"uid": "${DS_PROMETHEUS}"
          },
          "exemplar": true,
          "expr": "time() - max(minio_node_process_starttime_seconds{job=~\"$scrape_jobs\"})",
          "format": "time_series",
          "instant": true,
          "interval": "",
          "intervalFactor": 1,
          "legendFormat": "{{instance}}",
          "metric": "process_start_time_seconds",
          "refId": "A",

assertEquals("application/json; charset=utf-8", request.getHeader("Content-Type"));
assertEquals("{}", request.getBody().readUtf8());
```

#### Dispatcher

By default MockWebServer uses a queue to specify a series of responses. Use a
Dispatcher (`import okhttp3.mockwebserver.Dispatcher`) to handle requests using another policy. One natural policy is to
dispatch on the request path.

    title: How to monitor FastAPI application performance using Python agent
  - author: Jean-Baptiste Rocher
    author_link: https://hashnode.com/@jibrocher
    link: https://dev.indooroutdoor.io/series/fastapi-react-poll-app
    title: Building the Poll App From Django Tutorial With FastAPI And React
  - author: Silvan Melchior
    author_link: https://github.com/silvanmelchior

// Exactly one "target" type should be set.
message ExternalMetricSource {
  // metricName is the name of the metric in question.
  optional string metricName = 1;

  // metricSelector is used to identify a specific time series
  // within a given metric.
  // +optional
  optional k8s.io.apimachinery.pkg.apis.meta.v1.LabelSelector metricSelector = 2;

  // targetValue is the target value of the metric (as a quantity).

          },
          "editorMode": "code",
          "exemplar": false,
          "expr": "max(minio_node_drive_online_total{job=~\"$scrape_jobs\",server=\"$server\"})",
          "format": "time_series",
          "hide": false,
          "instant": true,
          "interval": "",
          "intervalFactor": 1,
          "legendFormat": ".",
          "metric": "process_start_time_seconds",

		Long: `
Prints the metrics for the specified service(s) when running in Kubernetes.

This command finds a Prometheus pod running in the specified istio system
namespace. It then executes a series of queries per requested workload to
find the following top-level workload metrics: total requests per second,
error rate, and request latency at p50, p90, and p99 percentiles. The

        Challenge(
          schemeName,
          Collections.singletonMap<String, String>(null, peek + "=".repeat(eqCount)),
        ),
      )
      peek = null
      continue
    }

    // It's a series of parameter names and values.
    val parameters = mutableMapOf<String?, String>()
    eqCount += skipAll('='.code.toByte())
    while (true) {
      if (peek == null) {
        peek = readToken()

          "datasource": {
            "type": "prometheus",
            "uid": "${datasource}"
          },
          "expr": "sum(container_memory_working_set_bytes{container=\"istio-proxy\"})",
          "format": "time_series",
          "hide": false,
          "intervalFactor": 2,
          "legendFormat": "Total (k8s)",
          "refId": "A",
          "step": 2
        }
      ],
      "title": "Memory",

[Prometheus](https://prometheus.io) is a cloud-native monitoring platform. Prometheus offers a multi-dimensional data model with time series data identified by metric name and key/value pairs. The data collection happens via a pull model over HTTP/HTTPS. Users looking to monitor their MinIO instances can point Prometheus configuration to scrape data from following endpoints. 

      apply {
        this.socketPolicy = socketPolicy
      }

    /**
     * Throttles the request reader and response writer to sleep for the given period after each
     * series of [bytesPerPeriod] bytes are transferred. Use this to simulate network behavior.
     */
    fun throttleBody(
      bytesPerPeriod: Long,
      period: Long,
      unit: TimeUnit,
    ) = apply {