Neo4j数据库研究
最近开始研究数据库的数据库特性。 目前我正在研究 Neo4j 图形数据库。
不幸的是,我无法找到我需要的所有信息。 我找到了除以下内容之外的大多数信息:
- 支持数据类型? (整数,
- 最大数据库大小?
- 数据库中的最大节点数?
- 数据库中的最大关系数?
I recently started researching database features of databases.
At the moment I'm looking into Neo4j Graph database.
Unfortunately, I can't find every bit of information I need.
I found most information except the following:
- Supporting datatypes? (Integer,
- Max. database size?
- Max. nodes in db?
- Max. relations in db?
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支持的数据类型:
来源: Neo4j API 文档
数据库大小没有限制,但当前版本 (1.2) 对节点、关系和属性的数量有限制。其中每一项的限额为 40 亿。增加限制的工作现已完成,并将很快包含在里程碑版本中。节点和关系的新限制为 32B,属性的新限制为 64B。
在 1.3.M03 里程碑版本中,包含了对更有效的短字符串存储方式的支持,这将大大降低许多数据集的磁盘消耗。请参阅Neo4j 中对短字符串的更好支持。
The supported datatypes:
Source: Neo4j API docs
There's no limit on database size, but the current release (1.2) has limitations on the number of nodes, relationships and properties. The limit on each of these is 4 billion. The work on increasing the limits is done right now, and will be included in a milestone release soon. The new limit is 32B on nodes and relationships and 64B on properties.
In the 1.3.M03 milestone release support for a more efficient way of storing short strings was included, which will lower disk consumption considerably for many datasets. See Better support for short strings in Neo4j.
以下是一些关键区别和显着特征:
Neo4j 具有关系系统中未找到的数据类型:路径、列表。遍历是沿着可以收集数据的路径进行的。
关系可以具有与关系系统中的联接不同的属性。它们可以用来记录来源并加快查询速度。
增加节点和关系的数量对性能的影响最小。
用另一个图来扩充一个图很容易,或者可以在新的节点、关系或属性中记录分析/查询的结果……这就是逐步构建知识图的方式。
Here are some key differences and distinguishing features:
Neo4j has data types not found in a relational system: path, list. Traversals are along paths during which data can be collected.
Relationships can have properties unlike the joins in a relational system. They can be leverage to document provenance and speed queries.
Increasing the number f nodes and relationships has a minimal effect on performance.
It's easy to augment a graph with another graph or ny memorializing the results of analytics/queries in new nodes, relationships or properties ... which is how you incrementally build a knowledge graph.