Postgresql_根据执行计划优化SQL 我会带着你远行 2021-10-23 09:31 330阅读 0赞 # 执行计划路径选择 # postgresql查询规划过程中，查询请求的不同执行方案是通过建立不同的路径来表达的，在生成许多符合条件的路径之后，要从中选择出代价最小的路径，把它转化为一个计划，传递给执行器执行，规划器的核心工作就是生成多条路径，然后从中找出最优的那一条。 ## 代价评估 ## 评估路径优劣的依据是用系统表pg\_statistic中的统计信息估算出来的不同路径的代价(cost)，PostgreSQL估计计划成本的方式：基于统计信息估计计划中各个节点的成本。PostgreSQL会分析各个表来获取一个统计信息样本（这个操作通常是由autovacuum这个守护进程周期性的执行analyze，来收集这些统计信息，然后保存到pg\_statistic和pg\_class里面）。 ## 用于估算代价的参数postgresql.conf ## # - Planner Cost Constants - #seq_page_cost = 1.0 # measured on an arbitrary scale 顺序磁盘扫描时单个页面的开销 #random_page_cost = 4.0 # same scale as above 随机磁盘访问时单页面的读取开销 #cpu_tuple_cost = 0.01 # same scale as above cpu处理每一行的开销 #cpu_index_tuple_cost = 0.005 # same scale as above cpu处理每个索引行的开销 #cpu_operator_cost = 0.0025 # same scale as above cpu处理每个运算符或者函数调用的开销 #parallel_tuple_cost = 0.1 # same scale as above 计算并行处理的成本，如果成本高于非并行，则不会开启并行处理。 #parallel_setup_cost = 1000.0 # same scale as above #min_parallel_relation_size = 8MB #effective_cache_size = 4GB 再一次索引扫描中可用的文件系统内核缓冲区有效大小 也可以使用 show all的方式查看 ## 路径的选择 ## --查看表信息 db_jcxxglpt=# \d t_jcxxgl_tjaj Table "db_jcxx.t_jcxxgl_tjaj" Column | Type | Modifiers --------------+--------------------------------+----------- c_bh | character(32) | not null c_xzdm | character varying(300) | c_jgid | character(32) | c_ajbm | character(22) | ... Indexes: "t_jcxxgl_tjaj_pkey" PRIMARY KEY, btree (c_bh) "idx_ttjaj_cah" btree (c_ah) "idx_ttjaj_dslrq" btree (d_slrq) 首先更新统计信息vacuum analyze t_jcxxgl_tjaj，许多时候可能因为统计信息的不准确导致了不正常的执行计划 --执行计划，全表扫描 db_jcxxglpt=# explain (analyze,verbose,costs,buffers,timing)select c_bh,c_xzdm,c_jgid,c_ajbm from t_jcxxgl_tjaj where d_slrq >='2018-03-18'; QUERY PLAN ------------------------------------------------------------------------------------------------------------ Seq Scan on db_jcxx.t_jcxxgl_tjaj (cost=0.00..9.76 rows=3 width=96) (actual time=1.031..1.055 rows=3 loops =1) Output: c_bh, c_xzdm, c_jgid, c_ajbm Filter: (t_jcxxgl_tjaj.d_slrq >= '2018-03-18'::date) Rows Removed by Filter: 138 Buffers: shared hit=8 Planning time: 6.579 ms Execution time: 1.163 ms (7 rows) --执行计划，关闭全表扫描 db_jcxxglpt=# set session enable_seqscan = off; SET db_jcxxglpt=# explain (analyze,verbose,costs,buffers,timing)select c_bh,c_xzdm,c_jgid,c_ajbm from t_jcxxgl_tjaj where d_slrq >='2018-03-18'; QUERY PLAN ------------------------------------------------------------------------------------------------------------ Index Scan using idx_ttjaj_dslrq on db_jcxx.t_jcxxgl_tjaj (cost=0.14..13.90 rows=3 width=96) (actual time=0.012..0.026 rows=3 loops=1) Output: c_bh, c_xzdm, c_jgid, c_ajbm Index Cond: (t_jcxxgl_tjaj.d_slrq >= '2018-03-18'::date) Buffers: shared hit=4 Planning time: 0.309 ms Execution time: 0.063 ms (6 rows) d_slrq上面有btree索引，但是查看执行计划并没有走索引，这是为什么呢？ 