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Unified Diff: README

Issue 2828017: Update libmetrics docs to cover some recent questions and issues. (Closed) Base URL: ssh://git@chromiumos-git/metrics.git
Patch Set: Created 10 years, 6 months ago
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Index: README
diff --git a/README b/README
index 42193796ee428ef4a867a0549f7f4af43b868f0d..677e6b75c5fa88d6148d8189a11ba2d20c75fdbf 100644
--- a/README
+++ b/README
@@ -116,3 +116,36 @@ signals to or communicate in some alternative way with the metrics
daemon. Then the metrics daemon needs to monitor for the relevant
events and take appropriate action -- for example, aggregate data and
send the histogram samples.
+
+
+================================================================================
+FAQ
+================================================================================
+
+Q. What should my histogram's |min| and |max| values be set at?
+
+A. You should set the values to a range that covers the vast majority
+ of samples that would appear in the field. Note that samples below
+ the |min| will still be collected in the underflow bucket and
+ samples above the |max| will end up in the overflow bucket. Also,
+ the reported mean of the data will be correct regardless of the
+ range.
+
+Q. How many buckets should I use in my histogram?
+
+A. You should allocate as many buckets as necessary to perform proper
+ analysis on the collected data. Note, however, that the memory
+ allocated in Chrome for each histogram is proportional to the
+ number of buckets. Therefore, it is strongly recommended to keep
+ this number low (e.g., 50 is normal, while 100 is probably high).
+
+Q. When should I use an enumeration (linear) histogram vs. a regular
+ (exponential) histogram?
+
+A. Enumeration histograms should really be used only for sampling
+ enumerated events and, in some cases, percentages. Normally, you
+ should use a regular histogram with exponential bucket layout that
+ provides higher resolution at the low end of the range and lower
+ resolution at the high end. Regular histograms are generally used
+ for collecting performance data (e.g., timing, memory usage, power)
+ as well as aggregated event counts.
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