def fire(self, expr):
# If not a Project, who cares?
if not isinstance(expr, algebra.Project):
return expr
mappings = [(None, x) for x in expr.columnlist]
col_select = algebra.Apply(mappings, expr.input)
return algebra.Distinct(input=col_select)
def fire(self, expr):
if isinstance(expr, algebra.ProjectingJoin):
return algebra.Apply([(None, x) for x in expr.output_columns],
algebra.Join(expr.condition,
expr.left,
expr.right))
return expr
def fire(self, expr):
if not isinstance(expr, algebra.GroupBy):
return expr
# A simple grouping expression is an AttributeRef
def is_simple_grp_expr(grp):
return isinstance(grp, expression.AttributeRef)
complex_grp_exprs = [(i, grp)
for (i, grp) in enumerate(expr.grouping_list)
if not is_simple_grp_expr(grp)]
complex_agg_exprs = [agg for agg in expr.aggregate_list
if not is_simple_agg_expr(agg)]
# There are no complicated expressions, we're okay with the existing
# GroupBy.
if not complex_grp_exprs and not complex_agg_exprs:
return expr
# Construct the Apply we're going to stick before the GroupBy
child_scheme = expr.input.scheme()
# First: copy every column from the input verbatim
mappings = [(None, UnnamedAttributeRef(i))
for i in range(len(child_scheme))]
# Next: move the complex grouping expressions into the Apply, replace
# with simple refs
for i, grp_expr in complex_grp_exprs:
mappings.append((None, grp_expr))
expr.grouping_list[i] = UnnamedAttributeRef(len(mappings) - 1)
# Finally: move the complex aggregate expressions into the Apply,
# replace with simple refs
for agg_expr in complex_agg_exprs:
mappings.append((None, agg_expr.input))
agg_expr.input = UnnamedAttributeRef(len(mappings) - 1)
# Construct and prepend the new Apply
new_apply = algebra.Apply(mappings, expr.input)
expr.input = new_apply
# Don't overwrite expr.grouping_list or expr.aggregate_list, instead we
# are mutating the objects it contains when we modify grp_expr or
# agg_expr in the above for loops.
return expr
def fire(self, expr):
if isinstance(expr, algebra.GroupBy) and len(expr.aggregate_list) == 0:
# We can turn an empty GroupBy into a Distinct. However,
# we must ensure that the GroupBy does not do any column
# re-ordering.
group_cols = expr.get_unnamed_grouping_list()
if all(e.position == i for i, e in enumerate(group_cols)):
# No reordering is done
return algebra.Distinct(input=expr.input)
# Some reordering is done, so shim in the Apply to mimic it.
reorder_cols = algebra.Apply(
emitters=[(None, e) for e in group_cols], input=expr.input)
return algebra.Distinct(input=reorder_cols)
return expr
def fire(self, expr):
if not isinstance(expr, algebra.GroupBy):
return expr
aggs = expr.get_unnamed_aggregate_list()
# Maps aggregate index j to index i < j that j duplicates
dups = {}
# Maps non-dup aggregate index i to index i' <= i it will have
# in the output aggregate list.
orig = {}
for i, a in enumerate(aggs):
if i in dups:
continue
orig[i] = len(orig)
dups.update({(j + i + 1): i
for j, b in enumerate(aggs[(i + 1):])
if a == b})
if len(dups) == 0:
# All the aggregate expressions are unique, we're good
return expr
#################################
# Construct a new Apply that drops all duplicates and replaces
# them with repeated UnnamedAttributeRefs
#################################
# First keep the grouping list intact
num_grps = len(expr.grouping_list)
mappings = [(None, UnnamedAttributeRef(i))
for i in range(num_grps)]
# Construct the references to the grouping list
for i in range(len(aggs)):
if i in orig:
m = orig[i] + num_grps
else:
m = orig[dups[i]] + num_grps
mappings.append((None, UnnamedAttributeRef(m)))
# Drop any duplicates from the agg list
expr.aggregate_list = [aggs[i] for i in sorted(orig)]
return algebra.Apply(emitters=mappings, input=expr)
def fire(self, expr):
# don't allow swap-created join to be swapped
if (isinstance(expr, algebra.Join) or
isinstance(expr, algebra.CrossProduct)) \
and not hasattr(expr, '__swapped__'):
assert (
isinstance(
expr,
algebra.Join)) or (
isinstance(
expr,
algebra.CrossProduct))
# An apply will undo the effect of the swap on the scheme,
# so above operators won't be affected
left_sch = expr.left.scheme()
right_sch = expr.right.scheme()
leftlen = len(left_sch)
rightlen = len(right_sch)
assert leftlen + rightlen == len(expr.scheme())
emitters_left = [(left_sch.getName(i),
UnnamedAttributeRef(rightlen + i))
for i in range(leftlen)]
emitters_right = [(right_sch.getName(i), UnnamedAttributeRef(i))
for i in range(rightlen)]
emitters = emitters_left + emitters_right
if isinstance(expr, algebra.Join):
# reindex the expression
index_map = dict([(oldpos, attr[1].position)
for (oldpos, attr) in enumerate(emitters)])
expression.reindex_expr(expr.condition, index_map)
newjoin = algebra.Join(expr.condition, expr.right, expr.left)
else:
newjoin = algebra.CrossProduct(expr.right, expr.left)
newjoin.__swapped__ = True
return algebra.Apply(emitters=emitters, input=newjoin)
else:
return expr
def renameIDB(self, plan):
"""Rename the attributes of the plan to match the current rule. Used
when chaining multiple rules."""
