Intuitively, or rather theoretically (since this doesn't even require intuition), using hash indexes over Btree indexes for equality lookups should be faster: O(1) vs O(log n). And as is expected, various benchmarks confirm this. However, when it comes to enforcing uniqueness in a given column, hash indexes don't perform so well.
The standard way of adding a uniqueness constraint to a column, is by declaring it when creating the table. For example:
create table item( id int unique, name text );
On my computer, it takes 5.563 seconds to insert a million sequentially ordered IDs. It takes 7.533 seconds to add a million randomly ordered IDs. Without any constraint on the
id column, it takes roughly 2 seconds to insert a million items regardless of whether they are ordered sequentially or randomly.
Postgres provides another round-about way of adding a uniqueness constraint to a column - exclusion constraints. Postgres' documentation defines exclusion constraints as follows:
Exclusion constraints ensure that if any two rows are compared on the specified columns or expressions using the specified operators, at least one of these operator comparisons will return false or null.
For our case, enforcing uniqueness, the above statement could be restated in this way: Suppose we only consider a single column and provide one operator for the exclusion constraint (such as the equals operator '=' for uniqueness). Then, when inserting or updating a row, the comparison with all other pre-existing rows should result in false, (or null since sql has three-valued logic). Otherwise, it will be excluded. This is easier demonstrated with actual sql:
create table item( id int, name text, exclude (id with =) );
Better yet, Postgres allows us to speed up the exclusion check using an index as follows:
create table item( id int, name text, exclude using hash (id with =) );
Now, when using a unique constraint, Postgres' documentation states that it "will automatically create a unique B-tree index on the column or group of columns listed in the constraint". Given that the exclusion constraint above is using hash index, I expected it to be faster. However, inserting a million sequential ID's took 9.192 seconds, which is almost twice as slow as relying on the plain old unique constraint. Inserting randomly ordered IDs took 8.443 seconds.
At first, I presumed it has something to do with the way the underlying hash indexes are structured, but even when using btree, it took roughly the same amount of time as the hash index exclusion constraint. The btree though was way much slower when inserting randomly ordered IDs, taking 12.058 seconds. My current presumption is that Postgres developers have put a lot of work into optimizing the standard unique constraint, since it's expected that the users will opt for it over exclusion constraints, which are better left for more interesting stuff, like overlapping intervals.
Other than being slower, by relying on exclusion constraints to enforce uniqueness, we also lose the ability to have the column be referenced by foreign keys in other tables. This is because in Postgres, only unique columns and primary key columns can be referenced. For example, the second table definition below fails:
create table item( id int, name text, constraint unique_book_id_hash exclude using hash (id with =) ); create table orders( id int, item_id int references item(id) -- ❌ );
So far though, if you've noticed, I've been using integers. Before making any further conclusions and dismissing hash indexes entirely, it's only fair that they're measured up in the one area where they excel quite well, comparing lengthy strings. And as expected, they do truly shine here. I began with UUID's since I didn't have to write an extra function for generating random strings. With plain old
unique, it takes 32.96 seconds to insert a million UUIDs. It gets worse when inserting the next million UUIDS, 50.557 seconds. On the other hand, when using the hash-index based exclusion check, it takes 12.537 seconds to insert the first set of a million UUIDs, 12.764 to insert the next set and finally 16.24 seconds to insert the third set - quite impressive. I'll be sure to try comparing both with random strings of different lengths but I expect similar results. And yeah, that's definitely one way to speed up uniqueness constraint checks if the column's type is a string, rather than an integer and it won't be referenced elsewhere in the database.