Add startup metrics that measure the performance of the first web contents.

chrome/browser/metrics/first_web_contents_profiler.cc

Index: chrome/browser/metrics/first_web_contents_profiler.cc
diff --git a/chrome/browser/metrics/first_web_contents_profiler.cc b/chrome/browser/metrics/first_web_contents_profiler.cc
new file mode 100644
index 0000000000000000000000000000000000000000..b1d2a90cd9cc1dd5ce8d13d95af5c1ea27d9bb76
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+++ b/chrome/browser/metrics/first_web_contents_profiler.cc
@@ -0,0 +1,112 @@
+// Copyright 2014 The Chromium Authors. All rights reserved.
+// Use of this source code is governed by a BSD-style license that can be
+// found in the LICENSE file.
+
+#include "chrome/browser/metrics/first_web_contents_profiler.h"
+
+#include "base/metrics/histogram.h"
+#include "base/process/process_info.h"
+#include "base/time/time.h"
+#include "chrome/browser/ui/browser.h"
+#include "chrome/browser/ui/browser_iterator.h"
+#include "chrome/browser/ui/tabs/tab_strip_model.h"
+
+namespace {
+
+const int kHistogramMinTimeMilliseconds = 200;
+const int kHistogramMaxTimeSeconds = 45;
+
+// Startup histograms are always bimodal. I strongly suspect that the two modes
+// correspond to devices with an SSD vs. devices with an HDD. As such, neither
+// the mean nor the median are particularly relevant.
+//
+// Most changes to startup improvement will be incremenetal, and have a
+// relatively small effect on one or both of the modes. It's important that the
+// bucket count is sufficiently large that these changes can be measured to
+// determine whether their intended effect matches the actual effect.
+//
+// I want to be able to differentiate between 1% improvements, so the
+// logarithmic difference between buckets must be 0.01.

[Ilya Sherman, 2014/11/26 22:04:08]

What is the connection between 1% and a logarithmic difference of 0.01?  Are you
interested in 1% improvements in the log space?

Moreover, I'm not convinced that you need 1%-sized deltas between consecutive
buckets in order to get 1% granularity on the means, even for a bimodal
distribution.  The best option would be to identify why the distribution is
bimodal, and split it up into two sets of unimodal histograms.  If that's not
feasible, I'd recommend at least checking with a statistician for how best to
compute the number you're interested in.  I'm going to be pretty strict in
asking you for a solid statistical basis for a time-tracking histogram that has
oodles of buckets.

+//
+// Total logarithmic difference between top/bottom buckets = lg(45 * 1000 /
+// 200) = 7.81
+// The number of buckets needed for a logarithmic difference of 0.01 is 7.81 /
+// 0.01 = 781.
+const int kHistogramBucketSize = 781;
+
+}  // namespace
+
+scoped_ptr<FirstWebContentsProfiler>
+FirstWebContentsProfiler::CreateProfilerForFirstWebContents(
+    FirstWebContentsProfilerDelegate* delegate) {
+  DCHECK(delegate);
+  for (chrome::BrowserIterator iterator; !iterator.done(); iterator.Next()) {
+    Browser* browser = *iterator;
+    content::WebContents* web_contents =
+        browser->tab_strip_model()->GetActiveWebContents();
+    if (web_contents) {
+      return scoped_ptr<FirstWebContentsProfiler>(
+          new FirstWebContentsProfiler(web_contents, delegate));
+    }
+  }
+  return nullptr;
+}
+
+FirstWebContentsProfiler::FirstWebContentsProfiler(
+    content::WebContents* web_contents,
+    FirstWebContentsProfilerDelegate* delegate)
+    : content::WebContentsObserver(web_contents),
+      collected_paint_metric_(false),
+      collected_load_metric_(false),
+      delegate_(delegate) {
+}
+
+void FirstWebContentsProfiler::DidFirstVisuallyNonEmptyPaint() {
+  if (collected_paint_metric_)
+    return;
+  collected_paint_metric_ = true;
+  const base::Time process_creation_time =
+      base::CurrentProcessInfo::CreationTime();
+  if (!process_creation_time.is_null()) {
+    base::TimeDelta elapsed = base::Time::Now() - process_creation_time;
+    UMA_HISTOGRAM_CUSTOM_TIMES(
+        "Startup.FirstWebContents.NonEmptyPaint", elapsed,
+        base::TimeDelta::FromMilliseconds(kHistogramMinTimeMilliseconds),
+        base::TimeDelta::FromSeconds(kHistogramMaxTimeSeconds),
+        kHistogramBucketSize);
+  }
+
+  if (IsFinishedCollectingMetrics())
+    FinishedCollectingMetrics();
+}
+
+void FirstWebContentsProfiler::DocumentOnLoadCompletedInMainFrame() {
+  if (collected_load_metric_)
+    return;
+  collected_load_metric_ = true;
+  const base::Time process_creation_time =
+      base::CurrentProcessInfo::CreationTime();
+  if (!process_creation_time.is_null()) {
+    base::TimeDelta elapsed = base::Time::Now() - process_creation_time;
+    UMA_HISTOGRAM_CUSTOM_TIMES(
+        "Startup.FirstWebContents.MainFrameLoad", elapsed,
+        base::TimeDelta::FromMilliseconds(kHistogramMinTimeMilliseconds),
+        base::TimeDelta::FromSeconds(kHistogramMaxTimeSeconds),
+        kHistogramBucketSize);
+  }
+
+  if (IsFinishedCollectingMetrics())
+    FinishedCollectingMetrics();
+}
+
+void FirstWebContentsProfiler::WebContentsDestroyed() {
+  FinishedCollectingMetrics();
+}
+
+bool FirstWebContentsProfiler::IsFinishedCollectingMetrics() {
+  return collected_paint_metric_ && collected_load_metric_;
+}
+
+void FirstWebContentsProfiler::FinishedCollectingMetrics() {
+  delegate_->ProfilerFinishedCollectingMetrics();
+}