![]() For example, let’s say each product has a specific category ID, which is a numeric value. Virtual fields minimize the number of reference tables needed for creating a query.Ī database usually stores information about a single entity in multiple tables, which makes data searches much more complicated. Group adjacent entities into virtual objectsĬreating virtual fields and objects really helps give you a clearer database schema view, making it easier to manipulate a smaller number of objects. It also makes sense to create different views according to roles or user groups (for example, the accounting department sees one structure, business analysts see another). You can put a few main groups in the root folder, and divide the rest into subfolders (similar to a file manager). If the number of objects you need is large enough, you can group objects in folders in accordance with areas of business use and subject. This way, the database will become more transparent from the user’s perspective, featuring a tree of only the most relevant data for current business processes. As you work, you can selectively display only the tables you need to see. There’s no point in showing an entire database structure with thousands of tables. Aliases can include text in any language. ![]() Your queries will be self descriptive and easy to understand. The best solution here going forward is to assign clear and descriptive names when creating a new database, but what if you’re stuck working with an existing database that supports a large system and is impossible to change?Īctive Query Builder allows you to assign aliases to any table or field name. Automatic machine names (e.g., TBL-001, TBL-002).Įither way is human-unreadable, and it doesn’t serve you well when you need to find relevant data quickly.Indistinct names given by humans (e.g., AGC_SPAN2WAY_COL).To recap, here we have two main problems: Another time this type of issue comes up is when website CMSs create databases automatically and assign ‘machine’ names. Unfortunately, this issue can’t be resolved by simply changing the names, because that will cause addressing problems in linked services. When creating a database, an engineer may give tables and fields nondescript names, making it difficult to navigate in the future as the DB continues to grow. Make it simple! Self-explanatory name aliases Solving these problems will benefit data analysts and operators who need to get data, as well as developers who control DB infrastructure. In this article, we’ll talk about the most common problems that crop up in large relational databases, and how to optimize data searching mechanisms. Another trap is searching in the wrong place by selecting the wrong object as the data source when typing a query. Documentation and even professional advice isn’t always such a great help for intimately understanding a database schema and navigating it like your own backyard.Ĭorporate databases usually contain thousands of tables, and a simple search by name may not work. Sometimes, even database designers have a hard time guessing which object contains the required data. Knowing where to look for the necessary data in a scarily large database schema is vital when you write SQL queries.
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