[gRPC via C#] gRPC本质的探究与实践

鉴于内容过多，先上太长不看版：

• grpc 就是请求流&响应流特殊一点的 Http 请求，性能和 WebAPI 比起来只快在 Protobuf 上；

附上完整试验代码：GrpcWithOutSDK.zip

另附小Demo，基于 Controller 和 HttpClient 的在线聊天室：ChatRoomOnController.zip

---

本文内容有点长，涉及较多基础知识点，某些结论可能直接得出，没有上下文，限于篇幅，不会在本文内详细描述，如有疑惑请友好交流或尝试搜索互联网。

本文仅代表个人试验结果和观点，可能会有偏颇，请自行判断。

---

一、背景

个人经常在网上看到 grpc、高性能 字眼的文章；有幸也面试过一些同僚，问及 grpc 对比 WebAPI，答案都是更快、性能更高；至于能快多少，答案就各种各样了，几倍到几十倍的回答都有，但大概是统一的：“grpc 要快得多”。那么具体快在哪里呢？回答我就觉得不那么准确了。

现在我们就来探索一下 grpc 和 WebAPI 的差别是什么？ grpc 快在哪里？

二、验证请求模型

就是个常规的 asp.net core 使用 grpc 的步骤

创建服务端

• 建立一个 asp.net core grpc 项目

• 添加一个测试的 reverse.proto 用于测试 grpc 的几种通讯模式，并为其生成服务端

syntax = "proto3";

option csharp_namespace = "GrpcWithOutSDK";

package reverse;

service Reverse {
 rpc Simple (Request) returns (Reply);
 rpc ClientSide (stream Request) returns (Reply);
 rpc ServerSide (Request) returns (stream Reply);
 rpc Bidirectional (stream Request) returns (stream Reply);
}

message Request {
 string message = 1;
}

message Reply {
 string message = 1;
}

• 新建 ReverseService.cs 实现具体的方法逻辑

public class ReverseService : Reverse.ReverseBase
{
   private readonly ILogger<ReverseService> _logger;

   public ReverseService(ILogger<ReverseService> logger)
   {
       _logger = logger;
   }

   private static Reply CreateReplay(Request request)
   {
       return new Reply
       {
           Message = new string(request.Message.Reverse().ToArray())
       };
   }

   private void DisplayReceivedMessage(Request request, [CallerMemberName] string? methodName = null)
   {
       _logger.LogInformation($"{methodName} Received: {request.Message}");
   }

   public override async Task Bidirectional(IAsyncStreamReader<Request> requestStream, IServerStreamWriter<Reply> responseStream, ServerCallContext context)
   {
       while (await requestStream.MoveNext())
       {
           DisplayReceivedMessage(requestStream.Current);
           await responseStream.WriteAsync(CreateReplay(requestStream.Current));
       }
   }

   public override async Task<Reply> ClientSide(IAsyncStreamReader<Request> requestStream, ServerCallContext context)
   {
       var total = 0;
       while (await requestStream.MoveNext())
       {
           total++;
           DisplayReceivedMessage(requestStream.Current);
       }
       return new Reply
       {
           Message = $"{nameof(ServerSide)} Received Over. Total: {total}"
       };
   }

   public override async Task ServerSide(Request request, IServerStreamWriter<Reply> responseStream, ServerCallContext context)
   {
       DisplayReceivedMessage(request);

       for (int i = 0; i < 5; i++)
       {
           await responseStream.WriteAsync(CreateReplay(request));
       }
   }

   public override Task<Reply> Simple(Request request, ServerCallContext context)
   {
       return Task.FromResult(CreateReplay(request));
   }
}

最后记得 app.MapGrpcService<ReverseService>();

