第十四封信中英文对照：

To our shareowners:

致我们的股东：

Random forests, naïve Bayesian estimators, RESTful services, gossip protocols, eventual consistency, data sharding, anti-entropy, Byzantine quorum, erasure coding, vector clocks … walk into certain Amazon meetings, and you may momentarily think you’ve stumbled into a computer science lecture.

随机森林（Random Forest）、朴素贝叶斯分类器（Naïve Bayesian Estimator）、表现层状态转换（RESTful Service）、Gossip Protocol、最终一致性（Eventual Consistency）、Data Sharding、逆熵（Anti-Entropy）、Byzantine Quorum、抹除码（Erasure Code）、向量时钟 （Vector Clock），走进某个Amazon会议中，你可能会以为自己走进某个计算器科学讲座。

Look inside a current textbook on software architecture, and you’ll find few patterns that we don’t apply at Amazon. We use high-performance transactions systems, complex rendering and object caching, workflow and queuing systems, business intelligence and data analytics, machine learning and pattern recognition, neural networks and probabilistic decision making, and a wide variety of other techniques. And while many of our systems are based on the latest in computer science research, this often hasn’t been sufficient: our architects and engineers have had to advance research in directions that no academic had yet taken. Many of the problems we face have no textbook solutions, and so we — happily — invent new approaches.

摊开一本最新的软件架构教科书，你会发现我们用上里头不少知识。我们使用了高性能的交易系统、Complex Rendering、Object Caching、工作流和排队系统、商业智能、数据分析、机器学习、模式识别、神经网络、概率决策以及其他多种技术。尽管我们很多系统都用上了最新的计算器科学研究，这依然还是不够。我们的架构师和工程师，不得不朝学术界的未知领域拓展技术边界。我们面临的很多问题，教科书上都没有答案。因此，我们乐于发明新方法。

Our technologies are almost exclusively implemented as services: bits of logic that encapsulate the data they operate on and provide hardened interfaces as the only way to access their functionality. This approach reduces side effects and allows services to evolve at their own pace without impacting the other components of the overall system. Service-oriented architecture — or SOA — is the fundamental building abstraction for Amazon technologies. Thanks to a thoughtful and far-sighted team of engineers and architects, this approach was applied at Amazon long before SOA became a buzzword in the industry. Our e-commerce platform is composed of a federation of hundreds of software services that work in concert to deliver functionality ranging from recommendations to order fulfillment to inventory tracking. For example, to construct a product detail page for a customer visiting Amazon.com, our software calls on between 200 and 300 services to present a highly personalized experience for that customer.

我们的技术几乎都是用服务的形式呈现：逻辑位封装了操作数据，并强化存取功能的接口。这样的做法降低了副作用，同时让服务以既有的步调迭代，而不影响系统的其他组件。服务导向架构（Service-Oriented Architecture）是Amazon的技术基石。感谢我们极富远见的工程师与架构师团队，我们在服务导向架构一词尚未成为业界流行语时就开始这么做了。我们的电子商务平台，由数百个联合工作的软件服务组成，以提供客户从推荐、订单执行到库存追踪的功能。举个例子，为了在产品详情页上提供客户个人化的推荐，我们的软件需要调用200到300个服务。

State management is the heart of any system that needs to grow to very large size. Many years ago, Amazon’s requirements reached a point where many of our systems could no longer be served by any commercial solution: our key data services store many petabytes of data and handle millions of requests per second. To meet these demanding and unusual requirements, we’ve developed several alternative, purpose-built persistence solutions, including our own key-value store and single table store. To do so, we’ve leaned heavily on the core principles from the distributed systems and database research communities and invented from there. The storage systems we’ve pioneered demonstrate extreme scalability while maintaining tight control over performance, availability, and cost. To achieve their ultra-scale properties these systems take a novel approach to data update management: by relaxing the synchronization requirements of updates that need to be disseminated to large numbers of replicas, these systems are able to survive under the harshest performance and availability conditions. These implementations are based on the concept of eventual consistency. The advances in data management developed by Amazon engineers have been the starting point for the architectures underneath the cloud storage and data management services offered by Amazon Web Services (AWS). For example, our Simple Storage Service, Elastic Block Store, and SimpleDB all derive their basic architecture from unique Amazon technologies.

