Databases Reference
In-Depth Information
Table 6-2.
Relational Data Model vs. Cassandra Data Model
Relational Data Model
Cassandra Data Model
(Standard)
Cassandra Data Model
(Super)
Server based
Cluster based
Database
Key Space
Table
Column Family
Primary Key
Key
Column Name
Column Name
Super Column Name
Column Value
Column Value
Column Value
Designing Cassandra Data Structures
1.
Entities and Point of Interest:
The best way to model a
Cassandra data structure is to identify the entities that would
be subjected to most queries and creating the entire structure
around the entity. The activities performed (generally the
use cases) by the user applications, how the data is retrieved
and displayed would be the areas of interest for designing the
Cassandra column families.
2.
De-normalization:
Normalization is the set of rules
established to aid in the design of tables and their
relationships in any RDBMS. The benefits of normalization
would be:
•
Avoiding repetitive entries
•
Reduction of storage space
•
Prevention of schema restructuring for future needs.
•
Improved speed and flexibility of SQL queries, joins, sorts,
and search results.
Achieving similar kind of performance for big data scale is a challenge in traditional
relational data models. Therefore, in most of the big appl data ications de-normalization
approaches are adopted to achieve performance. Cassandra does not support foreign
key relationships like a relational database, and the better way is to de-normalize the
data model. The important fact is that instead of modeling the data first and framing the
queries, with Cassandra the queries would be modeled first and then the data be framed
around them.