创建客户端

• 新建一个控制台项目，并添加Google.Protobuf、Grpc.Net.Client、Grpc.Tools这几个包的引用
• 引用之前写好的 reverse.proto 并为其生成客户端
• 写几个用于测试各种通讯模式的方法

private static async Task Bidirectional(Reverse.ReverseClient client)
{
   var stream = client.Bidirectional();

   var sendTask = Task.Run(async () =>
   {
       for (int i = 0; i < 10; i++)
       {
           await stream.RequestStream.WriteAsync(new() { Message = $"{nameof(Bidirectional)}-{i}" });
       }
       await stream.RequestStream.CompleteAsync();
   });

   var receiveTask = Task.Run(async () =>
   {
       while (await stream.ResponseStream.MoveNext(default))
       {
           DisplayReceivedMessage(stream.ResponseStream.Current);
       }
   });

   await Task.WhenAll(sendTask, receiveTask);
}

private static async Task ClientSide(Reverse.ReverseClient client)
{
   var stream = client.ClientSide();

   for (int i = 0; i < 5; i++)
   {
       await stream.RequestStream.WriteAsync(new() { Message = $"{nameof(ClientSide)}-{i}" });
   }

   await stream.RequestStream.CompleteAsync();

   var reply = await stream.ResponseAsync;

   DisplayReceivedMessage(reply);
}

private static async Task Sample(Reverse.ReverseClient client)
{
   var reply = await client.SimpleAsync(new() { Message = nameof(Sample) });
   DisplayReceivedMessage(reply);
}

private static async Task ServerSide(Reverse.ReverseClient client)
{
   var stream = client.ServerSide(new() { Message = nameof(ServerSide) });

   while (await stream.ResponseStream.MoveNext(default))
   {
       DisplayReceivedMessage(stream.ResponseStream.Current);
   }
}

• 测试代码

const string Host = "http://localhost:5035";
var channel = GrpcChannel.ForAddress(Host);
var grpcClient = new Reverse.ReverseClient(channel);

await Sample(grpcClient);
await ClientSide(grpcClient);
await ServerSide(grpcClient);
await Bidirectional(grpcClient);

进行验证

• 将服务端的 Microsoft.AspNetCore 日志等级调整为 Information 以打印请求日志
• 运行服务端与客户端
• 不出意外的话服务端会看到如下输出（为便于观察，已按方法进行分段，不重要的信息已省略）

info: Microsoft.AspNetCore.Hosting.Diagnostics[1]
     Request starting HTTP/2 POST http://localhost:5035/reverse.Reverse/Simple application/grpc -
info: Microsoft.AspNetCore.Routing.EndpointMiddleware[0]
     Executing endpoint 'gRPC - /reverse.Reverse/Simple'
info: Microsoft.AspNetCore.Routing.EndpointMiddleware[1]
     Executed endpoint 'gRPC - /reverse.Reverse/Simple'
info: Microsoft.AspNetCore.Hosting.Diagnostics[2]
     Request finished HTTP/2 POST http://localhost:5035/reverse.Reverse/Simple application/grpc - - 200 - application/grpc 99.1956ms

info: Microsoft.AspNetCore.Hosting.Diagnostics[1]
      Request starting HTTP/2 POST http://localhost:5035/reverse.Reverse/ClientSide application/grpc -
info: Microsoft.AspNetCore.Routing.EndpointMiddleware[0]
      Executing endpoint 'gRPC - /reverse.Reverse/ClientSide'
info: Microsoft.AspNetCore.Routing.EndpointMiddleware[1]
      Executed endpoint 'gRPC - /reverse.Reverse/ClientSide'
info: Microsoft.AspNetCore.Hosting.Diagnostics[2]
      Request finished HTTP/2 POST http://localhost:5035/reverse.Reverse/ClientSide application/grpc - - 200 - application/grpc 21.9445ms

info: Microsoft.AspNetCore.Hosting.Diagnostics[1]
      Request starting HTTP/2 POST http://localhost:5035/reverse.Reverse/ServerSide application/grpc -
info: Microsoft.AspNetCore.Routing.EndpointMiddleware[0]
      Executing endpoint 'gRPC - /reverse.Reverse/ServerSide'
info: Microsoft.AspNetCore.Routing.EndpointMiddleware[1]
      Executed endpoint 'gRPC - /reverse.Reverse/ServerSide'
info: Microsoft.AspNetCore.Hosting.Diagnostics[2]
      Request finished HTTP/2 POST http://localhost:5035/reverse.Reverse/ServerSide application/grpc - - 200 - application/grpc 12.7054ms

