MySQL, primary key and normal index

I. What is an index?
Indexes are used to quickly look for records with specific values, and all MySQL indexes are saved as B-trees. If there is no index, MySQL must start scanning all records of the entire table from the first record until it finds a record that meets the requirements. The higher the number of records in the table, the higher the cost of this operation. If an index has been created on a column that is a search condition, MySQL can quickly get to where the target record is without scanning any records. If a table has 1000 records, finding records by index is at least 100 times times faster than sequential scan records.

Let's say we've created a table named people:

CREATE TABLE people (Peopleid SMALLINT not NULL, name CHAR (a) not null);

Then, we completely randomly insert 1000 different name values into the People table. Shows a small portion of the data file where the people table resides:

As you can see, the Name column in the data file does not have any definite order. If we create the index of the name column, MySQL will sort the name column in the index:

For each item in the index, MySQL internally holds a pointer to the location of the actual record in the data file. So if we want to find the name equals "Mike" Record Peopleid (SQL command for "Select Peopleid from people WHERE Name= ' Mike ';" ), MySQL can find the "Mike" value in the index of name, then go directly to the corresponding line in the data file and return exactly the line's Peopleid (999). In this process, MySQL only has to process one row to return the results. If there is no index to the "name" column, MySQL will scan all records in the data file, that is, 1000 records! Obviously, the fewer records that require MySQL to process, the faster it can complete the task.

Ii. Types of indexes
MySQL offers a variety of index types to choose from:

Normal index
This is the most basic type of index, and it has no limitations such as uniqueness. Normal indexes can be created in the following ways:
Create indexes, such as the name of the CREATE INDEX < index > on tablename (List of columns);
Modify the table, such as ALTER TABLE TableName ADD index [name of index] (list of columns);
Specify an index when creating a table, such as CREATE TABLE TableName ([...], index [name of indexed] (List of columns));

Uniqueness Index
This index is basically the same as the previous "normal index", but there is one difference: all the values of an indexed column can only occur once, that is, they must be unique. A unique index can be created in the following ways:
Create indexes such as the name of the Create UNIQUE Index < index > on tablename (List of columns);
Modify the table, such as ALTER TABLE TableName ADD UNIQUE [index name] (List of columns);
Specify indexes when creating tables, such as CREATE TABLE TableName ([...], UNIQUE [index name] (List of columns));

Primary key
The primary key is a unique index, but it must be specified as "PRIMARY key". If you've ever used a auto_increment type column, you're probably already familiar with concepts like the primary key. The primary key is typically specified when creating the table, such as "CREATE TABLE TableName ([...], PRIMARY KEY (List of columns)"; ”。 However, we can also add the primary key by modifying the table, such as "ALTER table tablename Add PRIMARY key (List of columns); ”。 There can be only one primary key per table.

Full-Text Indexing
MySQL supports full-text indexing and full-text retrieval starting from version 3.23.23. In MySQL, the index type of the full-text index is fulltext. A full-text index can be created on a varchar or text-type column. It can be created by the CREATE TABLE command or by the ALTER TABLE or CREATE INDEX command. For large datasets, it is faster to create a full-text index by using the ALTER TABLE (or CREATE INDEX) command than to insert the record into an empty table with a full-text index. The following discussion in this article no longer involves full-text indexing, see MySQL documentation for more information.

Third, single-row index and multi-column index
The index can be a single-column index or a multicolumn index. Let's take a concrete example to illustrate the differences between the two indexes. Suppose there is such a people table:

CREATE TABLE people (Peopleid SMALLINT NOT NULL auto_increment, FirstName char (a) NOT NULL, LastName char (.) NOT NULL, Age SMALLINT isn't null, Townid SMALLINT NOT NULL, PRIMARY KEY (Peopleid));

Here is the data we inserted into this people table:

The data fragment contains four people named "Mikes" (two of them Sullivans, two surname McConnells), two are 17 years of age, and a different name for Joe Smith.

