Introduction
In a relational database, indexing plays a crucial role in improving the performance of queries. By creating indexes on specific columns, the database system creates a data structure that allows for quicker retrieval of data. In this blog post, we will explore the impact of database indexing on query performance execution plans.
What are Execution Plans?
Execution plans are the roadmap that the database follows to execute a query. It outlines the steps the database needs to take to retrieve and manipulate the data requested by the query. These plans are generated by the query optimizer, which is responsible for determining the most efficient way to execute a query.
How Indexing Affects Execution Plans
When a query is executed, the database optimizer evaluates different execution plans and chooses the one it believes will yield the best performance. The presence or absence of indexes on the queried columns greatly influences the optimizer's decision.
With appropriate indexing, the optimizer can utilize the indexes to quickly locate the required data without scanning the entire table. On the other hand, when indexing is lacking or poorly designed, the optimizer may resort to slower methods such as full table scans. This can result in significantly slower query execution times.
Index Selection and Execution Plans
The optimizer considers multiple factors when selecting an appropriate index for a query. These factors include selectivity, cardinality, data distribution, and query patterns. Let's look at some of the scenarios where indexing can impact execution plans:
Improved Selectivity
By creating an index on a column with high selectivity, the optimizer can quickly filter out rows that do not meet the query's criteria. This leads to a reduced number of rows to be scanned and processed, resulting in faster query execution.
Index Join
When joining multiple tables, indexes on the join columns can greatly impact the execution plan. The optimizer can leverage these indexes to perform index joins, where the join operation is executed using the indexes rather than the actual table data. This can significantly improve the query's performance.
Covered Queries
A covered query is one where all the columns referenced in the query are included in the index. In such cases, the optimizer can avoid accessing the actual table and retrieve the required data directly from the index. This eliminates the need for additional disk I/O operations, resulting in faster execution.
Beware of Over-Indexing
While indexing can greatly improve query performance, having too many indexes can have a negative impact. Each index requires additional disk space and maintenance overhead. Moreover, indexes need to be updated whenever the underlying table data changes, leading to slower write operations. It's important to strike a balance between the number of indexes and the performance gains they provide.
Conclusion
Database indexing plays a vital role in optimizing query performance execution plans. With the right indexes in place, the optimizer can choose efficient execution plans that lead to faster query execution. However, it's crucial to carefully design and maintain indexes to avoid over-indexing and unnecessary performance overhead. By fine-tuning indexing strategies, organizations can greatly enhance the performance of their database queries.
本文来自极简博客，作者：魔法学徒喵，转载请注明原文链接：The Impact of Database Indexing on Query Performance