info: Microsoft.AspNetCore.Hosting.Diagnostics[1]
      Request starting HTTP/2 POST http://localhost:5035/reverse.Reverse/Bidirectional application/grpc -
info: Microsoft.AspNetCore.Routing.EndpointMiddleware[0]
      Executing endpoint 'gRPC - /reverse.Reverse/Bidirectional'
info: Microsoft.AspNetCore.Routing.EndpointMiddleware[1]
      Executed endpoint 'gRPC - /reverse.Reverse/Bidirectional'
info: Microsoft.AspNetCore.Hosting.Diagnostics[2]
      Request finished HTTP/2 POST http://localhost:5035/reverse.Reverse/Bidirectional application/grpc - - 200 - application/grpc 41.2414ms

对日志进行一些分析我们可以发现：

• 所有类型的 grpc 通讯模式执行逻辑都是相同的，都是一次完整的http请求周期；
• 请求的协议使用的是 HTTP/2；
• 方法都为 POST；
• 所有grpc方法都映射到了对应的终结点 /{package名}.{service名}/{方法名}；
• 请求&响应的 ContentType 都为 application/grpc；

三、进一步验证请求模型

如果我们上一步的分析是对的，那么数据只能承载在 请求流 & 响应流 中，我们可以尝试获取流中的数据，进一步分析具体细节；

dump请求&响应数据

借助 asp.net core 的中间件，我们可以比较容易的进行 请求流 & 响应流 的内容 dump；

请求流 是只读的，响应流 是只写的，我们需要两个代理流替换原有的流，进行数据dump，将数据保存到 MemoryStream 中，以便我们观察；

这两个流分别为 ReadCacheProxyStream.cs 和 WriteCacheProxyStream.cs，直接上代码：

public class ReadCacheProxyStream : Stream
{
    private readonly Stream _innerStream;

    public MemoryStream CachedStream { get; } = new MemoryStream(1024);

    public override bool CanRead => _innerStream.CanRead;

    public override bool CanSeek => false;

    public override bool CanWrite => false;

    public override long Length => _innerStream.Length;

    public override long Position { get => _innerStream.Length; set => throw new NotSupportedException(); }

    public ReadCacheProxyStream(Stream innerStream)
    {
        _innerStream = innerStream;
    }

    public override void Flush() => throw new NotSupportedException();

    public override Task FlushAsync(CancellationToken cancellationToken) => _innerStream.FlushAsync(cancellationToken);

    public override int Read(byte[] buffer, int offset, int count) => throw new NotSupportedException();

    public override async ValueTask<int> ReadAsync(Memory<byte> buffer, CancellationToken cancellationToken = default)
    {
        var len = await _innerStream.ReadAsync(buffer, cancellationToken);
        if (len > 0)
        {
            CachedStream.Write(buffer.Span.Slice(0, len));
        }
        return len;
    }

    public override long Seek(long offset, SeekOrigin origin) => throw new NotSupportedException();

    public override void SetLength(long value) => throw new NotSupportedException();

    public override void Write(byte[] buffer, int offset, int count) => throw new NotSupportedException();
}

public class WriteCacheProxyStream : Stream
{
    private readonly Stream _innerStream;

    public MemoryStream CachedStream { get; } = new MemoryStream(1024);

    public override bool CanRead => false;

    public override bool CanSeek => false;

    public override bool CanWrite => _innerStream.CanWrite;

    public override long Length => _innerStream.Length;

    public override long Position { get => _innerStream.Length; set => throw new NotSupportedException(); }

    public WriteCacheProxyStream(Stream innerStream)
    {
        _innerStream = innerStream;
    }

    public override void Flush() => throw new NotSupportedException();

    public override Task FlushAsync(CancellationToken cancellationToken) => _innerStream.FlushAsync(cancellationToken);

    public override int Read(byte[] buffer, int offset, int count) => throw new NotSupportedException();

    public override long Seek(long offset, SeekOrigin origin) => throw new NotSupportedException();

    public override void SetLength(long value) => throw new NotSupportedException();

    public override void Write(byte[] buffer, int offset, int count) => throw new NotSupportedException();

    public override async ValueTask WriteAsync(ReadOnlyMemory<byte> buffer, CancellationToken cancellationToken = default)
    {
        await _innerStream.WriteAsync(buffer, cancellationToken);
        CachedStream.Write(buffer.Span);
    }
}

