self.base(title, targets, desc)
      + timeSeries.standardOptions.withUnit('Bps')
      + timeSeries.standardOptions.withOverrides([
        fieldOverride.byQuery.new('B')
        + fieldOverride.byQuery.withProperty('custom.axisPlacement', 'right')
        + fieldOverride.byQuery.withProperty('unit', 'c/s'),
      ])
    ,

    durationQuantile(title, targets, desc=''):
      self.base(title, targets, desc)

query.gsa.default.lang=en
query.gsa.default.sort=
query.gsa.meta.prefix=MT_
query.gsa.index.field.charset=charset
query.gsa.index.field.content_type.=content_type
query.collapse.max.concurrent.group.results=4
query.collapse.inner.hits.name=similar_docs
query.collapse.inner.hits.size=0
query.collapse.inner.hits.sorts=
query.default.languages=
query.json.default.preference=_query
query.gsa.default.preference=_query
query.language.mapping=\

{* ../../docs_src/background_tasks/tutorial002.py hl[13,15,22,25] *}

Dans cet exemple, les messages seront écrits dans le fichier `log.txt` après que la réponse soit envoyée.

S'il y avait une `query` (paramètre nommé `q`) dans la requête, alors elle sera écrite dans `log.txt` via une tâche d'arrière-plan.

				</div>
			</div>
		</main>
		<jsp:include page="footer.jsp" />
	</la:form>
	<input type="hidden" id="contextPath" value="${contextPath}" />
	<script type="text/javascript"
		src="${fe:url('/js/jquery-3.7.1.min.js')}"></script>
	<script type="text/javascript" src="${fe:url('/js/bootstrap.min.js')}"></script>
	<script type="text/javascript" src="${fe:url('/js/suggestor.js')}"></script>

                        suggestor.suggest();
                        interval = 0;
                    }
                }
            }
        }, 100);

    }

Их тяжелее настраивать, также им нужен брокер сообщений наподобие RabbitMQ или Redis, но зато они позволяют вам запускать фоновые задачи в нескольких процессах и даже на нескольких серверах.

Для примера, посмотрите [Project Generators](../project-generation.md){.internal-link target=_blank}, там есть проект с уже настроенным Celery.

    String QUERY_JSON_DEFAULT_PREFERENCE = "query.json.default.preference";

    /** The key of the configuration. e.g. _query */
    String QUERY_GSA_DEFAULT_PREFERENCE = "query.gsa.default.preference";

    /** The key of the configuration. e.g. ar=ar<br>
     * bg=bg<br>
     * bn=bn<br>
     * ca=ca<br>
     * ckb-iq=ckb-iq<br>
     * ckb_IQ=ckb-iq<br>
     * cs=cs<br>

{!> ../../docs_src/background_tasks/tutorial002.py!}
```

////

In obigem Beispiel werden die Nachrichten, *nachdem* die Response gesendet wurde, in die Datei `log.txt` geschrieben.

Wenn im Request ein Query-Parameter enthalten war, wird dieser in einem Hintergrundtask in das Log geschrieben.

They tend to require more complex configurations, a message/job queue manager, like RabbitMQ or Redis, but they allow you to run background tasks in multiple processes, and especially, in multiple servers.

To see an example, check the [Project Generators](../project-generation.md){.internal-link target=_blank}, they all include Celery already configured.

# Comparações

As comparações independentes da TechEmpower mostram as aplicações **FastAPI** rodando com Uvicorn como <a href="https://www.techempower.com/benchmarks/#section=test&runid=7464e520-0dc2-473d-bd34-dbdfd7e85911&hw=ph&test=query&l=zijzen-7" class="external-link" target="_blank">um dos _frameworks_ Python mais rápidos disponíveis</a>, somente atrás dos próprios Starlette e Uvicorn (utilizados internamente pelo FastAPI). (*)