///

---

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).

```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.

        includeFilter.processUrl("http://example.com/");

        assertEquals(1, includeFilter.cachedIncludeSet.size());
        assertEquals(0, includeFilter.cachedExcludeSet.size());
        assertEquals("http://example.com/.*", includeFilter.cachedIncludeSet.toArray()[0]);

        includeFilter.processUrl("https://test.com");

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"]
}

* There can be **multiple processes** of the **same program** running at the same time.

If you check out the "task manager" or "system monitor" (or similar tools) in your operating system, you will be able to see many of those processes running.

- Worker processes. Long-running daemon processes that the Gradle daemon starts to run specific kinds of work, such as compilation or test execution.

The diagram below shows the relationships between the different processes:

```mermaid
    graph LR
        
        gradle["Gradle CLI client"]
    

* 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**.

         * Only kill local Gradle processes (classpath in checkout directory).
         * Not clean up global Gradle processes (i.e. classpath in ~/.gradle/...).
         */
        KILL_LEAKED_PROCESSES_FROM_PREVIOUS_BUILDS,
        /**
         * Run at the end of each build. Kill potentially leaked processes in the current build.

This way, containers consume **little resources**, an amount comparable to running the processes directly (a virtual machine would consume much more).

Containers also have their own **isolated** running processes (commonly just one process), file system, and network, simplifying deployment, security, development, etc.

## What is a Container Image

        List<InterpolationPostProcessor> processors = new ArrayList<>(2);
        if (projectDir != null) {
            processors.add(new PathTranslatingPostProcessor(
                    getProjectPrefixes(config), TRANSLATED_PATH_EXPRESSIONS, projectDir, pathTranslator));
        }
        processors.add(new UrlNormalizingPostProcessor(urlNormalizer));
        return processors;
    }