• 在请求管道中替换流
  
  将如下中间件添加到请求管道的最开始

app.Use(async (context, next) =>
{
    var originRequestBody = context.Request.Body;
    var originResponseBody = context.Response.Body;
    var requestCacheStream = new ReadCacheProxyStream(originRequestBody);
    var responseCacheStream = new WriteCacheProxyStream(originResponseBody);

    context.Request.Body = requestCacheStream;
    context.Response.Body = responseCacheStream;
    try
    {
        await next();
    }
    finally
    {
        await context.Response.CompleteAsync();

        //要不要还回去不在这里进行讨论了
        context.Request.Body = originRequestBody;
        context.Response.Body = originResponseBody;

        var requestData = requestCacheStream.CachedStream.ToArray();
        var responseData = requestCacheStream.CachedStream.ToArray();
    }
});

• 接下来在 finally 块的最后打上断点，然后运行服务端和客户端，即可在中间件中通过 requestData 和 responseData 观察数据交互

分析数据结构

---

理论上我们可以直接使用 Protobuf 进行解析，不过这里我们目的是为了手动实现一个超级简单的编码器。。。

---

客户端执行 Sample 方法，并在服务端获取 requestData 和 responseData：

分析requestData

这个样子太不直观了，由于我们的消息定义 Request 只有一个 string 类型的字段，那么如果之前猜测正确，这个数据里面必定有对应字符串。我们直接尝试拿来看看：

果然有对应的数据 Sample ，我们尝试去掉多余的数据看看：

那么前7个byte是干什么的呢，我们改一下请求的消息内容，将 Sample 修改为 Sample1 再次进行分析：

这样就比较明显了，稍做分析，我们可以先做个简单的总结，第5个字节为消息的总长度，第6个字节应该是字段描述之类的，当前消息体固定为10，第7个字节为Request.message字段的长度；

不过这样有点草率，byte最大为255，我们再探索一下内容超过255时，是什么结构。将 Sample 修改为 50 个重复的 Sample 再次进行分析：

情况一下就复杂了。。。不过第6个字节仍然是10，那么前5个字节应该有描述消息总长度，[0,0,0,1,47] 和长度 303 (注：308-5)之间的关系是什么呢；稍微试了一下，数据的第1个字节目前假设固定为0，第2-5字节应该是一个大端序的uint32，用来声明消息总长度但是第7、8个字节如何转换为300，就有点难琢磨了。。。算了，我们先不处理内容过大的情况吧（具体编码逻辑可参见 protocol-buffers-encoding）

分析responseData

查看后发现结构和 requestData 是一样的（因为 Request 和 Reply 消息声明的结构相同），这里就不多描述了，可以自行Debug查看。

分析流式请求的requestData和responseData

分析后发现流式请求里面的多个消息每个都是单个消息的结构，然后顺序放到请求或响应流中，这里也不多描述了，可以自行Debug进行查看，直接上基于以上总结的解码器代码：

public static IEnumerable<string> ReadMessages(byte[] originData)
{
    var slice = originData.AsMemory();

    while (!slice.IsEmpty)
    {
        var messageLen = BinaryPrimitives.ReadInt32BigEndian(slice.Slice(1, 4).Span);

        var messageData = slice.Slice(5, messageLen);
        slice = slice.Slice(5 + messageLen);

        int len = messageData.Span[1];
        var content = Encoding.UTF8.GetString(messageData.Slice(2, len).Span);

        yield return content;
    }
}

然后在中间件中展示内容

TempMessageCodecUtil.DisplayMessages(requestData);
TempMessageCodecUtil.DisplayMessages(responseData);

