使用 R2DBC 的动机是什么？
What is the motivation for using R2DBC?
我对反应式 spring 堆栈非常陌生，目前正在探索 R2DBC。
你能解释一下使用 R2dbcRepository 比将阻塞 JpaRepository 包装到 Mono/Flux 有什么好处吗？
让我举几个例子：
val producer: Mono<BookEntity> = booksReactiveRepository.findById(id)
对
val producer: Mono<BookEntity> = Mono.fromSupplier { booksBlockingRepository.findById(id) }
执行上有概念上的区别吗？
主要区别在于 JDBC/JPA 使用阻塞 I/O 这意味着每个请求都需要一个专用线程。在高并发系统中，这很容易导致扩展问题。
另一方面，R2DBC 使用非阻塞 I/O，这意味着它能够仅使用固定的、少量的线程来处理请求，这使得扩展更容易且成本更低。
查看以下文章：
https://spring.io/blog/2018/12/07/reactive-programming-and-relational-databases
Java uses JDBC as the primary technology to integrate with relational
databases. JDBC is of a blocking nature – there’s nothing sensible one
could do to mitigate the blocking nature of JDBC. The first idea for
how to make calls non-blocking is offloading JDBC calls to an Executor
(typically Thread pool). While this approach somewhat works, it comes
with several drawbacks that neglect the benefits of a reactive
programming model.
Thread pools require – no surprise – threads to run. Reactive runtimes
typically use a limited number of threads that match the number of CPU
cores. Additional threads introduce overhead and reduce the effect of
thread limiting. Additionally, JDBC calls typically pile up in a
queue, and once the threads are saturated with requests, the pool will
block again. So, JDBC is right now not an option.
我对反应式 spring 堆栈非常陌生，目前正在探索 R2DBC。
你能解释一下使用 R2dbcRepository 比将阻塞 JpaRepository 包装到 Mono/Flux 有什么好处吗？
让我举几个例子：
val producer: Mono<BookEntity> = booksReactiveRepository.findById(id)
对
val producer: Mono<BookEntity> = Mono.fromSupplier { booksBlockingRepository.findById(id) }
执行上有概念上的区别吗？
主要区别在于 JDBC/JPA 使用阻塞 I/O 这意味着每个请求都需要一个专用线程。在高并发系统中，这很容易导致扩展问题。
另一方面，R2DBC 使用非阻塞 I/O，这意味着它能够仅使用固定的、少量的线程来处理请求，这使得扩展更容易且成本更低。
查看以下文章： https://spring.io/blog/2018/12/07/reactive-programming-and-relational-databases
Java uses JDBC as the primary technology to integrate with relational databases. JDBC is of a blocking nature – there’s nothing sensible one could do to mitigate the blocking nature of JDBC. The first idea for how to make calls non-blocking is offloading JDBC calls to an Executor (typically Thread pool). While this approach somewhat works, it comes with several drawbacks that neglect the benefits of a reactive programming model.
Thread pools require – no surprise – threads to run. Reactive runtimes typically use a limited number of threads that match the number of CPU cores. Additional threads introduce overhead and reduce the effect of thread limiting. Additionally, JDBC calls typically pile up in a queue, and once the threads are saturated with requests, the pool will block again. So, JDBC is right now not an option.