任何一个打算增长到极大规模的系统，其系统的核心都是状态管理。很多年前，Amazon就达到一个很大的规模，当时市面上的所有解决方案都无法满足我们的需求：我们的服务储存了好几PB的数据，每秒处理了上百万个请求。为了满足这些非同寻常的需求，我们开发出数个长期解决方案，包括我们自己的键-值数据库（Key-Value Store）和单表数据库（Single Table Store）。为此，我们以分布式系统及数据库的核心原理为基础进行发明创造。我们首创的数据库系统，展示出极高的可扩展性，同时保持了对性能、可用性和成本的良好平衡。为了实现超大规模的效能，这些系统采用了一种新办法来管理数据更新：降低同时发送大量副本的更新需求，使系统可以撑过高强度的挑战。这些做法都是为了同一个最终目标－数据同步。Amazon工程师开发的数据管理服务，已经成为AWS云计算服务的基础架构。举个例子，我们的Simple Storage Service、Elastic Block Store和SimpleDB服务，其基础架构都来自于Amazon的独家技术。

Other areas of Amazon’s business face similarly complex data processing and decision problems, such as product data ingestion and categorization, demand forecasting, inventory allocation, and fraud detection. Rulebased systems can be used successfully, but they can be hard to maintain and can become brittle over time. In many cases, advanced machine learning techniques provide more accurate classification and can self-heal to adapt to changing conditions. For example, our search engine employs data mining and machine learning algorithms that run in the background to build topic models, and we apply information extraction algorithms to identify attributes and extract entities from unstructured descriptions, allowing customers to narrow their searches and quickly find the desired product. We consider a large number of factors in search relevance to predict the probability of a customer’s interest and optimize the ranking of results. The diversity of products demands that we employ modern regression techniques like trained random forests of decision trees to flexibly incorporate thousands of product attributes at rank time. The end result of all this behind-the-scenes software? Fast, accurate search results that help you find what you want.

Amazon的各个业务领域也遇到类似的数据处理和决策问题，像是产品数据的采集与分类、需求预测、库存分配和诈欺预防。起初，建立于规则之上的系统可以很好地运行，但随着时间的推移，系统会越来越脆弱，越来越难以维持。在很多情况下，先进的机器学习可以提供更精准的分类，而且可以自我修复，以适应复杂多变的情况。举个例子，我们的搜索引擎使用数据挖掘和机器学习算法来建构模型，运用信息提取算法标识属性，从非结构化的描述中提取信息。如此一来，消费者可以缩小搜寻范围，快速找到所需产品。我们在相关性中考虑了很多因素，因此我们的搜寻功能可以预测消费者的兴趣，并优化搜寻结果的排行。商品的多样性，使我们必须采用现代回归技术，例如随机森林，让上千种商品属性可以灵活地排行。所有这些幕后努力换得什么？快速且精准的搜寻结果，帮助你找到你要的东西。

All the effort we put into technology might not matter that much if we kept technology off to the side in some sort of R&D department, but we don’t take that approach. Technology infuses all of our teams, all of our processes, our decision-making, and our approach to innovation in each of our businesses. It is deeply integrated into everything we do.

如果我们单纯将技术只放在研发部门，那我们迄今的努力可能没有什么效益。因此，我们并不是这么做的。技术为我们的团队、流程、决策和业务创新注入活力，技术与我们所做的一切紧密结合。

One example is Whispersync, our Kindle service designed to ensure that everywhere you go, no matter what devices you have with you, you can access your reading library and all of your highlights, notes, and bookmarks, all in sync across your Kindle devices and mobile apps. The technical challenge is making this a reality for millions of Kindle owners, with hundreds of millions of books, and hundreds of device types, living in over 100 countries around the world—at 24×7 reliability. At the heart of Whispersync is an eventually consistent replicated data store, with application defined conflict resolution that must and can deal with device isolation lasting weeks or longer. As a Kindle customer, of course, we hide all this technology from you. So when you open your Kindle, it’s in sync and on the right page. To paraphrase Arthur C. Clarke, like any sufficiently advanced technology, it’s indistinguishable from magic.