再次运行程序，能够正确看到控制台直接输出的请求和响应消息内容，形如：

四、使用 Controller 实现能够与 Grpc Client SDK 交互的服务端

基于之前的分析，理论上我们只需要满足：

 - 请求的协议使用的是 `HTTP/2`；
 - 方法都为 `POST`；
 - 所有grpc方法都映射到了对应的终结点 `/{package名}.{service名}/{方法名}`；
 - 请求&响应的 `ContentType` 都为 `application/grpc`；

然后正确的从请求流中解析数据结构，将正确的数据结构写入响应流，就可以响应 Grpc Client 的请求了。

• 现在我们需要一个编码器，能够将字符串编码为 Reply 消息格式；以及一个解码器，从请求流中读取 Request 消息。直接上代码。编码器：

public static byte[] BuildMessage(string message)
{
    var contentData = Encoding.UTF8.GetBytes(message);
    if (contentData.Length > 127)
    {
        throw new ArgumentException();
    }
    var messageData = new byte[contentData.Length + 7];
    Array.Copy(contentData, 0, messageData, 7, contentData.Length);
    messageData[5] = 10;
    messageData[6] = (byte)contentData.Length;
    BinaryPrimitives.WriteInt32BigEndian(messageData.AsSpan().Slice(1), contentData.Length + 2);
    return messageData;
}

解码器：

private async IAsyncEnumerable<string> ReadMessageAsync([EnumeratorCancellation] CancellationToken cancellationToken)
{
    var pipeReader = Request.BodyReader;

    while (!cancellationToken.IsCancellationRequested)
    {
        var readResult = await pipeReader.ReadAsync(cancellationToken);

        var buffer = readResult.Buffer;

        if (readResult.IsCompleted
            && buffer.IsEmpty)
        {
            yield break;
        }

        if (buffer.Length < 5)
        {
            pipeReader.AdvanceTo(buffer.Start, buffer.End);
            continue;
        }

        var messageBuffer = buffer.IsSingleSegment
                            ? buffer.First
                            : buffer.ToArray();

        var messageLen = BinaryPrimitives.ReadInt32BigEndian(messageBuffer.Slice(1, 4).Span);

        if (buffer.Length < messageLen + 5)
        {
            pipeReader.AdvanceTo(buffer.Start, buffer.End);
            continue;
        }

        messageBuffer = messageBuffer.Slice(5);

        int len = messageBuffer.Span[1];
        var content = Encoding.UTF8.GetString(messageBuffer.Slice(2, len).Span);

        yield return content;

        pipeReader.AdvanceTo(readResult.Buffer.GetPosition(7 + len));
    }
}

• 实现一个 ReverseController.cs ，映射 reverse.proto 中对应的方法，实现和 ReverseService.cs 中相同的执行逻辑。代码如下：

[Route("reverse.Reverse")]
[ApiController]
public class ReverseController : ControllerBase
{
    [HttpPost]
    [Route(nameof(Bidirectional))]
    public async Task Bidirectional()
    {
        await foreach (var item in ReadMessageAsync(HttpContext.RequestAborted))
        {
            DisplayReceivedMessage(item);
            await ReplayReverseAsync(item);
        }
    }

    [HttpPost]
    [Route(nameof(ClientSide))]
    public async Task ClientSide()
    {
        var total = 0;

        await foreach (var item in ReadMessageAsync(HttpContext.RequestAborted))
        {
            total++;
            DisplayReceivedMessage(item);
        }

        await ReplayAsync($"{nameof(ServerSide)} Received Over. Total: {total}");
    }

    [HttpPost]
    [Route(nameof(ServerSide))]
    public async Task ServerSide()
    {
        string message = null!;
        await foreach (var item in ReadMessageAsync(HttpContext.RequestAborted))
        {
            message = item;
        }

        DisplayReceivedMessage(message);

        for (int i = 0; i < 5; i++)
        {
            await ReplayReverseAsync(message);
        }
    }

    [HttpPost]
    [Route(nameof(Simple))]
    public async Task Simple()
    {
        string message = null!;
        await foreach (var item in ReadMessageAsync(HttpContext.RequestAborted))
        {
            message = item;
        }

