磨砺技术珠矶，践行数据之道，追求卓越价值

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参考官方文档：

如下的各个参数，都是只有相对意义，比较的基准是：seq_page_cost=1

 

 Unfortunately, there is no well-defined method for determining ideal values for the cost variables. They are best treated as averages over the entire mix of queries that a particular installation will receive. This means that changing them on the basis of just a few experiments is very risky.

基本上，如果系统Cache使用状况良好，那么，random_page_cost可以适当降低。

而如果Cache使用达到接近于内存数据库的程度，那么seq_page_cost和 random_page_cost还可相对于cpu开头的个参数再降低。

反之亦然。

 

 

The cost variables described in this section are measured on an arbitrary scale. Only their relative values matter, hence scaling them all up or down by the same factor will result in no change in the planner's choices. By default, these cost variables are based on the cost of sequential page fetches; that is, seq_page_cost is conventionally set to 1.0 and the other cost variables are set with reference to that. But you can use a different scale if you prefer, such as actual execution times in milliseconds on a particular machine.Note: Unfortunately, there is no well-defined method for determining ideal values for the cost variables. They are best treated as averages over the entire mix of queries that a particular installation will receive. This means that changing them on the basis of just a few experiments is very risky.seq_page_cost (floating point)Sets the planner's estimate of the cost of a disk page fetch that is part of a series of sequential fetches. The default is 1.0. This value can be overridden for tables and indexes in a particular tablespace by setting the tablespace parameter of the same name (see ALTER TABLESPACE).random_page_cost (floating point)Sets the planner's estimate of the cost of a non-sequentially-fetched disk page. The default is 4.0. This value can be overridden for tables and indexes in a particular tablespace by setting the tablespace parameter of the same name (see ALTER TABLESPACE).Reducing this value relative to seq_page_cost will cause the system to prefer index scans; raising it will make index scans look relatively more expensive. You can raise or lower both values together to change the importance of disk I/O costs relative to CPU costs, which are described by the following parameters.Random access to mechanical disk storage is normally much more expensive than four-times sequential access. However, a lower default is used (4.0) because the majority of random accesses to disk, such as indexed reads, are assumed to be in cache. The default value can be thought of as modeling random access as 40 times slower than sequential, while expecting 90% of random reads to be cached.If you believe a 90% cache rate is an incorrect assumption for your workload, you can increase random_page_cost to better reflect the true cost of random storage reads. Correspondingly, if your data is likely to be completely in cache, such as when the database is smaller than the total server memory, decreasing random_page_cost can be appropriate. Storage that has a low random read cost relative to sequential, e.g. solid-state drives, might also be better modeled with a lower value for random_page_cost.Tip: Although the system will let you set random_page_cost to less than seq_page_cost, it is not physically sensible to do so. However, setting them equal makes sense if the database is entirely cached in RAM, since in that case there is no penalty for touching pages out of sequence. Also, in a heavily-cached database you should lower both values relative to the CPU parameters, since the cost of fetching a page already in RAM is much smaller than it would normally be.cpu_tuple_cost (floating point)Sets the planner's estimate of the cost of processing each row during a query. The default is 0.01.cpu_index_tuple_cost (floating point)Sets the planner's estimate of the cost of processing each index entry during an index scan. The default is 0.005.cpu_operator_cost (floating point)Sets the planner's estimate of the cost of processing each operator or function executed during a query. The default is 0.0025.

 

[作者 高健@博客园  luckyjackgao@gmail.com] 

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磨砺技术珠矶，践行数据之道，追求卓越价值