索引顺序扫描引发的heap scan IO放大, 背后的统计学原理与解决办法

xiaoxiao2026-05-06 22

背景

通过B-TREE索引扫描可能会带来了巨大的heap page scan数目，即IO的放大.

为什么呢？

请接下去看完本文揭晓答案。

IO放大的后果：

如果数据库的单个数据块（block_size）很大的话, 这种情况带来的负面影响也将被放大. 例如32k的block_size显然比8k的block_size扫描开销更大.

本文将讲解一下索引扫描引发的heap page scan放大的原因, 以及解决办法。告诫大家注意这样的事情发生，以及如何对付。

正文

测试环境的成本因子如下 :

shared_buffers = 8192MB # min 128kB #seq_page_cost = 1.0 # measured on an arbitrary scale random_page_cost = 1.0 # same scale as above #cpu_tuple_cost = 0.01 # same scale as above cpu_index_tuple_cost = 0.005 # same scale as above #cpu_operator_cost = 0.0025 # same scale as above effective_cache_size = 96GB

我们先创建一个测试表, 插入一些测试数据, 创建一个索引 :

digoal=> create table test_indexscan(id int, info text); CREATE TABLE digoal=> insert into test_indexscan select generate_series(1,5000000),md5(random()::text); INSERT 0 5000000 digoal=> create index idx_test_indexscan_id on test_indexscan (id); CREATE INDEX

我们查看这个表和索引占用了多少数据块.

digoal=> select relpages from pg_class where relname='test_indexscan'; relpages ---------- 10396 (1 row) digoal=> select relpages from pg_class where relname='idx_test_indexscan_id'; relpages ---------- 3402 (1 row)

接下来分析以下查询, 我们看到走索引扫描, 并且扫描的数据块是13547个. (10209 +3338).

扫描的数据块和实际表占用的数据块和索引块相当.

digoal=> explain (analyze,verbose,costs,buffers,timing) select * from test_indexscan where id>90000; QUERY PLAN ------------------------------------------------------------------------------------------------------------------------------------ ----------------------------- Index Scan using idx_test_indexscan_id on digoal.test_indexscan (cost=0.43..99518.57 rows=4912065 width=37) (actual time=0.180..21 72.949 rows=4910000 loops=1) Output: id, info Index Cond: (test_indexscan.id > 90000) Buffers: shared hit=10209 read=3338 Total runtime: 2674.637 ms (5 rows)

这里使用索引扫描为什么没有带来heap page扫描的放大呢? 原因和值的顺序与物理存储顺序一致.

如下, 那么索引扫描的时候没有发生块的跳跃 :

digoal=> select correlation from pg_stats where tablename='test_indexscan' and attname='id'; correlation ------------- 1 (1 row) digoal=> select ctid,id from test_indexscan limit 10; ctid | id --------+--------- (0,1) | 1 (0,2) | 2 (0,3) | 3 (0,4) | 4 (0,5) | 5 (0,6) | 6 (0,7) | 7 (0,8) | 8 (0,9) | 9 (0,10) | 10 (10 rows)

接下来我们插入随机数据, 使得索引扫描时发生heap page的跳跃.

digoal=> truncate test_indexscan ; TRUNCATE TABLE digoal=> insert into test_indexscan select (random()*5000000)::int,md5(random()::text) from generate_series(1,100000); INSERT 0 100000

查询当前的ID列的顺性, 非常小, 说明这个值非常的离散.

digoal=> select correlation from pg_stats where tablename='test_indexscan' and attname='id'; correlation ------------- 0.00986802 (1 row)

从数据分布结果中也能看到这点.

digoal=> select ctid,id from test_indexscan limit 10; ctid | id --------+--------- (0,1) | 4217216 (0,2) | 2127868 (0,3) | 2072952 (0,4) | 62641 (0,5) | 4927312 (0,6) | 3000894 (0,7) | 2799439 (0,8) | 4165217 (0,9) | 2446438 (0,10) | 2835211 (10 rows)

