How Can We Efficiently Parse a Flat Table into a Hierarchical Tree Structure?

Analyze the flat table as a tree structure: high -efficiency and elegant method

When processing the layered data stored in a flat table, it usually needs to be parsed and presented as an intuitive tree structure. The key to efficient and elegant solutions is to use basic data structures and understand hierarchical relationships in data.

Efficient algorithm:

Assume that the table contains column "ID", "name", "Parentid" and "Order". We can use the hash table to build a tree structure efficiently. The steps are as follows:

Create a hash table, where the key is the node ID, and the value is a node object that contains nodes names and other related information.

Each line of traversing tables creates node objects for any unsighting ID, and adds them to the hash table.

1. For each node, find its parent node by quoting the "Parentid" column. If there is no parent node, it is the root node.
2. By updating the "Children" list in the parent node, the node is added to the sub -node of its parent node.
3. Repeat steps 3-4 until all nodes are processed.
This algorithm uses the constant time of the hash table to find the function to ensure the efficient time complexity of O (n), of which n is the number of nodes.
Extra content: Store the tree structure in the relationship database

There are limitations of the traditional method (adjacent table, path enumeration and nested sets) described in the problem of storage tree structure. The better method is the materialization path

method, PostgreSQL and other modern databases support this method. In this method, the "PATH" is added to the table, which contains the complete path from the root node to each node, and is separated by separators (for example, "/"). This allows efficient inquiries and traversing tree -shaped hierarchical structures without recursive operations.

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