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Showing posts with label MySQL Primary Key Optimization. Show all posts
Showing posts with label MySQL Primary Key Optimization. Show all posts

What is the difference between MySQL, MySQLi and PDO?

To understand the difference between MySQL, MySQLi, and PDO, we must know about each one of them individually.

These are nothing but the APIs of PHP that is used to access the MySQL databases and tables. The developers can choose either one of them for their project, however, it must be known that MySQLi cannot be used with PHP 7 and its newer versions. However, developers can use MySQLi with PHP 5, which is now deprecated.

Let’s have some more information about each of them:

  • MySQL: This was the main extension that was designed to help PHP applications send and receive data from MySQL database. However, use of MySQL has been deprecated and removed as of PHP 7 and its newer versions. This is why it is not recommended for new projects, and that’s the reason why MySQLi and PDO extensions are used more nowadays.
  • MySQLi: The ‘i’ in MySQLi stands for Improved. Therefore, this is also known as the improved version of MySQL. It has many new features that will be covered later in the article.
  • PDO – PHP Data Objects: The main advantage of using PDO is that it supports, and provides a uniform method of access to 11 different databases.

What is a foreign key? Write a query to implement the same in MySQL.

 A foreign key is used to connect two tables. A FOREIGN KEY is a field (or assortment of it) in one table that alludes to the PRIMARY KEY in another table. The FOREIGN KEY requirement is utilised to forestall activities that would crush joins between tables.

To assign a foreign key, it is important to mention it while creating the table. It can be assigned by invoking the FOREIGN KEY query. Something like this:

FOREIGN KEY (Any_ID) REFERENCES Table_to_reference(Any_ID)

The System Tablespace

 The system tablespace is the storage area for the InnoDB data dictionary, the doublewrite buffer, the change buffer, and undo logs. It may also contain table and index data if tables are created in the system tablespace rather than file-per-table or general tablespaces.

The system tablespace can have one or more data files. By default, a single system tablespace data file, named ibdata1, is created in the data directory. The size and number of system tablespace data files is defined by the innodb_data_file_path startup option. For configuration information, see System Tablespace Data File Configuration.

Additional information about the system tablespace is provided under the following topics in the section:

Resizing the System Tablespace

This section describes how to increase or decrease the size of the system tablespace.

Increasing the Size of the System Tablespace

The easiest way to increase the size of the system tablespace is to configure it to be auto-extending. To do so, specify the autoextend attribute for the last data file in the innodb_data_file_path setting, and restart the server. For example:

The ENUM Type

 An ENUM is a string object with a value chosen from a list of permitted values that are enumerated explicitly in the column specification at table creation time.

See Section 11.3.1, “String Data Type Syntax” for ENUM type syntax and length limits.

The ENUM type has these advantages:

  • Compact data storage in situations where a column has a limited set of possible values. The strings you specify as input values are automatically encoded as numbers. See Section 11.7, “Data Type Storage Requirements” for storage requirements for the ENUM type.

  • Readable queries and output. The numbers are translated back to the corresponding strings in query results.

and these potential issues to consider:

  • If you make enumeration values that look like numbers, it is easy to mix up the literal values with their internal index numbers, as explained in Enumeration Limitations.

  • Using ENUM columns in ORDER BY clauses requires extra care, as explained in Enumeration Sorting.

Multiple-Column Indexes

 MySQL can create composite indexes (that is, indexes on multiple columns). An index may consist of up to 16 columns. For certain data types, you can index a prefix of the column (see Section 8.3.5, “Column Indexes”).

MySQL can use multiple-column indexes for queries that test all the columns in the index, or queries that test just the first column, the first two columns, the first three columns, and so on. If you specify the columns in the right order in the index definition, a single composite index can speed up several kinds of queries on the same table.

A multiple-column index can be considered a sorted array, the rows of which contain values that are created by concatenating the values of the indexed columns.

Note

As an alternative to a composite index, you can introduce a column that is hashed based on information from other columns. If this column is short, reasonably unique, and indexed, it might be faster than a wide index on many columns. In MySQL, it is very easy to use this extra column:

SELECT * FROM tbl_name
  WHERE hash_col=MD5(CONCAT(val1,val2))
  AND col1=val1 AND col2=val2;

Suppose that a table has the following specification:

CREATE TABLE test (
    id         INT NOT NULL,
    last_name  CHAR(30) NOT NULL,
    first_name CHAR(30) NOT NULL,
    PRIMARY KEY (id),
    INDEX name (last_name,first_name)
);

The name index is an index over the last_name and first_name columns. The index can be used for lookups in queries that specify values in a known range for combinations of last_name and first_name values. It can also be used for queries that specify just a last_name value because that column is a leftmost prefix of the index (as described later in this section). Therefore, the name index is used for lookups in the following queries:

SELECT * FROM test WHERE last_name='Jones';

SELECT * FROM test
  WHERE last_name='Jones' AND first_name='John';

SELECT * FROM test
  WHERE last_name='Jones'
  AND (first_name='John' OR first_name='Jon');

SELECT * FROM test
  WHERE last_name='Jones'
  AND first_name >='M' AND first_name < 'N';

However, the name index is not used for lookups in the following queries:

SELECT * FROM test WHERE first_name='John';

SELECT * FROM test
  WHERE last_name='Jones' OR first_name='John';

Suppose that you issue the following SELECT statement:

SELECT * FROM tbl_name
  WHERE col1=val1 AND col2=val2;

If a multiple-column index exists on col1 and col2, the appropriate rows can be fetched directly. If separate single-column indexes exist on col1 and col2, the optimizer attempts to use the Index Merge optimization (see Section 8.2.1.3, “Index Merge Optimization”), or attempts to find the most restrictive index by deciding which index excludes more rows and using that index to fetch the rows.