按以下顺序扫描, 显然会出现大量的数据块的跳跃.

digoal=> select id,ctid from test_indexscan order by id limit 10; id | ctid -----+----------- 56 | (192,318) 73 | (119,163) 218 | (189,2) 235 | (7,209) 260 | (41,427) 340 | (37,371) 548 | (118,363) 607 | (143,174) 690 | (161,38) 714 | (1,21) (10 rows)

当前这个表和索引占用的数据块如下 :

digoal=> select relpages from pg_class where relname='test_indexscan'; relpages ---------- 208 (1 row) digoal=> select relpages from pg_class where relname='idx_test_indexscan_id'; relpages ---------- 86 (1 row)

接下来我们执行这个SQL, 发现走索引扫描了, 但是显然shared hit变得非常的大, 原因就是每扫描一个索引条目, 对应到heap page number都是跳跃的. 造成了heap page扫描的放大. 具体放大多少行呢, 和差出来的行差不多.

digoal=> explain (analyze,verbose,costs,buffers,timing) select * from test_indexscan where id>90000; QUERY PLAN ------------------------------------------------------------------------------------------------------------------------------------ ---------------------- Index Scan using idx_test_indexscan_id on digoal.test_indexscan (cost=0.29..2035.38 rows=99719 width=37) (actual time=0.027..87.45 6 rows=98229 loops=1) Output: id, info Index Cond: (test_indexscan.id > 90000) Buffers: shared hit=97837 Total runtime: 97.370 ms (5 rows)

heap page scan放大评估和索引扫描了多少条目有关, 但至少有98229个条目 :

digoal=> select count(*) from test_indexscan where id>90000; count ------- 98229 (1 row)

如果纯随机扫描, 那么将要扫描98229次heap page. 也就不难理解这里的Buffers: shared hit=97837.

但是实际上, PostgreSQL的优化器似乎没有关注这些开销, 因为我们看到的成本只有2035.38 (这里和random_page_cost以及effective_cache_size 大于整个表和索引的空间有关)

接下来把random_page_cost设置为2和1, 两个cost相减, 看看到底优化器评估了多少个块扫描.

digoal=> set random_page_cost=2; SET digoal=> set enable_seqscan=off; SET digoal=> explain (analyze,verbose,costs,buffers,timing) select * from test_indexscan where id>90000; QUERY PLAN ------------------------------------------------------------------------------------------------------------------------------------ ---------------------- Index Scan using idx_test_indexscan_id on digoal.test_indexscan (cost=0.29..2305.73 rows=98255 width=37) (actual time=0.045..81.76 8 rows=98229 loops=1) Output: id, info Index Cond: (test_indexscan.id > 90000) Buffers: shared hit=97837 Total runtime: 92.186 ms (5 rows) digoal=> set random_page_cost=1; SET digoal=> explain (analyze,verbose,costs,buffers,timing) select * from test_indexscan where id>90000; QUERY PLAN ------------------------------------------------------------------------------------------------------------------------------------ ---------------------- Index Scan using idx_test_indexscan_id on digoal.test_indexscan (cost=0.29..2012.75 rows=98255 width=37) (actual time=0.028..80.05 5 rows=98229 loops=1) Output: id, info Index Cond: (test_indexscan.id > 90000) Buffers: shared hit=97837 Total runtime: 90.549 ms (5 rows)

相减得到293, 即优化器认为index scan需要扫描293个数据块.

digoal=> select 2305-2012; ?column? ---------- 293 (1 row)

接下来我把enable_indexscan关闭, 让优化器选择bitmap scan.

