},
          "exemplar": true,
          "expr": "sum by (server) (rate(minio_s3_traffic_received_bytes{job=~\"$scrape_jobs\"}[$__rate_interval]))",
          "interval": "1m",
          "intervalFactor": 2,
          "legendFormat": "Data Received [{{server}}]",
          "refId": "A"
        }
      ],
      "title": "S3 API Ingress Rate ",
      "type": "timeseries"
    },
    {
      "datasource": {

		"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,

  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);

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

    }

    /**
     * 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;

# https://github.com/apache/lucene-solr/blob/master/lucene/analysis/common/src/resources/org/apache/lucene/analysis/eu/stopwords.txt
al
anitz
arabera
asko
baina
bat
batean
batek
bati
batzuei
batzuek
batzuetan
batzuk
bera
beraiek
berau
berauek
bere
berori
beroriek
beste
bezala
da
dago
dira
ditu
du
dute
edo
egin
ere
eta
eurak
ez
gainera
gu
gutxi
guzti

	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++ {

    @Test
    void testFormatRateEdgeCases() {
        FileSizeFormat format = new FileSizeFormat();

        // Test zero rate
        MessageBuilder builder = new DefaultMessageBuilder();
        format.formatRate(builder, 0.0);
        assertEquals("0.0 B/s", builder.build());

        // Test rate at exactly 1000 (1 kB/s)
        builder = new DefaultMessageBuilder();
        format.formatRate(builder, 1000.0);

     */
    int[] getSupportedAlgorithms();

    /**
     * Estimates the compression ratio for the given data and algorithm.
     * This can be used to decide whether compression is worthwhile.
     *
     * @param data the data to analyze
     * @param algorithm the compression algorithm
     * @return estimated compression ratio (0.0 to 1.0, where 0.5 means 50% size reduction)
     */

        cookey(kn, KnL);
    }

    private void cookey(final int[] raw, final int KnL[]) {
        int raw0, raw1;
        int rawi, KnLi;
        int i;

        for (i = 0, rawi = 0, KnLi = 0; i < 16; ++i) {
            raw0 = raw[rawi];
            rawi++;
            raw1 = raw[rawi++];
            KnL[KnLi] = (raw0 & 0x00fc0000) << 6;
            KnL[KnLi] |= (raw0 & 0x00000fc0) << 10;