Cassandra Data Modeling: What It Is and How To Use It
By Indeed Editorial Team
Published May 11, 2021
The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.
Data modeling is a useful tool for organizing and structuring large amounts of data so that you can analyze entities and their relationships. The data model you choose depends on the type of data you need to access and your query patterns. When using the Cassandra data management system, the data model you choose can be especially important. In this article, we discuss what Cassandra data modeling is, when to use it and best practices you can follow to help you design a successful model that works well with Cassandra.
What is Cassandra data modeling?
Cassandra data modeling is a way to optimize your data model for Cassandra, a database management system. The Cassandra data model is unique because users model the data to fit specific data requests rather than organize relations or objects. Using the model, you can structure data storage as a set of rows organized into tables or columns. The major components of the model are:
Columns: A column is a row of data.
Key spaces: Key spaces hold together columns.
Tables: Tables, also called column families, are a tool for organizing data.
Because of its specific uses, choosing an appropriate data model can be the most challenging part of using Cassandra. Cassandra doesn't support combined tables or table joins the way relational database models do. This means it can be extremely helpful to organize your model's columns into a single table. Each query requires a column family to continue to duplicate data and provide the high performance you need from the model.
The goals of your Cassandra data model are to:
Store large amounts of data
Model your data around your needs
Optimize data for specific queries
Provide fast reads and writes
Organize data to support Cassandra Query Language (CQL)
Disperse data around a cluster
Minimize returned partitions
Related: What Is Data Modeling?
When to use Cassandra data modeling
Cassandra is designed to support large amounts of structured or semi-structured data across general servers and shouldn't cause widespread system malfunction because of a single fault. This can be beneficial to companies scaling up because the platform's strength increases with the addition of new data centers, regardless of their location.
Cassandra modeling provides the following features, which your organization may find appealing:
Scalability: As additional data units, or nodes, are added and data is spread out more evenly among them, the load each node handles is reduced. A group of data points, called a cluster, may be deployed in multiple data centers and span global regions.
Reliability: Cassandra makes it easy to distribute data evenly across every node in a cluster, with each node able to handle read and write requests. This means the platform shouldn't fail because of a single malfunction.
Adjustability: In Cassandra, you can set the consistency level depending on your query needs.
Flexibility: The Cassandra data model applies in a wide range of use cases, so you can likely use Cassandra for your data.
Availability: Cassandra is highly available and can still work even with faults because of the way data replicates across nodes in a cluster.
Communicability: Peer-to-peer architecture allows all of the nodes in a Cassandra cluster to communicate with one another.
Accessibility: Cassandra is an open-source project, and you can easily integrate it with other open source projects.
Best practices for Cassandra data modeling
Here is a list of basic rules that may help you optimize your model's performance:
Map data and queries
If you're used to relational modeling, Cassandra modeling can look a little different. Instead of designing a relational table, consider creating a nested, sorted map. The nested structure can aid in efficient scans, and the map can help you with easy key lookup. A successful mapped structure depends on data discovery and identifying patterns. When designing your tables, try to think of them as two sorted maps: an inner map keyed by a column key and an outer map keyed by a row key.
Model your data around specific queries
Data modeling in Cassandra is query-driven, meaning it can be helpful to structure the data in your model around use patterns and planned queries. Try to consider your query patterns before you design your column families.
It can be helpful to analyze how frequently you use a query and if a query is prone to delays between your actions and the program's response. This way, you can ensure your model supports the most important and frequent query patterns. It can also be helpful to condense all entities involved in a query or query set into a single table to achieve faster data access reads.
Depending on your query, you may need to have a table containing over one entity or have a single entity included in multiple tables. This is because the platform doesn't support creating secondary indexes or complex SQLs. To avoid this, consider starting your design by identifying key entities and relationships and then mold them to fit specific queries and query patterns.
Understand your data
A key component to effectively modeling your data around your queries is understanding your data. A finished model contains accurately identified queries and complete data sets. To avoid the challenging task of inputting data in an existing model again, consider focusing on the development of a solid conceptual model so that you can better comprehend the data you need. You may want to begin with a high-level view of your data to understand your entities and their identifying attributes.
Related: What Is Data Management?
Follow big data modeling methods
If you've chosen to use Cassandra for your data storage and analysis needs, you're likely processing substantial amounts of data to support a large-scale business process. Consider following other big data modeling methods and taking a structured approach to ensure your model is both complete and high performing.
Expect higher instances of writes and data duplication
Depending on your background, you may be used to minimizing writes and denormalization in your models. While those goals hold some weight in Cassandra data modeling, they likely won't be your main priority. Writes in Cassandra are relatively inexpensive, and you may choose to execute extra writes to improve the efficiency of your read queries. The program can handle high writes throughput and can execute almost all writes with equal efficiency. Reads, however, can be more costly and challenging to tune.
Denormalization and data duplication are common in Cassandra. The system doesn't aim to conserve disk space because it's typically an inexpensive and available resource. Duplicated data is sometimes necessary for efficient reads, especially because the platform doesn't support table joins.
Distribute data evenly
In Cassandra, the system facilitates the equal distribution of data, but accomplishing this still requires the user to select an appropriate primary key to ensure you can distribute your data evenly.
Ultimately, the aim is to have approximately the same amount of data for each node in the cluster. Cassandra only supports sorting on the clustering columns of a specific primary key. When making your design decisions, consider how sorting occurs in your data model.
Minimize partition reads
A partition refers to a group of rows with the same partition key. Your partitions may reside in different nodes, and the partition read you request might require a unique command for each partition in each separate node. This can quickly become time-consuming and increase latency variation. Because of how Cassandra stores rows, it can also be costly to read from multiple partitions, even if it's only on a single node.
For this reason, aim to read rows from fewer partitions when you issue your read queries. Though it can sometimes be challenging to achieve both fewer partition reads and equal data distribution, finding a balance between the two can help you create a successful and efficient model.
Analyze the effectiveness of your model
Your model may need to be adjusted to fit the demands of your queries, data and scope of work. Thoroughly analyzing your model can help you change your schema to adjust for any special considerations or limitations like partition size and data redundancy. If disk space is limited, you might have to update your model to accommodate space needs as well. While duplicated data, lightweight transactions and multiple partitions can be inevitable features of your model, consider keeping them to a minimum to support efficient reads and optimized performance.
Once you've finished creating your physical tables, you can review and refine your physical data model to ensure it meets your goals.
Please note that none of the companies mentioned in this article are affiliated with Indeed.
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