And that's great, but for a lot of customers, they want transactional semantics, they want the things you get out of a relational database.

这很好， 但对于许多客户来说，他们需要事务性语义， 需要关系型数据库所提供功能。

He architected FreeCodeCamp's entire relational database certification curriculum, which you can run in your browser or right inside of your VS Code editor.

他计了FreeCodeCamp整个关系数据库认证课程，你可以在浏览器中或直接在VS Code编辑器内运行该课程。

The way we set it up makes that possible, because now it's a relational database, so we have development time.

我们方式使之成为可能，因为现在这是一个关系型数据库，所以我们有了开发时间。

That's why we give people that brick, that's a relational database, and they can build CRM themselves.

这就是为什么我们给人们这块砖，这实上是一个关系数据库， 他们可以用它自己搭建客户关系管理系統。

Yeah, and this is really the third big challenge that I see with scaling relational databases, is if you've got a good strategy for vertical scaling, you've got a good strategy for horizontal scaling.

是，这实上是我认为扩展关系型数据库时遇到大挑战：如果你已经制定了良好垂直扩展策略，并且也具备了有效水平扩展策略。

An archetype is more like a relational database.

原型更像是关系数据库。

The relational database curriculum, like, I don't know, tens of thousands of people have gone through that and claimed the cert.

关系型数据库课程，据我所知， 已有数万人完成了学习并获得了认证。

And I think, you know, when we talk about relational databases, that tends to be one of the main things that people get caught up on is how do I scale my relational database as my application grows?

我认为， 当我们讨论关系型数据库时，人们往往会纠结于一个主要问题：随着应用增长， 我该如何扩展我关系型数据库？

Really, there are three main challenges when we look at scaling relational databases.

实上，当我们探讨关系型数据库扩展问题时，主要面临大挑战。

But databases, specifically relational databases, you have to provision for that peak capacity.

但数据库，特别是关系型数据库，你必须为峰值容量进行资源配。

But there are challenges that come with relational databases.

然而，关系型数据库也带来了一些挑战。

However, at some point with a relational database, you tend to bump into scale problems.

然而，在使用关系型数据库到一定程度时，通常会遇到扩展性问题。

And additionally, when you're doing that scaling operation, traditionally, relational databases, you're provisioning for peak.

此外，在进行扩展操作时，传统关系型数据库通常是按峰值需求来配资源。

So Josh, you've done many, many things in your career, but the thing we're going to focus on today is really about databases and not just any old databases, but relational databases.

那么，乔什，你在职业生涯中涉猎广泛，但今天我们要聚焦是数据库领域， 而且不仅仅是普通数据库，而是关系型数据库。

And very often you see the relational database can become the bottleneck and it can become the sticking point as you start to want to scale your application.

通常情况下，随着应用程序开始需要扩展，您会发现关系型数据库可能成为瓶颈，并成为扩展过程中阻碍点。

So if you are provisioning a relational database during that time, you're going to have that kind of wasted compute resource.

因此，如果您在那个时间段内配关系型数据库，就会产生那种计算资源浪费。

And so it's not like we've forgotten about relational databases.

所以我们并没有忘记关系型数据库。

Relational databases are at the core of many applications and have been for many, many decades.

关系型数据库是众多应用程序核心，几十年来一直如此。

The challenge really with auto-scaling your relational database is that If we look at a traditional provisioned database, take for example PostgreSQL, which is one of the most popular relational database engines, open source database engine.

自动扩展关系型数据库真正挑战在于，如果我们看传统预配数据库，比如PostgreSQL，这是最受欢迎开源关系型数据库引擎之一。

These databases, relational database technology, predates cloud compute, where we have these horizontally scaling connection pools that can cause these connection storms very easily and overload the database.

这些关系型数据库技术早于云计算时代，在那时我们有这些水平扩展连接池，它们很容易引发连接风暴并导致数据库过载。