]),
  ], panelHeight=10, startY=1)
  + g.util.grid.makeGrid([
    row.new('Push Information')
    + row.withPanels([
      panels.timeSeries.xdsPushes(
        'XDS Pushes', queries.xdsPushes, |||
          Rate of XDS push operations, by type. This is incremented on a per-proxy basis.
        |||
      ),
      panels.timeSeries.base(
        'Events', queries.pilotEvents, |||
          Size of each xDS push.
        |||

            {
               "datasource": {
                  "type": "prometheus",
                  "uid": "$datasource"
               },
               "expr": "sum by (cluster, namespace, job) (\n    rate(\n        process_cpu_seconds_total{\n            cluster=~\"$cluster\",\n            namespace=~\"$namespace\",\n            job=~\"$job\"\n        }\n    [$__rate_interval])\n)\n",
               "intervalFactor": 2,

		"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,

{
  sum(query, by=[]):
    if std.length(by) == 0 then
      'sum (%s)' % [query]
    else
      'sum by (%s) (%s)' % [std.join(',', by), query],
  irate(query):
    'irate(%s[$__rate_interval])' % query,
  rate(query):
    'rate(%s[$__rate_interval])' % query,
  round(query, by=0.01):
    'round(%s, %s)' % [query, by],
  quantile(quantile, query):
    'histogram_quantile(%s, %s)' %[quantile, query],
  labels(metric_name, labels):

  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);

	isReady.Store(false)
}

// installationThrottle is a small wrapper around a rate limitter. It aims to avoid excessive writes to CNI configuration,
// and detect if there is a loop of requests, typically caused by another component constantly reverting our work.
// Where possible, the remediate steps are logged.
type installationThrottle struct {
	limiter *rate.Limiter
	hits    int
	in      *Installer
}

	mockProm := mockPromAPI{
		cannedResponse: map[string]prometheus_model.Value{
			"sum(rate(istio_requests_total{destination_workload=~\"details.*\", destination_workload_namespace=~\".*\",reporter=\"destination\"}[1m0s]))": prometheus_model.Vector{ // nolint: lll
				&prometheus_model.Sample{Value: 0.04},
			},

    row.new('Global Traffic')
    + row.withPanels([
      panels.timeSeries.statRps('Traffic Volume', queries.globalRequest, 'Total requests in the cluster'),
      panels.timeSeries.statPercent('Success Rate', queries.globalRequestSuccessRate, 'Total success rate of requests in the cluster'),
      panels.timeSeries.statRps('4xxs', queries.globalRequest4xx, 'Total 4xx requests in in the cluster'),

package bandwidth

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

import (
	"context"
	"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

	wname := parts[0]
	wns := ""
	if len(parts) > 1 {
		wns = parts[1]
	}

	rpsQuery := fmt.Sprintf(`sum(rate(%s{%s=~"%s.*", %s=~"%s.*",reporter="destination"}[%s]))`,
		reqTot, destWorkloadLabel, wname, destWorkloadNamespaceLabel, wns, duration)
	errRPSQuery := fmt.Sprintf(`sum(rate(%s{%s=~"%s.*", %s=~"%s.*",reporter="destination",response_code=~"[45][0-9]{2}"}[%s]))`,