In database management systems (DBMS), multiple granularity refers to the ability to define different levels of detail or granularity for accessing and manipulating data within a database. It allows users and applications to work with data at various levels of abstraction, depending on their specific needs.
Multiple granularity can be achieved in different ways, but one common approach is through the use of hierarchical data models or database schemas. In this approach, data is organized into a hierarchy of levels, with each level representing a different level of granularity. For example, a database may have a high-level schema that provides an overview of the entire database, and then lower-level schemas that provide more detailed views of specific subsets of the data.
At each level of granularity, users can interact with the data in different ways. They can query and retrieve data at a higher level of abstraction to get a general understanding of the database content. Alternatively, they can access data at a lower level of granularity for more detailed analysis and processing.
The multiple granularity concept is especially useful in large and complex databases where different users or applications have varying information requirements. It allows users to focus on the data that is relevant to their specific tasks without being overwhelmed by unnecessary details.
By providing multiple levels of granularity, DBMS can offer flexibility, efficiency, and scalability. Users can work with the level of detail that suits their needs, and the system can optimize data access and storage based on the granularity requirements.
Overall, multiple granularity in DBMS enhances the usability and performance of database systems, enabling users to access and manipulate data at different levels of abstraction.
Example of Multiple Granularity:
Sure! Let’s consider an example of a database for a retail store. The database contains information about products, customers, and sales. We can demonstrate multiple granularity by defining different levels of detail for accessing and manipulating the data.
- High-level Granularity: At the highest level of granularity, we can have a schema that provides an overview of the entire database. This schema may include tables like “Products,” “Customers,” and “Sales.” It gives a broad view of the data and allows users to retrieve general information about products, customers, and sales.
- Medium-level Granularity: At a medium level of granularity, we can have more specific schemas that provide additional details about subsets of the data. For example, we can have a schema for “Product Categories” that includes tables like “Electronics,” “Clothing,” and “Home Appliances.” These schemas allow users to retrieve information specific to product categories.
- Low-level Granularity: At a lower level of granularity, we can have schemas that provide even more detailed information. For instance, we can have a schema for “Product Details” that includes tables like “Product Specifications,” “Inventory,” and “Pricing.” These schemas allow users to access and manipulate specific attributes of products, such as their specifications, stock levels, and pricing details.
Using this multiple granularity approach, different users or applications can work with the data at the level of detail that suits their needs. A store manager might work with the high-level schema to get an overview of sales performance, while a salesperson might use the medium-level schema to focus on a specific product category. On the other hand, a data analyst might work with the low-level schema to perform detailed analysis on product specifications or inventory levels.
By providing multiple levels of granularity, the DBMS allows users to navigate through different levels of detail efficiently, retrieve relevant information, and perform various operations based on their specific requirements.
Intention Mode Lock:
Intention-shared (IS): It contains explicit locking at a lower level of the tree but only with shared locks.
Intention-Exclusive (IX): It contains explicit locking at a lower level with exclusive or shared locks.
Shared & Intention-Exclusive (SIX): In this lock, the node is locked in shared mode, and some node is locked in exclusive mode by the same transaction.
Compatibility Matrix with Intention Lock Modes: The below table describes the compatibility matrix for these lock modes: