To develop new features for Wope's AI expension we required extensive need of processing few thousand request per second for long-running tasks like LLM requests. We needed to handle up to 10,000 requests per second efficiently. As part of Cloudflare's startup program, we received significant support, including access to a GPU cluster. However, consuming this capacity on an average machine (8 cores, 16 GB RAM) presented significant challenges. Python's Global Interpreter Lock (GIL) limits prompted us to seek a compiled language solution. While our backend primarily uses Go, I decided to explore Rust's capabilities for this high-performance task.

Processing only 600 / sec to not make video size too large - but you can consume up to 10K req / sec.

rust-hyper-load-balanced-api-client is a high-performance Rust tool designed to send API requests (specifically to LLMs) with built-in weighted load balancing, retry mechanisms, and rate limiting. Utilizing the hyper library for fast request handling, this tool manages large volumes of asynchronous requests, optimized to handle up to 10,000 requests per second.

• Weighted Load Balancing: Distribute API requests across multiple LLM endpoints based on their weights.
• Retry Mechanisms: Automatically retry failed requests with exponential backoff.
• Rate Limiting: Control the number of requests sent per second.
• Asynchronous I/O: Read from and write to files asynchronously, ensuring high throughput.
• Error Logging: Save error responses to a dedicated errors.jsonl file.
• Configurable via CLI: Easily configure the tool using command-line flags.

• Rust toolchain installed
• Cargo package manager

Clone the repository and build the project:

git clone https://github.com/yourusername/rust-hyper-load-balanced-api-client.git
cd rust-hyper-load-balanced-api-client
cargo build --release

The tool can be configured using the following command-line flags:

• --requests_filepath: Path to the JSONL file containing the requests.
• --max_requests_per_second: Maximum number of requests to send per second.
• --max_attempts: Maximum number of retry attempts for failed requests.
• --save_filepath: Path to save the successful responses (optional).

Example usage:

./target/release/api_processor --requests_filepath "/path/to/requests.jsonl" --max_requests_per_second 10000 --max_attempts 3 --save_filepath "/path/to/save.jsonl"

The input JSONL file should contain one JSON object per line, structured as follows:

{
  "input": "User prompt or input text here"
}

Errors are logged in a separate errors.jsonl file, with each error entry structured as follows:

{
  "input": "User prompt or input text here",
  "error": "Error message"
}

{"input": "User prompt or input text here"}

{"response": "LLM response"}

{"input": "User prompt or input text here", "error": "Error message"}

The project consists of a single main Rust file (main.rs) that handles all the functionalities:

1. Reading Requests: The tool reads from a JSONL file asynchronously, ensuring it doesn't block the processing of other requests.
2. Sending Requests: It uses hyper to send HTTP requests to the specified LLM endpoints. Requests are sent at a rate controlled by the max_requests_per_second parameter.
3. Load Balancing: The tool uses weighted load balancing to distribute requests across multiple endpoints.
4. Retry Mechanisms: Failed requests are retried with exponential backoff until the maximum number of attempts (max_attempts) is reached.
5. Logging: Successful responses are saved to a specified file, while errors are logged to errors.jsonl.

• process_api_requests_from_file: Manages reading requests from the file and sending them asynchronously.
• send_request: Sends individual API requests and handles retries and logging.

rust-hyper-load-balanced-api-client is a robust and high-performance tool designed to handle the demanding task of sending a large volume of API requests efficiently. By leveraging Rust's capabilities and the hyper library, it achieves high throughput and reliability, making it an excellent choice for applications requiring extensive API interactions, such as consuming LLM services.

I welcome contributions and feedback from the community to further enhance this tool.