如何在 AWS Lambda 上使用 IronWord

建立 AWS Lambda 項目

使用 Visual Studio 建立容器化的 AWS Lambda 專案：

1. 安裝適用於 Visual Studio 的 AWS 工具包
2. 選擇 AWS Lambda 專案（.NET Core - C#）
3. 選擇 .NET 8（容器映像）藍圖並完成安裝
4. 選擇容器鏡像作為部署類型

新增包依賴項

透過 NuGet 將 IronWord 和 AWSSDK.S3 套件新增至您的專案。 只要配置正確，IronWord 函式庫就能在 AWS Lambda 上流暢運作。執行以下命令即可將 IronWord 無縫安裝到您的 AWS 專案中：

Install-Package IronWord

更新專案的 Dockerfile 以使用 .NET 8 Lambda 基礎映像，並複製建置產物：

來自 public.ecr.aws/lambda/dotnet:8

運行 dnf update -y

工作目錄 /var/task

複製"bin/Release/lambda-publish"。

修改函數處理程序程式碼

下面是一個 Lambda 函數範例，它會建立一個簡單的 Word 文檔，將其儲存為 .docx 文件，並將其上傳到 S3 儲存桶。

using Amazon.Lambda.Core;
using Amazon.S3;
using Amazon.S3.Model;
using IronWord;
using IronWord.Models;
using System.Text;

// Assembly attribute to enable the Lambda function's JSON input to be converted into a .NET class.
[assembly: LambdaSerializer(typeof(Amazon.Lambda.Serialization.SystemTextJson.DefaultLambdaJsonSerializer))]

namespace IronWordAwsLambda;

public class Function
{
    private static readonly IAmazonS3 _s3Client = new AmazonS3Client(Amazon.RegionEndpoint.APSoutheast1);

    public async Task FunctionHandler(ILambdaContext context)
    {
        var awsTmpPath = @"/tmp/";
        License.LicenseKey = "YOUR-LICENSE-KEY"; // Replace if you have one

        string filename = Guid.NewGuid() + ".docx";
        string localPath = Path.Combine(awsTmpPath, filename);
        string bucketName = "your-s3-bucket-name";
        string objectKey = $"IronWordAwsLambda/{filename}";

        try
        {
            // Create Word Document
            var doc = new WordDocument();
            Paragraph paragraph = new Paragraph(new TextContent("Hello from IronWord on AWS Lambda!"));
            doc.AddParagraph(paragraph);
            doc.SaveAs(localPath);

            context.Logger.LogLine("Word document created.");

            // Upload to S3
            byte[] fileBytes = await File.ReadAllBytesAsync(localPath);
            await UploadToS3Async(bucketName, objectKey, fileBytes);

            context.Logger.LogLine($"Document uploaded to S3: {bucketName}/{objectKey}");
        }
        catch (Exception ex)
        {
            context.Logger.LogLine($"[ERROR] {ex.Message}");
        }
    }

    private async Task UploadToS3Async(string bucketName, string objectKey, byte[] fileBytes)
    {
        using var stream = new MemoryStream(fileBytes);
        var request = new PutObjectRequest
        {
            BucketName = bucketName,
            Key = objectKey,
            InputStream = stream,
            ContentType = "application/vnd.openxmlformats-officedocument.wordprocessingml.document"
        };
        await _s3Client.PutObjectAsync(request);
    }
}

using Amazon.Lambda.Core;
using Amazon.S3;
using Amazon.S3.Model;
using IronWord;
using IronWord.Models;
using System.Text;

// Assembly attribute to enable the Lambda function's JSON input to be converted into a .NET class.
[assembly: LambdaSerializer(typeof(Amazon.Lambda.Serialization.SystemTextJson.DefaultLambdaJsonSerializer))]

namespace IronWordAwsLambda;

public class Function
{
    private static readonly IAmazonS3 _s3Client = new AmazonS3Client(Amazon.RegionEndpoint.APSoutheast1);

    public async Task FunctionHandler(ILambdaContext context)
    {
        var awsTmpPath = @"/tmp/";
        License.LicenseKey = "YOUR-LICENSE-KEY"; // Replace if you have one

        string filename = Guid.NewGuid() + ".docx";
        string localPath = Path.Combine(awsTmpPath, filename);
        string bucketName = "your-s3-bucket-name";
        string objectKey = $"IronWordAwsLambda/{filename}";

        try
        {
            // Create Word Document
            var doc = new WordDocument();
            Paragraph paragraph = new Paragraph(new TextContent("Hello from IronWord on AWS Lambda!"));
            doc.AddParagraph(paragraph);
            doc.SaveAs(localPath);

            context.Logger.LogLine("Word document created.");

            // Upload to S3
            byte[] fileBytes = await File.ReadAllBytesAsync(localPath);
            await UploadToS3Async(bucketName, objectKey, fileBytes);

            context.Logger.LogLine($"Document uploaded to S3: {bucketName}/{objectKey}");
        }
        catch (Exception ex)
        {
            context.Logger.LogLine($"[ERROR] {ex.Message}");
        }
    }

    private async Task UploadToS3Async(string bucketName, string objectKey, byte[] fileBytes)
    {
        using var stream = new MemoryStream(fileBytes);
        var request = new PutObjectRequest
        {
            BucketName = bucketName,
            Key = objectKey,
            InputStream = stream,
            ContentType = "application/vnd.openxmlformats-officedocument.wordprocessingml.document"
        };
        await _s3Client.PutObjectAsync(request);
    }
}

增加記憶體和超時

由於文件處理可能佔用大量內存，請在 aws-lambda-tools-defaults.json 中將 Lambda 函數內存增加到至少 512 MB，並將超時時間設置為 300 秒：

{
  "function-memory-size": 512,
  "function-timeout": 300
}

如果遇到類似"運行時退出並出現錯誤：訊號：終止"的錯誤，請增加記憶體或最佳化程式碼。

發布

發布 Lambda 函數：

• 在 Visual Studio 中右鍵點選您的專案
• 選擇"發佈到 AWS Lambda..."
• 依照精靈指示，根據需要配置設定。

試試看！

透過 AWS Lambda 控制台或 Visual Studio 呼叫 Lambda 函數。 執行完成後，檢查您的 S3 儲存桶中是否已找到新建立的 Word 文件。