Building an AWS C++ S3 Program on Windows with vcpk

-

Building an AWS C++ S3 Program on Windows with vcpkg

This guide shows how to set up a working C++ project on Windows that uses the AWS SDK to list S3 buckets. We will use vcpkg as the package manager to handle the AWS SDK installation and dependencies.

Step 1: Install vcpkg

If you haven't installed vcpkg yet, download it from the official repository and bootstrap it:

git clone https://github.com/microsoft/vcpkg.git C:\Users\YourName\CDK\vcpkg
cd C:\Users\YourName\CDK\vcpkg
.\bootstrap-vcpkg.bat

Note: On Windows, you must run bootstrap-vcpkg.bat in the Command Prompt.

---

Step 2: Install the AWS SDK for C++ using vcpkg

Install the core and S3 components for 64-bit Windows:

vcpkg install aws-sdk-cpp[core,s3]:x64-windows

This will install the required libraries and headers under installed\x64-windows\. vcpkg will handle dependencies like aws-c-common, aws-crt-cpp, and others.

---

Step 3: Set up your C++ project

Create a project folder, e.g., C:\AWS_CPP_S3, and add your main.cpp:

#include <iostream>
#include <aws/core/Aws.h>
#include <aws/s3/S3Client.h>
#include <aws/s3/model/ListBucketsRequest.h>

int main() {
    Aws::SDKOptions options;
    Aws::InitAPI(options);
    {
        Aws::S3::S3Client s3_client;
        auto outcome = s3_client.ListBuckets();
        if (outcome.IsSuccess()) {
            std::cout << "Buckets:\n";
            for (auto const &bucket : outcome.GetResult().GetBuckets()) {
                std::cout << " - " << bucket.GetName() << "\n";
            }
        } else {
            std::cerr << "Error listing buckets: " << outcome.GetError().GetMessage() << "\n";
        }
    }
    Aws::ShutdownAPI(options);
    return 0;
}
---

Step 4: Create CMakeLists.txt

Use CMake to build your project and integrate with vcpkg. Here’s a working CMakeLists.txt for Windows:

cmake_minimum_required(VERSION 3.20)
project(HelloS3 LANGUAGES CXX)

set(CMAKE_CXX_STANDARD 17)
set(CMAKE_CXX_STANDARD_REQUIRED ON)

# vcpkg toolchain
set(CMAKE_TOOLCHAIN_FILE "C:/Users/YourName/CDK/vcpkg/scripts/buildsystems/vcpkg.cmake" CACHE STRING "")

# Find individual AWS SDK components
find_package(aws-cpp-sdk-core REQUIRED)
find_package(aws-cpp-sdk-s3 REQUIRED)

# Executable
add_executable(HelloS3 main.cpp)

# Include directories and link libraries
target_include_directories(HelloS3 PRIVATE
    "C:/Users/YourName/CDK/vcpkg/installed/x64-windows/include"
)
target_link_libraries(HelloS3 PRIVATE aws-cpp-sdk-core aws-cpp-sdk-s3)

Note: Replace YourName with your actual Windows username.

---

Step 5: Build the project using CMake

Open the x64 Native Tools Command Prompt for VS 2022, navigate to your project folder, and run:

cd C:\AWS_CPP_S3
rmdir /s /q build
mkdir build
cd build
cmake .. -G "Visual Studio 17 2022" -A x64 -DCMAKE_TOOLCHAIN_FILE="C:/Users/YourName/CDK/vcpkg/scripts/buildsystems/vcpkg.cmake"
cmake --build . --config Release

This will generate a HelloS3.exe in build\Release.

---

Step 6: Run your program

Make sure your AWS credentials are configured (either in %USERPROFILE%\.aws\credentials or environment variables). Then run:

Release\HelloS3.exe

You should see a list of your S3 buckets, confirming that your program can communicate with AWS S3.

---

Troubleshooting Tips

  • If CMake cannot find the AWS SDK targets, make sure you specify the vcpkg toolchain with -DCMAKE_TOOLCHAIN_FILE.
  • If you switch architectures (e.g., from Win32 to x64), delete the build folder to clear the CMake cache.
  • On Windows, the vcpkg AWS SDK does not provide AWSSDKConfig.cmake — use the individual component targets like aws-cpp-sdk-core and aws-cpp-sdk-s3.
  • Always build from the Visual Studio Developer Command Prompt to ensure the correct compiler environment.
---

Conclusion

Using vcpkg simplifies dependency management for AWS SDK C++ on Windows. Following these steps, you can compile, link, and run a working AWS S3 program with C++ and CMake. From here, you can expand to other AWS services by installing additional components through vcpkg.

Comments

Post a Comment

Popular posts from this blog

Building and Deploying a Fargate Container that runs Python and performs CloudWatch Logging

-
Image
This guide walks you through building and deploying an AWS Fargate container that runs a Python script. The container will be tagged as the latest version, pushed to Amazon Elastic Container Registry (ECR), and configured to log messages to CloudWatch Logs. The process includes setting up the Dockerfile, creating the Python script, building and pushing the Docker image, and configuring AWS services to ensure your container runs smoothly on Fargate. By the end of this tutorial, you will have a running Fargate service that logs information to CloudWatch Logs, allowing you to monitor the status and output of your containerized application in real time. NOTE - In this guide, when you see aws_account_id replace it with your AWS account number and region with your AWS region.  Step-by-Step Guide Overview Setting up the Dockerfile Define a Dockerfile that uses Amazon Linux 2023 as a base image, installs Python, sets up the environment, and configures the CloudWatch logging agent. Wri...
Read more

Asynchronous DynamoDB Queries with Java Using GSIs and the AWS SDK v2 Enhanced Async Client

-
When building modern Java applications that interact with AWS DynamoDB , you often need to query data by attributes other than the primary key . This is where Global Secondary Indexes (GSIs) come into play. In this post, we’ll break down: Why GSIs are important How to use the Enhanced Async DynamoDB Client in Java How to query a GSI asynchronously using Reactive Streams How to process results efficiently using CompletableFuture 1. Understanding Global Secondary Indexes (GSIs) A Global Secondary Index (GSI) allows you to query DynamoDB tables using an alternative partition key and optional sort key. Unlike the primary key of the table: A GSI can have different partition and sort keys. It provides fast queries for attributes you don’t want to scan. GSIs are eventually consistent by default, but you can request strong consistency if needed. Why use a GSI here? In our scenario, we have a table of form templates with this schema...
Read more

Automate Your API Gateway Setup with Boto3: Rendering HTML from Lambda

-
Creating RESTful endpoints using the AWS Management Console can quickly become frustrating — especially when dealing with API Gateway. If you've ever felt like the Console experience is too slow for repeatable tasks, you're not alone. In this post, we’ll walk through a Python script using the boto3 library that automates the creation of an AWS API Gateway REST API backed by a Lambda function — and returns HTML, not JSON. This makes it ideal for rendering mobile-friendly forms or web content directly from serverless logic. Why Use Python Over the Console? Repeatability : Run your script anytime without clicking through dozens of console menus. Version Control : Store and track API configurations in Git. Speed : Automate CORS setup, deployment stages, permissions — in seconds. Precision : Avoid human error by defining everything in code. Use Case This script will: Create a REST API in API Gateway. Add a /form route with a GET method. Integrat...
Read more