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Optimize intersectAttrs performance

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Always traverse the shortest set.
This commit is contained in:
Robert Hensing 2021-11-11 10:31:19 +01:00
parent 3dbf9b5af5
commit 336908cf4c
3 changed files with 108 additions and 5 deletions
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62
src/libexpr/primops.cc
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@ -2448,12 +2448,62 @@ static void prim_intersectAttrs(EvalState & state, const PosIdx pos, Value * * a
state.forceAttrs(*args[0], pos);
state.forceAttrs(*args[1], pos);
auto attrs = state.buildBindings(std::min(args[0]->attrs->size(), args[1]->attrs->size()));
Bindings &left = *args[0]->attrs;
Bindings &right = *args[1]->attrs;
for (auto & i : *args[0]->attrs) {
Bindings::iterator j = args[1]->attrs->find(i.name);
if (j != args[1]->attrs->end())
attrs.insert(*j);
auto attrs = state.buildBindings(std::min(left.size(), right.size()));
// The current implementation has good asymptotic complexity and is reasonably
// simple. Further optimization may be possible, but does not seem productive,
// considering the state of eval performance in 2022.
//
// I have looked for reusable and/or standard solutions and these are my
// findings:
//
// STL
// ===
// std::set_intersection is not suitable, as it only performs a simultaneous
// linear scan; not taking advantage of random access. This is O(n + m), so
// linear in the largest set, which is not acceptable for callPackage in Nixpkgs.
//
// Simultaneous scan, with alternating simple binary search
// ===
// One alternative algorithm scans the attrsets simultaneously, jumping
// forward using `lower_bound` in case of inequality. This should perform
// well on very similar sets, having a local and predictable access pattern.
// On dissimilar sets, it seems to need more comparisons than the current
// algorithm, as few consecutive attrs match. `lower_bound` could take
// advantage of the decreasing remaining search space, but this causes
// the medians to move, which can mean that they don't stay in the cache
// like they would with the current naive `find`.
//
// Double binary search
// ===
// The optimal algorithm may be "Double binary search", which doesn't
// scan at all, but rather divides both sets simultaneously.
// See "Fast Intersection Algorithms for Sorted Sequences" by Baeza-Yates et al.
// https://cs.uwaterloo.ca/~ajsaling/papers/intersection_alg_app10.pdf
// The only downsides I can think of are not having a linear access pattern
// for similar sets, and having to maintain a more intricate algorithm.
//
// Adaptive
// ===
// Finally one could run try a simultaneous scan, count misses and fall back
// to double binary search when the counter hit some threshold and/or ratio.
if (left.size() < right.size()) {
for (auto & l : left) {
Bindings::iterator r = right.find(l.name);
if (r != right.end())
attrs.insert(*r);
}
}
else {
for (auto & r : right) {
Bindings::iterator l = left.find(r.name);
if (l != left.end())
attrs.insert(r);
}
}
v.mkAttrs(attrs.alreadySorted());
@ -2465,6 +2515,8 @@ static RegisterPrimOp primop_intersectAttrs({
.doc = R"(
Return a set consisting of the attributes in the set *e2* which have the
same name as some attribute in *e1*.
Performs in O(*n* log *m*) where *n* is the size of the smaller set and *m* the larger set's size.
)",
.fun = prim_intersectAttrs,
});