The primary purpose of this table is to return the corresponding Peopleid based on the specified user name, first name, and age. For example, we may need to find the Peopleid (SQL command for select Peopleid from people WHERE Firstname= ' Mike ' and Lastname= ' for the user who is named Mike Sullivan, who is 17 years of age Sullivan ' and age=17;). Since we don't want MySQL to scan the entire table every time it executes a query, we need to consider using an index.

First, we can consider creating an index on a single column, such as FirstName, LastName, or the Age column. If we create an index of the FirstName column (ALTER TABLE people add index FirstName (firstname), MySQL will quickly limit the search to those firstname= through this index) Mike ' 's record, and then search for other criteria on this "intermediate result set": It first excludes records whose lastname are not equal to "Sullivan", and then excludes records whose age is not equal to 17. When the record satisfies all search criteria, MySQL returns the final search results.

Because of the FirstName column index, MySQL is much more efficient than performing a full scan of the table, but we require that the number of logs scanned by MySQL still far exceeds what is actually needed. Although we can delete the index on the FirstName column, and then create an index of the LastName or age column, it seems that the efficiency of creating an index search is still similar, regardless of which column.

To improve search efficiency, we need to consider using multi-column indexes. If you create a multi-column index for the three columns of FirstName, LastName, and age, MySQL can find the correct results with a single search! Here is the SQL command to create this multi-column index:

ALTER TABLE People ADD INDEX fname_lname_age (firstname,lastname,age);

Since the index file is saved in the B-tree format, MySQL can immediately go to the appropriate FirstName and then go to the appropriate LastName, and finally to the appropriate age. In the absence of any record of the scanned data file, MySQL correctly finds the target record of the search!

So, if you create a single-column index on the three columns of FirstName, LastName, and age, will the effect be the same as creating a multicolumn index of FirstName, LastName, and age? The answer is no, the two are totally different. When we execute the query, MySQL can use only one index. If you have three single-column indexes, MySQL will try to select one of the most restrictive indexes. However, even the most restrictive single-column index is limited in its ability to be significantly less than a multicolumn index on the three columns of FirstName, LastName, and age.

Four, the leftmost prefix
A multi-column index has another advantage, which is manifested by the concept of the leftmost prefix (leftmost prefixing). Continuing to consider the previous example, we now have a multi-column index on the FirstName, LastName, and age columns, which we call the index fname_lname_age. When the search condition is a combination of the following columns, MySQL uses the fname_lname_age index:

Firstname,lastname,age
Firstname,lastname
FirstName
On the other hand, it is equivalent to the index we created (Firstname,lastname,age), (Firstname,lastname), and (FirstName) on these column combinations. The following queries all have the ability to use this Fname_lname_age index:

SELECT Peopleid from people WHERE firstname= ' Mike ' and lastname= ' Sullivan ' and age= ' 17′; SELECT Peopleid from people WHERE firstname= ' Mike ' and lastname= ' Sullivan '; SELECT Peopleid from people WHERE firstname= ' Mike '; The following queries cannot use the index at All:select Peopleid from people WHERE lastname= ' Sullivan '; SELECT Peopleid from people WHERE age= ' 17′; SELECT Peopleid from people WHERE lastname= ' Sullivan ' and age= ' 17′;

V. Select an indexed column
In the performance optimization process, choosing which columns to create indexes on is one of the most important steps. There are two main types of columns that you can consider using indexes: columns that appear in the WHERE clause, columns that appear in the join clause. Consider the following query:

SELECT Age # # does not use an index
From people WHERE firstname= ' Mike ' # # consider using an index
and Lastname= ' Sullivan ' # # consider using an index

This query is slightly different from the previous query, but it still belongs to a simple query. Because age is referenced in the Select section, MySQL does not use it to restrict column selection operations. Therefore, it is not necessary to create an index of the age column for this query. The following is a more complex example:

SELECT People.age, # #不使用索引
Town.name # #不使用索引
From people left JOIN
People.townid=town.townid # #考虑使用索引
WHERE firstname= ' Mike ' # #考虑使用索引
and Lastname= ' Sullivan ' # #考虑使用索引

As in the previous example, because FirstName and LastName appear in the WHERE clause, these two columns still have the necessary to create an index. In addition, because the Townid of the town table is listed in the JOIN clause now, we need to consider creating the index of the column.