代价计算： 一个路径的估算由三部分组成：启动代价(startup cost)，总代价(totalcost)，执行结果的排序方式(pathkeys) 代价估算公式：总代价=启动代价+I/O代价+CPU代价（cost=S+P+W*T） P:执行时要访问的页面数，反应磁盘的I/O次数 T:表示在执行时所要访问的元组数，反映了cpu开销 W:表示磁盘I/O代价和CPU开销建的权重因子 统计信息：统计信息的其中一部分是每个表和索引中项的总数，以及每个表和索引占用的磁盘块数。这些信息保存在pg_class表的reltuples和relpages列中。我们可以这样查询相关信息： --查看统计信息 db_jcxxglpt=# select relpages,reltuples from pg_class where relname ='t_jcxxgl_tjaj'; relpages | reltuples ----------+----------- 8 | 141 (1 row) total_cost = 1(seq_page_cost)*8(磁盘总页数)+0.01(cpu_tuple_cost)*141(表的总记录数)+0.0025(cpu_operation_cost)*141(表的总记录数)=9.7625 可以看到走索引的cost=13.90比全表扫描cost=9.76要大。在表较小的情况下，全表扫描比索引扫描更有效， index scan 至少要发生两次I/O，一次是读取索引块，一次是读取数据块。 ## seq\_scan源码 ## /* * cost_seqscan * Determines and returns the cost of scanning a relation sequentially. * * 'baserel' is the relation to be scanned * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL */ void cost_seqscan(Path *path, PlannerInfo *root, RelOptInfo *baserel, ParamPathInfo *param_info) { Cost startup_cost = 0; Cost cpu_run_cost; Cost disk_run_cost; double spc_seq_page_cost; QualCost qpqual_cost; Cost cpu_per_tuple; /* Should only be applied to base relations */ Assert(baserel->relid > 0); Assert(baserel->rtekind == RTE_RELATION); /* Mark the path with the correct row estimate */ if (param_info) path->rows = param_info->ppi_rows; else path->rows = baserel->rows; if (!enable_seqscan) startup_cost += disable_cost; /* fetch estimated page cost for tablespace containing table */ get_tablespace_page_costs(baserel->reltablespace, NULL,&spc_seq_page_cost); /* * disk costs */ disk_run_cost = spc_seq_page_cost * baserel->pages; /* CPU costs */ get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost); startup_cost += qpqual_cost.startup; cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple; cpu_run_cost = cpu_per_tuple * baserel->tuples; /* tlist eval costs are paid per output row, not per tuple scanned */ startup_cost += path->pathtarget->cost.startup; cpu_run_cost += path->pathtarget->cost.per_tuple * path->rows; /* Adjust costing for parallelism, if used. */ if (path->parallel_workers > 0) { double parallel_divisor = get_parallel_divisor(path); /* The CPU cost is divided among all the workers. */ cpu_run_cost /= parallel_divisor; /* * It may be possible to amortize some of the I/O cost, but probably * not very much, because most operating systems already do aggressive * prefetching. For now, we assume that the disk run cost can't be * amortized at all. */ /* * In the case of a parallel plan, the row count needs to represent * the number of tuples processed per worker. */ path->rows = clamp_row_est(path->rows / parallel_divisor); } path->startup_cost = startup_cost; path->total_cost = startup_cost + cpu_run_cost + disk_run_cost; } ## 一个SQL优化实例 ## 慢SQL： select c_ajbh, c_ah, c_cbfy, c_cbrxm, d_larq, d_jarq, n_dbjg, c_yqly from