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How to analyze the little knowledge of graph database and graph database, I believe that many inexperienced people do not know what to do about it. Therefore, this paper summarizes the causes and solutions of the problem. Through this article, I hope you can solve this problem.

Graph database-Wikipedia: in computer science, a graph database (English: graph database,GDB) is a database that uses graph structures for semantic queries, using nodes, edges, and attributes to represent and store data. The key concept of the system is graph, which directly associates the data items in storage with the set of edges that represent the relationship between data nodes and nodes. These relationships allow data in the store to be linked directly together and, in many cases, can be retrieved through a single operation. The graph database gives priority to the relationship between the data. The relationships in the query graph database are fast because they are permanently stored in the database itself. You can use a graph database to visually display relationships, making them useful for highly interconnected data.

Graph database is a kind of non-relational database, the limitation of database. The graph model explicitly lists the dependencies between the data nodes, while the relational model and other NoSQL database models link the data through implicit connections. From the design, the graph database can simply and quickly retrieve the complex hierarchical structure which is difficult to model in the relational system. The graph database is similar to the network model database in the 1970s, they all represent general graphs, but the network model database runs at a lower level of abstraction and can not easily traverse a series of edges.

The underlying storage mechanism of the graph database may be different. Some rely on the relational engine and "store" graph data into tables (although the table is a logical element, this approach imposes another layer of abstraction between the graph database, the graph database management system, and the physical device that actually stores the data). Others use key-value storage or document-oriented databases for storage so that they have an inherent NoSQL structure. Most graph databases based on non-relational storage engines also add the concept of tags or attributes, which are essentially relationships with pointers to another document. In this way, the data elements can be classified for centralized retrieval.

Retrieving data from a graph database requires a query language other than SQL. SQL is designed to deal with data in relational systems, so it is impossible to traverse the graph "elegantly". As of 2017, there is no common graph query language like SQL, which is usually limited to one product. However, there has been some standardization work that has made Gremlin, SPARQL, and Cypher multi-vendor query languages. In addition to having a query language interface, you can also access some graph databases through the application programming interface (API).

The graph database is different from the graph computing engine. Graph database is the technology of transforming relational OLTP database. The graph calculation engine is used for batch analysis in OLAP. Due to the success of major technology companies in the use of proprietary map databases and the introduction of open source graph databases, graph databases attracted considerable attention in the 2000s.

After reading the above, have you mastered the method of how to analyze the graph database and the little knowledge of the graph database? If you want to learn more skills or want to know more about it, you are welcome to follow the industry information channel, thank you for reading!

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