C++ Academy · Lesson

Performance Considerations

Buckets and load factor.

Performance Considerations is a free C++ Academy lesson on CoddyKit — lesson 4 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the C++ Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.

How Hash Tables Store Data

An unordered container holds an array of buckets. The hash of a key picks a bucket; multiple keys in one bucket form a chain that is searched linearly.

#include <iostream>
#include <unordered_map>

int main() {
    std::unordered_map<int, int> m{{1, 1}, {2, 2}, {3, 3}};
    std::cout << "bucket count: " << m.bucket_count() << '\n';
    return 0;
}

Which Bucket?

bucket(key) tells you which bucket index a key maps to right now.

#include <iostream>
#include <unordered_map>

int main() {
    std::unordered_map<int, int> m{{10, 1}, {20, 2}, {30, 3}};
    std::cout << "key 20 in bucket " << m.bucket(20) << '\n';
    return 0;
}

Load Factor

The load factor is size / bucket_count. A higher load means longer chains and slower lookups.

#include <iostream>
#include <unordered_map>

int main() {
    std::unordered_map<int, int> m{{1, 1}, {2, 2}};
    std::cout << "load factor: " << m.load_factor() << '\n';
    return 0;
}

Max Load Factor

max_load_factor() is the threshold. When the load factor exceeds it, the table rehashes into more buckets.

#include <iostream>
#include <unordered_map>

int main() {
    std::unordered_map<int, int> m;
    std::cout << "default max load: " << m.max_load_factor() << '\n';
    return 0;
}

Rehashing

Rehashing rebuilds the table with more buckets and is expensive. It happens automatically when the load factor is exceeded.

#include <iostream>
#include <unordered_map>

int main() {
    std::unordered_map<int, int> m;
    std::size_t before = m.bucket_count();
    for (int i = 0; i < 100; ++i) m[i] = i;
    std::cout << before << " -> " << m.bucket_count() << " buckets\n";
    return 0;
}

Reserve to Avoid Rehashes

If you know the size ahead of time, call reserve(n) to pre-allocate buckets and avoid repeated rehashing.

#include <iostream>
#include <unordered_map>

int main() {
    std::unordered_map<int, int> m;
    m.reserve(1000);
    std::cout << "buckets reserved: " << (m.bucket_count() >= 1000 ? "yes" : "no") << '\n';
    return 0;
}

rehash Directly

rehash(n) sets the bucket count to at least n. Use reserve for element counts and rehash for bucket counts.

#include <iostream>
#include <unordered_map>

int main() {
    std::unordered_map<int, int> m;
    m.rehash(64);
    std::cout << "buckets >= 64: " << (m.bucket_count() >= 64 ? "yes" : "no") << '\n';
    return 0;
}

Inspecting Bucket Sizes

bucket_size(i) reveals how many elements share bucket i, useful for diagnosing collisions.

#include <iostream>
#include <unordered_map>

int main() {
    std::unordered_map<int, int> m;
    for (int i = 0; i < 10; ++i) m[i] = i;
    std::cout << "bucket 0 holds " << m.bucket_size(0) << " elements\n";
    return 0;
}

Worst Case Is O(n)

With a bad hash that collides heavily, all keys chain in one bucket and operations degrade to linear time. A good hash keeps things O(1).

#include <iostream>
#include <unordered_map>

int main() {
    std::unordered_map<int, int> m;
    for (int i = 0; i < 5; ++i) m[i] = i * i;
    std::cout << "avg lookups stay fast with good hashing\n";
    std::cout << "load: " << m.load_factor() << '\n';
    return 0;
}

Lowering Max Load Factor

Setting a lower max_load_factor trades memory for speed: fewer collisions, but more buckets.

#include <iostream>
#include <unordered_map>

int main() {
    std::unordered_map<int, int> m;
    m.max_load_factor(0.5f);
    std::cout << "new max load: " << m.max_load_factor() << '\n';
    return 0;
}

Iterator Invalidation

A rehash invalidates iterators but keeps references and pointers to elements valid. Plan loops accordingly.

#include <iostream>
#include <unordered_map>

int main() {
    std::unordered_map<int, int> m{{1, 100}};
    int& ref = m[1];
    m.reserve(500);
    std::cout << "reference still valid: " << ref << '\n';
    return 0;
}

Quick Check

Test your understanding of hash table performance.

Recap

You learned hash table internals:

keys map to buckets; collisions form chains
load factor = size / bucket_count; exceeding max_load_factor triggers a rehash
use reserve to avoid rehashing; a rehash invalidates iterators but not references

Next course: reading and writing files with fstream.

Frequently asked questions

Is the “Performance Considerations” lesson free?

Yes — the full text of “Performance Considerations” is free to read here on the web, and the C++ Academy course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the C++ Academy course, upgrade to CoddyKit PRO.

What will I learn in “Performance Considerations”?

Buckets and load factor. You practise C++ Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.

Do I need any experience to start C++ Academy?

No prior experience is required. C++ Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 4 of 4, so you can start here or from the beginning and move at your own pace.

How long does the “Performance Considerations” lesson take?

Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.

Can I write and run code in this C++ Academy lesson?

Yes. Every C++ Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.

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