        DisplayReceivedMessage(message);
        await ReplayReverseAsync(message);
    }

    private async Task ReplayAsync(string message)
    {
        if (!Response.HasStarted)
        {
            Response.Headers.ContentType = "application/grpc";
            Response.AppendTrailer("grpc-status", "0");

            await Response.StartAsync();
        }

        await Response.Body.WriteAsync(TempMessageCodecUtil.BuildMessage(message));
    }

    private Task ReplayReverseAsync(string rawMessage) => ReplayAsync(new string(rawMessage.Reverse().ToArray()));

    //省略其他信息
}

最后记得 services.AddControllers() 和 app.MapControllers() 并取消Grpc的ServiceMap；

此时分别使用 Controller 和 GrpcService 运行服务端，并查看客户端日志，可以看到运行结果相同，如图：

五、使用 HttpClient 实现能够与 Grpc Server 交互的客户端

在上面我们已经使用原生 Controller 实现了一个可以让客户端正常运行的服务端，现在我们不使用 Grpc SDK 来实现一个可以和服务端交互的客户端。

• 服务端获取请求流和响应流比较简单，目前 HttpClient 没有直接获取请求流的办法，我们需要从 HttpContent 的 SerializeToStreamAsync 方法中获取到真正的请求流。具体细节不在这里赘述，直接上代码：

class LongAliveHttpContent : HttpContent
{
    private readonly TaskCompletionSource<Stream> _streamGetCompletionSource = new(TaskCreationOptions.RunContinuationsAsynchronously);
    private readonly TaskCompletionSource _taskCompletionSource = new(TaskCreationOptions.RunContinuationsAsynchronously);

    public LongAliveHttpContent()
    {
        Headers.ContentType = new MediaTypeHeaderValue("application/grpc");
    }

    protected override Task SerializeToStreamAsync(Stream stream, TransportContext? context)
    {
        _streamGetCompletionSource.SetResult(stream);
        return _taskCompletionSource.Task;
    }

    protected override bool TryComputeLength(out long length)
    {
        length = -1;
        return false;
    }

    public void Complete()
    {
        _taskCompletionSource.TrySetResult();
    }

    public Task<Stream> GetStreamAsync()
    {
        return _streamGetCompletionSource.Task;
    }
}

• 客户端同样需要满足对应的请求要求：

 - 请求的协议使用的是 `HTTP/2`；
 - 方法都为 `POST`；
 - 所有grpc方法都映射到了对应的终结点 `/{package名}.{service名}/{方法名}`；
 - 请求&响应的 `ContentType` 都为 `application/grpc`；

直接上代码，使用 HttpClient 发起请求，并获取 请求流 & 响应流：

private static (Task<Stream> RequestStreamGetTask, Task<Stream> ResponseStreamGetTask, LongAliveHttpContent HttpContent) CreateStreamGetTasksAsync(HttpClient client, string path)
{
    var content = new LongAliveHttpContent();

    var httpRequestMessage = new HttpRequestMessage()
    {
        Method = HttpMethod.Post,
        RequestUri = new Uri(path, UriKind.Relative),
        Content = content,
        Version = HttpVersion.Version20,
        VersionPolicy = HttpVersionPolicy.RequestVersionExact,
    };

    var responseStreamGetTask = client.SendAsync(httpRequestMessage, HttpCompletionOption.ResponseHeadersRead)
                                      .ContinueWith(m => m.Result.Content.ReadAsStreamAsync())
                                      .Unwrap();

    return (content.GetStreamAsync(), responseStreamGetTask, content);
}

• 实现和Grpc客户端相同的执行逻辑。代码如下：

private static async Task BidirectionalWithOutSDK(HttpClient client)
{
    var (requestStreamGetTask, responseStreamGetTask, httpContent) = CreateStreamGetTasksAsync(client, "reverse.Reverse/Bidirectional");

    var requestStream = await requestStreamGetTask;