term = self
# Do we know the actual scheme? If it's recursive, we don't
# So derive it from the rule head
# Not really satisfied with this.
try:
sch = plan.scheme()
except algebra.RecursionError:
sch = Scheme([make_attr(i, r, term.name)
for i, r in enumerate(term.valuerefs)])
oldscheme = [name for (name, _) in sch]
termscheme = [expr for expr in term.valuerefs]
if len(oldscheme) != len(termscheme):
raise TypeError("Rule with head %s does not match Term %s" % (term.name, term)) # noqa
pairs = zip(termscheme, oldscheme)
# Merge the old and new schemes. Use new where we can.
def choosename(new, old):
if isinstance(new, Var):
# Then use this new var as the column name
return new.var
else:
# It's an implicit selection condition, like R(x,3). In R's
# schema, don't call the column name '3' (new), instead call it
# whatever the column name was in the prior rule where R is the
# head variable R (old).
return old
mappings = [(choosename(new, old), expression.UnnamedAttributeRef(i))
for i, (new, old) in enumerate(pairs)]
# Use an apply operator to implement the renaming
plan = algebra.Apply(mappings, plan)
return plan
def toRA(self, program):
"""Emit a relational plan for this rule"""
if program.compiling(self.head):
# recursive rule
if not self.fixpoint:
self.fixpoint = algebra.Fixpoint()
state = algebra.State(self.head.name, self.fixpoint)
return state
else:
self.compiling = True
# get the terms, like A(X,Y,"foo")
terms = [c for c in self.body if isinstance(c, Term)]
# get the conditions, like Z=3
conditions = [c for c in self.body
if isinstance(c, expression.BinaryBooleanOperator)]
if len(conditions) > 0:
LOG.debug("found conditions: %s (type=%s) for program %s", conditions, type(conditions[0]), program) # noqa
else:
LOG.debug("found conditions: %s (type=%s) for program %s", conditions, None, program) # noqa
# construct the join graph
joingraph = nx.Graph()
N = len(terms)
for i, term1 in enumerate(terms):
# store the order for explaining queries later -- not strictly
# necessary
term1.originalorder = i
# for each term, add it as a vertex,
# and for each term it joins to, add an edge
joingraph.add_node(term1, term=term1)
for j in range(i + 1, N):
term2 = terms[j]
LOG.debug("joinsto? %s %s", term1, term2)
joins = term1.joinsto(term2, conditions)
if joins:
conjunction = reduce(expression.AND, joins)
LOG.debug("add edge: %s --[%s]--> %s", term1, conjunction,
term2)
joingraph.add_edge(term1, term2, condition=conjunction,
terms=(term1, term2))
# find connected components (some non-determinism in the order here)
comps = nx.connected_component_subgraphs(joingraph)
component_plans = []
# for each component, choose a join order
for component in comps:
cycleconditions = []
# check for cycles
cycles = nx.cycle_basis(component)
while cycles:
LOG.debug("found cycles: %s", cycles)
# choose an edge to break the cycle
# that edge will be a selection condition after the final join
# oneedge = cycles[0][-2:]
# try to make the chosen edge from cycle deterministic
oneedge = sorted(cycles[0], key=lambda v: v.originalorder)[-2:]
data = component.get_edge_data(*oneedge)
LOG.debug("picked edge: %s, data: %s", oneedge, data)
cycleconditions.append(data)
component.remove_edge(*oneedge)
cycles = nx.cycle_basis(component)
if len(component) == 1:
# no joins to plan
onlyterm = component.nodes()[0]
plan = onlyterm.makeLeaf(conditions, program)
else:
LOG.debug("component: %s", component)