digoal=> set enable_indexscan=off; SET digoal=> explain (analyze,verbose,costs,buffers,timing) select * from test_indexscan where id>90000; QUERY PLAN ------------------------------------------------------------------------------------------------------------------------------------ Bitmap Heap Scan on digoal.test_indexscan (cost=846.77..2282.96 rows=98255 width=37) (actual time=15.291..35.911 rows=98229 loops= 1) Output: id, info Recheck Cond: (test_indexscan.id > 90000) Buffers: shared hit=292 -> Bitmap Index Scan on idx_test_indexscan_id (cost=0.00..822.21 rows=98255 width=0) (actual time=15.202..15.202 rows=98229 loo ps=1) Index Cond: (test_indexscan.id > 90000) Buffers: shared hit=84 Total runtime: 45.838 ms (8 rows)

从bitmap scan的结果可以看到, 实际扫描的块为292个, 相比index scan少扫描了9.7万多数据块. 并且实际的执行时间也是bitmap scan要快很多.

本例PostgreSQL在计算index scan的random page的成本时, 评估得到的index scan成本小于bitmap index scan的成本, 然而实际上当correlation 很小时, index scan会扫描更多次的heap page, 成本远远大于bitmap scan.

本例发生这样的情况, 具体的原因和我们的成本因子设置有关系, 因为错误的设置了random_page_cost以及表和索引的大小小于effective_cache_size, PostgreSQL在使用这样的成本因子计算成本时, 出现了bitmap scan大于index scan成本的结果.

所以设置正确的成本因子非常重要, 这也是我们需要校准成本因子的原因.

例子 :

[postgres@digoal pgdata]$ psql psql (9.3.4) Type "help" for help.

默认的成本因子如下

digoal=# show seq_page_cost; seq_page_cost --------------- 1 (1 row) digoal=# show random_page_cost; random_page_cost ------------------ 4 (1 row) digoal=# show cpu_tuple_cost; cpu_tuple_cost ---------------- 0.01 (1 row) digoal=# show cpu_index_tuple_cost; cpu_index_tuple_cost ---------------------- 0.005 (1 row) digoal=# show cpu_operator_cost; cpu_operator_cost ------------------- 0.0025 (1 row) digoal=# show effective_cache_size; effective_cache_size ---------------------- 128MB (1 row)

表和索引的大小如下

把random_page_cost校准为10, 这个在一般的硬件环境中都适用.

digoal=# set random_page_cost=10; SET

默认选择了全表扫描

digoal=# explain (analyze,costs,buffers,timing,verbose) select * from tbl_cost_align where id>2000000; QUERY PLAN ----------------------------------------------------------------------------------------------------------------------------------- Seq Scan on public.tbl_cost_align (cost=0.00..65538.00 rows=2996963 width=45) (actual time=0.050..1477.028 rows=2997015 loops=1) Output: id, info, crt_time Filter: (tbl_cost_align.id > 2000000) Rows Removed by Filter: 2985 Buffers: shared hit=28038 Total runtime: 2011.742 ms (6 rows)

关闭全表扫描后, 选择了bitmap scan

digoal=# set enable_seqscan=off; SET digoal=# explain (analyze,costs,buffers,timing,verbose) select * from tbl_cost_align where id>2000000; QUERY PLAN ------------------------------------------------------------------------------------------------------------------------------------ ---------------- Bitmap Heap Scan on public.tbl_cost_align (cost=105426.89..170926.93 rows=2996963 width=45) (actual time=1221.104..2911.889 rows=2 997015 loops=1) Output: id, info, crt_time Recheck Cond: (tbl_cost_align.id > 2000000) Rows Removed by Index Recheck: 2105 Buffers: shared hit=36229 -> Bitmap Index Scan on tbl_cost_align_id (cost=0.00..104677.65 rows=2996963 width=0) (actual time=1214.865..1214.865 rows=2997 015 loops=1) Index Cond: (tbl_cost_align.id > 2000000) Buffers: shared hit=8191 Total runtime: 3585.699 ms (9 rows)

关闭bitmap scan后选择了index scan, index scan的cost远远大于评估到的bitmap scan. 因为我们使用了正确的成本因子.

