPart(c, part.p): yield x
[docs]def evalValues(ctx, part): for r in part.p.res: c = ctx.push() try: for k, v in r.iteritems(): if v != 'UNDEF': c[k] = v except AlreadyBound: continue yield c.solution()
[docs]def evalMultiset(ctx, part): if == 'values': return evalValues(ctx, part) return evalPart(ctx, part.p)
[docs]def evalPart(ctx, part): # 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 == '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 == 'Filter': return evalFilter(ctx, part) elif == 'Join': return evalJoin(ctx, part) elif == 'LeftJoin': return evalLeftJoin(ctx, part) elif == 'Graph': return evalGraph(ctx, part) elif == 'Union': return evalUnion(ctx, part) elif == 'ToMultiSet': return evalMultiset(ctx, part) elif == 'Extend': return evalExtend(ctx, part) elif == 'Minus': return evalMinus(ctx, part) elif == 'Project': return evalProject(ctx, part) elif == 'Slice': return evalSlice(ctx, part) elif == 'Distinct': return evalDistinct(ctx, part) elif == 'Reduced': return evalReduced(ctx, part) elif == 'OrderBy': return evalOrderBy(ctx, part) elif == 'Group': return evalGroup(ctx, part) elif == 'AggregateJoin': return evalAggregateJoin(ctx, part) elif == 'SelectQuery': return evalSelectQuery(ctx, part) elif == 'AskQuery': return evalAskQuery(ctx, part) elif == 'ConstructQuery': return evalConstructQuery(ctx, part) elif == 'ServiceGraphPattern': raise Exception('ServiceGraphPattern not implemented') elif == 'DescribeQuery': raise Exception('DESCRIBE not implemented') else: # import pdb ; pdb.set_trace() raise Exception('I dont know: %s' %
[docs]def evalGroup(ctx, group): """ """ # grouping should be implemented by evalAggregateJoin return evalPart(ctx, group.p)
[docs]def evalAggregateJoin(ctx, agg): # 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 = 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.itervalues(): yield FrozenBindings(ctx, aggregator.get_bindings()) # there were no matches if len(res) == 0: yield FrozenBindings(ctx)
[docs]def evalOrderBy(ctx, part): 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
[docs]def evalSlice(ctx, slice): # import pdb; pdb.set_trace() res = evalPart(ctx, slice.p) i = 0 while i < slice.start: i += 1 i = 0 for x in res: i += 1 if slice.length is None: yield x else: if i <= slice.length: yield x else: break
[docs]def evalReduced(ctx, part): """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: # 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 = 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)
[docs]def evalDistinct(ctx, part): res = evalPart(ctx, part.p) done = set() for x in res: if x not in done: yield x done.add(x)
[docs]def evalProject(ctx, project): res = evalPart(ctx, project.p) return (row.project(project.PV) for row in res)
[docs]def evalSelectQuery(ctx, query): res = {} res["type_"] = "SELECT" res["bindings"] = evalPart(ctx, query.p) res["vars_"] = query.PV return res
[docs]def evalAskQuery(ctx, query): res = {} res["type_"] = "ASK" res["askAnswer"] = False for x in evalPart(ctx, query.p): res["askAnswer"] = True break return res
[docs]def evalConstructQuery(ctx, query): 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 = {} res["type_"] = "CONSTRUCT" res["graph"] = graph return res
[docs]def evalQuery(graph, query, initBindings, base=None): initBindings = dict( ( Variable(k),v ) for k,v in initBindings.iteritems() ) ctx = QueryContext(graph, initBindings=initBindings) ctx.prologue = query.prologue main = query.algebra if main.datasetClause: if ctx.dataset is None: raise Exception( "Non-conjunctive-graph doesn't know about " + "graphs! Try a query without FROM (NAMED).") ctx = ctx.clone() # or push/pop? firstDefault = False for d in main.datasetClause: if d.default: if firstDefault: # replace current default graph dg = ctx.dataset.get_context(BNode()) ctx = ctx.pushGraph(dg) firstDefault = True ctx.load(d.default, default=True) elif d.named: g = d.named ctx.load(g, default=False) return evalPart(ctx, main)