How to implement hash mapping and prefix query functions from multi-dimensional to unique values ​​in Java?

Hash mapping and prefix query from Java multi-dimensional data to unique ID

This article discusses how to design a hash map in Java to implement the mapping of multi-dimensional data to unique IDs, and supports prefix query based on partial dimensions. For example, the function f(a, b, c, ...) needs to generate a unique ID, and f(a, b) != f(b, a) . We also need to be able to query all mapping results prefixed with a specific dimension, such as querying all mappings starting with a .

plan:

It is difficult to efficiently implement prefix query using a single HashMap directly. A more efficient solution is to use a tree structure, such as a Trie tree or a custom tree structure, with dimension information as keys and unique IDs as values.

Implementation steps:

1. Dimensional data structure: Define a class to represent dimensional data, for example:

 class Dimension {
    String a;
    String b;
    String c;
    // ... other dimensions

    public Dimension(String a, String b, String c) {
        this.a = a;
        this.b = b;
        this.c = c;
    }

    // equals() and hashCode() methods for HashMap comparison
    @Override
    public boolean equals(Object obj) {
        if (this == obj) return true;
        if (obj == null || getClass() != obj.getClass()) return false;
        Dimension that = (Dimension) obj;
        return Objects.equals(a, that.a) && Objects.equals(b, that.b) && Objects.equals(c, that.c);
    }

    @Override
    public int hashCode() {
        return Objects.hash(a, b, c);
    }
}

2. Trie tree structure (example): Use Trie tree to store dimension information and ID mapping. Each node represents a dimension value, and the leaf node stores a unique ID.

 class TrieNode {
    String value;
    Map<string trienode> children;
    String uniqueId; // Store unique ID at leaf nodes

    public TrieNode(String value) {
        this.value = value;
        this.children = new HashMap();
    }
}

class Trie {
    TrieNode root;

    public Trie() {
        root = new TrieNode("");
    }

    public void insert(Dimension dim, String uniqueId) {
        TrieNode node = root;
        node = insertRecursive(node, dim, uniqueId);
    }

    private TrieNode insertRecursive(TrieNode node, Dimension dim, String uniqueId) {
        if (dim == null) {
            node.uniqueId = uniqueId;
            return node;
        }
        if (dim.a != null) {
            node.children.computeIfAbsent(dim.a, k -> new TrieNode(k));
            node = node.children.get(dim.a);
            if (dim.b != null) {
                node.children.computeIfAbsent(dim.b, k -> new TrieNode(k));
                node = node.children.get(dim.b);
                if (dim.c != null) {
                    node.children.computeIfAbsent(dim.c, k -> new TrieNode(k));
                    node = node.children.get(dim.c);
                }
            }
        }
        node.uniqueId = uniqueId;
        return node;
    }


    public List<string> prefixSearch(String prefix) {
        List<string> result = new ArrayList();
        TrieNode node = root;
        for (String part : prefix.split(",")) {
            if (!node.children.containsKey(part)) {
                return result; // Prefix not found
            }
            node = node.children.get(part);
        }
        collectIds(node, result);
        return result;
    }

    private void collectIds(TrieNode node, List<string> result) {
        if (node.uniqueId != null) {
            result.add(node.uniqueId);
        }
        for (TrieNode child : node.children.values()) {
            collectIds(child, result);
        }
    }
}</string></string></string></string>

3. Example of usage:

 public class Main {
    public static void main(String[] args) {
        Trie trie = new Trie();
        trie.insert(new Dimension("a", "b", "c"), "u1");
        trie.insert(new Dimension("a", "b", "d"), "u2");
        trie.insert(new Dimension("x", "y", "z"), "v1");

        List<string> results = trie.prefixSearch("a,b");
        System.out.println(results); // Output: [u1, u2]

        results = trie.prefixSearch("a");
        System.out.println(results); // Output: [u1, u2]

        results = trie.prefixSearch("x");
        System.out.println(results); // Output: [v1]
    }
}</string>

This example shows how to use a Trie tree to implement mapping and prefix query of multi-dimensional data to unique IDs. You can adjust the dimensional data structure and implementation details of the Trie tree according to actual needs. For very large data sets, consider using more advanced data structures and algorithms to optimize performance. For example, consider using database indexes to speed up queries.

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