Apologies if this is unwanted, but I ran this thread through ChatGPT and liked its answer. I've provided its take on the situation below. I'm also a bit interested on how more experienced users view its response. Let me know if you want it removed! Anyways, here goes...
[Reply #4]
User: ChatGPT
Replying To: Kura.Den
Content:
Hello Kura.Den,
I can provide a slightly more specific approach to handling your task, using a combination of Tokio and Reqwest which have the advantage of being async-friendly and providing a fairly straightforward interface for HTTP(S) requests.
Here's a general idea of how to structure your code to send HTTP requests in chunks, and update your DB records asynchronously. This isn't a complete program, but an outline:
use reqwest::Client;
use tokio::task;
use std::sync::Arc;
async fn process_batch(client: Arc<Client>, batch: Vec<Record>) {
// Create a Vec of Futures that you can await later
let tasks: Vec<_> = batch.into_iter().map(|record| {
let client = Arc::clone(&client);
task::spawn(async move {
// Make your request using `client` and `record` here.
// Then, update your DB record with the response.
// Handle errors appropriately.
})
}).collect();
// Wait for all requests in the batch to complete.
let _results: Vec<_> = futures::future::join_all(tasks).await;
}
#[tokio::main]
async fn main() {
// Client is wrapped in an Arc to allow sharing across tasks
let client = Arc::new(Client::new());
// Fetch your 1M records from the DB
let records = fetch_records_from_db();
// Process your records in batches
let chunks = records.chunks(100);
for batch in chunks {
process_batch(Arc::clone(&client), batch.to_vec()).await;
}
}
This assumes you have a Record
type that represents a single record from your DB. Replace Record
and fetch_records_from_db()
with your actual data fetching code.
As for the batch size, it depends on a number of factors. The more requests you send at once, the more memory your program will use. But if you send too few requests at once, you won't fully utilize your network resources. You might want to experiment with different batch sizes to see what works best for your specific use case.
For your second question regarding N workers pulling off a queue, you may consider using a work-stealing model. Rust has a few work-stealing queue libraries such as rayon
, async_nursery
, async-task
which can be used along with async channels (tokio::sync::mpsc
). The concept is to put all your tasks (http requests in this case) into an async channel and let your workers pull from it and process them.
Remember to handle errors appropriately and perhaps add some backoff or rate limiting mechanism to prevent overloading the server with requests.
I hope this gives you a clearer direction. Let me know if there's anything you'd like me to expand upon.