Detailed explanation of the technical principles of the Java class library medium and high -frequency transaction (HFT) collection framework
High -frequency trading (HFT) is a strategy to transaction by using fast and efficient algorithms.The high -frequency trading set framework in the Java class library is designed to meet the needs of HFT. It provides some high -performance data structures and algorithms to support high -speed data processing and large -scale concurrent operations.This article will introduce the technical principles of the high -frequency transaction set framework of the Java class library in detail, and provide the corresponding Java code example.
In order to achieve the rapid and efficient high -frequency transactions, the high -frequency trading set framework in the Java class library mainly uses the following technical principles:
1. No lock data structure: In high -frequency transactions, the read -write operation of data needs to be completed quickly and accurately, and the traditional lock mechanism will introduce large expenses.Therefore, the high -frequency transaction set framework uses some lock -free data structures, such as no lock queue, no lock mapping, etc. to improve the read and write efficiency of data.The following is an example code without lock queue:
import java.util.concurrent.atomic.AtomicReference;
public class LockFreeQueue<T> {
private final AtomicReference<Node<T>> head;
private final AtomicReference<Node<T>> tail;
public LockFreeQueue() {
Node<T> dummy = new Node<>(null);
head = new AtomicReference<>(dummy);
tail = new AtomicReference<>(dummy);
}
public void enqueue(T value) {
Node<T> newNode = new Node<>(value);
while (true) {
Node<T> last = tail.get();
Node<T> next = last.next.get();
if (last == tail.get()) {
if (next == null) {
if (last.next.compareAndSet(next, newNode)) {
tail.compareAndSet(last, newNode);
return;
}
} else {
tail.compareAndSet(last, next);
}
}
}
}
public T dequeue() {
while (true) {
Node<T> first = head.get();
Node<T> last = tail.get();
Node<T> next = first.next.get();
if (first == head.get()) {
if (first == last) {
if (next == null) {
return null;
}
tail.compareAndSet(last, next);
} else {
T value = next.value;
if (head.compareAndSet(first, next)) {
return value;
}
}
}
}
}
private static class Node<T> {
private final T value;
private final AtomicReference<Node<T>> next;
public Node(T value) {
this.value = value;
this.next = new AtomicReference<>(null);
}
}
}
2. Collective thread pool: High -frequency transactions need to handle large -scale concurrent operations. Traditional thread pools often cannot meet this demand.Therefore, the high -frequency transaction set framework introduces a thread pool that is optimized for the collection operation. Through the fine -grained task allocation and scheduling, and efficient concurrency operation, the performance and throughput of the collection operation are improved.
Here are a sample code that uses a collection thread pool:
import java.util.List;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;
public class CollectionThreadPoolExample {
public static void main(String[] args) {
ExecutorService executor = Executors.newWorkStealingPool();
List<Integer> list = new CopyOnWriteArrayList<>();
for (int i = 0; i < 1000; i++) {
executor.execute(() -> list.add(i));
}
executor.shutdown();
while (!executor.isTerminated()) {
// Waiting for all tasks to execute to complete
}
System.out.println("List size: " + list.size());
}
}
3. Memory optimization: High -frequency transactions need to process a large amount of data and require quickly and efficiently reading and writing memory.In order to improve the use of memory and access speed, the high -frequency trading set framework uses some memory optimization techniques, such as memory distribution, object pool, etc.These technologies can reduce the overhead of memory allocation and recycling, reduce the frequency of garbage recovery, and improve the performance and response speed of the system.
To sum up, the high -frequency trading set framework in the Java class library can provide high -performance data structures and algorithms by using technical principles such as lock -free data structure, collective thread pool and memory optimization to meet the needs of high -frequency transactions.Developers can choose the appropriate high -frequency trading set framework according to the specific scenes and needs to improve the performance and throughput of the system.
