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doc: make blooms docs match reality 

Parallel execution changed the way bloom queries are executed, so update
the EXPLAIN output, and restructure the docs to be clearer and more
accurate.

Reported-by: Daniel Westermann

Discussion: https://postgr.es/m/ZR0P278MB0122119FAE78721A694C30C8D2340@ZR0P278MB0122.CHEP278.PROD.OUTLOOK.COM

Author: Daniel Westermann and me

Backpatch-through: 9.6
This commit is contained in:
Bruce Momjian 2020-10-26 19:17:05 -04:00
parent 20d3fe9009
commit e9661f2b0f
1 changed files with 62 additions and 55 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

117
doc/src/sgml/bloom.sgml
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@ -110,75 +110,70 @@ CREATE INDEX bloomidx ON tbloom USING bloom (i1,i2,i3)
FROM
generate_series(1,10000000);
SELECT 10000000
=# CREATE INDEX bloomidx ON tbloom USING bloom (i1, i2, i3, i4, i5, i6);
CREATE INDEX
=# SELECT pg_size_pretty(pg_relation_size('bloomidx'));
pg_size_pretty
----------------
153 MB
(1 row)
=# CREATE index btreeidx ON tbloom (i1, i2, i3, i4, i5, i6);
CREATE INDEX
=# SELECT pg_size_pretty(pg_relation_size('btreeidx'));
pg_size_pretty
----------------
387 MB
(1 row)
</programlisting>
<para>
A sequential scan over this large table takes a long time:
<programlisting>
=# EXPLAIN ANALYZE SELECT * FROM tbloom WHERE i2 = 898732 AND i5 = 123451;
QUERY PLAN
-------------------------------------------------------------------&zwsp;-----------------------------------------
Seq Scan on tbloom (cost=0.00..213694.08 rows=1 width=24) (actual time=1445.438..1445.438 rows=0 loops=1)
QUERY PLAN
-------------------------------------------------------------------&zwsp;-----------------------------------
Seq Scan on tbloom (cost=0.00..2137.14 rows=3 width=24) (actual time=16.971..16.971 rows=0 loops=1)
Filter: ((i2 = 898732) AND (i5 = 123451))
Rows Removed by Filter: 10000000
Planning time: 0.177 ms
Execution time: 1445.473 ms
Rows Removed by Filter: 100000
Planning Time: 0.346 ms
Execution Time: 16.988 ms
(5 rows)
</programlisting>
</para>
<para>
So the planner will usually select an index scan if possible.
With a btree index, we get results like this:
Even with the btree index defined the result will still be a
sequential scan:
<programlisting>
=# CREATE INDEX btreeidx ON tbloom (i1, i2, i3, i4, i5, i6);
CREATE INDEX
=# SELECT pg_size_pretty(pg_relation_size('btreeidx'));
pg_size_pretty
----------------
3976 kB
(1 row)
=# EXPLAIN ANALYZE SELECT * FROM tbloom WHERE i2 = 898732 AND i5 = 123451;
QUERY PLAN
-------------------------------------------------------------------&zwsp;-------------------------------------------------------------
Index Only Scan using btreeidx on tbloom (cost=0.56..298311.96 rows=1 width=24) (actual time=445.709..445.709 rows=0 loops=1)
Index Cond: ((i2 = 898732) AND (i5 = 123451))
Heap Fetches: 0
Planning time: 0.193 ms
Execution time: 445.770 ms
QUERY PLAN
-------------------------------------------------------------------&zwsp;-----------------------------------
Seq Scan on tbloom (cost=0.00..2137.00 rows=2 width=24) (actual time=12.805..12.805 rows=0 loops=1)
Filter: ((i2 = 898732) AND (i5 = 123451))
Rows Removed by Filter: 100000
Planning Time: 0.138 ms
Execution Time: 12.817 ms
(5 rows)
</programlisting>
</para>
<para>
Bloom is better than btree in handling this type of search:
Having the bloom index defined on the table is better than btree in
handling this type of search:
<programlisting>
=# CREATE INDEX bloomidx ON tbloom USING bloom (i1, i2, i3, i4, i5, i6);
CREATE INDEX
=# SELECT pg_size_pretty(pg_relation_size('bloomidx'));
pg_size_pretty
----------------
1584 kB
(1 row)
=# EXPLAIN ANALYZE SELECT * FROM tbloom WHERE i2 = 898732 AND i5 = 123451;
QUERY PLAN
