原文地址：https://www.percona.com/blog/understanding-mysql-indexes-types-best-practices/
When it comes to MySQL databases, performance is everything. As more activities move online and data volumes grow exponentially, ensuring efficient data retrieval and query execution becomes crucial. Database indexing plays a significant role in this by providing powerful tools to optimize operations in MySQL. Without an index, MySQL must perform a full table scan, reading every row to find the desired data, which becomes increasingly inefficient as the table grows larger. By creating an index on one or more columns, MySQL can quickly locate the relevant rows, significantly reducing the amount of data that needs to be scanned.
However, while MySQL indexes offer substantial performance benefits, it’s important to balance their advantages with the overhead associated with index maintenance. Adding, modifying, or removing data in an indexed table requires updating the corresponding index, which can impact write performance. Carefully planning and implementing indexing strategies is crucial to optimizing query performance while minimizing unnecessary overhead.
In this blog post, we will discuss the different types of indexes in MySQL, when to use them, and how to create them. Additionally, we will provide tips for effective indexing.
Here are some key benefits of using MySQL indexes:
Improved query speed and optimization: MySQL indexes significantly enhance query processing, particularly with extensive datasets. By reducing the need for full table scans, indexes enable MySQL to quickly locate and retrieve necessary data. This optimization improves query response times and overall system performance, making it essential for efficient database operations.
Improved efficiency and resource usage: As stated above, if indexes were not used, MySQL would have to search through every row in a table to locate the necessary data, resulting in significant utilization of system resources like CPU cycles, memory, and disk I/O. By utilizing indexes, the database can swiftly pinpoint the required rows, minimizing the data that needs to be examined and processed to enhance response times for queries and the overall performance and scalability of the system.
Optimized JOIN operations: MySQL indexes can significantly improve the performance of JOIN operations between tables. When you join tables using indexed columns in MySQL, it can use the indexes to find matching rows quickly. This reduces the need for resource-heavy tasks like scanning the entire table or sorting. This optimization becomes increasingly important as the complexity of JOIN queries and the number of tables involved grows.
Enforcing data integrity: In addition to enhancing performance, MySQL indexes also aid in upholding data integrity rules. For instance, unique indexes prevent the insertion of duplicate values into a column, thereby preserving data uniqueness. Likewise, primary key indexes ensure that every row in a table possesses a unique identifier, avoiding unintentional duplicates or isolated data.
Improved sorting and range queries: MySQL indexes can significantly enhance the performance of queries that require sorting or filtering by a range of values. By organizing data in a structured order, indexes allow MySQL to quickly locate and retrieve the necessary rows without having to perform expensive sorting operations or search through unnecessary data.
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MySQL offers several types of indexes, each designed to optimize query performance for different use cases. Understanding these MySQL indexes and their purposes can help you select the best one for your needs.
A primary key index ensures each row in a table can be uniquely identified, which is crucial for database integrity. This index enforces the uniqueness of the column(s) it covers and prevents duplicate entries.
A unique index is similar to a primary key but allows for NULL values while ensuring non-NULL values are unique. This type of index helps maintain data uniqueness without the strict constraints of a primary key.
A standard index, also known simply as an index, speeds up searches on frequently queried columns. This type of index can be applied to any column and is beneficial for improving the performance of SELECT queries.
Full-text indexes are specialized for full-text search functionalities, allowing efficient text search and retrieval operations. They are ideal for applications requiring fast and accurate text searches.
Composite indexes cover multiple columns, optimizing queries that filter or sort by more than one column. They provide quick access to combined data and are useful for complex query scenarios.
In MySQL, InnoDB tables use the primary key as the clustered index, meaning the data is physically organized based on the primary key values. This improves the performance of queries that access large amounts of data. However, MySQL does not have an explicit clustered index type; it is automatically handled by InnoDB.
A secondary or non-clustered index is an additional index separate from the primary (clustered) index. In InnoDB tables, secondary indexes reference the primary key, allowing efficient access to rows based on non-primary key columns.
To understand how indexes enhance performance, let’s take a look at the underlying data structures MySQL uses to store and organize index data. Understanding how indexes work is essential for maximizing index performance and overall database efficiency.
B-trees (B+ Trees): B+ trees are the primary data structure used for indexing in MySQL, especially with the InnoDB storage engine. These balanced trees are efficient for insertion, deletion, and lookup operations, with data stored in a sorted order. This makes them ideal for range queries and ordered retrievals.
