与原始数据库相比，恢复转储的物化视图的搜索查询速度较慢
Search queries slow on Materialized Views of a restored dump as compared to the original DB
我有一个包含各种物化视图的 psql 数据库，在 运行 查询时，即 query_a 我们在 2800 毫秒内完成查询执行并重新 运行 相同再次查询我们得到 53ms 的执行时间。这可以通过 psql 完成的缓存来解释。现在是棘手的部分，我创建了这个数据库的转储并在 NewDB 中恢复它，当我重新 运行 query_a 我得到 2253ms 的执行时间和重新 运行ning得到相同的时间，即，似乎 psql 缓存在 NewDB 上不起作用。
我进行了各种实验来纠正相同的问题，并注意到当我明确刷新视图时没有任何改进，但如果我删除这些视图并在我的 NewDB 中重新创建它，它会给我原来的性能。
请注意，数据在 DB 和 NewDB 中是不变的，我使用了提到的命令 here 来创建和恢复转储。
在 DB 上重新 运行 查询的结果是 ->
运行第1次和第2次在NewDB上进行相同查询的结果如下->
QUERY PLAN
------------------------------------------------------------------------------------------------------------------------------------------------------------
Aggregate (cost=113790614477.61..113790614477.62 rows=1 width=8) (actual time=2284.605..2284.605 rows=1 loops=1)
Buffers: shared hit=3540872
CTE t
-> Merge Join (cost=40600.92..11846650.56 rows=763041594 width=425) (actual time=3.693..1909.916 rows=6005 loops=1)
Merge Cond: (n.node_id = nd.node_id)
Buffers: shared hit=3524063
-> Index Scan using nodes_node_id on nodes n (cost=0.43..350865.91 rows=3824099 width=389) (actual time=0.014..1651.025 rows=3598491 loops=1)
Buffers: shared hit=3523372
-> Sort (cost=40600.49..40700.26 rows=39907 width=40) (actual time=3.668..4.227 rows=6005 loops=1)
Sort Key: nd.node_id
Sort Method: quicksort Memory: 623kB
Buffers: shared hit=691
-> Bitmap Heap Scan on nodes_depths nd (cost=1153.11..37550.73 rows=39907 width=40) (actual time=0.627..2.846 rows=6005 loops=1)
Recheck Cond: ((ancestor_1 = 1) OR (ancestor_2 = 1))
Heap Blocks: exact=658
Buffers: shared hit=691
-> BitmapOr (cost=1153.11..1153.11 rows=40007 width=0) (actual time=0.547..0.547 rows=0 loops=1)
Buffers: shared hit=33
-> Bitmap Index Scan on nodes_depths_1 (cost=0.00..566.58 rows=20003 width=0) (actual time=0.032..0.032 rows=156 loops=1)
Index Cond: (ancestor_1 = 1)
Buffers: shared hit=4
-> Bitmap Index Scan on nodes_depths_2 (cost=0.00..566.58 rows=20003 width=0) (actual time=0.515..0.515 rows=5849 loops=1)
Index Cond: (ancestor_2 = 1)
Buffers: shared hit=29
-> Merge Right Join (cost=169565733.26..97549168801.28 rows=6491839610305 width=0) (actual time=1915.721..2284.175 rows=6005 loops=1)
Merge Cond: (nodes_fts.node_id = t.node_id)
Buffers: shared hit=3540872
-> Index Only Scan using nodes_fts_idx on nodes_fts (cost=0.43..97055.96 rows=1701569 width=4) (actual time=0.041..277.890 rows=1598712 loops=1)
Heap Fetches: 1598712
Buffers: shared hit=16805
-> Materialize (cost=169565732.84..173380940.81 rows=763041594 width=4) (actual time=1915.675..1916.583 rows=6005 loops=1)
Buffers: shared hit=3524067
-> Sort (cost=169565732.84..171473336.82 rows=763041594 width=4) (actual time=1915.672..1916.057 rows=6005 loops=1)
Sort Key: t.node_id
Sort Method: quicksort Memory: 474kB
Buffers: shared hit=3524067
-> CTE Scan on t (cost=0.00..15260831.88 rows=763041594 width=4) (actual time=3.698..1914.771 rows=6005 loops=1)