digoal=# set enable_bitmapscan=off; SET digoal=# explain (analyze,costs,buffers,timing,verbose) select * from tbl_cost_align where id>2000000; QUERY PLAN ------------------------------------------------------------------------------------------------------------------------------------ ---------------------------- Index Scan using tbl_cost_align_id on public.tbl_cost_align (cost=0.43..16601388.04 rows=2996963 width=45) (actual time=0.064..566 2.361 rows=2997015 loops=1) Output: id, info, crt_time Index Cond: (tbl_cost_align.id > 2000000) Buffers: shared hit=3005084 Total runtime: 6173.067 ms (5 rows)

当错误的设置了random_page_cost=1=seq_page_cost时, 执行计划会有所改变(改变出现在effective_cache_size大于表和索引的大小时).

the wrong plan cost occur when i set random_page_cost to 1, and effective_cache_size big then index size and table size in this case.

重新进入psql, 所有因子重回默认值.

digoal=# set random_page_cost=1; SET digoal=# explain (analyze,costs,buffers,timing,verbose) select * from tbl_cost_align where id>2000000; QUERY PLAN ----------------------------------------------------------------------------------------------------------------------------------- Seq Scan on public.tbl_cost_align (cost=0.00..65538.00 rows=2996963 width=45) (actual time=0.040..1692.712 rows=2997015 loops=1) Output: id, info, crt_time Filter: (tbl_cost_align.id > 2000000) Rows Removed by Filter: 2985 Buffers: shared hit=28038 Total runtime: 2249.313 ms (6 rows)

目前看来还正确

digoal=# set enable_seqscan=off; SET digoal=# explain (analyze,costs,buffers,timing,verbose) select * from tbl_cost_align where id>2000000; QUERY PLAN ------------------------------------------------------------------------------------------------------------------------------------ -------------- Bitmap Heap Scan on public.tbl_cost_align (cost=31446.89..96946.93 rows=2996963 width=45) (actual time=1224.445..2454.797 rows=299 7015 loops=1) Output: id, info, crt_time Recheck Cond: (tbl_cost_align.id > 2000000) Rows Removed by Index Recheck: 2105 Buffers: shared hit=36229 -> Bitmap Index Scan on tbl_cost_align_id (cost=0.00..30697.65 rows=2996963 width=0) (actual time=1220.404..1220.404 rows=29970 15 loops=1) Index Cond: (tbl_cost_align.id > 2000000) Buffers: shared hit=8191 Total runtime: 2955.816 ms (9 rows)

当effective_cache_size还是小于表和索引时, 执行计划依旧正确

digoal=# set effective_cache_size='280MB'; SET digoal=# explain (analyze,costs,buffers,timing,verbose) select * from tbl_cost_align where id>2000000; QUERY PLAN ------------------------------------------------------------------------------------------------------------------------------------ ------------- Bitmap Heap Scan on public.tbl_cost_align (cost=31446.89..96946.93 rows=2996963 width=45) (actual time=963.845..2060.463 rows=2997 015 loops=1) Output: id, info, crt_time Recheck Cond: (tbl_cost_align.id > 2000000) Rows Removed by Index Recheck: 2105 Buffers: shared hit=36229 -> Bitmap Index Scan on tbl_cost_align_id (cost=0.00..30697.65 rows=2996963 width=0) (actual time=959.673..959.673 rows=2997015 loops=1) Index Cond: (tbl_cost_align.id > 2000000) Buffers: shared hit=8191 Total runtime: 2515.649 ms (9 rows)

当effective_cache_size大于表和索引的大小时, index scan的成本低于bitmap scan的成本了.

