在 Neo4J 中遍历树结构时的建模替代方案和性能
我使用 Neo4J 图形数据库建模了树结构。所有节点都代表一个具有特征名称的类别。所以我必须经常从根到特定的节点/类别遍历我的树。哪个节点取决于作为输入的列表。该列表包含表示从根到目标节点的类别名称的字符串。
我想知道，将这些名称存储为边的类型而不是特定节点中的名称属性是否有效。 我认为，当我这样做时，Neo4J 不必每次在树中深入一步时都寻找每个子节点的合适名称属性。相反，Neo4J 可以在包含传出边缘的映射中查找名称。
你怎么认为？
I modelled a tree structure using the Neo4J graph database. All nodes represent a category with a characterising name. So I have to traverse my tree very often from the root to a specific node / category. To which node depends on a list coming as input. This list contains strings representing the names of the categories from the root to the target node.
I wonder, if it would be effective to store these names as the types of the edges instead of a name property in the particular nodes.
I thought that when I do so, Neo4J doesn't have to look for the fitting name property of every child node every time going a step deeper in the tree. Instead Neo4J could lookup the name in the map that contains the outgoing edges.
What do you think?
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听起来很有道理。你有多少个不同的名字？如果只是类别，那不应该是数百万。
您是否将数据加载到图表中并在两种方法之间进行性能比较？它在你的图表中对性能至关重要吗？
Sounds sensible. How many different names do you have? If it is just categories those shouldn't be millions.
Did you load your data into the graph and run a performance comparison between both approaches? Is it a performance critical thing in your graph?