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.

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) {

```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>]

```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

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);

	}
	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()
		maps.Copy(cache.iamPolicyDocsMap, m)
		store.unlock()

		for policy, p := range m {

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.

Dennoch besteht in beiden Fällen eine gute Chance, dass **FastAPI** [immer noch schneller](index.md#performance){.internal-link target=_blank} als Ihr bisheriges Framework (oder zumindest damit vergleichbar) ist.

### Abhängigkeiten { #dependencies }

PLkotlinx/coroutines/JobCancellationException;->fillInStackTrace()Ljava/lang/Throwable;
PLkotlinx/coroutines/JobImpl;->getOnCancelComplete$kotlinx_coroutines_core()Z
PLkotlinx/coroutines/JobKt;->cancel$default(Lkotlin/coroutines/CoroutineContext;Ljava/util/concurrent/CancellationException;ILjava/lang/Object;)V
PLkotlinx/coroutines/JobSupport$Finishing;-><init>(Lkotlinx/coroutines/NodeList;ZLjava/lang/Throwable;)V

        }

        try {
            if (log.isTraceEnabled()) {
                log.trace("Sending " + request);
            }
            if (request.isCancel()) {
                doSend0(request);
                return null;
            }
            if (request instanceof SmbComTransaction) {
                response = sendComTransaction(request, response, params);