Re: GIN improvements part 3: ordering in index
От | Heikki Linnakangas |
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Тема | Re: GIN improvements part 3: ordering in index |
Дата | |
Msg-id | 51C9B7DB.1080706@vmware.com обсуждение исходный текст |
Ответ на | Re: GIN improvements part 3: ordering in index (Alexander Korotkov <aekorotkov@gmail.com>) |
Ответы |
Re: GIN improvements part 3: ordering in index
(Alexander Korotkov <aekorotkov@gmail.com>)
|
Список | pgsql-hackers |
On 25.06.2013 01:24, Alexander Korotkov wrote: > On Wed, Jun 19, 2013 at 1:21 AM, Alexander Korotkov<aekorotkov@gmail.com>wrote: > >> On Mon, Jun 17, 2013 at 10:27 PM, Heikki Linnakangas< >> hlinnakangas@vmware.com> wrote: >> >> That has some obvious limitations. First of all, you can run out of >>> memory. >> >> Yes, it is so. qsort should be replaced with tuplesort. > > In attached patch qsort is replaced with tuplesort. As expected, it leads > to some performance drawback, but it's not dramatic. > Also, some doc is added for new distance method of GIN. Thanks. But the fact remains that with this patch, you fetch all the tuples and then you sort them, which is exactly the same thing you do without the patch. The way it happens without the patch just seems to be slower. Time to break out the profiler.. I downloaded what I believe to be the same DBLP titles dataset that you tested with. Without the patch: postgres=# explain analyze select * from dblp_titles where tsvector @@ to_tsquery('english', 'statistics') order by ts_rank(tsvector, to_tsquery('english', 'statistics')) limit 10; QUERY PLAN --------------------------------------------------------------------------------- --------------------------------------------------------- Limit (cost=42935.81..42935.84 rows=10 width=64) (actual time=57.945..57.948 ro ws=10 loops=1) -> Sort (cost=42935.81..42980.86 rows=18020 width=64) (actual time=57.943..5 7.944 rows=10 loops=1) Sort Key: (ts_rank(tsvector, '''statist'''::tsquery)) Sort Method: top-N heapsort Memory: 28kB -> Bitmap Heap Scan on dblp_titles (cost=211.66..42546.41 rows=18020 w idth=64) (actual time=13.061..46.358 rows=15142 loops=1) Recheck Cond: (tsvector @@ '''statist'''::tsquery) -> Bitmap Index Scan on gin_title (cost=0.00..207.15 rows=18020 width=0) (actual time=8.339..8.339 rows=15142 loops=1) Index Cond: (tsvector @@ '''statist'''::tsquery) Total runtime: 57.999 ms (9 rows) And the profile looks like this: 6,94% postgres tbm_iterate 6,12% postgres hash_search_with_hash_value 4,40% postgres tbm_comparator 3,79% libc-2.17.so __memcpy_ssse3_back 3,68% postgres heap_hot_search_buffer 2,62% postgres slot_deform_tuple 2,47% postgres nocachegetattr 2,37% postgres heap_page_prune_opt 2,27% libc-2.17.so __memcmp_sse4_1 2,21% postgres heap_fill_tuple 2,18% postgres pg_qsort 1,96% postgres tas 1,88% postgres palloc0 1,83% postgres calc_rank_or Drilling into that, tbm_iterate, tbm_comparator and pq_sort calls come from the Tidbitmap code, as well as about 1/3 of the hash_search_with_hash_value calls. There seems to be a fair amount of overhead in building and iterating the tid bitmap. Is that what's killing us? For comparison, this is the same with your patch: postgres=# explain analyze select * from dblp_titles where tsvector @@ to_tsquery('english', 'statistics') order by tsvector >< to_tsquery('english', 'statistics') limit 10; QUERY PLAN --------------------------------------------------------------------------------- -------------------------------------------------------- Limit (cost=16.00..52.81 rows=10 width=136) (actual time=9.957..9.980 rows=10 l oops=1) -> Index Scan using gin_title on dblp_titles (cost=16.00..52198.94 rows=1417 5 width=136) (actual time=9.955..9.977 rows=10 loops=1) Index Cond: (tsvector @@ '''statist'''::tsquery) Order By: (tsvector >< '''statist'''::tsquery) Total runtime: 10.084 ms (5 rows) 9,57% postgres scanGetItemFast 7,02% postgres