How They Work
"What is it? It's it" - Epic, Faith No More
Welcome back to this series on working with tablespaces in Navicat 16. Part 1 presented some advantages offered by tablespaces, including Recoverability, Ease of Adding More Tables, Automatic Storage Management, and the Ability to Isolate Data in Buffer Pools for Improved Performance or Memory Utilization. This second instalment will provide more information on what tablespaces are, how they work and the types of default tablespaces you'll find in the various relational database products. The next and final part of the series will focus on how to manage tablespaces in Navicat 16.
The Advantages
Did you know that Navicat 16 supports tablespaces? A table space is a storage structure for tables (as well as indexes, large objects, and long data) that organizes database data into logical storage groupings that relate to where data is stored on the filesystem. It's main function is to link the physical storage layer and the logical storage layer. By assigning tables to a tablespace you can control the physical storage layout by putting some tables on faster or more redundant disks, or to stripe tables across disks. This series is split into two parts: in the first couple of blogs, we'll cover the theoretical side, specifically, what sort of advantages tablespaces offer, as well as how they work and. The second part will focus on more practical matters, i.e., how to manage tablespaces in Navicat 16.
As you well know, multiple server hits can slow down an application. For that reason, developers are keen to find the most efficient ways to update data using as few statements as possible. As it turns out, the SQL UPDATE statement does support the setting of fields from multiple tables using this syntax:
In the Joins versus Subqueries: Which Is Faster? blog article we learned that joins tend to execute faster than subqueries. Having said that, it's not a universal rule, so you may not want to automatically assume that a join will be preferable. As mentioned in that article, if you need to add many joins to a query, the database server has to do more work, which can translate to slower data retrieval times. This article will present a couple of quick tests you can perform to compare a query that employs joins to one that contains subqueries so that you can choose which performs best.
Back in 2020, we learned about The NULL Value and its Purpose in Relational Database Systems. As stated in that article, the value NULL has become a special marker to mean that no value exists. You could also say that NULL values may indicate that a column could have a value, but you don't know what that value should be yet. In that context, they act as a placeholder until you finally collect the data needed to fill the table field with a real value.
Moreover, when you consider that all major database vendors support NULLs as default values, it only makes sense to use them, doesn't it? Well, not so fast. There are database designers who avoid using NULLs unless absolutely necessary. Do they know something that the rest of us don't? Read on to find out!
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