///

---

Dans ce scénario de burgers parallèles, vous êtes un ordinateur / programme 🤖 avec deux processeurs (vous et votre crush 😍) attendant 🕙 à deux et dédiant votre attention 🕙 à "attendre devant le comptoir" pour une longue durée.

Le fast-food a 8 processeurs (serveurs/cuisiniers) 👨‍🍳👨‍🍳👨‍🍳👨‍🍳👨‍🍳👨‍🍳👨‍🍳👨‍🍳. Alors que le fast-food de burgers concurrents en avait 2 (un serveur et un cuisinier).

            final Set<String> processedUrls = new HashSet<>();
            requestQueue.offer(new CrawlRequest(url, 0));
            while (!requestQueue.isEmpty() && (maxAccessCount < 0 || counter < maxAccessCount)) {
                final CrawlRequest crawlRequest = requestQueue.poll();
                if ((maxDepth != -1 && crawlRequest.getDepth() > maxDepth) || processedUrls.contains(crawlRequest.getUrl())) {

/**
 * Provides tools for processing Maven dependency injection annotations at compile time.
 * <p>
 * This package contains annotation processors that generate metadata files used by
 * the Maven dependency injection system. The main component is the {@link org.apache.maven.di.tool.DiIndexProcessor},
 * which processes classes annotated with {@link org.apache.maven.api.di.Named} and creates an index file

```Dockerfile
FROM tiangolo/uvicorn-gunicorn-fastapi:python3.7

COPY ./app /app/app
```

### Raspberry Pi et autres architectures

Si vous utilisez Docker sur un Raspberry Pi (qui a un processeur ARM) ou toute autre architecture, vous pouvez créer un `Dockerfile` à partir de zéro, basé sur une image de base Python (qui est multi-architecture) et utiliser Uvicorn seul.

func (cs *callbacks) Create() *processor {
	return cs.processors["create"]
}

func (cs *callbacks) Query() *processor {
	return cs.processors["query"]
}

func (cs *callbacks) Update() *processor {
	return cs.processors["update"]
}

func (cs *callbacks) Delete() *processor {
	return cs.processors["delete"]
}

func (cs *callbacks) Row() *processor {
	return cs.processors["row"]
}

* Others...

## Replication - Processes and Memory { #replication-processes-and-memory }

With a FastAPI application, using a server program like the `fastapi` command that runs Uvicorn, running it once in **one process** can serve multiple clients concurrently.

But in many cases, you will want to run several worker processes at the same time.

### Multiple Processes - Workers { #multiple-processes-workers }

import org.codelibs.fess.util.JobProcess;

import jakarta.annotation.PreDestroy;

/**
 * Helper class for managing system processes in Fess.
 * This class provides functionality to start, stop, and manage external processes
 * such as crawler processes, with proper resource cleanup and lifecycle management.
 */
public class ProcessHelper {
    /** Logger instance for this class */

        try {
            // Start multiple processes
            processHelper.startProcess(sessionId1, cmdList, pbCall);
            processHelper.startProcess(sessionId2, cmdList, pbCall);

            assertTrue(processHelper.isProcessRunning());

            // Wait a bit for the processes to start
            Thread.sleep(50);

            // Destroy all processes
            processHelper.destroy();

### Containers with Multiple Processes and Special Cases { #containers-with-multiple-processes-and-special-cases }

Of course, there are **special cases** where you could want to have **a container** with several **Uvicorn worker processes** inside.

In those cases, you can use the `--workers` command line option to set the number of workers that you want to run:

* Restarts
* **Replication (the number of processes running)**
* Memory
* Previous steps before starting

Up to this point, with all the tutorials in the docs, you have probably been running a **server program**, for example, using the `fastapi` command, that runs Uvicorn, running a **single process**.