If the table has a multiple-column index, any leftmost prefix of the index can be used by the optimizer to look up rows. For example, if you have a three-column index on (col1, col2, col3), you have indexed search capabilities on (col1)(col1, col2), and (col1, col2, col3).

MySQL cannot use the index to perform lookups if the columns do not form a leftmost prefix of the index. Suppose that you have the SELECT statements shown here:

SELECT * FROM tbl_name WHERE col1=val1;
SELECT * FROM tbl_name WHERE col1=val1 AND col2=val2;

SELECT * FROM tbl_name WHERE col2=val2;
SELECT * FROM tbl_name WHERE col2=val2 AND col3=val3;

If an index exists on (col1, col2, col3), only the first two queries use the index. The third and fourth queries do involve indexed columns, but do not use an index to perform lookups because (col2) and (col2, col3) are not leftmost prefixes of (col1, col2, col3).

Column Indexes

 The most common type of index involves a single column, storing copies of the values from that column in a data structure, allowing fast lookups for the rows with the corresponding column values. The B-tree data structure lets the index quickly find a specific value, a set of values, or a range of values, corresponding to operators such as =, >, , BETWEEN, IN, and so on, in a WHERE clause.

The maximum number of indexes per table and the maximum index length is defined per storage engine. See Chapter 15, The InnoDB Storage Engine, and Chapter 16, Alternative Storage Engines. All storage engines support at least 16 indexes per table and a total index length of at least 256 bytes. Most storage engines have higher limits.

For additional information about column indexes, see Section 13.1.15, “CREATE INDEX Statement”.

Index Prefixes

With col_name(N) syntax in an index specification for a string column, you can create an index that uses only the first N characters of the column. Indexing only a prefix of column values in this way can make the index file much smaller. When you index a BLOB or TEXT column, you must specify a prefix length for the index. For example:

CREATE TABLE test (blob_col BLOB, INDEX(blob_col(10)));

Prefixes can be up to 767 bytes long for InnoDB tables that use the REDUNDANT or COMPACT row format. The prefix length limit is 3072 bytes for InnoDB tables that use the DYNAMIC or COMPRESSED row format. For MyISAM tables, the prefix length limit is 1000 bytes.

Note

Prefix limits are measured in bytes, whereas the prefix length in CREATE TABLEALTER TABLE, and CREATE INDEX statements is interpreted as number of characters for nonbinary string types (CHARVARCHARTEXT) and number of bytes for binary string types (BINARYVARBINARYBLOB). Take this into account when specifying a prefix length for a nonbinary string column that uses a multibyte character set.

If a search term exceeds the index prefix length, the index is used to exclude non-matching rows, and the remaining rows are examined for possible matches.

For additional information about index prefixes, see Section 13.1.15, “CREATE INDEX Statement”.

FULLTEXT Indexes

FULLTEXT indexes are used for full-text searches. Only the InnoDB and MyISAM storage engines support FULLTEXT indexes and only for CHARVARCHAR, and TEXT columns. Indexing always takes place over the entire column and column prefix indexing is not supported. For details, see Section 12.10, “Full-Text Search Functions”.

Optimizations are applied to certain kinds of FULLTEXT queries against single InnoDB tables. Queries with these characteristics are particularly efficient:

  • FULLTEXT queries that only return the document ID, or the document ID and the search rank.

  • FULLTEXT queries that sort the matching rows in descending order of score and apply a LIMIT clause to take the top N matching rows. For this optimization to apply, there must be no WHERE clauses and only a single ORDER BY clause in descending order.

  • FULLTEXT queries that retrieve only the COUNT(*) value of rows matching a search term, with no additional WHERE clauses. Code the WHERE clause as WHERE MATCH(text) AGAINST ('other_text'), without any > 0 comparison operator.

For queries that contain full-text expressions, MySQL evaluates those expressions during the optimization phase of query execution. The optimizer does not just look at full-text expressions and make estimates, it actually evaluates them in the process of developing an execution plan.

An implication of this behavior is that EXPLAIN for full-text queries is typically slower than for non-full-text queries for which no expression evaluation occurs during the optimization phase.

EXPLAIN for full-text queries may show Select tables optimized away in the Extra column due to matching occurring during optimization; in this case, no table access need occur during later execution.

Spatial Indexes

You can create indexes on spatial data types. MyISAM and InnoDB support R-tree indexes on spatial types. Other storage engines use B-trees for indexing spatial types (except for ARCHIVE, which does not support spatial type indexing).

Indexes in the MEMORY Storage Engine

The MEMORY storage engine uses HASH indexes by default, but also supports BTREE indexes.

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