private var nextQueueName = 10000
  private var coordinatorWaiting = false
  private var coordinatorWakeUpAt = 0L

  /**
   * When we need a new thread to run tasks, we call [Backend.execute]. A few microseconds later we
   * expect a newly-started thread to call [Runnable.run]. We shouldn't request new threads until
   * the already-requested ones are in service, otherwise we might create more threads than we need.
   *

Instead of that, by being an "asynchronous" system, once finished, the task can wait in line a little bit (some microseconds) for the computer / program to finish whatever it went to do, and then come back to take the results and continue working with them.

   *
   * <p>This always holds: {@code 0 <= permitsToTake <= storedPermits}
   */
  abstract long storedPermitsToWaitTime(double storedPermits, double permitsToTake);

  /**
   * Returns the number of microseconds during cool down that we have to wait to get a new permit.
   */
  abstract double coolDownIntervalMicros();

  /** Updates {@code storedPermits} and {@code nextFreeTicketMicros} based on the current time. */

So, behind the scenes, it will actually work as if everything was the same single app.

///

/// check

You don't have to worry about performance when including routers.

This will take microseconds and will only happen at startup.

So it won't affect performance. ⚡

///

### Include an `APIRouter` with a custom `prefix`, `tags`, `responses`, and `dependencies`

				})

				for apiName, latency := range disk.Metrics.LastMinute {
					metrics = append(metrics, MetricV2{
						Description:    getNodeDriveAPILatencyMD(),
						Value:          float64(latency.Avg().Microseconds()),
						VariableLabels: map[string]string{"drive": disk.DrivePath, "api": "storage." + apiName},
					})
				}
			}
		}

		metrics = append(metrics, MetricV2{

    class ModelWithDatetimeField(BaseModel):
        dt_field: datetime

        @field_serializer("dt_field")
        def serialize_datetime(self, dt_field: datetime):
            return dt_field.replace(microsecond=0, tzinfo=timezone.utc).isoformat()

    app = FastAPI()
    model = ModelWithDatetimeField(dt_field=datetime(2019, 1, 1, 8))

    @app.get("/model", response_model=ModelWithDatetimeField)
    def get_model():

						duration = time.Duration(val) * time.Nanosecond
					case ts.Unit.IsSetMILLIS():
						duration = time.Duration(val) * time.Millisecond
					case ts.Unit.IsSetMICROS():
						duration = time.Duration(val) * time.Microsecond
					default:
						return nil, errors.New("Invalid LogicalType annotation found")
					}
					value = sql.FormatSQLTimestamp(time.Unix(0, 0).Add(duration))
				}

    class ModelWithCustomEncoder(BaseModel):
        dt_field: datetime

        @field_serializer("dt_field")
        def serialize_dt_field(self, dt):
            return dt.replace(microsecond=0, tzinfo=timezone.utc).isoformat()

    class ModelWithCustomEncoderSubclass(ModelWithCustomEncoder):
        pass

    model = ModelWithCustomEncoder(dt_field=datetime(2019, 1, 1, 8))

À la place, en étant "asynchrone", une fois terminée, une tâche peut légèrement attendre (quelques microsecondes) que l'ordinateur / le programme finisse ce qu'il était en train de faire, et revienne récupérer le résultat.

pkg testing, method (*B) ReportMetric(float64, string)
pkg testing, type BenchmarkResult struct, Extra map[string]float64
pkg text/template, method (ExecError) Unwrap() error
pkg time, method (Duration) Microseconds() int64
pkg time, method (Duration) Milliseconds() int64
pkg unicode, const Version = "11.0.0"
pkg unicode, var Dogra *RangeTable
pkg unicode, var Gunjala_Gondi *RangeTable