"""
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
import itertools
import re
import requests
from pyparsing import ParseException
from rdflib import Variable, Graph, BNode, URIRef, Literal
from six import iteritems, itervalues
from rdflib.plugins.sparql import CUSTOM_EVALS
from rdflib.plugins.sparql.parserutils import value
from rdflib.plugins.sparql.sparql import (
QueryContext, AlreadyBound, FrozenBindings, Bindings, 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
from rdflib.plugins.sparql import parser
[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):
for x in evalPart(ctx, union.p1):
yield x
for x in evalPart(ctx, union.p2):
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.items():
if v != 'UNDEF':
c[k] = v
except AlreadyBound:
continue
yield c.solution()
[docs]def evalMultiset(ctx, part):
if part.p.name == '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 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)
#raise Exception('ServiceGraphPattern not implemented')
elif part.name == 'DescribeQuery':
raise Exception('DESCRIBE not implemented')
else:
raise Exception('I dont know: %s' % part.name)
[docs]def evalServiceQuery(ctx, part):
res = {}
match = re.match('^service <(.*)>[ \n]*{(.*)}[ \n]*$',
part.get('service_string', ''), re.DOTALL)
if match:
service_url = match.group(1)
service_query = _buildQueryStringForServiceCall(ctx, match)
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 = requests.get(service_url, params=query_settings, headers=headers)
else:
response = requests.post(service_url, params=query_settings, headers=headers)
if response.status_code == 200:
json = response.json();
variables = res["vars_"] = json['head']['vars']
# or just return the bindings?
res = json['results']['bindings']
if len(res) > 0:
for r in res:
for bound in _yieldBindingsFromServiceCallResult(ctx, r, variables):
yield bound
else:
raise Exception("Service: %s responded with code: %s", service_url, response.status_code);
"""
Build a query string to be used by the service call.
It is supposed to pass in the existing bound solutions.
Re-adds prefixes if added and sets the base.
Wraps it in select if needed.
"""
def _buildQueryStringForServiceCall(ctx, match):
service_query = match.group(2)
try:
parser.parseQuery(service_query)
except ParseException:
# This could be because we don't have a select around the service call.
service_query = 'SELECT REDUCED * WHERE {' + service_query + '}'
for p in ctx.prologue.namespace_manager.store.namespaces():
service_query = 'PREFIX ' + p[0] + ':' + p[1].n3() + ' ' + service_query
# re add the base if one was defined
base = ctx.prologue.base
if base is not None and len(base) > 0:
service_query = 'BASE <' + base + '> ' + service_query
sol = ctx.solution();
if len(sol) > 0:
variables = ' '.join(map(lambda v:v.n3(), sol))
variables_bound = ' '.join(map(lambda v: ctx.get(v).n3(), sol))
service_query = service_query + 'VALUES (' + variables + ') {(' + variables_bound + ')}'
return service_query
def _yieldBindingsFromServiceCallResult(ctx, r, variables):
res_dict = {}
for var in variables:
if var in r and r[var]:
if r[var]['type'] == "uri":
res_dict[Variable(var)] = URIRef(r[var]["value"])
elif r[var]['type'] == "bnode":
res_dict[Variable(var)] = BNode(r[var]["value"])
elif r[var]['type'] == "literal" and 'datatype' in r[var]:
res_dict[Variable(var)] = Literal(r[var]["value"], datatype=r[var]['datatype'])
elif r[var]['type'] == "literal" and 'xml:lang' in r[var]:
res_dict[Variable(var)] = Literal(r[var]["value"], lang=r[var]['xml:lang'])
yield FrozenBindings(ctx, res_dict)
[docs]def evalGroup(ctx, group):
"""
http://www.w3.org/TR/sparql11-query/#defn_algGroup
"""
# 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 itervalues(res):
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):
res = evalPart(ctx, slice.p)
return itertools.islice(res, slice.start, slice.start + slice.length if slice.length is not None else None)
[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:
# 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 = 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 iteritems(initBindings))
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)
