Alexis Beingessner March 20th, 2019 -- Rust Nightly 1.35.0 I recently finished a detailed review of hashbrown, which will likely become the new implementation for rust's std::collections::HashMap. One…

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**Article content:**Alexis Beingessner

March 20th, 2019 -- Rust Nightly 1.35.0

I recently finished a detailed review of [1]hashbrown, which will likely become the new implementation for rustʼs std::collections::HashMap. One of the most surprising things I found was in the implementation of insert, which was essentially:

fn insert(&mut self, key: K, val: V) { let hash = self.hash(&key); if let Some(old_val) = self.get_mut(hash, &key) { *old_val = val; } else { self.really_insert(hash, key, val); }

}

This shocked me because it was doing something that was so offensive to people who care about collection performance that we had [2]designed an entire API to help people avoid it: it did two lookups in the map.

However, after some more discussion and review, I concluded that this implementation was reasonable. This post will try to cover why that is.

Before we get into the details, we need a quick primer on how hashmaps work.

A hashmap is basically just an array, and a way to convert keys to a seemingly-random index in that array (a hash function). We wonʼt be talking about hash functions, so just assume weʼve solved that problem well.

Given this, doing insertions and lookups is conceptually very simple:

fn insert(&mut self, key: K, val: V) { let index = self.hash(key) % self.vec.len(); self.vec [index]= Some((key, val));

} fn get(&mut self, key: &K) -> Option<&V> { let index = self.hash(key) % self.vec.len(); self.vec[index].as_ref().map(|pair| &pair.1);

}

Wow easy, canʼt be more effecient than that, hashmaps solved forever.

Not quite. Hereʼs the issue: two different keys can be assigned the same index in our array, and we need to handle that in a way that lets both keys exist in our map. How we choose to solve this problem is basically the entire problem with implementing a HashMap.

A very simple solution is as follows: just walk along the array until we find an empty index (or matching key to overwrite). This approach of just trying to find a new index is called "open addressing", which will be the focus of our discussion (the most common alternative being an approach called "chaining").

Hereʼs a simple implementation of open addressing that literally just iterates through the array indices until it finds an empty slot (this is known as first-come-first-serve linear probing, and is usually a bad strategy for complicated reasons we donʼt care about here):

fn insert(&mut self, key: K, val: V) { let mut index = self.hash(key) % self.vec.len(); while self.vec[index].is_some() && self.vec[index].unwrap().0 != key { index = (index + 1) % self.vec.len(); } self.vec [index]= Some((key, val));

}

However now that weʼre using open addressing, we have another problem: how to remove things. If you just delete an entry where you found it, this will corrupt the map. For instance, say we have 3 keys, A, B, and C, which all wanted to go in the same index:

[..., A, B, C, _, ...] ^ | where all 3 keys wanted to be inserted (A came first, then B, then C)

If we delete B naively, then C will become "lost":

[..., A, _, C, _, ...] ^ ^ | | | ...and gives up here, because we found an empty index | search for C starts here...

There are two solutions to this problem: backshifting, and tombstones.

With backshifting, if we see that the element after B would want to be in Bʼs spot, we move it there (and possibly repeat this process over and over until we hit an empty spot):

[..., A, C, _, _, ...]

With tombstones, if we see that thereʼs an element after B, we mark the entry as "deleted" ("leaving a tombstone in its place"):

[..., A, *, C, _, ...]

Tombstones are like free spots for insertion, but they let our search algorithm know that there used to be something there, so we should keep searching past that spot in case it displaced the key weʼre looking for.

Whether tombstones or backshifting are better for you depends on your implementation and workload. All of the implementations Rustʼs standard library has used over the years have been backshift based. Hashbrown is interesting because it bucks this trend and uses tombstones. So weʼll be focusing on tombstones from here on out.

Tombstones lead to an interesting property of our insertion algorithm: the place that you prove that the key isnʼt in the table can be incredibly far from where you perform the actual insertion:

[A, B, C, *, *, *, D, *, *, E, _] ^ ^ ^ | | | | where to insert where you prove that the key | isnʼt in the table where you start

This can lead you to two different implementation strategies: the double simple loop, and the single complex loop (these implementations fleshed out to be a bit more implementation agnostic):

fn insert(&mut self, key: K, val: V) { let index = self.hash(key); for bucket in self.iterate_buckets_from(index) { if bucket.has_key() { if bucket.key() == key { bucket.set(key, val); return; } } else if bucket.is_empty() { break; } } for bucket in self.iterate_buckets_from(index) { if !bucket.has_key() { bucket.set(key, val); return; } }

}

fn insert(&mut self, key: K, val: V) { let index = self.hash(key); let first_tombstone = None; for bucket in self.iterate_buckets_from(index) { if bucket.has_key() { if bucket.key() == key { bucket.set(key, val); return; } } else if bucket.is_tombstone() { if first_tombstone.is_none() { first_tombstone = Some(bucket); } } else if let Some(target_bucket) = first_tombstone { target_bucket.set(key, val); return; } else { bucket.set(key, val); return; } }

}

Knowing nothing else about our implementation, I would tend to favour the "single complex loop" approach, because it seems like it would do less work under heavy loads. Definitely something worth testing and benchmarking, though!

But hey, that double simple loop looks familiar, what was that implementation that was troubling us again?

fn insert(&mut self, key: K, val: V) { let hash = self.hash(&key); if let Some(old_val) = self.get_mut(hash, &key) { *old_val = val; } else { self.really_insert(hash, key, val); }

}

Hey thatʼs the same thing!

So now we have a sense for why we might come to such an implementation, but why does it seem to be the better one for hashbrown? Well, hashbrownʼs big idea is to be optimized for SIMD. Which is to say: hashbrown is optimized to churn through buckets in our map in big chunks all at once. For instance, when compiled with sse2 support it can search 16 buckets at once. The odds that we take more than 16 elements to find where to insert is quite low.

And so our "two loops" are usually just "do two simple SIMD operations". Not quite so offensive when you say it like that!

Hereʼs a sketch of the implementation in hashbrown:

fn insert(&mut self, key: K, val: V) { let hash = self.hash(key); let hash_fragment = top7_bits_of(hash); for bucket_cluster in self.iterate_buckets_from(hash) { for bucket in bucket_cluster.get_all_matches(hash_fragment).iter_bits() { if bucket.key() == key { bucket.set(hash_fragment, key, val); return; } } if bucket_cluster.get_all_matches(EMPTY) != 0 { break; } } for bucket_cluster in self.iterate_buckets_from(hash) { let bitvec = bucket_cluster.get_all_empty_or_tombstone(); if bitvec != 0 { let idx = bitvec.lowest_set_bit(); bucket_cluster.get_bucket(idx).set(hash_fragment, key, val); return; } }

}

And thatʼs why insertion in hashbrown does a double-lookup.

edit: a bunch of people have asked so just to be clear, the Entry API still saves a good amount of work:

if !map.contains(&k) { map.insert(k, v);

}

This will result in hash(); get(); hash(); get(); really_insert(); in the worst-case. The entry API:

map.entry(k).or_insert(v);

only ever does hash(); get(); really_insert();

References

Visible links

1.

https://github.com/Amanieu/hashbrown 2.

https://doc.rust-lang.org/std/collections/index.html#entriesHackerNewsBot debug: Calculated post rank: 178 - Loop: 307 - Rank min: 100 - Author rank: 77