dosubot[bot] commented on issue #2524: URL: https://github.com/apache/incubator-hugegraph/issues/2524#issuecomment-2058507877
<!-- Answer --> The discussion around HugeGraph's use of relational/SQL-based backend storage systems like MySQL, RocksDB, Cassandra, and HBase for graph data storage raises important points about the nature and capabilities of graph databases. The essence of HugeGraph being classified as a graph database is not strictly about the storage mechanism it employs but rather about how it models, manages, and queries data. By representing data as vertices and edges, HugeGraph adopts a graph data model that is fundamentally different from the tabular model used in relational databases. This model is optimized for efficiently querying relationships and patterns within the data, which is a core advantage of graph databases. HugeGraph's support for various backend systems is a strategic choice that allows it to leverage the robustness, scalability, and persistence features of these established databases. On top of these systems, HugeGraph implements graph-specific functionalities such as schema management, indexing for efficient query processing, and optimizations for graph traversal. These capabilities enable HugeGraph to offer the key benefits associated with graph databases, such as enhanced search speeds and more effective data relationship exploration, irrespective of the underlying storage system. The clarification provided by the maintainer, @imbajin, further enriches our understanding by highlighting that HugeGraph's design is centered around a binary key-value (KV) structure on disk and a graph structure in memory. This approach is not solely dependent on relational databases but is compatible with them, facilitating easier migration or understanding for users familiar with relational models. It's important to note that the use of relational databases is not equivalent to KV storage but rather a compatible option. From a broader perspective, the choice to build a graph system on top of stable and reliable KV storage is a common and mainstream approach in the industry. This strategy not only ensures the reliability and efficiency of the storage system but also simplifies expansion into distributed environments. The underlying KV/Map semantics provided by the storage system serve as a foundational layer upon which the graph data model is implemented, allowing HugeGraph to fully leverage the unique advantages of graph databases for handling complex queries and relationships. <!-- Dosu Comment Footer --> <details> <summary> #### About Dosu </summary> This response is meant to be useful and save you time. It is **not meant to be a precise solution**, but rather a starting point for your own research. Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant. If you want to continue the conversation, start your reply with @dosu-bot. </details> -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected] --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
