Netty is a high-performance, asynchronous event-driven NIO framework. It provides support for TCP, UDP and file transfer. All IO operations of Netty are asynchronous and non-blocking. Through the Future-Listener mechanism, users can conveniently Actively obtain or obtain the IO operation results through the notification mechanism.
The advantages of Netty are:
a. Rich functions, built-in multiple data encoding and decoding functions, and support for multiple network protocols.
b. High performance. Compared with other mainstream NIO network frameworks, its overall performance is the best.
c. It has good scalability and can flexibly expand network communication through the ChannelHandler component it provides.
d. Ease of use, the API is simple to use.
e. It has passed the test of many commercial applications and has been successfully commercialized in many industries such as the Internet, online games, big data, and telecommunications software.
Netty adopts a typical three-layer network architecture for design. The logical architecture diagram is as follows:
The first layer: Reactor communication scheduling layer. The main responsibility of this layer is to monitor network connections and read and write operations. It is responsible for reading network layer data into the memory buffer and then triggering various network events, such as connection creation, connection activation, read events, write events, etc. Trigger these events into the Pipeline, and then the Pipeline acts as a chain of responsibilities for subsequent processing.
The second layer: the responsibility chain Pipeline layer. Responsible for the orderly forward (backward) propagation of events in the responsibility chain, and also responsible for the dynamic orchestration of the responsibility chain. Pipeline can choose to listen and handle the events it cares about.
The third layer: business logic processing layer, which can generally be divided into two categories: a. Pure business logic processing, such as logs and order processing. b. Application layer protocol management, such as HTTP(S) protocol, FTP protocol, etc.
We all know that the main factors that affect network service communication performance are: network I/O model, thread (process) scheduling model and data serialization method.
In terms of network I/O model, Netty adopts an implementation based on non-blocking I/O, and the bottom layer relies on the Selector of the JDKNIO framework.
In terms of thread scheduling model, Netty adopts the Reactor thread model. There are three commonly used Reactor thread models, namely:
a. Reactor single-threaded model: Reactor single-threaded model means that all I/O operations are completed on the same NIO thread. For some small-capacity application scenarios, the single-threaded model can be used.
b. Reactor multi-threaded model: The biggest difference between the Rector multi-threaded model and the single-threaded model is that there is a set of NIO threads to handle I/O operations. Mainly used in high concurrency and large business volume scenarios.
c. Master-slave Reactor multi-thread model: The characteristic of the master-slave Reactor thread model is that the server is no longer a separate NIO thread for receiving client connections, but an independent NIO thread pool. Using the master-slave NIO thread model can solve the problem of insufficient performance of a server-side listening thread that cannot effectively handle all client connections. Netty threading model is not fixed, it can support three Reactor threading models.
In terms of data serialization, the main factors that affect serialization performance are:
a. The size of the code stream after serialization (network bandwidth occupation).
b. Performance of serialization and deserialization operations (CPU resource usage).
c. Performance during concurrent calls: stability, linear growth, etc.
Netty provides support for the GoogleProtobuf binary serialization framework by default, but by extending Netty's encoding and decoding interface, other high-performance serialization frameworks can be implemented, such as Avro and Thrift's compressed binary encoding and decoding frameworks.
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