Should I create a new instance of boto3 client for each file upl

ghz 17hours ago ⋅ 3 views

Should I create a new instance of boto3 client for each file upload request, or use a shared instance

I was wondering, should I create a new instance of boto3 client for each file upload request, or use a shared instance? Which is the correct way to do so?

Create a new instance each upload request

@app.route('/', methods=['POST'])
def upload_file():
    file = request.files['file']

    file.save('/tmp/file.ext')

    client = boto3.client('s3')

    client.upload_file('/tmp/file.ext',  # Path to local file
        'my-space',  # Name of Space
        'file.ext')  # Name for remote file

Shared instance for each upload request

client = boto3.client('s3')

@app.route('/', methods=['POST'])
def upload_file():
    file = request.files['file']

    file.save('/tmp/file.ext')

    client.upload_file('/tmp/file.ext',  # Path to local file
        'my-space',  # Name of Space
        'file.ext')  # Name for remote file

Answer

When deciding whether to create a new boto3 client for each file upload request or to use a shared instance, the correct approach generally depends on the trade-offs between efficiency and the architecture of your application.

Let's analyze the two options:

1. Create a New Instance for Each Upload Request

@app.route('/', methods=['POST'])
def upload_file():
    file = request.files['file']

    file.save('/tmp/file.ext')

    client = boto3.client('s3')

    client.upload_file('/tmp/file.ext',  # Path to local file
        'my-space',  # Name of Space
        'file.ext')  # Name for remote file

Pros:

  • Thread-safety: Each request creates a new client, so no need to worry about thread safety or sharing a single client across different requests.
  • Fresh Credentials: If you're using credentials that might change per request, creating a new instance ensures that any potential changes are reflected immediately (though this is rare in practice).

Cons:

  • Performance Overhead: Creating a new boto3 client on each request introduces overhead because it requires initializing resources like connections and authentication for every upload. For high-throughput applications, this could become a bottleneck.
  • Network Connections: Each new client may establish a new network connection to AWS, which can increase latency and resource consumption.

2. Shared Instance for Each Upload Request

client = boto3.client('s3')

@app.route('/', methods=['POST'])
def upload_file():
    file = request.files['file']

    file.save('/tmp/file.ext')

    client.upload_file('/tmp/file.ext',  # Path to local file
        'my-space',  # Name of Space
        'file.ext')  # Name for remote file

Pros:

  • Performance: Reusing a single boto3 client reduces the overhead of creating new clients for every request, which can improve performance, especially when handling multiple requests simultaneously.
  • Efficient Resource Management: A single boto3 client will maintain its network connection pool, reducing connection latency and improving throughput for multiple file uploads.
  • Reuse of Client Features: Once the client is set up (with credentials, config, etc.), it can be reused across multiple requests, saving time and resources.

Cons:

  • Thread Safety: boto3 clients are designed to be thread-safe, but you must be careful when using them in a multithreaded environment (e.g., if you're using a WSGI server like Gunicorn or uWSGI). If you're using Flask with multi-threading, the boto3 client should still be safe, but it's important to understand that you'll need to ensure no shared mutable state across requests.
  • Potential for Stale Configuration: If credentials or configurations change during the application's lifetime, a single client may be out of sync with the current state unless the client is recreated.

Best Practice: Use a Shared Client Instance

For most applications, using a shared client instance is the better option. This is especially true for web applications where you expect a high volume of requests. By reusing the same client instance across requests, you avoid the overhead of client initialization and benefit from connection pooling, which reduces latency and improves overall throughput.

Thread Safety Considerations

  • Thread Safety: As mentioned earlier, boto3 clients are thread-safe, so if your application is multi-threaded (e.g., using Gunicorn), a shared client should be fine. However, you should make sure that you're not modifying the client instance in a way that affects other threads.
  • WSGI Servers: Most production WSGI servers (e.g., Gunicorn, uWSGI) handle multiple requests using worker processes or threads. If you're running in a multithreaded or multi-process environment, each worker (process/thread) will have its own instance of the boto3 client, so the potential issues of thread safety are minimal.

Optimized Approach (Shared Client with Lazy Initialization)

You can also combine the shared client approach with lazy initialization. This way, the client is created only when needed (i.e., when the first request comes in), and it remains shared across requests.

For example, you can initialize the boto3 client as a global variable or use a factory pattern to create the client:

import boto3

# Create the S3 client only once when the app starts up
s3_client = None

def get_s3_client():
    global s3_client
    if s3_client is None:
        s3_client = boto3.client('s3')
    return s3_client

@app.route('/', methods=['POST'])
def upload_file():
    file = request.files['file']
    file.save('/tmp/file.ext')

    client = get_s3_client()

    client.upload_file('/tmp/file.ext',  # Path to local file
        'my-space',  # Name of Space
        'file.ext')  # Name for remote file

This approach ensures that the client is created only once and reused for subsequent requests, which strikes a balance between performance and resource management.

Conclusion

  • Shared Instance is generally the correct approach for most cases as it improves performance and reduces overhead.
  • New Instance for Each Request may be required in cases where you have special needs (e.g., dynamic credentials or multi-process apps with isolated state).

In most cases, a shared instance (with proper thread safety considerations) should be preferred.

#py