https://github.com/hussein-awala/async-batcher

A service to batch the http requests.
https://github.com/hussein-awala/async-batcher

asyncio batch-processing fastapi grpc http-requests python

Last synced: about 2 months ago
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A service to batch the http requests.

• Host: GitHub
• URL: https://github.com/hussein-awala/async-batcher
• Owner: hussein-awala
• License: apache-2.0
• Created: 2024-02-15T23:50:01.000Z (12 months ago)
• Default Branch: main
• Last Pushed: 2024-09-02T20:56:15.000Z (5 months ago)
• Last Synced: 2024-12-10T03:40:24.213Z (2 months ago)
• Topics: asyncio, batch-processing, fastapi, grpc, http-requests, python
• Language: Python
• Homepage:
• Size: 3.9 MB
• Stars: 22
• Watchers: 1
• Forks: 3
• Open Issues: 4
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md

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README

        
# Async Batcher

This project provides a Python library to batch the asynchronous requests and handle them in batches.

## How to use

To use the library, you need to install the package in your environment. You can install the package using pip:

```bash

pip install async-batcher

```

Then, you can create a new `AsyncBatcher` class by implementing the `process_batch` method:

```python

from async_batcher.batcher import AsyncBatcher

class MyAsyncBatcher(AsyncBatcher):

    async def process_batch(self, batch):

        # Process the batch

        print(batch)

# Create a new instance of the `MyAsyncBatcher` class

async_batcher = MyAsyncBatcher(max_batch_size=20)

async_batcher.start()

```

## Benchmark

The benchmark is available in the [BENCHMARK.md](https://github.com/hussein-awala/async-batcher/blob/main/BENCHMARK.md)

file.

## Use cases

The `AsyncBatcher` library can be used in any application that needs to handle asynchronous requests in batches,

such as:

- Serving machine learning models that optimize the batch processing (e.g. TensorFlow, PyTorch, Scikit-learn, etc.)

- Storing multiple records in a database in a single query to optimize the I/O operations (or to reduce the cost of the

  database operations, e.g. AWS DynamoDB)

- Sending multiple messages in a single request to optimize the network operations (or to reduce the cost of the network

  operations, e.g. Kafka, RabbitMQ, AWS SQS, AWS SNS, etc.)