*       thoroughly as possible.
 *   <li><b>bit-dispersing:</b> masking out any <i>single bit</i> from a hash code should yield only
 *       the expected <i>twofold</i> increase to all collision rates. Informally, the "information"
 *       in the hash code should be as evenly "spread out" through the hash code's bits as possible.
 *       The result is that, for example, when choosing a bucket in a hash table of size 2^8,

  int maximumSize;

  @Param("5000")
  int distinctKeys;

  @Param("4")
  int segments;

  // 1 means uniform likelihood of keys; higher means some keys are more popular
  // tweak this to control hit rate
  @Param("2.5")
  double concentration;

  Random random = new Random();

  LoadingCache<Integer, Integer> cache;

  int max;

  static AtomicLong requests = new AtomicLong(0);

        now = datetime.now(tz=timezone.utc)

        # Handle primary rate limits
        primary_limit_wait_time = 0.0
        if self.remaining_points <= self.last_query_cost:
            primary_limit_wait_time = (self.reset_at - now).total_seconds() + 2
            logging.warning(
                f"Approaching GitHub API rate limit, remaining points: {self.remaining_points}, "

    }

    /**
     * Configure rate limiting
     *
     * @param windowMs time window in milliseconds
     * @param maxEvents maximum events per window
     */
    public void setRateLimit(long windowMs, int maxEvents) {
        this.rateLimitWindow = windowMs;
        this.maxEventsPerWindow = maxEvents;
        log.info("Rate limiting set to {} events per {} ms", maxEvents, windowMs);
    }

            }
        }

        // 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:"-"`

        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();

            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) {

          },
          "exemplar": true,
          "expr": "sum by (bucket) (rate(minio_bucket_traffic_sent_bytes{job=~\"$scrape_jobs\"}[$__rate_interval]))",
          "interval": "1m",
          "intervalFactor": 2,
          "legendFormat": "Data Sent [{{bucket}}]",
          "refId": "A"
        }
      ],
      "title": "Total Data Sent Rate",
      "type": "timeseries"
    },
    {
      "datasource": {