# TODO: clean this up.
# joingraph -> joinsequence -> relational plan
planner = BFSLeftDeepPlanner(component)
joinsequence = planner.chooseplan()
LOG.debug("join sequence: %s", joinsequence)
# create a relational plan, finally
# pass in the conditions to make the leaves of the plan
plan = joinsequence.makePlan(conditions, program)
LOG.debug("cycleconditions: %s", cycleconditions)
for condition_info in cycleconditions:
predicate = condition_info["condition"]
terms = condition_info["terms"]
# change all UnnamedAttributes based on the
# offset of its Term
termsToOffset = dict((t, joinsequence.offset(t))
for t in terms)
LOG.debug("before add offset %s", predicate)
predicate.add_offset_by_terms(termsToOffset)
LOG.debug("after add offset %s", predicate)
# create selections after each cycle
plan = algebra.Select(predicate, plan)
component_plans.append(plan)
# link the components with a cross product
plan = component_plans[0]
for newplan in component_plans[1:]:
plan = algebra.CrossProduct(plan, newplan)
try:
scheme = plan.scheme()
except AttributeError:
scheme = Scheme([make_attr(i, r, self.head.name) for i, r in enumerate(self.head.valuerefs)]) # noqa
# Helper function for the next two steps (TODO: move this to a method?)
def findvar(variable):
var = variable.var
if var not in scheme:
msg = "Head variable %s does not appear in rule body: %s" % (var, self) # noqa
raise SyntaxError(msg)
return expression.UnnamedAttributeRef(scheme.getPosition(var))
class FindVarExpressionVisitor(SimpleExpressionVisitor):
def __init__(self):
self.stack = []
def getresult(self):
assert len(self.stack) == 1
return self.stack.pop()
def visit_unary(self, unaryexpr):
inputexpr = self.stack.pop()
self.stack.append(unaryexpr.__class__(inputexpr))
def visit_binary(self, binaryexpr):
right = self.stack.pop()
left = self.stack.pop()
self.stack.append(binaryexpr.__class__(left, right))
def visit_zeroary(self, zeroaryexpr):
self.stack.append(zeroaryexpr.__class__())
def visit_literal(self, literalexpr):
self.stack.append(literalexpr.__class__(literalexpr.value))
def visit_nary(self, naryexpr):
raise NotImplementedError(
"TODO: implement findvar visit of nary expression")
def visit_attr(self, attr):
assert False, \
"FindVar should not be used on expressions with attributes"
def visit_Case(self, caseExpr):
raise NotImplementedError("Case now implemented for Datalog?")
def visit_Var(self, var):
asAttr = findvar(var)
self.stack.append(asAttr)
# TODO: add the other aggregates
# TODO and move aggregates to expression-visitor
def visit_SUM(self, x):
self.visit_unary(x)
def visit_COUNT(self, x):
self.visit_unary(x)
# if this Rule includes a server specification, add a partition
# operator
if self.isParallel():
if isinstance(self.head.serverspec, Broadcast):
plan = algebra.Broadcast(plan)
if isinstance(self.head.serverspec, PartitionBy):
positions = [findvar(v)
for v in self.head.serverspec.variables]
plan = algebra.PartitionBy(positions, plan)
def toAttrRef(e):
"""
Resolve variable references in the head; pass through aggregate
expressions
If expression requires an Apply then return True, else False
"""
LOG.debug("find reference for %s", e)
visitor = FindVarExpressionVisitor()
e.accept(visitor)
return visitor.getresult()
columnlist = [toAttrRef(v) for v in self.head.valuerefs]
LOG.debug("columnlist for Project (or group by) is %s", columnlist)
# If any of the expressions in the head are aggregate expression,
# construct a group by
if any(expression.expression_contains_aggregate(v)
for v in self.head.valuerefs):
emit_clause = [(None, a_or_g) for a_or_g in columnlist]
return raco.myrial.groupby.groupby(plan, emit_clause, [])
elif any([not isinstance(e, Var) for e in self.head.valuerefs]):