Buffers: shared hit=3524063
Planning time: 68.064 ms
Execution time: 2285.084 ms
(40 rows)
第二个 运行 ->
QUERY PLAN
------------------------------------------------------------------------------------------------------------------------------------------------------------
Aggregate (cost=113790614477.61..113790614477.62 rows=1 width=8) (actual time=2295.319..2295.319 rows=1 loops=1)
Buffers: shared hit=3540868
CTE t
-> Merge Join (cost=40600.92..11846650.56 rows=763041594 width=425) (actual time=15.324..1926.744 rows=6005 loops=1)
Merge Cond: (n.node_id = nd.node_id)
Buffers: shared hit=3524063
-> Index Scan using nodes_node_id on nodes n (cost=0.43..350865.91 rows=3824099 width=389) (actual time=0.027..1648.277 rows=3598491 loops=1)
Buffers: shared hit=3523372
-> Sort (cost=40600.49..40700.26 rows=39907 width=40) (actual time=15.254..15.903 rows=6005 loops=1)
Sort Key: nd.node_id
Sort Method: quicksort Memory: 623kB
Buffers: shared hit=691
-> Bitmap Heap Scan on nodes_depths nd (cost=1153.11..37550.73 rows=39907 width=40) (actual time=3.076..10.752 rows=6005 loops=1)
Recheck Cond: ((ancestor_1 = 1) OR (ancestor_2 = 1))
Heap Blocks: exact=658
Buffers: shared hit=691
-> BitmapOr (cost=1153.11..1153.11 rows=40007 width=0) (actual time=2.524..2.525 rows=0 loops=1)
Buffers: shared hit=33
-> Bitmap Index Scan on nodes_depths_1 (cost=0.00..566.58 rows=20003 width=0) (actual time=0.088..0.088 rows=156 loops=1)
Index Cond: (ancestor_1 = 1)
Buffers: shared hit=4
-> Bitmap Index Scan on nodes_depths_2 (cost=0.00..566.58 rows=20003 width=0) (actual time=2.434..2.435 rows=5849 loops=1)
Index Cond: (ancestor_2 = 1)
Buffers: shared hit=29
-> Merge Right Join (cost=169565733.26..97549168801.28 rows=6491839610305 width=0) (actual time=1933.113..2294.894 rows=6005 loops=1)
Merge Cond: (nodes_fts.node_id = t.node_id)
Buffers: shared hit=3540868
-> Index Only Scan using nodes_fts_idx on nodes_fts (cost=0.43..97055.96 rows=1701569 width=4) (actual time=0.077..271.313 rows=1598712 loops=1)
Heap Fetches: 1598712
Buffers: shared hit=16805
-> Materialize (cost=169565732.84..173380940.81 rows=763041594 width=4) (actual time=1933.030..1933.903 rows=6005 loops=1)
Buffers: shared hit=3524063
-> Sort (cost=169565732.84..171473336.82 rows=763041594 width=4) (actual time=1933.026..1933.375 rows=6005 loops=1)
Sort Key: t.node_id
Sort Method: quicksort Memory: 474kB
Buffers: shared hit=3524063
-> CTE Scan on t (cost=0.00..15260831.88 rows=763041594 width=4) (actual time=15.336..1932.145 rows=6005 loops=1)
Buffers: shared hit=3524063
Planning time: 1.154 ms
Execution time: 2295.801 ms
(40 rows)
估计的行数与实际数量相差几个数量级：
CTE Scan on t (cost=0.00..15260831.88 rows=763041594 width=4)
(actual time=15.336..1932.145 rows=6005 loops=1)
当 Postgres 无法准确估计执行查询的特定方式与另一种方式相比的工作量时，它将生成低效的查询计划，这就是为什么即使所有数据都是相同的查询也可能很慢的原因在 RAM 中。
