When you are working with OLTP systems, there are wide possibilities for the different types of database designs that are available in the market. Here are some database design performance tips you need to take care of when you are working with OLTP systems.
If you work with database performance, you probably already know that there are different kinds of database designs. As such, there are many things you need to look at in order to thoroughly analyze database schema designs. Here are five database design performance tips that you should consider when working with OLTP Systems:
1. Is normalization apparent in the database design? The starting point when it comes to critically evaluating database design is checking if database normalization is present – to be particular, in 3NF or Third Normal Form (or even BCNF). There is a reason why normalization processes are always endorsed, and that is because it is highly effective when it comes to identifying all the update, insert, and delete data types of anomalies as well as support the application data’s integrity. If there are too many tables with the exact same unique keys, you will know that you have over normalized things (in 4NF or 5NF) – which will kill the database design performance tips.
2. Are the tables able to leverage the natural keys of the business? Solid database design leverages, filters and accesses through natural keys found within the business. Ensure that you confirm such natural business keys in the database design. There have been many times (especially when it comes to the newer type of NoSQL databases or ORM products for web development) when a SEQUENCE, ROWID, or IDENTITY column gets used or are required. Such keys limit database integration through the business environment. They are detrimental in the SQL application’s performance.
3. Can you spot good column definitions in the database? The names and definitions of database columns are used provided that the database remains active (so as to confirm the right names, abbreviations, and also short standard names used as column names). Make sure all your columns do not appear as single data types like XML, CHAR, or VARCHAR, lest it fails to reflect the data’s business diversity. While column definitions are easy to change, take care to select numeric types and value ranges so that the right SMALLINT, BIGINT, INT, or DECIMAL are used. Well-defined tables that have solid numeric and business centric keys are always preferred.
4. Is NULLable present across all columns? Analyze, evaluate, and prototype everything for good business applications. The presence of good default values and limiting the scope of values is the best thing to do, performance-wise and for a robust application logic. NULLable columns can only be considered as good when your data is not known or when it does not have any values yet. For example, a person’s death data is one type of NULLable column since it is not known unless you directly find out that the person is indeed dead already. As such, ensuring that your database design has a good representation of known data and uses a small number of NULLable columns is the best route to go. These database design performance tips can help you in working with the OLTP systems.
5. And finally, were you able to leverage the right amount of RI or Referential Integrity? RI is defined right within database schema definitions simply because it implements business policies, and is more efficient compared to multiple applications that enforce it. In contrast, a database designed referential integrity will still be there many years later to validate all data relationship. It is during the development phase of application that RI needs to have definition right within database schemas so that coders will be able to understand it and be aware of how to minimize RI overhead efficiently. This is done by referencing as well as performing the operations in the right RI sequence.
Database design best practices will help you make the correct decision. The RI ought to be developed within the business application’s correct depth as well as the different kinds of transactions found within the schema. Solid RI database structures can be made within 5-10 levels. Going over this will lead to performance issues, which will go back to over-normalization of table design.These five database design performance tips are quite simple, but these are great starting criteria for the next time that you need to make database developments.
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