Hacker News Reader

Comment by colanderman | Hacker News Reader

Keep in mind that the log n factor of a B+tree is generally very low; B+tree branching factors are typically in the 100s. Also, the first few levels are generally kept in cache, so you'll only have to hit disk for inner nodes past 100 million entries or so.

Finally, hash indexes always require that the found row be confirmed in the data table, even for simple existence queries, since the keys themselves aren't stored in the hash table. (This is why hash indexes can't be UNIQUE.) B+trees can often answer such queries without the extra lookup (an "index-only scan"). If your B+tree is so large that its inner nodes spill onto disk (necessitating a 2nd disk seek), chances are the equivalent hash index will as well, which, combined with the consult of the data table, kind of negates the benefit.

View on HN

You're right, but I don't think the use case you mentioned (as a competitor to B+ trees for direct lookups) is the target use case for a hash index.

A hash index can be a big win for nested loop joins, especially at high concurrency. It is quite common to build a hash table over a subset of the inner table of an equijoin. This is (a) slow to construct and (b) memory-intensive (especially if many of these queries are run concurrently).

With a hash index, a lot of cases that required building a hash table to speed up a query can just use the hash index directly. Furthermore, every concurrent instance of the query can use the same hash index. This is a big win for both performance of a single query (latency) and query scalability.

That's a hash join which has existed in Postgres for many years. Hash indexes are unrelated.

The primary use case of hash indexes is situations where the indexes fields are very large. The hash index uses a fixed size regardless of the width in bytes of the key so it "wins" storage wise when the key is wide.

> That's a hash join which has existed in Postgres for many years. Hash indexes are unrelated.

I think the OP ankrgyl is aware of that. To quote:

>> It is quite common to build a hash table over a subset of the inner table of an equijoin. This is (a) slow to construct and (b) memory-intensive (especially if many of these queries are run concurrently).

Ah I think I misread that section with the "nested loop join" stuck in my mind.

I think the win for that kind of scenario is smaller than you make it out to be. I agree on memory usage, but the concurrency argument seems largely faulty because you at the same time increase page-level contention on a shared resource (postgres' buffer cache).

I personally want a lazily filled hash-join / something like a index nested loop join that has a fixed size cache for both positive and negative lookups.

> I don't think the use case you mentioned (as a competitor to B+ trees for direct lookups) is the target use case for a hash index.

Exactly – my comment is directed at those who think they might be, because O(1) sounds way better than O(log(n)). When really, except for data sets in the 10s of billions of rows, there are more subtle reasons to use them (as you point out).

Too late to edit now but "100 million" should read "20 billion"… I missed a tree layer. (Assuming a branching factor of 200 and 4 GiB of index cache: 500 thousand inner nodes of 8 KiB each can fit in cache, corresponding to 100 million leaves, which can contain 20 billion entries.)

So hash indexes really only begin to show benefits for individual lookups when your data is terabyte-scale, and below that can even be harmful for that use case (if you could otherwise benefit from an index-only lookup). But see ankrgyl's comment (sibling to this one) for better reasons to consider hash indexes.

> Finally, hash indexes always require that the found row be confirmed in the data table, even for simple existence queries, since the keys themselves aren't stored in the hash table. (This is why hash indexes can't be UNIQUE.)

I don't think that's really true - even for btree indexes the heap is accessed to check row visibility. That's necessary because postgres doesn't store visibility information in indexes. There's imo no really big problem making hash indexes support uniqueness - it's "just" work.

The heap need only be accessed if the visibility bitmap indicates it ought to be. (Otherwise index-only scans would be pretty useless.)

"Visibility information is not stored in index entries, only in heap entries; so at first glance it would seem that every row retrieval would require a heap access anyway. And this is indeed the case, if the table row has been modified recently. However, for seldom-changing data there is a way around this problem. PostgreSQL tracks, for each page in a table's heap, whether all rows stored in that page are old enough to be visible to all current and future transactions." [1]

But you are right regarding UNIQUE. No reason it couldn't be implemented for hash indexes.

[1] https://www.postgresql.org/docs/9.6/static/indexes-index-onl...

I think you might have misunderstood what I meant - I was talking about the index insertion case, to verify uniqueness. And that indeed checks the heap regardless of the VM bit. Check _bt_doinsert() invocation of _bt_check_unique() and the latter's content. Therefore there's no fundamental problem with having to do the same in hash insertion cases, even if more heap fetches are likely to be necessary.

https://git.postgresql.org/gitweb/?p=postgresql.git;a=blob;f...

Ah gotcha, I thought you were making two separate comments. Makes sense.

> Finally, hash indexes always require that the found row be confirmed in the data table

Probably to avoid returning the wrong result for statistically-inevitable collisions, right?

Probably it is needed to check if the row is still visible to that transaction.

Postgres uses a visibility bitmap [1] to avoid hitting the heap merely to determine visibility of stable data.

[1] https://www.postgresql.org/docs/9.6/static/indexes-index-onl...

Correct.

Reply on news.ycombinator.com