当您备份 table 时，转储不包含优化器使用的统计信息，因此您需要等待 autovacuum 守护程序或 运行 'ANALYZE ' 在从 'ANALYZE ' 恢复后手动转储。
我有一个包含各种物化视图的 psql 数据库，在 运行 查询时，即 query_a 我们在 2800 毫秒内完成查询执行并重新 运行 相同再次查询我们得到 53ms 的执行时间。这可以通过 psql 完成的缓存来解释。现在是棘手的部分，我创建了这个数据库的转储并在 NewDB 中恢复它，当我重新 运行 query_a 我得到 2253ms 的执行时间和重新 运行ning得到相同的时间，即，似乎 psql 缓存在 NewDB 上不起作用。
我进行了各种实验来纠正相同的问题，并注意到当我明确刷新视图时没有任何改进，但如果我删除这些视图并在我的 NewDB 中重新创建它，它会给我原来的性能。
请注意，数据在 DB 和 NewDB 中是不变的，我使用了提到的命令 here 来创建和恢复转储。
在 DB 上重新 运行 查询的结果是 ->
运行第1次和第2次在NewDB上进行相同查询的结果如下->
QUERY PLAN
------------------------------------------------------------------------------------------------------------------------------------------------------------
Aggregate (cost=113790614477.61..113790614477.62 rows=1 width=8) (actual time=2284.605..2284.605 rows=1 loops=1)
Buffers: shared hit=3540872
CTE t
-> Merge Join (cost=40600.92..11846650.56 rows=763041594 width=425) (actual time=3.693..1909.916 rows=6005 loops=1)
Merge Cond: (n.node_id = nd.node_id)
Buffers: shared hit=3524063
-> Index Scan using nodes_node_id on nodes n (cost=0.43..350865.91 rows=3824099 width=389) (actual time=0.014..1651.025 rows=3598491 loops=1)
Buffers: shared hit=3523372
-> Sort (cost=40600.49..40700.26 rows=39907 width=40) (actual time=3.668..4.227 rows=6005 loops=1)
Sort Key: nd.node_id
Sort Method: quicksort Memory: 623kB
Buffers: shared hit=691
-> Bitmap Heap Scan on nodes_depths nd (cost=1153.11..37550.73 rows=39907 width=40) (actual time=0.627..2.846 rows=6005 loops=1)
Recheck Cond: ((ancestor_1 = 1) OR (ancestor_2 = 1))
Heap Blocks: exact=658
Buffers: shared hit=691
-> BitmapOr (cost=1153.11..1153.11 rows=40007 width=0) (actual time=0.547..0.547 rows=0 loops=1)
Buffers: shared hit=33
-> Bitmap Index Scan on nodes_depths_1 (cost=0.00..566.58 rows=20003 width=0) (actual time=0.032..0.032 rows=156 loops=1)
Index Cond: (ancestor_1 = 1)
Buffers: shared hit=4
-> Bitmap Index Scan on nodes_depths_2 (cost=0.00..566.58 rows=20003 width=0) (actual time=0.515..0.515 rows=5849 loops=1)
Index Cond: (ancestor_2 = 1)
Buffers: shared hit=29
-> Merge Right Join (cost=169565733.26..97549168801.28 rows=6491839610305 width=0) (actual time=1915.721..2284.175 rows=6005 loops=1)
Merge Cond: (nodes_fts.node_id = t.node_id)
Buffers: shared hit=3540872
-> Index Only Scan using nodes_fts_idx on nodes_fts (cost=0.43..97055.96 rows=1701569 width=4) (actual time=0.041..277.890 rows=1598712 loops=1)
Heap Fetches: 1598712
Buffers: shared hit=16805
-> Materialize (cost=169565732.84..173380940.81 rows=763041594 width=4) (actual time=1915.675..1916.583 rows=6005 loops=1)
Buffers: shared hit=3524067
-> Sort (cost=169565732.84..171473336.82 rows=763041594 width=4) (actual time=1915.672..1916.057 rows=6005 loops=1)
Sort Key: t.node_id
Sort Method: quicksort Memory: 474kB
Buffers: shared hit=3524067
-> CTE Scan on t (cost=0.00..15260831.88 rows=763041594 width=4) (actual time=3.698..1914.771 rows=6005 loops=1)
Buffers: shared hit=3524063
