Apache Cassandra’s column-oriented data model offers high scalability and performance for big data applications, but requires careful schema design that is heavily influenced by query patterns. Current Cassandra modeling remains a manual, expert-dependent process prone to errors and inefficiencies. This paper presents AutoCassandra, a novel tool that automates the generation of optimized Cassandra schemas from conceptual models and query workflows. Building on established mapping rules and patterns, AutoCassandra translates UML conceptual models and application queries directly into production-ready CQL schemas. Our evaluation demonstrates that AutoCassandra reduces schema design time by 68% while producing schemas that outperform manually designed equivalents by 23% in query execution time across diverse use cases. The tool represents a significant step toward making Cassandra’s performance benefits accessible to non-expert developers while ensuring best practices in schema design.
Apache Cassandra’s column-oriented data model offers high scalability and performance for big data applications, but requires careful schema design that is heavily influenced by query patterns. Current Cassandra modeling remains a manual, expert-dependent process prone to errors and inefficiencies. This paper presents AutoCassandra, a novel tool that automates the generation of optimized Cassandra schemas from conceptual models and query workflows. Building on established mapping rules and patterns, AutoCassandra translates UML conceptual models and application queries directly into production-ready CQL schemas. Our evaluation demonstrates that AutoCassandra reduces schema design time by 68% while producing schemas that outperform manually designed equivalents by 23% in query execution time across diverse use cases. The tool represents a significant step toward making Cassandra’s performance benefits accessible to non-expert developers while ensuring best practices in schema design.
