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1 // Copyright 2014 The Chromium Authors. All rights reserved. | |
2 // Use of this source code is governed by a BSD-style license that can be | |
3 // found in the LICENSE file. | |
4 | |
5 #include "chrome/browser/metrics/first_web_contents_profiler.h" | |
6 | |
7 #include "base/metrics/histogram.h" | |
8 #include "base/process/process_info.h" | |
9 #include "base/time/time.h" | |
10 #include "chrome/browser/ui/browser.h" | |
11 #include "chrome/browser/ui/browser_iterator.h" | |
12 #include "chrome/browser/ui/tabs/tab_strip_model.h" | |
13 | |
14 namespace { | |
15 | |
16 const int kHistogramMinTimeMilliseconds = 200; | |
17 const int kHistogramMaxTimeSeconds = 45; | |
18 | |
19 // Startup histograms are always bimodal. I strongly suspect that the two modes | |
20 // correspond to devices with an SSD vs. devices with an HDD. As such, neither | |
21 // the mean nor the median are particularly relevant. | |
22 // | |
23 // Most changes to startup improvement will be incremenetal, and have a | |
24 // relatively small effect on one or both of the modes. It's important that the | |
25 // bucket count is sufficiently large that these changes can be measured to | |
26 // determine whether their intended effect matches the actual effect. | |
27 // | |
28 // I want to be able to differentiate between 1% improvements, so the | |
29 // logarithmic difference between buckets must be 0.01. | |
Ilya Sherman
2014/11/26 22:04:08
What is the connection between 1% and a logarithmi
| |
30 // | |
31 // Total logarithmic difference between top/bottom buckets = lg(45 * 1000 / | |
32 // 200) = 7.81 | |
33 // The number of buckets needed for a logarithmic difference of 0.01 is 7.81 / | |
34 // 0.01 = 781. | |
35 const int kHistogramBucketSize = 781; | |
36 | |
37 } // namespace | |
38 | |
39 scoped_ptr<FirstWebContentsProfiler> | |
40 FirstWebContentsProfiler::CreateProfilerForFirstWebContents( | |
41 FirstWebContentsProfilerDelegate* delegate) { | |
42 DCHECK(delegate); | |
43 for (chrome::BrowserIterator iterator; !iterator.done(); iterator.Next()) { | |
44 Browser* browser = *iterator; | |
45 content::WebContents* web_contents = | |
46 browser->tab_strip_model()->GetActiveWebContents(); | |
47 if (web_contents) { | |
48 return scoped_ptr<FirstWebContentsProfiler>( | |
49 new FirstWebContentsProfiler(web_contents, delegate)); | |
50 } | |
51 } | |
52 return nullptr; | |
53 } | |
54 | |
55 FirstWebContentsProfiler::FirstWebContentsProfiler( | |
56 content::WebContents* web_contents, | |
57 FirstWebContentsProfilerDelegate* delegate) | |
58 : content::WebContentsObserver(web_contents), | |
59 collected_paint_metric_(false), | |
60 collected_load_metric_(false), | |
61 delegate_(delegate) { | |
62 } | |
63 | |
64 void FirstWebContentsProfiler::DidFirstVisuallyNonEmptyPaint() { | |
65 if (collected_paint_metric_) | |
66 return; | |
67 collected_paint_metric_ = true; | |
68 const base::Time process_creation_time = | |
69 base::CurrentProcessInfo::CreationTime(); | |
70 if (!process_creation_time.is_null()) { | |
71 base::TimeDelta elapsed = base::Time::Now() - process_creation_time; | |
72 UMA_HISTOGRAM_CUSTOM_TIMES( | |
73 "Startup.FirstWebContents.NonEmptyPaint", elapsed, | |
74 base::TimeDelta::FromMilliseconds(kHistogramMinTimeMilliseconds), | |
75 base::TimeDelta::FromSeconds(kHistogramMaxTimeSeconds), | |
76 kHistogramBucketSize); | |
77 } | |
78 | |
79 if (IsFinishedCollectingMetrics()) | |
80 FinishedCollectingMetrics(); | |
81 } | |
82 | |
83 void FirstWebContentsProfiler::DocumentOnLoadCompletedInMainFrame() { | |
84 if (collected_load_metric_) | |
85 return; | |
86 collected_load_metric_ = true; | |
87 const base::Time process_creation_time = | |
88 base::CurrentProcessInfo::CreationTime(); | |
89 if (!process_creation_time.is_null()) { | |
90 base::TimeDelta elapsed = base::Time::Now() - process_creation_time; | |
91 UMA_HISTOGRAM_CUSTOM_TIMES( | |
92 "Startup.FirstWebContents.MainFrameLoad", elapsed, | |
93 base::TimeDelta::FromMilliseconds(kHistogramMinTimeMilliseconds), | |
94 base::TimeDelta::FromSeconds(kHistogramMaxTimeSeconds), | |
95 kHistogramBucketSize); | |
96 } | |
97 | |
98 if (IsFinishedCollectingMetrics()) | |
99 FinishedCollectingMetrics(); | |
100 } | |
101 | |
102 void FirstWebContentsProfiler::WebContentsDestroyed() { | |
103 FinishedCollectingMetrics(); | |
104 } | |
105 | |
106 bool FirstWebContentsProfiler::IsFinishedCollectingMetrics() { | |
107 return collected_paint_metric_ && collected_load_metric_; | |
108 } | |
109 | |
110 void FirstWebContentsProfiler::FinishedCollectingMetrics() { | |
111 delegate_->ProfilerFinishedCollectingMetrics(); | |
112 } | |
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