Introduction to Cassandra Data Modeling
To counter a colossal amount of information, new data management technologies have emerged. These techniques are different from traditional relational database approaches. They are collectively referred to as NoSQL. Cassandra is one of the widely known NoSQL databases. Other popular NoSQL database products include MongoDB, Riak, Redis, Neo4j, etc. In this topic, we are going to learn about Cassandra Data Modeling.
These NoSQL databases defeat the shortcomings uncovered by the relational database by incorporating enormous volume that contains organized, semi-organized, and unstructured information. Scalability and performance for web-applications, Lower cost, and Support for agile software development are some of its advantages. Cassandra is a functioning open-source platform in Apache Software Foundation and consequently, it is known as Apache Cassandra too. Cassandra can oversee an immense volume of organized, semi-organized, and unstructured data in a large distributed cluster across multiple centers. It provides high scalability, high performance and supports a flexible model.
Data modeling is an understanding of flow and structure that needs to be used to develop the software. It identifies the main objects, their features and the relationship with other objects. This is often the first step and the most essential step in creating any software. Just like how the blueprint design is for an architect, A data model is for a software developer. This not only helps to analyze the structure but also allows you to anticipate any functional or technical difficulties that may happen later.
Traditional data modeling flow starts with conceptual data modeling. This conceptual data model is then mapped to a relational data model that finally produces a relational database schema. In this process, the primary thing is data sorting which is done based on correlation by understanding and querying it.
Data modeling in Cassandra differs from data modeling in the relational database. Relational data modeling is based on the conceptual data model alone. Which uses SQL to retrieve and perform actions. Cassandra uses CQL (Cassandra Query Language) having SQL like syntax. Data modeling in Cassandra begins with organizing the data and understanding its relationship with its objects. Here, the keyspace is analogous to a database that contains different records and tables. A cluster can have multiple keyspaces. Different nodes connect to create one cluster. On the keyspace level, we can define attributes like the replication factor.
The understanding of a table in Cassandra is completely different from an existing notion. A CQL table can be considered as a group of partitions called the column family that contains rows with the same structure. Every partition holds a unique partition key and every row contains an optional singular cluster key. The combination of partition and a cluster key is called a primary key which is used to identify a row in the table. A table with a cluster key will have multi-row partitions whereas a table with no clustered key will solely have single row partition.
Casandra flow starts from a conceptual data model along with the application workflow which is given as inputs to obtain the logical data model and at last to get the physical data model.
User queries are defined in the application workflow. Conceptual Data Modelling is used to capture the relationship between different entities and their attributes. Hence the name E-R model.
Logical Data Modeling
The core of the Cassandra data modeling methodology is logical data modeling. A conceptual data model is mapped to a logical data model based on queries defined in an application workflow. This query-driven conceptual to logical mapping is defined by data modeling principles, mapping rules, and mapping patterns.
Data Modeling Principles
The following four principles provide a foundation for the mapping of conceptual to logical data models.
- Know your data: To organize data correctly, entities, attributes, and their relationships must be well known to develop a conceptual data model.
- Know your queries: To organize data efficiently, queries are used. The best option to be performed is partition per query.
- Data nesting: To organize multiple entities of the same type together on known criterion, data nesting is used. It is used to retrieve multiple entities from a single partition.
- Data Duplication: It is always better to have data duplication over joins in Cassandra as it helps efficiently support different queries over the same data.
Based on the data modeling principles, mapping rules are defined to carry out the transition from a conceptual data model to a logical data model
- Entities and Relationships: Entity and relationship types map to tables, while entities and relationships map to table rows.
- Equality Search Attributes: Equality search attributes are used at the columns containing the primary key to participate in equality search.
- Inequality Search Attributes: Inequality search attributes are also used in the columns containing the primary key to produce different search results.
- Ordering Attribute: Ordering attribute is used to group by data in a specific order
- Key Attribute: This characteristic helps to identify the unique rows
Based on the above mapping rules, we design mapping patterns that serve as the basis for automating the database design. Through the given query and conceptual data model, each pattern defines the final schema design outline.
Once the logical model is in place developing a physical model is relatively easy. A physical data model represents data in the database. After assigning of data types the partition size is estimated and testing is performed to analyze the model for better optimization.
To conclude we can say that when there are a huge volume and variety of data at disposal to be analyzed and processed. It is necessary to choose an approach that can efficiently extract the data to be analyzed. Cassandra with its high scalability and ability to store massive data offers fast retrieval of information to design data models for complex structures. Cassandra data modeling and all its functionality can be encompassed in the following ways. Here, we create a query-driven conceptual data design and with the help of outlined mapping rules and mapping patterns it enables the transition from conceptual model to the logical model occurs. We then describe a physical model to get a completely unique mental image of the design.
This is a guide to Cassandra Data Modeling. Here we discuss the Table Model, Query Model, Logical Data Modeling and Data Modeling Principles. You may also have a look at the following articles to learn more –