Kindle的Whispersync技术就是一个很好的例子。无论你在哪里，手里拿着什么装置，你都可以透过Kindle和移动应用，存取你的阅读纪录、重点集锦、笔记和书签。我们遇到的技术挑战是，提供一天24小时全年无休的服务，让遍及全球的用户可以在上百种装置中随时取得数以亿计的书籍。Whispersync的核心是数据同步技术，它可以解决装置多周不联网后，数据同步时会遇到的冲突问题。当然，作为Kindle的用户，你不用也不需要知道这些繁复的技术细节。当你打开Kindle之后，它会出现在右侧进行同步。用英国科幻作家Clarke的话来说就是，先进的科技和魔术没什么区别。

Now, if the eyes of some shareowners dutifully reading this letter are by this point glazing over, I will awaken you by pointing out that, in my opinion, these techniques are not idly pursued – they lead directly to free cash flow.

如果你们之中的有些人，读到这里时感到茫然，不知道追求技术的意义何在，那就由我来揭示技术的价值。这些技术不是全然盲目的追求，技术和自由现金流是直接相关的。

We live in an era of extraordinary increases in available bandwidth, disk space, and processing power, all of which continue to get cheap fast. We have on our team some of the most sophisticated technologists in the world – helping to solve challenges that are right on the edge of what’s possible today. As I’ve discussed many times before, we have unshakeable conviction that the long-term interests of shareowners are perfectly aligned with the interests of customers.

我们生活在一个带宽增加、硬盘空间增加和处理能力增加的美好时代，而且他们会持续越来越快、越来越便宜。我们团队中有世界上最好的技术人员，帮助我们解决现今遇到的挑战。如同我之前多次讨论过的，我们坚信，股东的长期利益与客户的利益完全一致。

And we like it that way. Invention is in our DNA and technology is the fundamental tool we wield to evolve and improve every aspect of the experience we provide our customers. We still have a lot to learn, and I expect and hope we’ll continue to have so much fun learning it. I take great pride in being part of this team.

我们喜欢如此。发明存在于我们的DNA，技术是我们的发展和改善客户体验的基本工具。我们还有很多东西要学，我希望我们持续享受从中学习的乐趣。对于身为团队的一份子，我感到很骄傲。

As always, I attach a copy of our original 1997 letter. Our approach remains the same, and it’s still Day 1.

如同往常，我把我们在1997年写的致股东信附在文末。我们的价值观依然不变，今天依旧是Day 1。

Jeffrey P. Bezos

Founder and Chief Executive Officer

Amazon.com, Inc.

杰夫·贝索斯

Amazon创始人暨CEO

我的观点

这封信里面提到了很多技术名词，大部分我还是有了解过，这其实很多就是亚马逊提出来的。说到亚马逊，其实很多人会提到他的推荐系统。

是的，我还是喜欢里面的那句话，英国科幻大师Clarke曾说过：「在任何一项足够先进的技术和魔法之间，人们看不出有何区别。」

亚马逊为了支撑业务，对技术进程了大量的投入研究，发明了很多东西。贝索斯说，他们乐于发明新方法，这其实是非常好的现象。技术为我们的团队、流程、决策和业务创新注入活力，技术与我们所做的一切紧密结合。其实就证明亚马逊很看重技术，也乐于投入来进行技术研究。

其实，就以前亚马逊的口碑来说，Google才是那个被证明技术能力很强的公司，亚马逊经常从开源社区吸血，而且还不参与贡献，这是开源届的毒瘤。 后面这几年，观感才好一点。