The editor of Downcodes will take you to understand the two main types of databases: analytical databases and transactional databases. They have significant differences in application scenarios and data processing methods. Analytical database (OLAP) focuses on data analysis and mainly uses SELECT statements to perform complex query operations for decision support; while transactional database (OLTP) focuses on daily transaction processing, such as bank transactions, using short DML statements. Lord. This article will deeply explore the differences between OLTP and OLAP, and introduce graph database, a non-relational database. With its unique graph structure and priority processing of relationships, it shows strong advantages in processing complex data relationships.
The analytical type is divided based on the role of the database. It is mainly used for data analysis (OLAP), and most of them are select statements. There is also one that is specially used for transaction processing, usually a short dml (OLTP). OLTP is the main application of traditional relational databases.
The analytical type is divided based on the role of the database. It is mainly used for data analysis (OLAP), and most of them are select statements. There is also one that is specially used for transaction processing, usually a short dml (OLTP).
OLTP is the main application of traditional relational databases, mainly for basic, daily transaction processing, such as bank transactions.
OLAP is the main application of data warehouse systems, supporting complex analysis operations, focusing on decision support, and providing intuitive and easy-to-understand query results.
Relational databases are divided into database types: databases can be divided into relational (RDBMS) and non-relational (no sql)
Further reading:
In computer science, a graph database (English: graph database, GDB) is a database that uses a graph structure for semantic queries. It uses nodes, edges, and attributes to represent and store data. The key concept of the system is a graph, which directly associates data items in storage with data nodes and a collection of edges representing relationships between nodes. These relationships allow data in the store to be linked directly together and, in many cases, retrieved with a single operation. Graph databases prioritize relationships between data. Querying relationships in a graph database is fast because they are permanently stored in the database itself. Relationships can be visualized using graph databases, making them useful for highly interconnected data.
Graph database is a non-relational database to solve the limitations of existing relational databases. Graph models explicitly list dependencies between data nodes, while relational models and other NoSQL database models link data through implicit connections. Graph databases are designed to easily and quickly retrieve complex hierarchical structures that are difficult to model in relational systems. Graph databases are similar to network model databases from the 1970s in that they represent general graphs, but network model databases operate at a lower level of abstraction and cannot easily traverse a sequence of edges.
I hope this article can help you better understand the types and applications of databases. The editor of Downcodes will continue to bring you more exciting content!