-------------------------------------------------------------------&zwsp;--------------------------------------------------------
Bitmap Heap Scan on tbloom (cost=178435.39..178439.41 rows=1 width=24) (actual time=76.698..76.698 rows=0 loops=1)
QUERY PLAN
-------------------------------------------------------------------&zwsp;--------------------------------------------------
Bitmap Heap Scan on tbloom (cost=1792.00..1799.69 rows=2 width=24) (actual time=0.388..0.388 rows=0 loops=1)
Recheck Cond: ((i2 = 898732) AND (i5 = 123451))
Rows Removed by Index Recheck: 2439
Heap Blocks: exact=2408
-&gt; Bitmap Index Scan on bloomidx (cost=0.00..178435.39 rows=1 width=0) (actual time=72.455..72.455 rows=2439 loops=1)
Rows Removed by Index Recheck: 29
Heap Blocks: exact=28
-&gt; Bitmap Index Scan on bloomidx (cost=0.00..1792.00 rows=2 width=0) (actual time=0.356..0.356 rows=29 loops=1)
Index Cond: ((i2 = 898732) AND (i5 = 123451))
Planning time: 0.475 ms
Execution time: 76.778 ms
Planning Time: 0.099 ms
Execution Time: 0.408 ms
(8 rows)
</programlisting>
Note the relatively large number of false positives: 2439 rows were
selected to be visited in the heap, but none actually matched the
query. We could reduce that by specifying a larger signature length.
In this example, creating the index with <literal>length=200</literal>
reduced the number of false positives to 55; but it doubled the index size
(to 306 MB) and ended up being slower for this query (125 ms overall).
</para>
<para>
@ -187,24 +182,36 @@ CREATE INDEX
A better strategy for btree is to create a separate index on each column.
Then the planner will choose something like this:
<programlisting>
=# CREATE INDEX btreeidx1 ON tbloom (i1);
CREATE INDEX
=# CREATE INDEX btreeidx2 ON tbloom (i2);
CREATE INDEX
=# CREATE INDEX btreeidx3 ON tbloom (i3);
CREATE INDEX
=# CREATE INDEX btreeidx4 ON tbloom (i4);
CREATE INDEX
=# CREATE INDEX btreeidx5 ON tbloom (i5);
CREATE INDEX
=# CREATE INDEX btreeidx6 ON tbloom (i6);
CREATE INDEX
=# EXPLAIN ANALYZE SELECT * FROM tbloom WHERE i2 = 898732 AND i5 = 123451;
QUERY PLAN
-------------------------------------------------------------------&zwsp;-----------------------------------------------------------
Bitmap Heap Scan on tbloom (cost=9.29..13.30 rows=1 width=24) (actual time=0.148..0.148 rows=0 loops=1)
QUERY PLAN
-------------------------------------------------------------------&zwsp;--------------------------------------------------------
Bitmap Heap Scan on tbloom (cost=24.34..32.03 rows=2 width=24) (actual time=0.028..0.029 rows=0 loops=1)
Recheck Cond: ((i5 = 123451) AND (i2 = 898732))
-&gt; BitmapAnd (cost=9.29..9.29 rows=1 width=0) (actual time=0.145..0.145 rows=0 loops=1)
-&gt; Bitmap Index Scan on tbloom_i5_idx (cost=0.00..4.52 rows=11 width=0) (actual time=0.089..0.089 rows=10 loops=1)
-&gt; BitmapAnd (cost=24.34..24.34 rows=2 width=0) (actual time=0.027..0.027 rows=0 loops=1)
-&gt; Bitmap Index Scan on btreeidx5 (cost=0.00..12.04 rows=500 width=0) (actual time=0.026..0.026 rows=0 loops=1)
Index Cond: (i5 = 123451)
-&gt; Bitmap Index Scan on tbloom_i2_idx (cost=0.00..4.52 rows=11 width=0) (actual time=0.048..0.048 rows=8 loops=1)
-&gt; Bitmap Index Scan on btreeidx2 (cost=0.00..12.04 rows=500 width=0) (never executed)
Index Cond: (i2 = 898732)
Planning time: 2.049 ms
Execution time: 0.280 ms
Planning Time: 0.491 ms
Execution Time: 0.055 ms
(9 rows)
</programlisting>
Although this query runs much faster than with either of the single
indexes, we pay a large penalty in index size. Each of the single-column
btree indexes occupies 214 MB, so the total space needed is over 1.2GB,
more than 8 times the space used by the bloom index.
indexes, we pay a penalty in index size. Each of the single-column
btree indexes occupies 2 MB, so the total space needed is 12 MB,
eight times the space used by the bloom index.
</para>
</sect2>