有两种方法可以解决这个问题：

1. 增加Lambda函数的内存和执行时间限制。在默认设置中，Lambda函数只有128 MB内存和3秒的执行时间。这意味着它无法处理大于5 MB的文件。通过增加内存和执行时间限制，Lambda函数可以更快地处理大型文件。

以下是示例代码：

import boto3

s3 = boto3.resource('s3')

def lambda_handler(event, context): src_bucket = event['Records'][0]['s3']['bucket']['name'] dst_bucket = 'destination-bucket-name' key = event['Records'][0]['s3']['object']['key'] file_size = event['Records'][0]['s3']['object']['size']

if file_size > 5242880:
    print('File size greater than 5 MB. Increase Lambda function memory and timeout limits.')
    return 'Lambda function memory and timeout limits too low.'
      
copy_source = {
    'Bucket': src_bucket,
    'Key': key
}
     
s3.meta.client.copy(copy_source, dst_bucket, key)
      
return 'File successfully copied to destination bucket.'

2. 使用S3 Multipart Upload API。S3 Multipart Upload API允许您在多个请求之间上传大型对象。您可以使用此API将大于5 MB的文件上传到S3存储桶中。

以下是示例代码：

import boto3 import os

s3 = boto3.client('s3')

def upload_to_s3(bucket_name, file_name): # set the part size and the file size part_size = 5242880 file_size = os.path.getsize(file_name)

# initiate the multipart upload
response = s3.create_multipart_upload(
    Bucket=bucket_name,
    Key=file_name
)

upload_id = response["UploadId"]
parts = []

# upload the file in parts
with open(file_name, "rb") as file:
    for i in range(0, file_size, part_size):
        part_number = int(i / part_size) + 1
        data = file.read(part_size)
        response = s3.upload_part(
            Body=data,
            Bucket=bucket_name,
            Key=file_name,
            PartNumber=part_number,
            UploadId=upload_id
        )
        parts.append({
            "PartNumber": part_number,
            "ETag": response["ETag"]
        })

# complete the multipart upload
response = s3.complete_multipart_upload(
    Bucket=bucket_name,
    Key=file_name,
    MultipartUpload={
        "Parts": parts
    },
    UploadId=upload_id
)

return response['Location']

example usage

upload_to_s3('destination-bucket-name', 'large-file.mp4')