Quickstart: Deploy a C++ service to Cloud Run

Write the sample application

To write an application in C++:

1. Create a new directory named helloworld-cpp and change directory into it:

   mkdir helloworld-cpp
   cd helloworld-cpp
   

2. Create a new file named CMakeLists.txt and paste the following code into it:

   cmake_minimum_required(VERSION 3.20)
   
   # Define the project name and where to report bugs.
   set(PACKAGE_BUGREPORT
       "https://github.com/GoogleCloudPlatform/cpp-samples/issues")
   project(cpp-samples-cloud-run-hello-world CXX)
   
   find_package(functions_framework_cpp REQUIRED)
   find_package(Threads)
   
   add_executable(cloud_run_hello cloud_run_hello.cc)
   target_compile_features(cloud_run_hello PRIVATE cxx_std_17)
   target_link_libraries(cloud_run_hello functions-framework-cpp::framework)
   
   include(GNUInstallDirs)
   install(TARGETS cloud_run_hello RUNTIME DESTINATION ${CMAKE_INSTALL_BINDIR})

3. Create a new file named vcpkg.json and paste the following code into it:

   {
     "name": "cpp-samples-cloud-run-hello-world",
     "version-string": "unversioned",
     "homepage": "https://github.com/GoogleCloudPlatform/cpp-samples/",
     "description": [
       "Shows how to deploy a C++ application to Cloud Run."
     ],
     "dependencies": [
       "functions-framework-cpp"
     ]
   }
   

4. Create a new file named cloud_run_hello.cc and paste the following code into it:

   #include <google/cloud/functions/framework.h>
   #include <cstdlib>
   
   namespace gcf = ::google::cloud::functions;
   
   auto hello_world_http() {
     return gcf::MakeFunction([](gcf::HttpRequest const& /*request*/) {
       std::string greeting = "Hello ";
       auto const* target = std::getenv("TARGET");
       greeting += target == nullptr ? "World" : target;
       greeting += "\n";
   
       return gcf::HttpResponse{}
           .set_header("Content-Type", "text/plain")
           .set_payload(greeting);
     });
   }
   
   int main(int argc, char* argv[]) {
     return gcf::Run(argc, argv, hello_world_http());
   }
   

   This code creates a basic web server that listens on the port defined by the PORT environment variable.

5. Create a new file named Dockerfile in the same directory as the source files. The C++ Dockerfile starts the application listening on the port defined by the PORT environment variable:

   # We chose Alpine to build the image because it has good support for creating
   # statically-linked, small programs.
   FROM alpine:3.20 AS build
   
   # Install the typical development tools for C++, and
   # the base OS headers and libraries.
   RUN apk update && \
       apk add \
           build-base \
           cmake \
           curl \
           git \
           gcc \
           g++ \
           libc-dev \
           linux-headers \
           ninja \
           pkgconfig \
           tar \
           unzip \
           zip
   
   # Use `vcpkg`, a package manager for C++, to install
   WORKDIR /usr/local/vcpkg
   ENV VCPKG_FORCE_SYSTEM_BINARIES=1
   RUN curl -sSL "https://github.com/Microsoft/vcpkg/archive/2024.04.26.tar.gz" | \
       tar --strip-components=1 -zxf - \
       && ./bootstrap-vcpkg.sh -disableMetrics
   
   # Copy the source code to /v/source and compile it.
   COPY . /v/source
   WORKDIR /v/source
   
   # Run the CMake configuration step, setting the options to create
   # a statically linked C++ program
   RUN cmake -S/v/source -B/v/binary -GNinja \
       -DCMAKE_TOOLCHAIN_FILE=/usr/local/vcpkg/scripts/buildsystems/vcpkg.cmake \
       -DCMAKE_BUILD_TYPE=Release
   
   # Compile the binary and strip it to reduce its size.
   RUN cmake --build /v/binary
   RUN strip /v/binary/cloud_run_hello
   
   # Create the final deployment image, using `scratch` (the empty Docker image)
   # as the starting point. Effectively we create an image that only contains
   # our program.
   FROM scratch AS cloud-run-hello
   WORKDIR /r
   
   # Copy the program from the previously created stage and the shared libraries it
   # depends on.
   COPY --from=build /v/binary/cloud_run_hello /r
   COPY --from=build /lib/ld-musl-x86_64.so.1 /lib/ld-musl-x86_64.so.1
   COPY --from=build /usr/lib/libstdc++.so.6 /usr/lib/libstdc++.so.6
   COPY --from=build /usr/lib/libgcc_s.so.1 /usr/lib/libgcc_s.so.1
   
   # Make the program the entry point.
   ENTRYPOINT [ "/r/cloud_run_hello" ]

Your app is finished and ready to be deployed.

Deploy to Cloud Run from source

Important: This quickstart assumes that you have owner or editor roles in the project you are using for the quickstart. Otherwise, refer to the Cloud Run Source Developer role for the required permissions for deploying a Cloud Run resource from source.

Use Cloud Build to create an image from the source code, and then deploy it.

1. In the source directory, use Cloud Build to create a docker image for your service

   gcloud builds submit --machine-type=e2_highcpu_32 --tag gcr.io/PROJECT_ID/cloud-run-hello-world

2. Deploy the image using the following command:

   gcloud run deploy --image=gcr.io/PROJECT_ID/cloud-run-hello-world

   If prompted to enable the API, Reply y to enable.

   1. When you are prompted for the service name, press Enter to accept the default name, for example helloworld.

   2. If you are prompted to enable additional APIs on the project, for example, the Artifact Registry API, respond by pressing y.

   3. When you are prompted for region: select the region of your choice, for example us-central1.

   4. If you are prompted to create a repository in the specified region, respond by pressing y.

   5. If you are prompted to allow unauthenticated invocations: respond y. You might not see this prompt if there is a domain restriction organization policy that prevents it; for more details see the Before you begin section.

   Then wait a few moments until the deployment is complete. On success, the command line displays the service URL.

3. Visit your deployed service by opening the service URL in a web browser.

Congratulations! You have just deployed a container image from source code to Cloud Run. Cloud Run automatically and horizontally scales out your container image to handle the received requests, then scales in when demand decreases. You only pay for the CPU, memory, and networking consumed during request handling.