数据库中树状数据模型存储

背景

典型的树状结构的场景有:

树状数据模型方案分析

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方案A: Model Tree Structures with Parent References

db.categories.insert( { _id: "MongoDB", parent: "Databases" } )
db.categories.insert( { _id: "dbm", parent: "Databases" } )
db.categories.insert( { _id: "Databases", parent: "Programming" } )
db.categories.insert( { _id: "Languages", parent: "Programming" } )
db.categories.insert( { _id: "Programming", parent: "Books" } )
db.categories.insert( { _id: "Books", parent: null } )

优点

  1. 数据模型简洁, 含义清晰;
  2. 改变所属父级时, 只需要修改一条数据的parent;

缺点

  1. 通过父级节点查找子孙节点需要递归操作;
  2. 通过子孙节点查找祖先节点需要递归操作;

方案B: Model Tree Structures with an Array of Ancestors

db.categories.insert( { _id: "MongoDB", ancestors: [ "Books", "Programming", "Databases" ], parent: "Databases" } )
db.categories.insert( { _id: "dbm", ancestors: [ "Books", "Programming", "Databases" ], parent: "Databases" } )
db.categories.insert( { _id: "Databases", ancestors: [ "Books", "Programming" ], parent: "Programming" } )
db.categories.insert( { _id: "Languages", ancestors: [ "Books", "Programming" ], parent: "Programming" } )
db.categories.insert( { _id: "Programming", ancestors: [ "Books" ], parent: "Books" } )
db.categories.insert( { _id: "Books", ancestors: [ ], parent: null } )

优点

  1. 通过父级节点查找子孙节点只需一次请求;
  2. 任一节点都直接保存了祖先节点的信息;
db.categories.find({ _id: { $in: doc.ancestors } }) // 找祖先
db.categories.find({ ancestors: 'Books' }) // 找子孙
db.categories.find({ 'ancestors.0': 'Books' }) // 找儿子
db.categories.find({ 'ancestors.1': 'Books' }) // 找孙子

缺点

  1. 改变所属父级时, 除自身的ancestors需要更新外, 所有子孙节点的ancestors信息都需要更新;

总结

方案A 和 方案B 各有优劣, 需要依据实际场景判断方案选择: 业务场景经常需要通过一个节点查找祖先或子孙, 则选择方案B, 否则选择方案A

参考资料