Hashes: Hash indexes in MySQL are specific to the MEMORY (HEAP) storage engine. They use a hash table to generate a hash value from the indexed column, which points to the corresponding rows. These indexes are highly efficient for exact-match queries but do not support range queries or ordered retrievals. While InnoDB doesn’t natively support hash indexes, it uses adaptive hash indexing, which dynamically optimizes frequently accessed B+ tree pages by converting them into a hash structure for faster exact-match lookups. This process is automatic and transparent to users.
Learn more: MySQL Performance Tuning 101 – Key Tips to Improve MySQL Database Performance
When creating indexes in MySQL, it’s important to strike a balance between the number of indexes and the performance benefits they provide. Too many indexes can slow down data insertion and updates, while too few indexes can result in slow query performance. It’s also important to regularly analyze and optimize indexes to ensure they are still providing the desired performance improvements.
Overall, creating indexes in MySQL is a crucial step in optimizing database performance and improving query speed. By carefully selecting the columns to index and choosing the appropriate index types, users can significantly enhance the efficiency of their database operations.
Storage impact: Indexes are stored as separate data structures within the database, occupying additional disk space. The amount of space required depends on the size of the indexed columns, the number of indexes, and the underlying data structure used (e.g., B-tree, hash). It’s important to balance the performance gains of indexes and the additional storage requirements.
Read performance: Indexes significantly improve read performance by allowing MySQL to quickly locate and retrieve the desired data without performing full table scans. Without indexes, MySQL performs full table scanning, which can be inefficient for large datasets. The performance improvement becomes more evident as the dataset grows larger and queries become more complex.
Write performance: While indexes enhance read performance, they can negatively impact write performance (INSERT, UPDATE, and DELETE operations). When data is modified in an indexed table, MySQL must update the table data and the corresponding index entries. This additional overhead can slow down write operations, especially for tables with multiple indexes or frequent write workloads.
Let’s look at various scenarios where indexes can significantly enhance query efficiency, strategies for managing large datasets, and techniques for optimizing join operations.
Indexes can drastically improve query performance, especially in scenarios involving frequent searches, joins, range queries, and ORDER BY clauses. Consider a slow query that involves searching for records in a large table without an index. Such queries can take significant time to complete because the database must scan every row. By creating an index on the column used in the WHERE clause, the database can quickly locate the relevant rows, resulting in much faster query execution.
Indexing large datasets requires careful planning to balance the benefits of faster read operations with the additional storage and maintenance overhead. One approach is to index only the most frequently queried columns, such as those used in WHERE clauses or JOIN operations. Additionally, partitioning large tables and creating indexes on each partition can help distribute the workload and improve performance. Regularly monitoring and analyzing query performance is also essential for fine-tuning indexes and ensuring they meet the application’s needs.
Joins are common operations in relational databases, where multiple tables are combined based on related columns. Indexing these columns can significantly optimize join operations. As an example, if two tables are frequently joined on a specific column, creating an index on that column in both tables can reduce the time required to match rows. This is particularly beneficial in complex queries involving multiple joins, as indexed columns allow the database to efficiently retrieve and combine the relevant rows, leading to faster query execution.
We’ve learned how MySQL indexes work and when certain kinds can be useful, so we’ll now cover some important practices for creating and removing them, monitoring their effectiveness, and dealing with index fragmentation.
Regular analysis and monitoring of indexes are essential for maintaining their effectiveness. Tools like EXPLAIN and SHOW INDEX provide valuable insights into how indexes are used in queries. The EXPLAIN command helps you understand the execution plan of a query, indicating which indexes are being used. SHOW INDEX provides details about the indexes on a table, including their cardinality and uniqueness. By knowing how to analyze this information, you can identify underutilized or redundant indexes and make informed decisions about their management.
Creating and dropping indexes are fundamental tasks in database management. The commands for creating indexes involve specifying the type of index and the columns to be indexed, while dropping indexes requires identifying the index to be removed. It’s important to carefully consider the impact of these operations, as creating indexes can improve query performance but also increase storage requirements and slow down write operations. On the flip side, dropping an index might reduce storage usage and improve write performance but could slow down read operations.
Index fragmentation occurs when the logical order of index pages no longer matches the physical order, leading to inefficient disk usage and slower query performance. MySQL primarily supports index rebuilding to deal with fragmentation, which reorganizes the table and indexes to improve performance.
Over time, indexes can become redundant or inefficient due to query patterns and data structure changes. Dropping unused or rarely used indexes can reduce storage overhead and improve write performance while combining multiple similar indexes into a single composite index can also streamline index management and enhance performance. Tools and commands like EXPLAIN and SHOW INDEX are invaluable in this process, providing the necessary insights to make data-driven decisions.