So, can we simply assume that each column that appears in the WHERE clause and the JOIN clause should be indexed? Almost so, but not entirely. We also have to take into account the type of operator that compares the columns. MySQL uses the index only for the following operators: <,<=,=,>,>=,between,in, and sometimes like. The case in which you can use an index in a like operation is when another operand is not preceded by a wildcard character (% or _). For example, "Select Peopleid from people WHERE firstname like ' mich% ';" This query will use the index, but "select Peopleid from people WHERE firstname like '%ike ';" This query does not use indexes.

Vi. Analysis of index efficiency
Now that we know a few things about how to choose indexed columns, we can't tell which one is the most effective. MySQL provides a built-in SQL command to help us complete this task, which is the explain command. The general syntax for the EXPLAIN command is: EXPLAIN. You can find more instructions on this command in the MySQL documentation. Here is an example:

EXPLAIN SELECT Peopleid from people WHERE firstname= ' Mike ' and lastname= ' Sullivan ' and age= ' 17′;

This command will return the following analysis results:

Let's take a look at the meaning of this explain analysis result.

Table: This is the name of the watch.
Type: Types of connection operations. The following is a description of the MySQL documentation about the ref connection type:

"For each combination of records in another table, MySQL reads all records with matching index values from the current table. If the connection operation uses only the leftmost prefix of the key, or if the key is not a unique or primary key type (in other words, if the connection operation cannot select a unique row based on the key value), then MySQL uses the ref connection type. If the key used by the connection operation matches only a small number of records, ref is a good type of connection. ”

In this example, because the index is not a unique type, ref is the best connection type we can get.

If explain shows that the connection type is "All" and you do not want to select most of the records from the table, then MySQL will be very inefficient because it will scan the entire table. You can add more indexes to solve this problem. For more information, see the MySQL manual for instructions.

Possible_keys:
The name of the index that may be available. The index name here is the index nickname specified when the index was created, and if the index does not have a nickname, the first column in the index is displayed by default (in this case, it is "FirstName"). The meaning of the default index name is often not obvious.

Key:
It shows the name of the index that MySQL actually uses. If it is empty (or null), then MySQL does not use the index.

Key_len:
The length of the part to be used in the index, in bytes. In this example, Key_len is 102, where FirstName accounts for 50 bytes, lastname accounts for 50 bytes, and age is 2 bytes. If MySQL only uses the FirstName portion of the index, then Key_len will be 50.

Ref
It shows the name of the column (or the word "const"), and MySQL will select rows based on these columns. In this example, MySQL selects rows based on three constants.

Rows
MySQL considers the number of records that it must scan before it can find the correct results. Obviously, the ideal number here is 1.

Extra:
Many different options may appear here, most of which will adversely affect the query. In this case, MySQL just reminds us that it will restrict the search result set with a WHERE clause.

Vii. Disadvantages of the index
So far, we've been talking about the advantages of indexes. In fact, indexes also have drawbacks.

First, the index takes up disk space. Usually, this problem is not very prominent. However, if you create an index of each possible combination of columns, the index file volume will grow much faster than the data file. If you have a large table, the size of the index file may reach the maximum file limit allowed by the operating system.

Second, indexes can slow down operations that require writing data, such as delete, update, and insert operations. This is because MySQL not only writes the change data to the data file, but it also writes the changes to the index file.

"Concluding remarks" in large databases, indexes are a key factor in increasing speed. No matter how simple the table structure is, a 500000-row table scan operation will not be fast anyway. If you have such a large-scale table on your site, you really should take the time to analyze which indexes you can take and consider whether you can rewrite the query to optimize your application. To learn more, see MySQL manual. Also note that this article assumes that the MySQL you are using is version 3.23, and some queries cannot be performed on MySQL version 3.22.

MySQL, primary key and normal index