public void CanReadv1Logs_Skip_PreStart(string logPath, int skip, int take,
int expectedHistogramCount, int expectedCombinedValueCount,
int expectedCombined999, long expectedCombinedMaxLength,
double expectedStartTime)
{
var readerStream = GetEmbeddedFileStream(logPath);
var reader = new HistogramLogReader(readerStream);
int histogramCount = 0;
long totalCount = 0;
HistogramBase accumulatedHistogram = Create(OneHourOfNanoseconds, DefaultSignificantDigits);
var histograms = reader.ReadHistograms()
.Where(h => h.StartTimeStamp >= reader.GetStartTime().MillisecondsSinceUnixEpoch())
.Skip(skip)
.Take(take);
foreach (var histogram in histograms)
{
histogramCount++;
totalCount += histogram.TotalCount;
accumulatedHistogram.Add(histogram);
}
Assert.Equal(expectedHistogramCount, histogramCount);
Assert.Equal(expectedCombinedValueCount, totalCount);
Assert.Equal(expectedCombined999, accumulatedHistogram.GetValueAtPercentile(99.9));
Assert.Equal(expectedCombinedMaxLength, accumulatedHistogram.GetMaxValue());
Assert.Equal(expectedStartTime, reader.GetStartTime().SecondsSinceUnixEpoch());
}
/// <summary>
/// Initializes a new instance of the <see cref="HdrSnapshot" /> class.
/// </summary>
/// <param name="histogram">The histogram.</param>
/// <param name="minValue">The minimum value.</param>
/// <param name="minUserValue">The minimum user value.</param>
/// <param name="maxValue">The maximum value.</param>
/// <param name="maxUserValue">The maximum user value.</param>
public HdrSnapshot(HistogramBase histogram, long minValue, string minUserValue, long maxValue, string maxUserValue)
{
_histogram = histogram;
Min = !string.IsNullOrWhiteSpace(minUserValue)
? minValue
: histogram.HighestEquivalentValue(histogram.RecordedValues().Select(hiv => hiv.ValueIteratedTo).FirstOrDefault());
MinUserValue = minUserValue;
Max = !string.IsNullOrWhiteSpace(maxUserValue) ? maxValue : _histogram.GetMaxValue();
MaxUserValue = maxUserValue;
}
public void WriteFooter(HistogramBase histogram)
{
// Calculate and output mean and std. deviation.
// Note: mean/std. deviation numbers are very often completely irrelevant when
// data is extremely non-normal in distribution (e.g. in cases of strong multi-modal
// response time distribution associated with GC pauses). However, reporting these numbers
// can be very useful for contrasting with the detailed percentile distribution
// reported by outputPercentileDistribution(). It is not at all surprising to find
// percentile distributions where results fall many tens or even hundreds of standard
// deviations away from the mean - such results simply indicate that the data sampled
// exhibits a very non-normal distribution, highlighting situations for which the std.
// deviation metric is a useless indicator.
var mean = histogram.GetMean() / _outputValueUnitScalingRatio;
var stdDeviation = histogram.GetStdDeviation() / _outputValueUnitScalingRatio;
_printStream.Write(_footerLine1FormatString, mean, stdDeviation);
_printStream.Write(_footerLine2FormatString, histogram.GetMaxValue() / _outputValueUnitScalingRatio, histogram.TotalCount);
_printStream.Write(_footerLine3FormatString, histogram.BucketCount, histogram.SubBucketCount);
}
public void WriteFooter(HistogramBase histogram)
{
// Calculate and output mean and std. deviation.
// Note: mean/std. deviation numbers are very often completely irrelevant when
// data is extremely non-normal in distribution (e.g. in cases of strong multi-modal
// response time distribution associated with GC pauses). However, reporting these numbers
// can be very useful for contrasting with the detailed percentile distribution
// reported by outputPercentileDistribution(). It is not at all surprising to find
// percentile distributions where results fall many tens or even hundreds of standard
// deviations away from the mean - such results simply indicate that the data sampled
// exhibits a very non-normal distribution, highlighting situations for which the std.
// deviation metric is a useless indicator.
var mean = histogram.GetMean() / _outputValueUnitScalingRatio;
var stdDeviation = histogram.GetStdDeviation() / _outputValueUnitScalingRatio;
_printStream.Write(_footerLine1FormatString, mean, stdDeviation);
_printStream.Write(_footerLine2FormatString, histogram.GetMaxValue() / _outputValueUnitScalingRatio, histogram.TotalCount);
_printStream.Write(_footerLine3FormatString, histogram.BucketCount, histogram.SubBucketCount);
}
public void TestScalingEquivalence()
{
Assert.AreEqual(
LongHistogram.GetMean() * 512,
ScaledHistogram.GetMean(), ScaledHistogram.GetMean() * 0.000001,
"averages should be equivalent");
Assert.AreEqual(
LongHistogram.TotalCount,
ScaledHistogram.TotalCount,
"total count should be the same");
Assert.AreEqual(
LongHistogram.LowestEquivalentValue(LongHistogram.GetValueAtPercentile(99.0)) * 512,
ScaledHistogram.LowestEquivalentValue(ScaledHistogram.GetValueAtPercentile(99.0)),
"99%'iles should be equivalent");
Assert.AreEqual(
ScaledHistogram.HighestEquivalentValue(LongHistogram.GetMaxValue() * 512),
ScaledHistogram.GetMaxValue(),
"Max should be equivalent for scaled data");
// Same for post-corrected:
Assert.AreEqual(
LongHistogram.GetMean() * 512,
ScaledHistogram.GetMean(), ScaledHistogram.GetMean() * 0.000001,
"averages should be equivalent");
Assert.AreEqual(
PostCorrectedHistogram.TotalCount,
PostCorrectedScaledHistogram.TotalCount,
"total count should be the same");
Assert.AreEqual(
PostCorrectedHistogram.LowestEquivalentValue(PostCorrectedHistogram.GetValueAtPercentile(99.0)) * 512,
PostCorrectedScaledHistogram.LowestEquivalentValue(PostCorrectedScaledHistogram.GetValueAtPercentile(99.0)),
"99%'iles should be equivalent");
Assert.AreEqual(
PostCorrectedScaledHistogram.HighestEquivalentValue(PostCorrectedHistogram.GetMaxValue() * 512),
PostCorrectedScaledHistogram.GetMaxValue(),
"Max should be equivalent for post-corrected data");
}