calc_rank_or 5,71% postgres FunctionCall10Coll 5,59% postgres entryGetNextItem 5,19% postgres keyGetOrdering 5,13% postgres ginDataPageLeafReadItemPointer 4,89% postgres entryShift 4,85% postgres ginCompareItemPointers 3,44% postgres ginDataPageLeafRead 3,28% postgres AllocSetAlloc 3,27% postgres insertScanItem 3,18% postgres gin_tsquery_distance 2,38% postgres puttuple_common 2,26% postgres checkcondition_gin 2,20% postgres cmpEntries 2,17% postgres AllocSetFreeIndex 2,11% postgres calc_rank Unsurprisingly, the tidbitmap overhead is not visible in the profile with your patch. To see how much of the difference is caused by the tidbitmap overhead, I wrote the attached quick & dirty patch (it will crash and burn with queries that require tidbitmap unions or intersects etc.). When there are fewer than 10 items on a page, the tidbitmap keeps the offsets of those items in an ordered array of offsets, instead of setting the bits in the bitmap. In the test query, most pages have only 1, maybe 2 hits, so that's a lot cheaper. Here's what the results look like with that patch: postgres=# explain analyze select * from dblp_titles where tsvector @@ to_tsquery('english', 'statistics') order by ts_rank(tsvector, to_tsquery('english', 'statistics')) limit 10; QUERY PLAN --------------------------------------------------------------------------------- ------------------------------------------------------- Limit (cost=36396.80..36396.82 rows=10 width=136) (actual time=19.532..19.532 r ows=0 loops=1) -> Sort (cost=36396.80..36432.23 rows=14175 width=136) (actual time=19.531.. 19.531 rows=0 loops=1) Sort Key: (ts_rank(tsvector, '''statist'''::tsquery)) Sort Method: quicksort Memory: 25kB -> Bitmap Heap Scan on dblp_titles (cost=169.86..36090.48 rows=14175 w idth=136) (actual time=19.525..19.525 rows=0 loops=1) Recheck Cond: (tsvector @@ '''statist'''::tsquery) -> Bitmap Index Scan on gin_title (cost=0.00..166.31 rows=14175 width=0) (actual time=8.310..8.310 rows=15142 loops=1) Index Cond: (tsvector @@ '''statist'''::tsquery) Total runtime: 19.583 ms (9 rows) So, that's about 3x faster than with no patch, but your patch is still 2x faster than this. However, I believe there's still room for improvement with this approach. The profile with this quick & dirty patch looks like this: 13,01% postgres hash_search_with_hash_value 11,03% postgres tbm_comparator 7,10% postgres heap_page_prune_opt 6,60% postgres pg_qsort 4,47% postgres expand_table 4,06% postgres scanGetItemFast 3,82% postgres tas 3,40% postgres tbm_iterate 2,70% postgres PinBuffer 2,30% postgres hash_any 2,16% postgres hash_seq_search 2,07% postgres tbm_get_pageentry 1,86% postgres calc_bucket So, a lot of time is still spent in tidbitmap. The overhead of tbm_iterate is gone, but much overhead remains elsewhere. The crux of the overhead is that when you have only 1-2 hits per page, the tidbitmap is really expensive, as it allocates an PagetableEntry struct for each page. I believe it would be much cheaper to just accumulate the ItemPointers into a simple array in tbm_add_tuples, sort it once, and return results. In summary: The test case you presented as motivation for this patch is a bit of a worst-case scenario for the current tidbitmap implementation. The speedup from your patch comes from avoiding the tidbitmap. However, it would be fairly easy to optimize the tidbitmap to handle this scenario better, which would benefit all kinds of queries that use bitmap scans. There is really no reason to complicate the GIN API for this. Let's just optimize tidbitmap. I'm not sure if I fullly understand your patch, though. Is there some other test scenario where it performs significantly better, which can not be attributed to a tidbitmap overhead? I'm assuming 'no' for now, and marking this patch as rejected in the commitfest app, but feel free to reopen if there is. - Heikki
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