db_zxzhld.t_zhld_db dbxx join db_zxzhld.t_zhld_ajdbxx dbaj on dbxx.c_bh = dbaj.c_dbbh where dbxx.n_valid=1 and dbxx.n_state in (1,2,3) and dbxx.c_dbztbh='1003' and dbaj.c_zblx='1003' and dbaj.c_dbfy='0' and dbaj.c_gy = '2550' and c_ajbh in (select distinct c_ajbh from db_zxzhld.t_zhld_zbajxx where n_dbzt = 1 and c_zblx = '1003' and c_gy = '2550' ) order by d_larq asc, c_ajbh asc limit 15 offset 0 慢sql耗时：7s 咋们先过下这个sql是干什么的、首先dbxx和dbaj的一个join连接然后dbaj.c_ajbh要包含在zbaj表里面，做了个排序，取了15条记录、大概就这样。 Sql有个缺点就是我不知道查询的字段是从那个表里面取的、建议加上表别名.字段。 查看该sql的表的数据量： db_zxzhld.t_zhld_db ：1311 db_zxzhld.t_zhld_ajdbxx ：341296 db_zxzhld.t_zhld_zbajxx ：1027619 执行计划： 01 Limit (cost=36328.67..36328.68 rows=1 width=107) (actual time=88957.677..88957.729 rows=15 loops=1) 02 -> Sort (cost=36328.67..36328.68 rows=1 width=107) (actual time=88957.653..88957.672 rows=15 loops=1) 03 Sort Key: dbaj.d_larq, dbaj.c_ajbh 04 Sort Method: top-N heapsort Memory: 27kB 05 -> Nested Loop Semi Join (cost=17099.76..36328.66 rows=1 width=107) (actual time=277.794..88932.662 rows=8605 loops=1) 06 Join Filter: ((dbaj.c_ajbh)::text = (t_zhld_zbajxx.c_ajbh)::text) 07 Rows Removed by Join Filter: 37018710 08 -> Nested Loop (cost=0.00..19200.59 rows=1 width=107) (actual time=199.141..601.845 rows=8605 loops=1) 09 Join Filter: (dbxx.c_bh = dbaj.c_dbbh) 10 Rows Removed by Join Filter: 111865 11 -> Seq Scan on t_zhld_ajdbxx dbaj (cost=0.00..19117.70 rows=219 width=140) (actual time=198.871..266.182 rows=8605 loops=1) 12 Filter: ((n_valid = 1) AND ((c_zblx)::text = '1003'::text) AND ((c_dbfy)::text = '0'::text) AND ((c_gy)::text = '2550'::text)) 13 Rows Removed by Filter: 332691 14 -> Materialize (cost=0.00..66.48 rows=5 width=33) (actual time=0.001..0.017 rows=14 loops=8605) 15 -> Seq Scan on t_zhld_db dbxx (cost=0.00..66.45 rows=5 width=33) (actual time=0.044..0.722 rows=14 loops=1) 16 Filter: ((n_valid = 1) AND ((c_dbztbh)::text = '1003'::text) AND (n_state = ANY ('{1,2,3}'::integer[]))) 17 Rows Removed by Filter: 1297 18 -> Materialize (cost=17099.76..17117.46 rows=708 width=32) (actual time=0.006..4.890 rows=4303 loops=8605) 19 -> HashAggregate (cost=17099.76..17106.84 rows=708 width=32) (actual time=44.011..54.924 rows=8605 loops=1) 20 Group Key: t_zhld_zbajxx.c_ajbh 21 -> Bitmap Heap Scan on t_zhld_zbajxx (cost=163.36..17097.99 rows=708 width=32) (actual time=5.218..30.278 rows=8605 loops=1) 22 Recheck Cond: ((n_dbzt = 1) AND ((c_zblx)::text = '1003'::text)) 23 Filter: ((c_gy)::text = '2550'::text) 24 Rows Removed by Filter: 21849 25 Heap Blocks: exact=960 26 -> Bitmap Index Scan on i_tzhldzbajxx_zblx_dbzt (cost=0.00..163.19 rows=5876 width=0) (actual time=5.011..5.011 rows=30458 loops=1) 27 Index Cond: ((n_dbzt = 1) AND ((c_zblx)::text = '1003'::text)) 28 Planning time: 1.258 ms 29 Execution time: 88958.029 ms 执行计划解读： 1：第27->21行，通过索引i_tzhldzbajxx_zblx_dbzt过滤表t_zhld_zbajxx的数据，然后根据过滤条件(c_gy)::text = '2550'::text过滤最终返回8605条数据 2：第17->15行，根据条件过滤t_zhld_db表的数据，最终返回了14条数据 3：第20->19行，对表t_zhld_zbajxx做group by的操作 4：第13->11行，全表扫描t_zhld_ajdbxx 最终返回了8605条数据 5：第08行，根据t_zhld_ajdbxx返回的8605条结果集作为驱动表和t_zhld_db的结果集(14条)做嵌套循环，t_zhld_db的结果集被循环了8605次。