- 授权协议: 未知
- 开发语言: Java
- 操作系统: 跨平台
- 软件首页: http://code.google.com/p/jetlang/
软件介绍
Jetlang 提供了一个高性能的Java线程库,该库是 JDK 1.5 中的 java.util.concurrent 包的补充,可用于基于并发消息机制的应用。该类库不提供远程的消息功能,其设计的宗旨是实现一个内存中的消息传递机制:
主要特点有:
- All messages to a particular Fiber are delivered sequentially. Components can easily keep state without synchronizing data access or worrying about thread races.
- Single Fiber interface that can be backed by a dedicated thread or a thread pool.
- Supports single or multiple subscribers for messages.
- Subscriptions for single events or event batching
- Single or recurring event scheduling
- High performance design optimized for low latency and high scalability
- Publishing is thread safe, allowing easy integration with other threading models.
- Low Lock Contention – Minimizing lock contention is critical for performance. Other concurrency solutions are limited by a single lock typically on a central thread pool or message queue. Jetlang is optimized for low lock contention. Without a central bottleneck, performance easily scales to the needs of the application.
// start thread backed receiver.
// Lighweight fibers can also be created using a thread pool
Fiber receiver = new ThreadFiber();
receiver.start();
// create java.util.concurrent.CountDownLatch to notify when message arrives
final CountDownLatch latch = new CountDownLatch(1);
// create channel to message between threads
Channel<String> channel = new MemoryChannel<String>();
Callback<String> onMsg = new Callback<String>() {
public void onMessage(String message) {
//open latch
latch.countDown();
}
};
//add subscription for message on receiver thread
channel.subscribe(receiver, onMsg);
//publish message to receive thread. the publish method is thread safe.
channel.publish("Hello");
//wait for receiving thread to receive message
latch.await(10, TimeUnit.SECONDS);
//shutdown thread
receiver.dispose();
智能Web算法(第2版)
【英】Douglas G. McIlwraith(道格拉斯 G. 麦基尔雷思)、【美】Haralambos Marmanis(哈若拉玛 玛若曼尼斯)、【美】Dmitry Babenko(德米特里•巴邦科) / 达观数据、陈运文 等 / 电子工业出版社 / 2017-7 / 69.00
机器学习一直是人工智能研究领域的重要方向,而在大数据时代,来自Web 的数据采集、挖掘、应用技术又越来越受到瞩目,并创造着巨大的价值。本书是有关Web数据挖掘和机器学习技术的一本知名的著作,第2 版进一步加入了本领域最新的研究内容和应用案例,介绍了统计学、结构建模、推荐系统、数据分类、点击预测、深度学习、效果评估、数据采集等众多方面的内容。《智能Web算法(第2版)》内容翔实、案例生动,有很高的阅......一起来看看 《智能Web算法(第2版)》 这本书的介绍吧!
