在Lambda函数中提高内存大小和设置适当的时间限制，以便更快地处理S3文件，并使用异步导入将数据插入数据库，以避免阻塞Lambda执行。示例代码如下：

import boto3    
import json    
import requests    
import logging    
import time    
    
s3 = boto3.client('s3')    
dynamodb = boto3.resource('dynamodb')    
table = dynamodb.Table('my_dynamodb_table')    
    
def lambda_handler(event, context):    
    bucket_name = event['Records'][0]['s3']['bucket']['name']    
    key = urllib.parse.unquote_plus(event['Records'][0]['s3']['object']['key'])    
    response = s3.get_object(Bucket=bucket_name, Key=key)    
    data = json.loads(response['Body'].read().decode())    
    
    with table.batch_writer(overwrite_by_pkeys=['id']) as batch:    
        for item in data:    
            batch.put_item(    
                Item={    
                    'id': item['id'],    
                    'name': item['name'],    
                    'email': item['email'],    
                }    
            )    
    return {'statusCode': 200, 'body': json.dumps('Data inserted successfully')}