Click on the _"Configuration"_ tab to break this stage into component parts,
exposing the cause of the slowness.

image::performance/build-scan-configuration-breakdown.png[title="Build scan configuration breakdown"]

Here you can see the scripts and plugins applied to the project in descending order of how long they took to apply.
The slowest plugin and script applications are good candidates for optimization.

  window.location.search = params.toString();
}
</script>

<h1>Regions: {{.Name}}</h1>

Table of contents
<ul>
	<li><a href="#summary">Summary</a></li>
	<li><a href="#breakdown">Breakdown</a></li>
	<li><a href="#ranges">Special ranges</a></li>
</ul>

<h3 id="summary">Summary</h3>

{{ with $p := filterParams .Filter}}
<table class="summary">
	<tr>
		<td>Network wait profile:</td>

  params.set(key, value);
  window.location.search = params.toString();
}
</script>

<h1>Goroutines</h1>

Table of contents
<ul>
	<li><a href="#summary">Summary</a></li>
	<li><a href="#breakdown">Breakdown</a></li>
	<li><a href="#ranges">Special ranges</a></li>
</ul>

<h3 id="summary">Summary</h3>

<table class="summary">
	<tr>
		<td>Goroutine start location:</td>

They are suitable for complex build logic that needs to be shared across projects, builds, and teams.
You can also write them in Scala or Groovy but that is not recommended.

Here is a breakdown of all options for implementing Gradle plugins:

[cols="~,~,~,~,~"]
|===
|*#* |*Using:* |*Type:* |*The Plugin is:* |*Recommended?*

|1
|Kotlin DSL
|Script plugin

# collection of packages and supplying a -coverpkg pattern that
# matches some but not all of the collection. In addition, some of the
# packages have Go code but no tests, and other packages have tests
# but no Go code. Package breakdown:
#
# Package         Code?           Tests?          Stmts           Imports
# a               yes             yes             2               f
# b               yes             yes             1               a, d

As your build logic is evolving and changing it is also important to make sure that the cache effectiveness is not regressing.
Build scans provide a detailed performance breakdown which show you how effectively your build is using the build cache:

[.screenshot]
image::build-cache/performance-task-execution.png[]

Ideally, this column should be empty.
+
3. Focusing on a script, plugin, or lifecycle callback will show a breakdown of the tasks that were created.

[[sec:task_configuration_avoidance_pitfalls]]
=== Migration pitfalls
[[task_configuration_avoidance_pitfall_build_scan_plugin]]

          "legendFormat": "Pool {{pool}} / Set {{set}} - Status",
          "range": true,
          "refId": "E",
          "useBackend": false
        }
      ],
      "title": "Health Breakdown",
      "type": "bargauge"
    },
    {
      "datasource": {
        "type": "prometheus",
        "uid": "${DS_PROMETHEUS}"
      },
      "fieldConfig": {
        "defaults": {

}

func getAttemptsLabel(p *framework.QueuedPodInfo) string {
	// We breakdown the pod scheduling duration by attempts capped to a limit
	// to avoid ending up with a high cardinality metric.
	if p.Attempts >= 15 {
		return "15+"
	}
	return strconv.Itoa(p.Attempts)
}

		// Try again.
	}

	// We couldn't observe the expected idle time even once.
	for i, dt := range failureIdleTimes {
		t.Logf("try %2d: idle time = %.5fs\n", i+1, dt)
	}
	t.Logf("try %d breakdown:\n", len(failureIdleTimes))
	for i := range names {
		if m1[i].Value.Kind() == metrics.KindBad {
			continue
		}
		t.Logf("\t%s %0.3f\n", names[i], m2[i].Value.Float64()-m1[i].Value.Float64())
	}