JinXiaozhao opened a new issue, #1770:
URL: https://github.com/apache/incubator-brpc/issues/1770
**Describe the bug (描述bug)**
serve端：brpc
client端：brpc(FLAGS_protocol=h2:grpc)
./echo_image_client --h2_client_stream_window_size=536870912
--h2_client_connection_window_size=536870912
重复发起100次请求，只有第一次请求可以正常；
其他请求报错：
E2004]remote_window_left is not enough, data_size=4915254
[R1][E2004]remote_window_left is not enough, data_size=4915254
[R2][E2004]remote_window_left is not enough, data_size=4915254
[R3][E2004]remote_window_left is not enough, data_size=4915254
**To Reproduce (复现方法)**
grpc传输数据为4.69M
**Expected behavior (期望行为)**
在客户端如何更改grpc传输数据的最大值
**Versions (各种版本)**
OS:
Compiler:
brpc:
protobuf:
**Additional context/screenshots (更多上下文/截图)**
客户端代码：
`
#include <chrono>
#include <gflags/gflags.h>
#include <butil/logging.h>
#include <butil/time.h>
#include <brpc/channel.h>
#include "echo.pb.h"
using namespace std::chrono;
using google::protobuf::int32;
DEFINE_string(attachment, "", "Carry this along with requests");
// DEFINE_string(protocol, "baidu_std", "Protocol type. Defined in
src/brpc/options.proto");
DEFINE_string(protocol, "h2:grpc", "Protocol type. Defined in
src/brpc/options.proto");
DEFINE_string(connection_type, "", "Connection type. Available values:
single, pooled, short");
DEFINE_string(server, "0.0.0.0:8000", "IP Address of server");
DEFINE_string(load_balancer, "", "The algorithm for load balancing");
DEFINE_int32(timeout_ms, 100, "RPC timeout in milliseconds");
DEFINE_int32(max_retry, 3, "Max retries(not including the first RPC)");
DEFINE_int32(interval_ms, 1000, "Milliseconds between consecutive requests");
// DEFINE_int32(h2_client_stream_window_size, 256*1024*1024, "256M");
// DEFINE_int32(h2_client_connection_window_size, 256*1024*1024, "256M");
tensorflow::TensorProto make_tensor_proto(int dims[3],
const int32 maxBatchSize,
const tensorflow::DataType dataType) {
tensorflow::TensorProto result;
result.set_dtype(dataType);
result.mutable_tensor_shape()->add_dim()->set_size(maxBatchSize);
auto numPixels = maxBatchSize;
for (int i=0; i<3; ++i) {
int dim_i = dims[i];
result.mutable_tensor_shape()->add_dim()->set_size(dim_i);
numPixels *= dim_i;
}
// here should be your true input data
std::vector<float> fakeData(numPixels, 0.5);
for (auto it = fakeData.begin(); it != fakeData.end(); ++it) {
union {
float source;
char target[sizeof(float)];
} converter;
converter.source = *it;
std::copy(
std::begin(converter.target), std::end(converter.target),
std::back_inserter(*result.mutable_tensor_content()));
}
return result;
}
int main(int argc, char* argv[]) {
// Parse gflags. We recommend you to use gflags as well.
GFLAGS_NS::ParseCommandLineFlags(&argc, &argv, true);
std::cout << "h2_client_stream_window_size: " << FLAGS_protocol <<
std::endl;
// A Channel represents a communication line to a Server. Notice that
// Channel is thread-safe and can be shared by all threads in your
program.
brpc::Channel channel;
// Initialize the channel, NULL means using default options.
brpc::ChannelOptions options;
options.protocol = FLAGS_protocol;
options.connection_type = FLAGS_connection_type;
options.timeout_ms = FLAGS_timeout_ms/*milliseconds*/;
options.max_retry = FLAGS_max_retry;
if (channel.Init(FLAGS_server.c_str(), FLAGS_load_balancer.c_str(),
&options) != 0) {
LOG(ERROR) << "Fail to initialize channel";
return -1;
}
// Normally, you should not call a Channel directly, but instead
construct
// a stub Service wrapping it. stub can be shared by all threads as well.
example::EchoService_Stub stub(&channel);
// Send a request and wait for the response every 1 second.
int log_id = 0;
int num = 100;
int time_us = 0;
// We will receive response synchronously, safe to put variables
// on stack.
example::EchoRequest request;
int dims[3] = {3,640,640};
const tensorflow::DataType dataType = tensorflow::DataType::DT_FLOAT;
int batchSize = 1;
tensorflow::TensorProto inputProto = make_tensor_proto(dims, batchSize,
dataType);
request.mutable_frame()->CopyFrom(inputProto);
// request.set_allocated_frame(&inputProto);
request.set_model_name("hello-world");
request.set_model_version("v1");
std::cout << "input_proto size:" << inputProto.ByteSize() << std::endl;
// std::string inputProtoStr;
// google::protobuf::util::MessageToJsonString(request, &inputProtoStr);
// std::cout << "input_proto :" << inputProtoStr << std::endl;
time_point<high_resolution_clock> timeStart =
high_resolution_clock::now();
while (!brpc::IsAskedToQuit() && num) {
std::cout << num << std::endl;
brpc::Controller cntl;
example::EchoResponse response;
cntl.set_log_id(log_id ++); // set by user
// Set attachment which is wired to network directly instead of
// being serialized into protobuf messages.
cntl.request_attachment().append(FLAGS_attachment);
// Because `done'(last parameter) is NULL, this function waits until
// the response comes back or error occurs(including timedout).
stub.Echo(&cntl, &request, &response, NULL);
if (!cntl.Failed()) {
time_us += cntl.latency_us();
LOG(INFO) << "Received response from " << cntl.remote_side()
<< " to " << cntl.local_side()
<< ": "
<< " model_name: " << response.model_name()
<< " , model_version: " << response.model_version()
<< " , tensor_size: " << response.image().ByteSize()
<< " (attached="
<< cntl.response_attachment() << ")"
<< " latency=" << cntl.latency_us() << "us";
} else {
LOG(WARNING) << cntl.ErrorText();
}
num -= 1;
// usleep(FLAGS_interval_ms * 1000L);
}
time_point<high_resolution_clock> timeEnd = high_resolution_clock::now();
float timeMs = duration_cast<milliseconds>(timeEnd - timeStart).count();
std::cout << "100 times is :" << timeMs / 100 << "ms" << std::endl;
std::cout << "100 times latency us is :" << time_us << std::endl;
LOG(INFO) << "EchoClient is going to quit";
return 0;
}
`
服务端代码：[https://github.com/apache/incubator-brpc/blob/master/example/echo_c%2B%2B/server.cpp](url)
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