Will be retired the SDK and all applications using the SDK When a single client is served, all other requests are queued up.On Augthe Azure Cosmos DB Async Java SDK v2.x The simplest implementation just opens a ServerSocket and handles client connections as they come. We will be implementing an extremely simple HTTP server that responds with 200 OKs for every request.Īs a matter of fact, for the sake of simplicity we will ignore the request altogether. This is the exercise that we probably all went through during our education: writing a server on top of raw sockets. The purpose of this section is to compare how blocking servers, even when written properly, behave under high load. In the end, you will notice that the version using RxJava combines both relative simplicity and outstanding performance. The idea behind this exercise is to compare the implementation complexity versus performance and throughput. This chapter takes you on a journey through different ways of implementing an HTTP server.įrom single-threaded servers, through thread pools, to entirely event-driven architectures.
This number is not a coincidence a couple of years ago, a properly designed Java application reached that enormous level on a typical server. However, with blocking I/O we need a disproportional amount of memory compared to heavily underutilized CPU.Įven if a big enterprise server can handle hundreds of thousands of concurrent connections (at very high price), it is far from solving C10M problem-ten million concurrent connections. On the other hand, vertical scalability means purchasing bigger and more capable servers. This requires a frontend load-balancer and does not solve the original C10k problem that expects just one server. To handle more concurrent connections we can simply spin up more servers, each managing a subset of the load. There are two independent approaches to scalability: horizontal and vertical. However, each thread occupies a little bit of memory (stack space), regardless of whether it is computing something or just waiting idle for data. However, after you reach certain level of concurrency, the number of threads becomes dangerous.Ī thousand concurrent connections handled by a single commodity server is not something unusual, especially with long-living TCP/IP connections like HTTP with a Keep-Alive header, server-sent events, or WebSockets. The classic thread-per- Socket model served us really well, and as a matter of fact it works quite good in many applications to this day. Waste significant amount of CPU time simply switching cores to run different threads (context switching). Put great pressure on the garbage collection mechanism, despite that stack space is not garbage-collected (lots of GC roots and live objects) Learn moreĬonsume several gigabytes of RAM for stack space
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The classic thread per connection model struggles to solve the C10k problem. But with RxJava the additional complexity will be reduced significantly. Admittedly, performance and scalability does have a complexity price tag. We will go through several examples of a simple HTTP server and observe how it behaves with respect to design choices we made. The architecture must be entirely event-driven and asynchronous in order to avoid blocking. Such an application should never synchronously wait for any computation or action. If you are lucky enough to work on a greenfield project, you might consider implementing your application in a reactive manner top to bottom. All of them will circle around the concept of reactive programming. In this chapter, we explore several implementation techniques that will improve scalability by several orders of magnitude.
There are many reactive approaches that easily achieve C10k, and RxJava makes them very approachable. The C10k problem was an area of research and optimization that tried to achieve 10,000 concurrent connections on a single commodity server.Įven these days, solving this engineering task with the traditional Java toolkit is a challenge. This is an excerpt from Reactive Programming with RxJava, by Tomasz Nurkiewicz and Ben Christensen.
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