登录查看更多内容

What is the impact of normalization on database performance?

由人工智能和领英社区提供技术支持

Normalization is a process of organizing the data in a relational database to reduce redundancy and improve integrity. It involves dividing the data into multiple tables that are linked by foreign keys, and applying certain rules or normal forms to ensure consistency and avoid anomalies. But how does normalization affect the performance of your database? In this article, we will explore the pros and cons of normalization for database performance, and provide some tips on how to balance the trade-offs.

此文章中的业界达人

由社区从 3 条内容中精选。了解更多

Cedric Prin-Derre

1 Benefits of normalization

Normalization can improve the performance of your database in several ways. First, it can reduce the storage space required for your data, as you eliminate duplicate or unnecessary values. This can also save bandwidth and memory when you transfer or load the data. Second, it can enhance the data quality and accuracy, as you avoid conflicting or inconsistent information across tables. This can also simplify the maintenance and update of the data, as you only need to modify one table instead of multiple ones. Third, it can facilitate the design and implementation of your database, as you follow a logical and standardized structure that conforms to the rules of the relational model.

添加您的观点

Cedric Prin-Derre
举报内容
Normalization will reduce the size of the database; that (should) means the Buffer Cache Hit ratio will increase reducing Physical I/O ans so improving I/O performance. I recommand a high normalisation on OLTP DB. Just remember that each rule got exceptions.

已翻译

赞

2 Drawbacks of normalization

Normalization can also have some negative impacts on the performance of your database. First, it can increase the complexity and overhead of your queries, as you need to join multiple tables to retrieve the data you need. This can also affect the readability and maintainability of your queries, as you have to deal with more tables and columns. Second, it can degrade the response time and throughput of your database, as you have to perform more operations and calculations to process the data. This can also increase the load and contention on your database server, as you have to handle more transactions and locks. Third, it can limit the flexibility and scalability of your database, as you have to adhere to a rigid and predefined schema that may not suit your changing or evolving needs.

添加您的观点

Cedric Prin-Derre
举报内容
More join means more query complexity. The optimizer is not perfect so it means that you increase probability that the optimizer doesn t find an optimal one. Just remember that an optimizer cannot evaluate all the possibilities if the number of join is too high. Also remember that most of the time a mapping table should be joined using as the first element of a hash join. Small mapping tables should be locked in memory to avoid performance/concurrency issues. Index ar most of the time useless on them

已翻译

赞

3 Tips for balancing normalization

Normalization is not a universal solution for maximizing database performance. Depending on your data characteristics, requirements, and goals, you may need to adjust the level of normalization or denormalization to achieve optimal performance. To help balance the trade-offs of normalization, analyze your data to identify sources of redundancy, inconsistency, and dependency. Apply the normal forms progressively and evaluate the impact on performance at each step. Then consider your query patterns and frequency to optimize indexes, views, and stored procedures accordingly. Furthermore, monitor your database performance using execution plans, query statistics, wait events, resource utilization, and performance counters. Finally, use techniques such as caching, partitioning, parallelism, compression, and replication to tune and optimize your database performance. By understanding the impact of normalization on database performance, you can design and manage your database more effectively.

添加您的观点

Cedric Prin-Derre
举报内容
Just rememeber that OLTP and Datawarehouses are two totally different strategies. on OLTP we want really fast small queries with mostly first rows importance. Indexes are normalisation are a must. but be aware of concurrency issues. Datawarehouses are big guys with large queries; indexes should be rare as partionning are most of the time a better solution if your applications are well designed. denormalization is very common as you are not responsible of the data quality and Buffer hit ratio is less important as you are very often doing some big table full scan. on

已翻译

赞

4 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

添加您的观点

Data Engineering

+ 关注

给文章评分

我们借助人工智能创建了此文章。您认为这篇文章怎么样？

很棒不太好

举报此文章

查看全部

What is the impact of normalization on database performance?

1

2

3

4

1 Benefits of normalization

2 Drawbacks of normalization

3 Tips for balancing normalization

4 Here’s what else to consider

Data Engineering

给文章评分

感谢您的反馈

更多Data Engineering相关文章

更多相关阅读内容

What is the impact of normalization on database performance?

1

2

3

4

1 Benefits of normalization

2 Drawbacks of normalization

3 Tips for balancing normalization

4 Here’s what else to consider

Data Engineering

给文章评分

感谢您的反馈

查看其他技能