In this case, it would be better to get **one extra server** and run some processes on it so that they all have **enough RAM and CPU time**.

There's also the chance that for some reason you have a **spike** of usage of your API. Maybe it went viral, or maybe some other services or bots start using it. And you might want to have extra resources to be safe in those cases.

		} else {
			t.Log("ok")
		}
	}
	result := string(buf[0:n])
	if result != "hello" {
		t.Errorf("got: %q; want: %q", result, "hello")
	}
}

func timeout(after time.Duration) (cancel func()) {
	c := time.After(after)
	cc := make(chan struct{})
	go func() {
		select {
		case <-cc:
			return
		case <-c:
			buf := make([]byte, 1<<20)
			stacklen := runtime.Stack(buf, true)

Nesse caso, seria melhor obter **um servidor extra** e executar alguns processos nele para que todos tenham **RAM e tempo de CPU suficientes**.

Também há a chance de que, por algum motivo, você tenha um **pico** de uso da sua API. Talvez ela tenha se tornado viral, ou talvez alguns outros serviços ou bots comecem a usá-la. E você pode querer ter recursos extras para estar seguro nesses casos.

## Alternativ (alt): `Query` als Defaultwert

Frühere Versionen von FastAPI (vor <abbr title="vor 2023-03">0.95.0</abbr>) benötigten `Query` als Defaultwert des Parameters, statt es innerhalb von `Annotated` unterzubringen. Die Chance ist groß, dass Sie Quellcode sehen, der das immer noch so macht, darum erkläre ich es Ihnen.

/// tip | "Tipp"

```console
$ pip install "harry==3"
```

</div>

And then you would end up with `harry` version `3` installed in your global Python environment.

And if you try to run `philosophers-stone` again, there's a chance it would **not work** because it needs `harry` version `1`.

```mermaid
flowchart LR
    subgraph global[global env]
        harry-1[<strike>harry v1</strike>]
        style harry-1 fill:#ccc,stroke-dasharray: 5 5

	}
	store.runlock()

	if len(missingPolicies) > 0 {
		m := make(map[string]PolicyDoc)
		for _, policy := range missingPolicies {
			ctx, cancel := context.WithTimeout(context.Background(), 5*time.Second)
			_ = store.loadPolicyDoc(ctx, policy, m)
			cancel()
		}

		cache := store.lock()
		for policy, p := range m {
			cache.iamPolicyDocsMap[policy] = p
		}
		store.unlock()

```console
$ pip install "harry==3"
```

</div>

E então você acabaria com `harry` versão `3` instalado em seu ambiente Python global.

E se você tentar executar `philosophers-stone` novamente, há uma chance de que **não funcione** porque ele precisa de `harry` versão `1`.

```mermaid
flowchart LR
    subgraph global[global env]
        harry-1[<strike>harry v1</strike>]

func (t *transitionState) ActiveTasks() int64 {
	return t.activeTasks.Load()
}

// MissedImmediateTasks returns the number of tasks - deferred to scanner due
// to tasks channel being backlogged.
func (t *transitionState) MissedImmediateTasks() int64 {
	return t.missedImmediateTasks.Load()
}

// worker waits for transition tasks
func (t *transitionState) worker(objectAPI ObjectLayer) {

TEST(CAPI, Status) {
  TF_Status* s = TF_NewStatus();
  EXPECT_EQ(TF_OK, TF_GetCode(s));
  EXPECT_EQ(string(), TF_Message(s));
  TF_SetStatus(s, TF_CANCELLED, "cancel");
  EXPECT_EQ(TF_CANCELLED, TF_GetCode(s));
  EXPECT_EQ(string("cancel"), TF_Message(s));
  TF_DeleteStatus(s);
}

void Deallocator(void* data, size_t, void* arg) {
  tensorflow::cpu_allocator()->DeallocateRaw(data);

Donc, pendant ce temps, l'ordinateur pourra effectuer d'autres tâches, pendant que "fichier-lent" 📝 se termine.

Ensuite l'ordinateur / le programme 🤖 reviendra à chaque fois qu'il en a la chance que ce soit parce qu'il attend à nouveau, ou car il 🤖 a fini tout le travail qu'il avait à faire. Il 🤖 regardera donc si les tâches qu'il attend ont terminé d'être effectuées.