In-depth Analysis of Memory Consumption and Performance Optimization of unordered_map

# Analysis of Unordered_map Memory Usage The `unordered_map` is one of the associative containers in the C++ STL, implemented using a hash table to store key-value pairs. Its underlying data structure includes arrays and linked lists, mapping keys to array indices through hash functions, and handling collisions with linked list methods. The space complexity of `unordered_map` depends on the number of elements stored and the number of buckets, being O(n) in terms of space complexity without considering the load factor. For collision handling, `unordered_map` maintains a linked list inside each bucket to connect colliding elements. When inserting elements, if a collision occurs, the new element is inserted at the head of the list. By analyzing the memory structure of `unordered_map`, we can better understand its inner workings and lay the groundwork for subsequent performance optimizations. # Exploring Performance Optimization of Unordered_map When optimizing the performance of `unordered_map`, it is essential to delve into the bottlenecks of lookup, insertion, and deletion operations, as well as the impact of resizing strategies. ## Analysis of Unordered_map Performance Bottlenecks ### Lookup Performance Analysis of Unordered_map In `unordered_map`, the design of the hash function is crucial. A good hash function can evenly distribute elements and reduce collisions. The design principles of hash functions include efficiency, uniformity, and consistency. The lookup efficiency of `unordered_map` depends on the quality of the hash function, directly affecting the time complexity of lookup operations. ### Insertion and Deletion Performance Analysis of Unordered_map In `unordered_map`, the efficiency of insertion operations is influenced by the resizing of the hash table. When the number of elements exceeds a certain threshold, a rehash operation is needed to adjust the size of the hash table, resulting in performance loss. Optimization strategies for deletion operations include marking deleted items for delayed deletion or merging deletions. ## Analysis of Unordered_map Resizing Strategies ### The Rehash Mechanism of Unordered_map Rehash is a critical mechanism in `unordered_map`, used to adjust the size of the hash table to keep the load factor within a certain range. The timing of rehash depends on the frequency of insertion operations and the threshold of the load factor. A reasonable rehash strategy can reduce performance overhead. ```mermaid graph TD A[Element to Insert] --> B{Trigger Rehash?} B -->|Yes| C[Resize Hash Table] B -->|No| D[Insert Element Directly] ``` ### Performance Optimization During Rehash To reduce performance overhead during the rehash process, a common optimization method is incremental rehash. This involves gradually migrating elements in batches from the old hash table to the new one, alleviating performance jitters caused by rehashing. The above analysis provides an in-depth understanding of `unordered_map` performance, and grasping these bottlenecks and optimization strategies is vital for enhancing the efficiency of `unordered_map`. # Methods for Optimizing Memory Usage of Unordered_map When optimizing the memory usage of `unordered_map`, several effective methods can be adopted, which will be introduced one by one below. ### Optimizing the Initialization of Unordered_map #### Using Reserve to Preallocate Space If you anticipate the number of elements to be stored in `unordered_map` approximately, the `reserve` method can be used to preallocate space, reducing the number of rehash operations during element insertion, enhancing performance, and minimizing additional mem