MySQL optimization rules for index establishment

the data structures commonly used in indexes are B + trees . The structure is as follows

For example, is a B + tree, the definition of B + tree can be seen in the B + tree, here is only a few points, the light blue block we call a disk block, you can see each disk block contains several data items (dark blue) and pointers (shown in yellow), such as disk Block 1 contains data items 17 and 35, including pointers P1, P3,P1 represents a disk block that is less than 17, P2 represents a disk block between 17 and 35, and P3 represents a disk block greater than 35. Real data exists at leaf nodes 3, 5, 9, 10, 13, 15, 28, 29, 36, 60, 75, 79, 90, 99. Non-leaf nodes do not store real data, only data items that guide the direction of the search, such as 17 and 35, do not exist in the data table.

The discovery process of B + trees

, if you want to find the data item 29, then the disk Block 1 is loaded into memory by disk, at this time Io, in memory with a binary lookup to determine 29 between 17 and 35, locking disk Block 1 P2 pointer, memory time because of very short (compared to the disk IO) can be negligible, Disk Block 1 through disk address of the P2 pointer to the disk block 3 is loaded into memory, the second io,29 between 26 and 30, locking disk block 3 of the P2 pointer, loading disk blocks 8 through the pointer to memory, a third Io, while in-memory binary find found 29, the end of the query, a total of three IO. The real situation is, the 3-tier B + tree can represent millions of data, if millions of data to find only three Io, the performance will be huge, if there is no index, each data item will occur once IO, then a total of millions of Io, it is obviously very expensive.

B + Tree Nature

1. Through the above analysis, we know that the number of IO depends on the height of B + H, assuming that the current data table data is N, the number of data items per disk block is M, then there is H=㏒ (m+1) n, when the amount of data n a certain case, m larger, h smaller, and m = size of disk block/data item The size of the disk block is also a data page size, is fixed, if the data items occupy less space, the more data items, the lower the height of the tree. This is why each data item, the index field, is as small as possible, such as an int accounting for 4 bytes, which is less than half the bigint8 byte. This is why the B + tree requires the real data to be placed on the leaf node instead of the inner node, and once placed in the inner node, the data items of the disk block will be greatly reduced, resulting in a higher tree. When the data item equals 1 o'clock, it will degenerate into a linear table.
2. When the data item of the B + tree is a composite data structure, such as (Name,age,sex), the B + number is based on the left-to-right order to establish the search tree, such as when the data (Zhang San, 20,f) is retrieved, the B + tree will first compare the name to determine the direction of the next search, If name is the same, then compare age and sex, and finally get the retrieved data, but when the (20,F) does not have the name of the data, B + tree does not know which node to check next, because the search tree when the name is the first comparison factor, You must search by name first to know where to go next. For example, when (Zhang San, F) such data to retrieve, B + tree can use name to specify the direction of the search, but the next field of age is missing, so only the name equal to Zhang San data are found, and then match the gender is the data of F, this is very important property, that is, the index of the leftmost matching characteristics.

Several principles of index building

1. The leftmost prefix matching principle, very important principle, MySQL will always match right until it encounters a range query (>, <, between, like) to stop the match, such as a = 1 and B = 2 and C > 3 and D = 4 if established (a,b,c,d The index of the order, D is not indexed, if the establishment (A,B,D,C) of the index can be used, the order of a,b,d can be arbitrarily adjusted.
2.= and in can be disorderly, such as a = 1 and B = 2 and c = 3 build (a,b,c) index can be arbitrary order, the MySQL query optimizer will help you to optimize the form of the index can be recognized
3. Try to choose a high-differentiated column as the index, the formula for the degree of sensitivity is count (distinct col)/count (*), indicating that the field does not repeat the scale, the greater the proportion of the number of records we scan, the difference between the unique key is 1, and some states, The gender field may be 0 in front of big data, and one might ask, what is the empirical value of this ratio? Using different scenarios, this value is also difficult to determine, generally need to join the field we are required to be more than 0.1, that is, the average 1 scan 10 records
4. The index column cannot participate in the calculation, keep the column "clean", such as from_unixtime (create_time) = ' 2014-05-29 ' can not be used to the index, the reason is simple, B + tree is stored in the Data table field values, but when the retrieval, You need to apply all the elements to the function to compare, obviously the cost is too large. So the statement should be written create_time = Unix_timestamp (' 2014-05-29 ');
5. Expand the index as much as possible and do not create a new index. For example, the table already has an index of a, now to add (A, b) of the index, then only need to modify the original index

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MySQL optimization rules for index establishment