While indexes are a powerful tool for optimizing database performance, their effectiveness depends on careful planning and management. Here are some important considerations and best practices for using indexes effectively:
Don’t overuse indexes: While indexes can significantly improve query performance, striking a balance and avoiding indexing every column in a table is essential. Excessive indexing can lead to increased storage requirements, slower write operations (inserts, updates, and deletes), and potential performance degradation. Each index added to a table incurs overhead for maintenance, so it’s important to be cautious and index only the columns frequently used in queries or requiring performance optimization.
Choose the right index type: MySQL offers several index types, including B-tree, hash, full-text, and spatial indexes. Selecting the appropriate index type is crucial for maximizing performance gains. For example, B-tree indexes are suitable for a wide range of queries, while hash indexes excel at equality lookups but perform poorly for range queries. Consider the data types, query patterns, and usage scenarios when choosing the appropriate index type.
Analyze index usage: MySQL tools like EXPLAIN and other utilities help you identify which indexes are being used, how often they are accessed, and their effectiveness. By analyzing this information, you can locate redundant, unused, or inefficient indexes and make informed decisions about index maintenance, such as dropping or rebuilding indexes as needed.
Index selective columns: When creating indexes, focus on columns with a high degree of selectivity, meaning they contain a wide range of unique values. Indexing low-cardinality columns (columns with few distinct values) may not provide significant performance benefits and can even degrade performance in some cases. Analyze your data distribution and indexing requirements to identify the most selective columns.
Optimize index length for large columns: For large column values, such as text or binary data, it may be beneficial to create a prefix index, which indexes only the first few characters or bytes of the column. This can reduce the index size and improve performance while providing useful indexing capabilities for queries that rely on prefix matching or partial column values.
Use composite indexes for multi-column queries: If your queries frequently involve multiple columns, consider creating a composite index (also known as a multi-column index) that includes all or a subset of those columns. Composite indexes can improve performance for queries that filter or sort on multiple columns simultaneously.
Periodically rebuild and reorganize indexes: As data is inserted, updated, and deleted over time, indexes can become fragmented, leading to performance degradation. Consider periodically rebuilding indexes to optimize their structure and improve query performance.
Leverage database tools and monitoring: There are various tools available to help you manage and optimize indexes. MySQL Workbench, the Performance Schema, and third-party monitoring solutions like Percona Monitoring and Management can provide valuable insights into index usage, performance metrics, and potential bottlenecks, enabling you to make informed decisions about indexing strategies.
Effectively using indexes is critical to optimizing your MySQL database performance. For those seeking even greater optimization, we highly recommend our eBook, “MySQL Performance Tuning: Strategies, Best Practices, and Tips from Percona MySQL Experts.” It is filled with advanced techniques and insights from Percona experts to help you take your database performance skills to the next level.
Composite indexes can be highly effective for large databases. They allow MySQL to use multiple columns in filtering data, which reduces the amount of data scanned. B-tree indexes (the default index type in MySQL) are efficient for a variety of queries, including range searches and ordered retrieval. For large datasets with complex queries, composite B-tree indexes are commonly used for optimal performance.
Index maintenance should be performed regularly, but the frequency depends on your database’s workload and size. For InnoDB tables, regular maintenance tasks like OPTIMIZE TABLE can help reduce fragmentation and reclaim space. While automatic mechanisms like InnoDB’s background operations handle some maintenance, periodic index rebuilding or defragmenting may still be necessary for heavily used tables.
Yes, while indexes speed up read operations by reducing the data scanned, they add overhead to write operations such as INSERT, UPDATE, and DELETE. Each write operation must also update the relevant indexes, which can slow down performance. It’s essential to strike a balance by indexing only the most frequently queried columns to optimize both read and write performance.
Columns that frequently appear in WHERE, JOIN, and ORDER BY clauses are good candidates for indexing. Indexing these columns can significantly improve query performance. Additionally, analyzing slow query logs and query execution plans can provide insights into which columns to index. Indexes should also cover all parts of the AND conditions in WHERE clauses when possible.
Several tools can help manage and optimize MySQL indexes, including:
MySQL Workbench: A graphical interface for creating and managing indexes.
Percona Monitoring and Management: Provides advanced tools for monitoring index usage and optimizing database performance.
phpMyAdmin: A web-based interface for managing indexes with a simple user interface.