    var sendTask = Task.Run(async () =>
    {
        for (int i = 0; i < 10; i++)
        {
            await requestStream.WriteAsync(TempMessageCodecUtil.BuildMessage($"{nameof(Bidirectional)}-{i}"));
        }

        httpContent.Complete();
    });

    var receiveTask = DisplayReceivedMessageAsync(responseStreamGetTask);

    await Task.WhenAll(sendTask, receiveTask);
}

private static async Task ClientSideWithOutSDK(HttpClient client)
{
    var (requestStreamGetTask, responseStreamGetTask, httpContent) = CreateStreamGetTasksAsync(client, "reverse.Reverse/ClientSide");

    var requestStream = await requestStreamGetTask;

    for (int i = 0; i < 5; i++)
    {
        await requestStream.WriteAsync(TempMessageCodecUtil.BuildMessage($"{nameof(ClientSide)}-{i}"));

        await requestStream.FlushAsync();
    }

    httpContent.Complete();

    await DisplayReceivedMessageAsync(responseStreamGetTask);
}

private static async Task SampleWithOutSDK(HttpClient client)
{
    var (requestStreamGetTask, responseStreamGetTask, httpContent) = CreateStreamGetTasksAsync(client, "reverse.Reverse/Simple");

    var requestStream = await requestStreamGetTask;

    await requestStream.WriteAsync(TempMessageCodecUtil.BuildMessage(nameof(Sample)));

    httpContent.Complete();

    await DisplayReceivedMessageAsync(responseStreamGetTask);
}

private static async Task ServerSideWithOutSDK(HttpClient client)
{
    var (requestStreamGetTask, responseStreamGetTask, httpContent) = CreateStreamGetTasksAsync(client, "reverse.Reverse/ServerSide");

    var requestStream = await requestStreamGetTask;

    await requestStream.WriteAsync(TempMessageCodecUtil.BuildMessage(nameof(ServerSide)));

    httpContent.Complete();

    await DisplayReceivedMessageAsync(responseStreamGetTask);
}

此时分别进行如下测试：

• 使用 GrpcService 运行服务端，并分别使用sdk客户端和HttpClient客户端进行请求；
• 使用 Controller 运行服务端，并分别使用sdk客户端和HttpClient客户端进行请求；

可以看到客户端运行结果相同，如下：

Sample Received: elpmaS
ClientSide Received: ServerSide Received Over. Total: 5
ServerSide Received: ediSrevreS
ServerSide Received: ediSrevreS
ServerSide Received: ediSrevreS
ServerSide Received: ediSrevreS
ServerSide Received: ediSrevreS
Bidirectional Received: 0-lanoitceridiB
Bidirectional Received: 1-lanoitceridiB
Bidirectional Received: 2-lanoitceridiB
Bidirectional Received: 3-lanoitceridiB
Bidirectional Received: 4-lanoitceridiB
Bidirectional Received: 5-lanoitceridiB
Bidirectional Received: 6-lanoitceridiB
Bidirectional Received: 7-lanoitceridiB
Bidirectional Received: 8-lanoitceridiB
Bidirectional Received: 9-lanoitceridiB
  ----------------- WithOutSDK -----------------
SampleWithOutSDK Received: elpmaS
ClientSideWithOutSDK Received: ServerSide Received Over. Total: 5
ServerSideWithOutSDK Received: ediSrevreS
ServerSideWithOutSDK Received: ediSrevreS
ServerSideWithOutSDK Received: ediSrevreS
ServerSideWithOutSDK Received: ediSrevreS
ServerSideWithOutSDK Received: ediSrevreS
BidirectionalWithOutSDK Received: 0-lanoitceridiB
BidirectionalWithOutSDK Received: 1-lanoitceridiB
BidirectionalWithOutSDK Received: 2-lanoitceridiB
BidirectionalWithOutSDK Received: 3-lanoitceridiB
BidirectionalWithOutSDK Received: 4-lanoitceridiB
BidirectionalWithOutSDK Received: 5-lanoitceridiB
BidirectionalWithOutSDK Received: 6-lanoitceridiB
BidirectionalWithOutSDK Received: 7-lanoitceridiB
BidirectionalWithOutSDK Received: 8-lanoitceridiB
BidirectionalWithOutSDK Received: 9-lanoitceridiB