When effective_cache_size large then table and index's size. then use index scan first than bitmap scan. digoal=# set effective_cache_size='283MB'; SET digoal=# explain (analyze,costs,buffers,timing,verbose) select * from tbl_cost_align where id>2000000; QUERY PLAN ------------------------------------------------------------------------------------------------------------------------------------ ------------------------- Index Scan using tbl_cost_align_id on public.tbl_cost_align (cost=0.43..92030.24 rows=2996963 width=45) (actual time=0.045..5238.3 61 rows=2997015 loops=1) Output: id, info, crt_time Index Cond: (tbl_cost_align.id > 2000000) Buffers: shared hit=3005084 Total runtime: 5689.583 ms (5 rows)

如果这个时候再把random_page_cost调回正常值10, 则执行计划回归正常.

digoal=# set random_page_cost=10; SET digoal=# explain (analyze,costs,buffers,timing,verbose) select * from tbl_cost_align where id>2000000; QUERY PLAN ------------------------------------------------------------------------------------------------------------------------------------ --------------- Bitmap Heap Scan on public.tbl_cost_align (cost=105426.89..170926.93 rows=2996963 width=45) (actual time=918.225..2195.414 rows=29 97015 loops=1) Output: id, info, crt_time Recheck Cond: (tbl_cost_align.id > 2000000) Rows Removed by Index Recheck: 2105 Buffers: shared hit=36229 -> Bitmap Index Scan on tbl_cost_align_id (cost=0.00..104677.65 rows=2996963 width=0) (actual time=913.935..913.935 rows=299701 5 loops=1) Index Cond: (tbl_cost_align.id > 2000000) Buffers: shared hit=8191 Total runtime: 2698.429 ms (9 rows) digoal=# set enable_seqscan=on; SET digoal=# explain (analyze,costs,buffers,timing,verbose) select * from tbl_cost_align where id>2000000; QUERY PLAN ----------------------------------------------------------------------------------------------------------------------------------- Seq Scan on public.tbl_cost_align (cost=0.00..65538.00 rows=2996963 width=45) (actual time=0.020..1522.791 rows=2997015 loops=1) Output: id, info, crt_time Filter: (tbl_cost_align.id > 2000000) Rows Removed by Filter: 2985 Buffers: shared hit=28038 Total runtime: 2104.057 ms (6 rows)

本例说明了成本因子的重要性. 千万不能随意设置, 即使完全内存命中, random_page_cost也应该大于seq_page_cost.

我在前一篇BLOG中测试了这样的场景, 完全内存命中的场景可以设置 random_page_cost=1.6; seq_page_cost=1;

《优化器成本因子校对 - PostgreSQL explain cost constants alignment to timestamp》

B-TREE扫描，对于线性相关性不好的列，会放大HEAP SCAN 的IO消耗，使用bitmap可以解决。

线性相关性的知识如下

《PostgreSQL 计算任意类型字段之间的线性相关性》

《PostgreSQL 统计信息之 - 逻辑与物理存储的线性相关性》

小结

1. 当字段的存储与值线性相关性差时，使用index scan会导致大量的HEAP SCAN IO放大。

2. bitmap index scan巧妙的解决了放大的问题，bitmap index scan对index item按照ctid(heap行号)排序后再取数据，避免了单个HEAP PAGE的重复IO。

3. 使用cluster对heap数据按索引顺序进行重排，也可以解决HEAP SCAN IO放大的问题。

参考

1. http://www.postgresql.org/message-id/flat/13668.1398541533@sss.pgh.pa.us#13668.1398541533@sss.pgh.pa.us

2. 《优化器成本因子校对 - PostgreSQL explain cost constants alignment to timestamp》

3. src/backend/optimizer/path/costsize.c

cost_index function : /* * Now interpolate based on estimated index order correlation to get total * disk I/O cost for main table accesses. */ csquared = indexCorrelation * indexCorrelation; run_cost += max_IO_cost + csquared * (min_IO_cost - max_IO_cost); 相关资源：python入门教程(PDF版)

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索引顺序扫描引发的heap scan IO放大, 背后的统计学原理与解决办法

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