Btree 의 속도를 향상시키는 방법 ?

Choosing between BTree and Hash For small working datasets that fit entirely in memory, there is no difference between BTree and Hash. Both will perform just as well as the other. In this situation, you might just as well use BTree, if for no other reason than the majority of DB applications use BTree.

Note that the main concern here is your working dataset, not your entire dataset. Many applications maintain large amounts of information but only need to access some small portion of that data with any frequency. So what you want to consider is the data that you will routinely use, not the sum total of all the data managed by your application.

However, as your working dataset grows to the point where you cannot fit it all into memory, then you need to take more care when choosing your access method. Specifically, choose:

* BTree if your keys have some locality of reference. That is, if they sort well and you can expect that a query for a given key will likely be followed by a query for one of its neighbors.

* Hash if your dataset is extremely large. For any given access method, DB must maintain a certain amount of internal information. However, the amount of information that DB must maintain for BTree is much greater than for Hash. The result is that as your dataset grows, this internal information can dominate the cache to the point where there is relatively little space left for application data. As a result, BTree can be forced to perform disk I/O much more frequently than would Hash given the same amount of data.

Moreover, if your dataset becomes so large that DB will almost certainly have to perform disk I/O to satisfy a random request, then Hash will definitely out perform BTree because it has fewer internal records to search through than does BTree.