然后过滤掉了其中的111865条记录，那么最终将得到（8605*14-111865） = 8605 6：第07->05行，根据第08和18行返回的结果集最终做了Nested Loop Semi Join，第18行的4303条结果集被循环了8605次,(4303*8605-37018710)=8605 7: 第04->02行，对最终的8605条记录进行排序 8：第01行，limit最终获取15条记录 整个执行计划中耗时最长的地方在05行Nested Loop Semi Join，actual time=277.794..88932.662， 表db_zxzhld.t_zhld_db dbxx和db_zxzhld.t_zhld_ajdbxx均是全表扫描 ## 具体优化步骤 ## 查看索引页并没有索引，创建c_ajbh，c_dbbh等逻辑外键的索引 drop index if exists I_T_ZHLD_AJDBXX_AJBH; create index I_T_ZHLD_AJDBXX_AJBH on T_ZHLD_AJDBXX (c_ajbh); commit; drop index if exists I_T_ZHLD_AJDBXX_DBBH; create index I_T_ZHLD_AJDBXX_DBBH on T_ZHLD_AJDBXX (c_dbbh); commit; 创建d_larq,c_ajbh的排序索引： drop index if exists I_T_ZHLD_AJDBXX_m6; create index I_T_ZHLD_AJDBXX_m6 on T_ZHLD_AJDBXX (c_zblx,c_dbfy,c_gy,d_larq asc,c_ajbh asc); commit; drop index if exists I_T_ZHLD_ZBAJXX_h3 ; create index I_T_ZHLD_ZBAJXX_h3 on db_zxzhld.t_zhld_zbajxx (n_dbzt,c_zblx,c_gy,c_gy); commit; 创建索引后执行计划有了改变，原来的dbaj表和dbxx表先做nestedloop变成了zbaj和dbaj表先做了nestedloop join,总的cost也从36328.68降到了12802.87， Limit (cost=12802.87..12802.87 rows=1 width=107) (actual time=4263.598..4263.648 rows=15 loops=1) -> Sort (cost=12802.87..12802.87 rows=1 width=107) (actual time=4263.592..4263.609 rows=15 loops=1) Sort Key: dbaj.d_larq, dbaj.c_ajbh Sort Method: top-N heapsort Memory: 27kB -> Nested Loop (cost=2516.05..12802.86 rows=1 width=107) (actual time=74.240..4239.723 rows=8605 loops=1) Join Filter: (dbaj.c_dbbh = dbxx.c_bh) Rows Removed by Join Filter: 111865 -> Nested Loop (cost=2516.05..12736.34 rows=1 width=140) (actual time=74.083..327.974 rows=8605 loops=1) -> HashAggregate (cost=2515.62..2522.76 rows=714 width=32) (actual time=74.025..90.185 rows=8605 loops=1) Group Key: ("ANY_subquery".c_ajbh)::text -> Subquery Scan on "ANY_subquery" (cost=2499.56..2513.84 rows=714 width=32) (actual time=28.782..59.823 rows=8605 loops=1) -> HashAggregate (cost=2499.56..2506.70 rows=714 width=32) (actual time=28.778..39.968 rows=8605 loops=1) Group Key: zbaj.c_ajbh -> Index Scan using i_t_zhld_zbajxx_h3 on t_zhld_zbajxx zbaj (cost=0.42..2497.77 rows=715 width=32) (actual time=0.062..15.104 rows=8605 loops=1) Index Cond: ((n_dbzt = 1) AND ((c_zblx)::text = '1003'::text) AND ((c_gy)::text = '2550'::text)) -> Index Scan using i_t_zhld_ajdbxx_ajbh on t_zhld_ajdbxx dbaj (cost=0.42..14.29 rows=1 width=140) (actual time=0.015..0.021 rows=1 loops=8605) Index Cond: ((c_ajbh)::text = ("ANY_subquery".c_ajbh)::text) Filter: (((c_zblx)::text = '1003'::text) AND ((c_dbfy)::text = '0'::text) AND ((c_gy)::text = '2550'::text)) Rows Removed by Filter: 1 -> Seq Scan on t_zhld_db dbxx (cost=0.00..66.45 rows=5 width=33) (actual time=0.015..0.430 rows=14 loops=8605) Filter: ((n_valid = 1) AND ((c_dbztbh)::text = '1003'::text) AND (n_state = ANY ('{1,2,3}'::integer[]))) Rows Removed by Filter: 1298 Planning time: 1.075 ms Execution time: 4263.803 ms 执行的时间还是要4s左右仍然不满足需求，并且没有使用上I\_T\_ZHLD\_AJDBXX\_m6这个索引。 ## 等价改写SQL(1) ## 等价改写：将排序条件加入db_zxzhld.t_zhld_ajdbxx让其先排序，再和t_zhld_db表连接。 修改后sql： Select dbaj.c_ajbh, dbaj.c_ah, dbaj.c_cbfy, dbaj.c_cbrxm, dbaj.d_larq, dbaj.d_jarq, dbaj.n_dbjg, dbaj.c_yqly from (select * from db_zxzhld.t_zhld_db where n_valid=1 and n_state in (1,2,3) and c_dbztbh='1003' )dbxx join (select * from db_zxzhld.t_zhld_ajdbxx where n_valid=1 and c_zblx='1003' and c_dbfy='0' and c_gy = '2550' and c_ajbh in (select distinct c_ajbh from db_zxzhld.t_zhld_zbajxx where n_dbzt = 1 and c_zblx = '1003' and c_gy = '2550' ) order by d_larq asc, c_ajbh asc)dbaj on dbxx.c_bh = dbaj.c_dbbh limit 15 offset 0 再次查看执行计划： Limit (cost=3223.92..3231.97 rows=1 width=107) (actual time=127.291..127.536 rows=15 loops=1) -> Nested Loop (cost=3223.92..3231.97 rows=1 width=107) (actual time=127.285..127.496 rows=15 loops=1) -> Sort (cost=3223.64..3223.65 rows=1 width=140) (actual time=127.210..127.225 rows=15 loops=1) Sort Key: t_zhld_ajdbxx.d_larq, t_zhld_ajdbxx.c_ajbh Sort Method: quicksort Memory: 2618kB -> Hash Semi Join (cost=2523.19..3223.63 rows=1 width=140) (actual time=55.913..107.265 rows=8605 loops=1) Hash Cond: ((t_zhld_ajdbxx.c_ajbh)::text = (t_zhld_zbajxx.c_ajbh)::text) -> Index Scan using i_t_zhld_ajdbxx_m6 on t_zhld_ajdbxx (cost=0.42..700.28 rows=219 width=140) (actual time=0.065..22.005 rows=8605 loops=1) Index Cond: (((c_zblx)::text = '1003'::text) AND ((c_dbfy)::text = '0'::text) AND ((c_gy)::text = '2550'::text)) -> Hash (cost=2513.84..2513.84 rows=714 width=32) (actual time=55.802..55.802 rows=8605 loops=1) Buckets: 16384 (originally 1024) Batches: 1 (originally 1) Memory Usage: 675kB -> HashAggregate (cost=2499.56..2506.70 rows=714 width=32) (actual time=30.530..43.275 rows=8605 loops=1) Group Key: t_zhld_zbajxx.c_ajbh -> Index Scan using i_t_zhld_zbajxx_h3 on t_zhld_zbajxx (cost=0.42..2497.77 rows=715 width=32) (actual time=0.043..15.552 rows=8605 loops=1) Index Cond: ((n_dbzt = 1) AND ((c_zblx)::text = '1003'::text) AND ((c_gy)::text = '2550'::text)) -> Index Scan using t_zhld_db_pkey on t_zhld_db (cost=0.28..8.30 rows=1 width=33) (actual time=0.009..0.011 rows=1 loops=15) Index Cond: (c_bh = t_zhld_ajdbxx.c_dbbh) Filter: (((c_dbztbh)::text = '1003'::text) AND (n_state = ANY ('{1,2,3}'::integer[]))) Planning time: 1.154 ms Execution time: 127.734 ms 这一次可以看出，ajdbxx和zbajxx表做了hash semi join 消除了nestedloop,cost降到了3231.97。