{ "expr": "process_virtual_memory_bytes{app=\"istiod\"}", "format": "time_series", "instant": false, "intervalFactor": 2, "legendFormat": "Virtual Memory", "refId": "I", "step": 2 }, { "expr": "process_resident_memory_bytes{app=\"istiod\"}", "format": "time_series", "intervalFactor": 2, "legendFormat": "Resident Memory", "refId": "H", "step": 2 }, { "expr": "go_memstats_heap_sys_bytes{app=\"istiod\"}", "format": "time_series", "hide": true, "intervalFactor": 2, "legendFormat": "heap sys", "refId":...

import java.util.Set;

/**
 * Filter to only retain objects in the given scope or better. This implementation allows the accumulation of multiple
 * scopes and their associated implied scopes, so that the user can filter apply a series of implication rules in a
 * single step. This should be a more efficient implementation of multiple standard {@link ScopeArtifactFilter}
 * instances ORed together.
 *
 */

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.

# Files that should be ignored by tools which do not want to consider generated
# code.
#
# https://github.com/kubernetes/test-infra/blob/master/prow/plugins/size/size.go
#
# This file is a series of lines, each of the form:
#     <type> <name>
#
# Type can be:
#    path - an exact path to a single file
#    file-name - an exact leaf filename, regardless of path
#    path-prefix - a prefix match on the file path

            "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",

// 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).

          ImmutableList.of(2, 3),
          ImmutableList.of(1, 2),
          ImmutableList.of(3, 5),
          ImmutableList.of(2, 4),
          ImmutableList.of(1, 2, 3, 5, 6, 8, 9));

  /*
   * We have a whole series of abstract test classes that "stack", so e.g. the tests for filtered
   * NavigableSets inherit the tests for filtered Iterables, Collections, Sets, and SortedSets. The
   * actual implementation tests are further down.
   */

          },
          "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",

    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

  public void add(double x, double y) {
    // We extend the recursive expression for the one-variable case at Art of Computer Programming
    // vol. 2, Knuth, 4.2.2, (16) to the two-variable case. We have two value series x_i and y_i.
    // We define the arithmetic means X_n = 1/n \sum_{i=1}^n x_i, and Y_n = 1/n \sum_{i=1}^n y_i.
    // We also define the sum of the products of the differences from the means