what happens when unordered_map keeps increasing in size?

 When a std::unordered_map keeps increasing in size, it dynamically manages its internal storage by resizing and rehashing the underlying hash table. This process helps to maintain efficient average-case performance for operations like insertions, lookups, and deletions. Here's what happens in more detail as the container grows:

1. Load Factor and Rehashing

The load factor is a key concept when it comes to how an unordered_map handles growth. It is defined as the ratio of the number of elements (n) in the container to the number of buckets (b) in the hash table:

bash
load factor = n / b

As the unordered_map grows, the load factor increases. If it exceeds a certain threshold (typically around 1.0), the map will rehash to maintain efficient performance. This threshold is dependent on the implementation, but most implementations will rehash when the load factor exceeds 1.0 (i.e., when the number of elements exceeds the number of buckets).

2. Resizing and Rehashing Process

When the unordered_map reaches its threshold for the load factor (or exceeds it), it will trigger a rehash. Rehashing involves:

• Increasing the number of buckets: The number of buckets is typically doubled (or increased by some other factor), which reduces the load factor and helps distribute elements more evenly across the new buckets.
• Rehashing the existing elements: All the elements in the current hash table are rehashed and redistributed across the new set of buckets. The elements are hashed again using the container's hash function.

3. Impact of Rehashing

Rehashing can have the following impacts:

• Time Complexity for Rehashing: Rehashing itself is an O(n) operation, where n is the number of elements in the unordered_map because all existing elements must be rehashed and placed in the new buckets. This means that rehashing is relatively expensive when the map grows.

• Performance During Rehashing: Even though rehashing is expensive, insertions and lookups after rehashing are still expected to be O(1) on average, assuming good hash distribution. However, during the rehashing process, the time taken for insertion will increase temporarily.

• Memory Usage: As the number of buckets increases, so does the memory usage. While the load factor decreases, the total memory used by the unordered_map increases due to the larger hash table, which may lead to higher memory consumption than the actual number of elements.

4. Overhead of Large unordered_map

If the map grows excessively large, there are some issues to consider:

• Memory Consumption: The memory overhead can become significant because the hash table must maintain extra space for each bucket, even though some buckets may remain empty. In extreme cases, the size of the hash table could become much larger than the actual number of elements stored.

• Frequent Rehashing: If the map keeps growing rapidly and the load factor remains high, it can lead to frequent rehashing as the map tries to keep the load factor low. This can significantly impact performance, especially if the number of insertions is high.

• Fragmentation: As the unordered_map resizes and reallocates its internal storage, memory fragmentation might occur, especially in cases where the size of the map grows rapidly and often.

5. Controlling Growth and Resizing

To optimize the performance and memory usage of a growing unordered_map, you can control its growth manually by using the following functions:

• reserve(size_t n): This function allows you to pre-allocate space for at least n elements. If you know that the unordered_map will grow to a large size, you can reserve enough space upfront to avoid frequent rehashing. This helps reduce the overhead caused by resizing the hash table as the container grows.

  cpp
  std::unordered_map<int, int> map;
  map.reserve(1000);  // Reserve space for at least 1000 elements
  

• rehash(size_t n): This function allows you to set the number of buckets to at least n, forcing the unordered_map to rehash and redistribute the elements across a specific number of buckets. This can be useful if you expect the map to hold a certain number of elements and want to control its size in advance.

  cpp
  map.rehash(2000);  // Set the number of buckets to 2000
  

6. Example: Growth and Resizing in Action

Let's look at a simple example to see how the unordered_map grows.

cpp
#include <iostream>
#include <unordered_map>

int main() {
    std::unordered_map<int, int> map;

    // Initially, unordered_map starts with a small number of buckets
    std::cout << "Initial bucket count: " << map.bucket_count() << std::endl;

    for (int i = 0; i < 100; ++i) {
        map[i] = i;  // Insert elements
        if (map.bucket_count() != map.max_bucket_count()) {
            std::cout << "Bucket count increased to: " << map.bucket_count() << std::endl;
        }
    }

    std::cout << "Final bucket count: " << map.bucket_count() << std::endl;
    std::cout << "Map size: " << map.size() << std::endl;

    return 0;
}

This example will show how the number of buckets changes as more elements are inserted. Typically, you'll see that as the number of elements exceeds the initial number of buckets, the unordered_map will double the number of buckets and rehash its contents.

Summary of What Happens During Growth:

• Rehashing occurs when the load factor exceeds the threshold (typically 1.0).
• The number of buckets increases (often doubling), which helps reduce the load factor.
• Memory usage increases because the number of buckets grows, and the elements are redistributed across the new buckets.
• Insertion and lookup operations remain O(1) on average, but rehashing itself is an expensive O(n) operation.
• You can use reserve() and rehash() to control growth and reduce the impact of resizing.

While unordered_map is efficient for average-case lookups and insertions, you need to be mindful of its growth behavior and control it when dealing with large datasets or high-performance requirements.