Planning time: 68.064 ms
Execution time: 2285.084 ms
(40 rows)
第二个 运行 ->
QUERY PLAN
------------------------------------------------------------------------------------------------------------------------------------------------------------
Aggregate (cost=113790614477.61..113790614477.62 rows=1 width=8) (actual time=2295.319..2295.319 rows=1 loops=1)
Buffers: shared hit=3540868
CTE t
-> Merge Join (cost=40600.92..11846650.56 rows=763041594 width=425) (actual time=15.324..1926.744 rows=6005 loops=1)
Merge Cond: (n.node_id = nd.node_id)
Buffers: shared hit=3524063
-> Index Scan using nodes_node_id on nodes n (cost=0.43..350865.91 rows=3824099 width=389) (actual time=0.027..1648.277 rows=3598491 loops=1)
Buffers: shared hit=3523372
-> Sort (cost=40600.49..40700.26 rows=39907 width=40) (actual time=15.254..15.903 rows=6005 loops=1)
Sort Key: nd.node_id
Sort Method: quicksort Memory: 623kB
Buffers: shared hit=691
-> Bitmap Heap Scan on nodes_depths nd (cost=1153.11..37550.73 rows=39907 width=40) (actual time=3.076..10.752 rows=6005 loops=1)
Recheck Cond: ((ancestor_1 = 1) OR (ancestor_2 = 1))
Heap Blocks: exact=658
Buffers: shared hit=691
-> BitmapOr (cost=1153.11..1153.11 rows=40007 width=0) (actual time=2.524..2.525 rows=0 loops=1)
Buffers: shared hit=33
-> Bitmap Index Scan on nodes_depths_1 (cost=0.00..566.58 rows=20003 width=0) (actual time=0.088..0.088 rows=156 loops=1)
Index Cond: (ancestor_1 = 1)
Buffers: shared hit=4
-> Bitmap Index Scan on nodes_depths_2 (cost=0.00..566.58 rows=20003 width=0) (actual time=2.434..2.435 rows=5849 loops=1)
Index Cond: (ancestor_2 = 1)
Buffers: shared hit=29
-> Merge Right Join (cost=169565733.26..97549168801.28 rows=6491839610305 width=0) (actual time=1933.113..2294.894 rows=6005 loops=1)
Merge Cond: (nodes_fts.node_id = t.node_id)
Buffers: shared hit=3540868
-> Index Only Scan using nodes_fts_idx on nodes_fts (cost=0.43..97055.96 rows=1701569 width=4) (actual time=0.077..271.313 rows=1598712 loops=1)
Heap Fetches: 1598712
Buffers: shared hit=16805
-> Materialize (cost=169565732.84..173380940.81 rows=763041594 width=4) (actual time=1933.030..1933.903 rows=6005 loops=1)
Buffers: shared hit=3524063
-> Sort (cost=169565732.84..171473336.82 rows=763041594 width=4) (actual time=1933.026..1933.375 rows=6005 loops=1)
Sort Key: t.node_id
Sort Method: quicksort Memory: 474kB
Buffers: shared hit=3524063
-> CTE Scan on t (cost=0.00..15260831.88 rows=763041594 width=4) (actual time=15.336..1932.145 rows=6005 loops=1)
Buffers: shared hit=3524063
Planning time: 1.154 ms
Execution time: 2295.801 ms
(40 rows)
估计的行数与实际数量相差几个数量级：
CTE Scan on t (cost=0.00..15260831.88 rows=763041594 width=4) (actual time=15.336..1932.145 rows=6005 loops=1)
当 Postgres 无法准确估计执行查询的特定方式与另一种方式相比的工作量时，它将生成低效的查询计划，这就是为什么即使所有数据都是相同的查询也可能很慢的原因在 RAM 中。
当您备份 table 时，转储不包含优化器使用的统计信息，因此您需要等待 autovacuum 守护程序或 运行 'ANALYZE ' 在从 'ANALYZE ' 恢复后手动转储。