# If complex expressions in head, then precede Project with Apply
# NOTE: should Apply actually just append emitters to schema
# instead of doing column select?
# we decided probably not in
# https://github.com/uwescience/raco/pull/209
plan = algebra.Apply([(None, e) for e in columnlist], plan)
else:
# otherwise, just build a Project
plan = algebra.Apply(emitters=[(None, c) for c in columnlist],
input=plan)
# If we found a cycle, the "root" of the plan is the fixpoint operator
if self.fixpoint:
self.fixpoint.loopBody(plan)
plan = self.fixpoint
self.fixpoint = None
self.compiling = False
return plan
def fire(self, op):
# Punt if it's not a group by or we've already converted this into an
# an instance of self.gb_class
if op.__class__ != algebra.GroupBy:
return op
if self._only_fire_on_multi_key and len(op.grouping_list) == 0:
out_op = self._only_fire_on_multi_key()
out_op.copy(op)
return out_op
# Do not shuffle and do not decompose if the data is shuffled already
if DecomposeGroupBy.check_no_shuffle(op):
out_op = self._gb_class()
out_op.copy(op)
return out_op
# Bail early if we have any non-decomposable aggregates
if not all(x.is_decomposable() for x in op.aggregate_list):
out_op = self._gb_class()
out_op.copy(op)
DecomposeGroupBy.do_transfer(out_op)
return out_op
num_grouping_terms = len(op.grouping_list)
local_emitters = []
local_statemods = []
remote_emitters = []
remote_statemods = []
finalizer_exprs = []
# The starting positions for the current local, remote aggregate
local_output_pos = num_grouping_terms
remote_output_pos = num_grouping_terms
requires_finalizer = False
for agg in op.aggregate_list:
# Multiple emit arguments can be associated with a single
# decomposition rule; coalesce them all together.
state = agg.get_decomposable_state()
assert state
################################
# Extract the set of emitters and statemods required for the
# local aggregate.
################################
laggs = state.get_local_emitters()
local_emitters.extend(laggs)
local_statemods.extend(state.get_local_statemods())
################################
# Extract the set of emitters and statemods required for the
# remote aggregate. Remote expressions must be rebased to
# remove instances of LocalAggregateOutput
################################
raggs = state.get_remote_emitters()
raggs = [rebase_local_aggregate_output(x, local_output_pos)
for x in raggs]
remote_emitters.extend(raggs)
rsms = state.get_remote_statemods()
for sm in rsms:
update_expr = rebase_local_aggregate_output(
sm.update_expr, local_output_pos)
remote_statemods.append(
StateVar(sm.name, sm.init_expr, update_expr))
################################
# Extract any required finalizers. These must be rebased to remove
# instances of RemoteAggregateOutput
################################
finalizer = state.get_finalizer()
if finalizer is not None:
requires_finalizer = True
finalizer_exprs.append(
rebase_finalizer(finalizer, remote_output_pos))
else:
for i in range(len(raggs)):
finalizer_exprs.append(
UnnamedAttributeRef(remote_output_pos + i))
local_output_pos += len(laggs)
remote_output_pos += len(raggs)
################################
# Glue together the local and remote aggregates:
# Local => Shuffle => Remote => (optional) Finalizer.
################################
local_gb = self._gb_class(op.grouping_list, local_emitters, op.input,
local_statemods)
grouping_fields = [UnnamedAttributeRef(i)
for i in range(num_grouping_terms)]
remote_gb = self._gb_class(grouping_fields, remote_emitters, local_gb,
remote_statemods)
DecomposeGroupBy.do_transfer(remote_gb)
if requires_finalizer:
# Pass through grouping terms
gmappings = [(None, UnnamedAttributeRef(i))
for i in range(num_grouping_terms)]
fmappings = [(None, fx) for fx in finalizer_exprs]
return algebra.Apply(gmappings + fmappings, remote_gb)
return remote_gb
def fire(self, op):
if isinstance(op, algebra.GroupBy):
child = op.input
child_scheme = child.scheme()
grp_list = op.get_unnamed_grouping_list()
agg_list = op.get_unnamed_aggregate_list()
up_names = [name for name, ex in op.updaters]
up_list = op.get_unnamed_update_exprs()
agg = [accessed_columns(a) for a in agg_list]
up = [accessed_columns(a) for a in up_list]
pos = [{g.position} for g in grp_list]
accessed = sorted(set(itertools.chain(*(up + agg + pos))))
if not accessed:
# Bug #207: COUNTALL() does not access any columns. So if the
# query is just a COUNT(*), we would generate an empty Apply.
# If this happens, just keep the first column of the input.
accessed = [0]
if len(accessed) != len(child_scheme):
emitters = [(None, UnnamedAttributeRef(i)) for i in accessed]
new_apply = algebra.Apply(emitters, child)
index_map = {a: i for (i, a) in enumerate(accessed)}
for agg_expr in itertools.chain(grp_list, agg_list, up_list):
expression.reindex_expr(agg_expr, index_map)
op.grouping_list = grp_list
op.aggregate_list = agg_list
op.updaters = [(name, ex) for name, ex in
zip(up_names, up_list)]
op.input = new_apply
return op
elif isinstance(op, algebra.ProjectingJoin):
l_scheme = op.left.scheme()
r_scheme = op.right.scheme()
in_scheme = l_scheme + r_scheme
condition = to_unnamed_recursive(op.condition, in_scheme)
column_list = [to_unnamed_recursive(c, in_scheme)
for c in op.output_columns]
accessed = (accessed_columns(condition) |
set(c.position for c in op.output_columns))
if len(accessed) == len(in_scheme):
return op
accessed = sorted(accessed)
left = [a for a in accessed if a < len(l_scheme)]
if len(left) < len(l_scheme):
emits = [(None, UnnamedAttributeRef(a)) for a in left]
apply = algebra.Apply(emits, op.left)
op.left = apply
right = [a - len(l_scheme) for a in accessed
if a >= len(l_scheme)]
if len(right) < len(r_scheme):
emits = [(None, UnnamedAttributeRef(a)) for a in right]
apply = algebra.Apply(emits, op.right)
op.right = apply
index_map = {a: i for (i, a) in enumerate(accessed)}
expression.reindex_expr(condition, index_map)
[expression.reindex_expr(c, index_map) for c in column_list]
op.condition = condition
op.output_columns = column_list
return op
return op
def fire(self, op):
if not isinstance(op, algebra.Apply):
return op
child = op.input
if isinstance(child, algebra.Apply):
emits = op.get_unnamed_emit_exprs()
child_emits = child.get_unnamed_emit_exprs()
def convert(n):
if isinstance(n, expression.UnnamedAttributeRef):
return child_emits[n.position]
n.apply(convert)
return n
emits = [convert(e) for e in emits]
new_apply = algebra.Apply(emitters=zip(op.get_names(), emits),
input=child.input)
return self.fire(new_apply)
elif isinstance(child, algebra.ProjectingJoin):
emits = op.get_unnamed_emit_exprs()
# If this apply is only AttributeRefs and the columns already
# have the correct names, we can push it into the ProjectingJoin
if (all(isinstance(e, expression.AttributeRef) for e in emits) and
len(set(emits)) == len(emits)):
new_cols = [child.output_columns[e.position] for e in emits]
# We need to ensure that left columns come before right cols
left_sch = child.left.scheme()
right_sch = child.right.scheme()
combined = left_sch + right_sch
left_len = len(left_sch)
new_cols = [expression.to_unnamed_recursive(e, combined)
for e in new_cols]
side = [e.position >= left_len for e in new_cols]
if sorted(side) == side:
# Left columns do come before right cols
new_pj = algebra.ProjectingJoin(
condition=child.condition, left=child.left,
right=child.right, output_columns=new_cols)
if new_pj.scheme() == op.scheme():
return new_pj
accessed = sorted(set(itertools.chain(*(accessed_columns(e)
for e in emits))))
index_map = {a: i for (i, a) in enumerate(accessed)}
child.output_columns = [child.output_columns[i] for i in accessed]
for e in emits:
expression.reindex_expr(e, index_map)
return algebra.Apply(emitters=zip(op.get_names(), emits),
input=child)
elif isinstance(child, algebra.GroupBy):
emits = op.get_unnamed_emit_exprs()
assert all(is_simple_agg_expr(agg) for agg in child.aggregate_list)
accessed = sorted(set(itertools.chain(*(accessed_columns(e)
for e in emits))))
num_grps = len(child.grouping_list)
accessed_aggs = [i for i in accessed if i >= num_grps]
if len(accessed_aggs) == len(child.aggregate_list):
return op
unused_map = {i: j + num_grps for j, i in enumerate(accessed_aggs)}
# copy the groupby operator so we can modify it
newgb = child.__class__()
newgb.copy(child)
# remove aggregates that are projected out
newgb.aggregate_list = [newgb.aggregate_list[i - num_grps]
for i in accessed_aggs]
for e in emits:
expression.reindex_expr(e, unused_map)
return algebra.Apply(emitters=zip(op.get_names(), emits),
input=newgb)
return op
