for i := 0; i < 2*b.N; i++ {
		if i%2 == 0 {
			l.Add(trace[i], trace[i])
		} else {
			if _, ok := l.Get(trace[i]); ok {
				hit++
			} else {
				miss++
			}
		}
	}
	b.Logf("hit: %d miss: %d ratio: %f", hit, miss, float64(hit)/float64(hit+miss))
}

func BenchmarkLRU_Freq_NoExpire(b *testing.B) {
	l := lru.NewLRU[int64, int64](8192, nil, 0)

	trace := make([]int64, b.N*2)
	for i := 0; i < b.N*2; i++ {

# Devolver una Response Directamente { #return-a-response-directly }

Cuando creas una *path operation* en **FastAPI**, normalmente puedes devolver cualquier dato desde ella: un `dict`, una `list`, un modelo de Pydantic, un modelo de base de datos, etc.

Por defecto, **FastAPI** convertiría automáticamente ese valor de retorno a JSON usando el `jsonable_encoder` explicado en [JSON Compatible Encoder](../tutorial/encoder.md){.internal-link target=_blank}.

    * Se você quer fazer comparações com o Uvicorn, compare com Daphne, Hypercorn, uWSGI, etc. Servidores de Aplicação.
* **Starlette**:
    * Terá a melhor performance, depois do Uvicorn. De fato, Starlette utiliza Uvicorn para rodar. Então, ele provavelmente será "mais lento" que Uvicorn por ter que executar mais código.

        return new VerticalLinearTransformation(x1);
      } else {
        return withSlope((y2 - y1) / (x2 - x1));
      }
    }

    /**
     * Finish building an instance with the given slope, i.e. the rate of change of {@code y} with
     * respect to {@code x}. The slope must not be {@code NaN}. It may be infinite, in which case
     * the transformation is vertical. (If it is zero, the transformation is horizontal.)
     */

# Request Body { #request-body }

Cuando necesitas enviar datos desde un cliente (digamos, un navegador) a tu API, los envías como un **request body**.

Un **request** body es un dato enviado por el cliente a tu API. Un **response** body es el dato que tu API envía al cliente.

        assertTrue(primaryScore > baseScore);

        // High error rate should reduce score
        channelInfo.setState(ChannelState.ESTABLISHED);
        channelInfo.setPrimary(false);
        for (int i = 0; i < 20; i++) {
            channelInfo.incrementRequestsSent();
            channelInfo.incrementErrors(); // 100% error rate
        }
        int highErrorScore = channelInfo.getScore();

            }
        }

        // If we've had many errors, consider fallback
        double errorRate = statistics.getErrorRate();
        if (errorRate > 0.1) { // More than 10% error rate
            log.warn("High RDMA error rate ({:.1f}%), TCP fallback recommended", errorRate * 100);
            return true;
        }

        return false;
    }

    /**

     * @return active memory region count
     */
    public long getMemoryRegionsActive() {
        return memoryRegionsActive.get();
    }

    /**
     * Calculate error rate as percentage of total operations
     *
     * @return error rate (0.0 to 1.0)
     */
    public double getErrorRate() {
        long total = rdmaReads.get() + rdmaWrites.get() + rdmaSends.get() + rdmaReceives.get();
        if (total == 0) {

	ReplicatedCount int64 `json:"replicationCount"`
	// Replication latency information
	Latency ReplicationLatency `json:"replicationLatency"`
	// transfer rate for large uploads
	XferRateLrg *XferStats `json:"largeTransferRate" msg:"lt"`
	// transfer rate for small uploads
	XferRateSml *XferStats `json:"smallTransferRate" msg:"st"`
	// Endpoint is the replication target endpoint
	Endpoint string `json:"-"`

            long avgResponseTime = metrics.getAverageResponseTime();

            // Adjust threshold based on failure rate and response time trends
            int currentThreshold = this.failureThreshold;
            int newThreshold = currentThreshold;

            // If failure rate is high but response time is normal, be more lenient
            if (failureRate > 0.5 && avgResponseTime < 5000) {