以并行方式执行DynamoDB查询（使用Global Secondary Index的BatchGetItems）

以下是使用并行方式执行DynamoDB查询的示例代码：

import boto3
from botocore.exceptions import NoCredentialsError
from multiprocessing import Pool

def query_items(table_name, key_name, keys):
    try:
        dynamodb = boto3.resource('dynamodb')
        table = dynamodb.Table(table_name)

        response = table.batch_get_item(
            RequestItems={
                table_name: {
                    'Keys': keys,
                    'AttributesToGet': [key_name]  # optional
                }
            }
        )

        return response['Responses'][table_name]

    except NoCredentialsError:
        print("AWS credentials not found")

if __name__ == "__main__":
    table_name = 'your_table_name'
    key_name = 'your_key_name'

    keys = [{'your_key_name': 'your_key_value1'}, {'your_key_name': 'your_key_value2'}, ...]

    # Number of parallel threads/processes
    num_processes = 4

    # Split the keys into chunks for parallel processing
    chunk_size = len(keys) // num_processes
    key_chunks = [keys[i:i+chunk_size] for i in range(0, len(keys), chunk_size)]

    # Create a pool of processes
    pool = Pool(processes=num_processes)

    results = []

    # Submit each chunk of keys to the pool for parallel processing
    for key_chunk in key_chunks:
        result = pool.apply_async(query_items, (table_name, key_name, key_chunk))
        results.append(result)

    # Wait for all processes to complete
    pool.close()
    pool.join()

    # Collect the results from all processes
    all_responses = []
    for result in results:
        all_responses.extend(result.get())

    # Process the combined responses
    for response in all_responses:
        print(response)

请注意，上述代码示例使用了Python的multiprocessing.Pool来实现并行处理。首先，将要查询的key按照要启动的并行线程数进行划分，然后将每个子集分配给不同的进程进行查询。最后，收集并处理所有进程的结果。