},
          "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": {

               },
               "expr": "sum by (pod) (rate(istio_tcp_connections_opened_total{pod=~\"ztunnel-.*\"}[$__rate_interval]))",
               "legendFormat": "Opened ({{pod}})"
            },
            {
               "datasource": {
                  "type": "prometheus",
                  "uid": "$datasource"
               },

               },
               "expr": "sum by (pod) (rate(go_memstats_alloc_bytes_total{app=\"istiod\"}[$__rate_interval]))",
               "legendFormat": "Bytes ({{pod}})"
            },
            {
               "datasource": {
                  "type": "prometheus",
                  "uid": "$datasource"
               },
               "expr": "sum by (pod) (rate(go_memstats_mallocs_total{app=\"istiod\"}[$__rate_interval]))",

    ]),
  ], 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.
        |||

    * Autocompletamento.
    * Controllo sulle annotazioni di tipo.
* Validazione dei dati:
    * Errori chiari e automatici quando i dati sono invalidi.
    * Validazione anche per gli oggetti JSON più complessi.
* <abbr title="anche noto come: serializzazione, parsing, marshalling">Conversione</abbr> dei dati di input: da risorse esterne a dati e tipi di Python. È possibile leggere da:
    * JSON.
    * Path parameters.

            {
               "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);

	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},
			},