Cassandra Database Limitations: What You Need to Know

When considering using Cassandra database for your application, it’s important to be aware of its limitations. Here are some key points to keep in mind:

1. Lack of Transactions

Cassandra does not support ACID transactions like traditional relational databases. It uses a different approach called eventual consistency, which may not be suitable for all use cases.

2. Query Limitations

Queries in Cassandra are limited to the columns that are part of the primary key or indexed columns. Complex queries involving joins or aggregations are not supported, making it less flexible for certain types of applications.

3. Storage and Disk Space

Cassandra requires a lot of disk space due to its design of writing data multiple times for durability and fault tolerance. This can lead to higher storage costs compared to other databases.

4. Read and Write Performance

While Cassandra is optimized for high write throughput, read performance can be impacted, especially when dealing with large datasets. Tuning the database for read-heavy workloads can be challenging.

5. Data Modeling Complexity

Data modeling in Cassandra requires a deep understanding of how data is structured and accessed. Denormalization is common to optimize queries, but it can make the data model complex and harder to manage.

6. Limited Support for Aggregations

Cassandra is not well-suited for applications that require complex aggregations or analytics. Performing aggregations across large datasets can be inefficient and resource-intensive.

7. Schema Changes

Modifying the schema in Cassandra can be cumbersome, especially for large tables. Adding or removing columns may require rewriting data, and schema changes can impact performance during the process.

8. Tooling and Ecosystem

Compared to popular relational databases like MySQL or PostgreSQL, Cassandra has a smaller ecosystem of tools and libraries. This can make it challenging to find suitable solutions for monitoring, backups, and other essential tasks.

9. Operational Complexity

Running and maintaining a Cassandra cluster can be complex and resource-intensive. Tasks like cluster management, backups, and tuning require specialized knowledge and experience.

10. Scalability Challenges

While Cassandra is designed for horizontal scalability, adding nodes to a cluster and rebalancing data can be tricky. Ensuring even distribution of data and maintaining performance as the cluster grows require careful planning.

Understanding these limitations of Cassandra is crucial for making informed decisions about using it in your applications. While it offers scalability and high availability, it may not be the best choice for every use case.