六、结论

至此，我们稍作分析和总结，可以得出结论：

• Grpc 所有类型的方法调用都是普通的Http请求，只是请求和响应的内容是经过 Protobuf 编码的数据；

我们再稍作拓展，可以得出更多结论：

• 多路复用、Header压缩 什么的，都是 Http2 带来的优化，不是和 Grpc 绑定的，使用 Http2 访问常规 WebAPI 也能享受到其带来的好处；
• Grpc 的 Unary 请求模式和和 WebAPI 逻辑是一样的；Server streaming、Client streaming 请求模式都可以通过 Http1.1 进行实现（但不能多路复用，每个请求会独占一个连接）；Bidirectional streaming 是基于 二进制分帧 的，只能在 Http2 及以上版本实现双向流通讯；

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基于以上结论，我们总结一下 Grpc 比 WebAPI 的优势在哪里：

• 运行速度更快（一定情况下），Protobuf 基于二进制的编码，在数据量较多时，比 json 这种基于文本的编码效率更高；但丢失了直接的可阅读性；（没做性能测试，理论是这样，如果性能打不过 json 的话，那就没有存在价值了。理论上数据量越大，性能差距越大）
• 传输数据更少，json 因为要自我描述，所有字段都有名字，在序列化 List 时这种浪费就比较多了，重复对象越多，浪费越多（但可阅读性也是这样来的）；Protobuf 没有这方面的浪费，还有一些其它的优化，参见 protocol-buffers-encoding；
• 开发速度更快，SDK使用 proto 文件直接生成服务端和客户端，上手更快，跨语言也能快速生成客户端（这点其实见仁见智，WebAPI 也有类似的工具）;

---

Grpc 比传统 WebAPI 的劣势有哪些呢：

• 可阅读性；不借助工具 Grpc 的消息内容是没法直接阅读的；
• HTTP2 强绑定；WebAPI 可以在低版本协议下运行，某些时候会方便一点；
• 依赖 Grpc SDK；虽然 Grpc SDK 已经覆盖了很多主流语言，但如果恰好某个需求要使用的语言没有SDK，那就有点麻烦了；相比之下基于文本的 WebAPI 会更通用一点；
• 类型不能完全覆盖某些语言的基础类型，需要额外的编码量（方法不能直接接收/返回基础类型、Nullable等）；
• Protobuf 要求严格的格式，字段增删
• 额外的学习成本；

---

最后再基于结论，总结一些我认为有问题的 grpc 使用方法吧：

• 把 grpc 当作一个封包/拆包工具；在消息体中放一个 json 之类的东西，拿到消息之后在反序列化一次。。。这又是何必呢。。。直接基于原生 Http 写一个 基于消息头指定消息长度 的分包逻辑并花不了多少工作量，也不会额外引入grpc的相关东西；这个用法也和 grpc 的 高性能 背道而驰，还多了一层 序列化/反序列化 操作；（我在这里没有说nacos）
• 使用单独的认证逻辑；grpc 调用就是 Http 请求，那么 Header 的工作逻辑是和 WebAPI 完全一样的；那么 grpc 请求完全可以使用现有的 Http 认证 和 Header处理 代码甚至请求管道；额外再自定义消息实现相关功能不是多此一举吗？（我在这里也没有说nacos）

综上，个人认为，不是别人说 grpc 高性能，就认为它碾压传统 WebAPI，就去用它；还是需要了解原理后好好考虑的，确认它能否为你带来理想的效果；有时候或许自己手写一个变体的 Http 请求处理逻辑能更快更好的满足需求；

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拓展

如果有闲心的话，理论上甚至可以做下列的玩具：

• WebAPI 的 grpc 兼容层，使 Controller 既能以 grpc 工作又能处理普通请求；通过 Controller 定义，反向生成 DTO 的 proto 消息定义，以及整个service的 proto 定义；
• grpc 的 WebAPI 兼容层，使 grpc 服务能工作的像 Controller 一样，对外输入输出 json；