Chapter Content
Nikolai's Custom In-Memory Storage Engines
12.1 Custom In-Memory Storage Concepts (Conceptual Model)
For real-time features like instant messaging, social graph indexing, news feeds, and search, traditional relational databases often introduce disk I/O bottlenecks. VK's engineering environment involved custom high-performance storage, caching, and data-management approaches designed for workloads where low-latency access and high throughput were important.
In such architectural designs, index structures and active datasets could be maintained in RAM for fast query response times, while transaction logs were written sequentially to persistent storage for durability and crash recovery. Note: This is an educational reconstruction illustrating how such a system could be designed; it is not a reconstruction of proprietary production source code.
[CONCEPTUAL ARCHITECTURE — BASED ON PUBLICLY AVAILABLE INFORMATION]
[Client Read Query] ====> [In-Memory Index (RAM)] ====> [Low-Latency Response]
[Client Write Query] ====> [In-Memory Update (RAM)]
| (Asynchronous Append)
v
[Sequential Disk Transaction Log]12.2 Illustrative Key-Value Bucket Index
To understand the memory allocation and hashing mechanics of custom in-memory storage concepts, examine this educational C++ data structure illustrating a simple in-memory key-value hash bucket index:
// =========================================================================
// EDUCATIONAL MODEL — NOT TELEGRAM'S PRODUCTION SOURCE CODE
// Illustrates low-overhead in-memory hashing and collision chaining concepts
// =========================================================================
#include <iostream>
#include <string>
#include <vector>
struct StorageNode {
uint32_t key_hash;
std::string key;
std::string value;
StorageNode* next;
};
class SimpleMemoryStore {
private:
std::vector<StorageNode*> buckets;
size_t capacity;
uint32_t ComputeHash(const std::string& str) {
uint32_t hash = 2166136261u;
for (char c : str) {
hash ^= static_cast<uint8_t>(c);
hash *= 16777619u;
}
return hash;
}
public:
SimpleMemoryStore(size_t cap = 256) : capacity(cap), buckets(cap, nullptr) {}
void Put(const std::string& key, const std::string& value) {
uint32_t hash = ComputeHash(key);
size_t idx = hash % capacity;
StorageNode* node = new StorageNode{hash, key, value, buckets[idx]};
buckets[idx] = node;
}
std::string Get(const std::string& key) {
uint32_t hash = ComputeHash(key);
size_t idx = hash % capacity;
StorageNode* curr = buckets[idx];
while (curr) {
if (curr->key == key) return curr->value;
curr = curr->next;
}
return "[NOT FOUND]";
}
};
int main() {
SimpleMemoryStore store;
store.Put("user:100", "Active Session Token");
std::cout << "Stored Token: " << store.Get("user:100") << std::endl;
return 0;
}
Architectural concept diagram illustrating in-memory hash buckets and sequential append-only transaction logging.