10M级排序唯一名称关联数字场景，最优内存数据结构选型咨询

Hey there! Let's break down your two questions with practical, in-memory data structure recommendations tailored to your needs.

When you need to store large volumes of unique records entirely in memory (avoiding disk hits), these structures are your top picks:

• Hash Table (Unordered Map)：The go-to choice for most unique record scenarios, offering average O(1) time complexity for insert, lookup, and delete operations. It enforces unique keys by design, and most languages have battle-tested built-in implementations (like Python's dict, Java's HashMap, or C++'s std::unordered_map). Memory overhead is minimal—just the key-value pairs plus minor handling for hash collisions.
• Balanced Binary Search Tree (e.g., Red-Black Tree)：Ideal if you need your records to stay sorted. It guarantees O(log n) time for all core operations while maintaining tree balance to avoid worst-case performance. Great for range queries or ordered traversals; look to Java's TreeMap or C++'s std::map for out-of-the-box implementations.
• Skip List：A simpler alternative to balanced BSTs that delivers comparable O(log n) performance for all operations. It uses layered index structures on top of a linked list, making it easier to implement than trees with rotation logic. Widely used in in-memory databases like Redis for sorted data, especially when you need efficient range queries.
• Trie (Prefix Tree)：Perfect if your unique records are strings and you need frequent prefix-based lookups (e.g., finding all names starting with "Alex"). It shares common string prefixes to save memory, and prefix queries run in time proportional to the prefix length—far faster than other structures for this use case.

Your specific use case (10 million sorted, unique string-to-number mappings, in-memory only) depends on how you plan to interact with the data. Here's a targeted breakdown:

Scenario 1: Static Data (Rarely Modified, Query-Heavy) #

If your data is loaded once and only queried (no frequent inserts/deletes), an ordered struct/object array + binary search is the optimal choice:

• Minimal memory footprint: No extra pointers or tree node overhead—just the raw name and number for each entry. For 10M records with an average 10-byte name and 4-byte number, total memory usage is roughly 140MB, which is trivial for modern systems.
• Fast enough queries: Binary search runs in O(log n) time, meaning only ~24 comparisons to find a record in 10M entries—plenty fast for most use cases.

Scenario 2: Dynamic Data (Needs Insert/Delete Operations) #

If you need to add or remove records regularly, or require efficient range queries, choose one of these:

• Skip List: Easier to implement than balanced trees while maintaining O(log n) performance for all operations. It's the backbone of Redis' sorted sets, making it proven for large-scale sorted in-memory data.
• Red-Black Tree: Use your language's built-in implementation (like Java TreeMap or C++ std::map) to avoid reinventing the wheel. It ensures strict ordering and consistent O(log n) performance, great for applications that rely on sorted traversals.

Scenario 3: Frequent Prefix Queries #

If your business relies heavily on prefix searches (e.g., autocomplete for names), a Trie Tree is your best bet:

• Prefix queries run in time equal to the length of the prefix, not the size of your dataset—far more efficient than scanning or using regex with other structures.
• Shared prefixes reduce memory usage compared to trees or hash tables, especially when many names share common starting characters.

内容的提问来源于stack exchange，提问作者Maana