并且使用上了i_t_zhld_ajdbxx_m6 子查询中in的结果集有一万多条数据，尝试使用exists等价改写in，看能否有更好的结果 ## 等价改写SQL(2) ## 等价改写：将in替换为exists： select c_ajbh, c_ah, c_cbfy, c_cbrxm, d_larq, d_jarq, n_dbjg, c_yqly from (select c_bh from db_zxzhld.t_zhld_db where n_state in (1,2,3) and c_dbztbh='1003' )dbxx join (select c_ajbh, c_ah, c_cbfy, c_cbrxm, d_larq, d_jarq, n_dbjg, c_yqly,c_dbbh from db_zxzhld.t_zhld_ajdbxx ajdbxx where c_zblx='1003' and c_dbfy='0' and c_gy = '2550' and exists (select distinct c_ajbh from db_zxzhld.t_zhld_zbajxx zbajxx where ajdbxx.c_ajbh = zbajxx.c_ajbh and n_dbzt = 1 and c_zblx = '1003' and c_gy = '2550' ) order by d_larq asc, c_ajbh asc)dbaj on dbxx.c_bh = dbaj.c_dbbh limit 15 offset 0 再次查看执行计划： Limit (cost=1.12..2547.17 rows=1 width=107) (actual time=0.140..0.727 rows=15 loops=1) -> Nested Loop (cost=1.12..2547.17 rows=1 width=107) (actual time=0.136..0.689 rows=15 loops=1) -> Nested Loop Semi Join (cost=0.85..2538.84 rows=1 width=140) (actual time=0.115..0.493 rows=15 loops=1) -> Index Scan using i_t_zhld_ajdbxx_m6 on t_zhld_ajdbxx t2 (cost=0.42..700.28 rows=219 width=140) (actual time=0.076..0.127 rows=15 loops=1) Index Cond: (((c_zblx)::text = '1003'::text) AND ((c_dbfy)::text = '0'::text) AND ((c_gy)::text = '2550'::text)) -> Index Scan using i_t_zhld_zbajxx_c_ajbh on t_zhld_zbajxx t3 (cost=0.42..8.40 rows=1 width=32) (actual time=0.019..0.019 rows=1 loops=15) Index Cond: ((c_ajbh)::text = (t2.c_ajbh)::text) Filter: (((c_zblx)::text = '1003'::text) AND ((c_gy)::text = '2550'::text) AND (n_dbzt = 1)) -> Index Scan using t_zhld_db_pkey on t_zhld_db (cost=0.28..8.30 rows=1 width=33) (actual time=0.007..0.008 rows=1 loops=15) Index Cond: (c_bh = t2.c_dbbh) Filter: (((c_dbztbh)::text = '1003'::text) AND (n_state = ANY ('{1,2,3}'::integer[]))) Planning time: 1.268 ms Execution time: 0.859 ms 可以看出使用exist效果更好，最终cost 2547.17 (1).少了t_zhld_zbajxx表的group by操作：Sort Key: t_zhld_ajdbxx.d_larq, t_zhld_ajdbxx.c_ajbh。(这一步是因为使用了索引中的排序) (2).少了分组的操作：Group Key: t_zhld_zbajxx.c_ajbh。 第(2)为什么这个查询消除了t_zhld_zbajxx表的group by操作呢？ 原因是exists替换了distinct的功能，一旦满足条件则立刻返回。所以使用exists的时候子查询可以直接去掉distinct。 优化无止境、、、 参考资料： [代价估算][Link 1] [索引和全表扫描][Link 2] [表连接方式][Link 3] pg11添加了并行hash\_join的功能，有兴趣可以了解： [pg11添加了并行hash join][pg11_hash join] 欢迎关注技术公众号：架构师成长营 ![20190820174522816.jpeg][] [Link 1]: http://blog.163.com/li_hx/blog/static/183991413201352083452277/?suggestedreading&wumii [Link 2]: https://www.cnblogs.com/gaojian/archive/2012/11/07/2758750.html [Link 3]: https://blog.csdn.net/wanghai__/article/details/6426941 [pg11_hash join]: https://github.com/digoal/blog/blob/master/201802/20180201_01.md?spm=a2c4e.11153959.blogcont501423.33.713f330aIK75lz&file=20180201_01.md [20190820174522816.jpeg]: /images/20211023/2aa149b0348544f19b156b4cc034e90a.png
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