}
            sendBlockedResponse(httpResponse);
            return;
        }

        // Check rate limit
        if (!rateLimitHelper.allowRequest(clientIp)) {
            if (logger.isDebugEnabled()) {
                logger.debug("Rate limit exceeded: clientIp={}", clientIp);
            }
            sendRateLimitResponse(httpResponse, rateLimitHelper.getRetryAfterSeconds());

		"Average replication data transfer rate in bytes/sec")
	replicationCurrentActiveWorkersMD = NewGaugeMD(replicationCurrentActiveWorkers,
		"Total number of active replication workers")
	replicationCurrentDataTransferRateMD = NewGaugeMD(replicationCurrentDataTransferRate,
		"Current replication data transfer rate in bytes/sec")
	replicationLastMinuteQueuedBytesMD = NewGaugeMD(replicationLastMinuteQueuedBytes,

        if (logger.isInfoEnabled()) {
            logger.info("RateLimitHelper initialized: windowMs={}, blockDurationMs={}", windowMs, blockDurationMs);
        }
    }

    /**
     * Check if rate limiting is enabled.
     * @return true if rate limiting is enabled
     */
    public boolean isEnabled() {
        return ComponentUtil.getFessConfig().isRateLimitEnabled();
    }

    /**

package bandwidth

//go:generate msgp -file=$GOFILE -unexported

import (
	"context"
	"slices"
	"sync"
	"time"

	"golang.org/x/time/rate"
)

//msgp:ignore bucketThrottle Monitor

type bucketThrottle struct {
	*rate.Limiter
	NodeBandwidthPerSec int64
}

// Monitor holds the state of the global bucket monitor
type Monitor struct {
	tlock sync.RWMutex // mutex for bucket throttling

  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);

    }

    @Test
    public void test_constructorWithMessageAndErrorCode() {
        final LlmException exception = new LlmException("Rate limit exceeded", LlmException.ERROR_RATE_LIMIT);
        assertEquals("Rate limit exceeded", exception.getMessage());
        assertEquals(LlmException.ERROR_RATE_LIMIT, exception.getErrorCode());
        assertNull(exception.getCause());
    }

    @Test

 *       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,

    }

    /**
     * Increment error counter
     */
    public void incrementErrors() {
        errors.incrementAndGet();
    }

    /**
     * Get error rate (errors / total requests)
     *
     * @return error rate between 0.0 and 1.0
     */
    public double getErrorRate() {
        long total = requestsSent.get();
        if (total == 0)
            return 0.0;

  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}, "