Graphite，显示数据时的平均算法
Graphite, averaging algorithm while displaying data
我每秒都在保存数据。
这里是碳时间序列的定义：
cat /etc/carbon/storage-schemas.conf
[requests]
pattern = ^requests\.
retentions = 1s:7d
出于测试目的，我向 Graphite 服务器发送了 step 函数的值：
setInterval(function() {
graphite.put('step', Math.round(Math.random()) );
}, 1000);
该函数以随机方式生成 1 或 0，并将其放在 requests.beryllium.step 目标上。
这是显示 1 分钟的石墨图：
/render?width=400&from=-2minutes&until=-1minute&height=250&target=requests.beryllium.step&_uniq=0.06224050732088515&title=requests.beryllium.step
图表上有预期的 60 个数据点。我通过获取 json 数据进行了检查：
/render?width=400&from=-2minutes&until=-1minute&height=250&target=requests.beryllium.step&_uniq=0.06224050732088515&title=requests.beryllium.step&format=json
结果（数组中的 60 个点，选中）：
[{"target": "requests.beryllium.step", "datapoints": [[1.0, 1472502764], [0.0, 1472502765], [1.0, 1472502766], [0.0, 1472502767], [1.0, 1472502768], [0.0, 1472502769], [1.0, 1472502770], [0.0, 1472502771], [1.0, 1472502772], [1.0, 1472502773], [0.0, 1472502774], [1.0, 1472502775], [0.0, 1472502776], [1.0, 1472502777], [1.0, 1472502778], [0.0, 1472502779], [1.0, 1472502780], [0.0, 1472502781], [1.0, 1472502782], [1.0, 1472502783], [0.0, 1472502784], [1.0, 1472502785], [0.0, 1472502786], [1.0, 1472502787], [0.0, 1472502788], [0.0, 1472502789], [1.0, 1472502790], [1.0, 1472502791], [0.0, 1472502792], [1.0, 1472502793], [0.0, 1472502794], [1.0, 1472502795], [0.0, 1472502796], [1.0, 1472502797], [0.0, 1472502798], [1.0, 1472502799], [1.0, 1472502800], [0.0, 1472502801], [0.0, 1472502802], [0.0, 1472502803], [1.0, 1472502804], [1.0, 1472502805], [1.0, 1472502806], [1.0, 1472502807], [1.0, 1472502808], [0.0, 1472502809], [0.0, 1472502810], [1.0, 1472502811], [1.0, 1472502812], [1.0, 1472502813], [0.0, 1472502814], [1.0, 1472502815], [1.0, 1472502816], [0.0, 1472502817], [0.0, 1472502818], [0.0, 1472502819], [0.0, 1472502820], [0.0, 1472502821], [0.0, 1472502822], [1.0, 1472502823]]}]
所有数据点都是零或一，这也是我们在图表上看到的。至此，一切正常。
现在，我用 15 分钟画了一张图：
/render?width=400&from=-16minutes&until=-1minute&height=250&target=requests.beryllium.step&_uniq=0.06224050732088515&title=requests.beryllium.step&format=png
在图表上我们可以看到不是 1 或 0 的点。让我们检查点数组中的内容：
/render?width=400&from=-16minutes&until=-1minute&height=250&target=requests.beryllium.step&_uniq=0.06224050732088515&title=requests.beryllium.step&format=json
结果：
[{"target": "requests.beryllium.step", "datapoints": [[1.0, 1472502324], [0.0, 1472502325], [0.0, 1472502326], [0.0, 1472502327], [1.0, 1472502328], [1.0, 1472502329], [1.0, 1472502330], [0.0, 1472502331], [1.0, 1472502332], [1.0, 1472502333], [0.0, 1472502334], [1.0, 1472502335], [0.0, 1472502336], [0.0, 1472502337], [0.0, 1472502338], [0.0, 1472502339], [0.0, 1472502340], [1.0, 1472502341], [1.0, 1472502342], [1.0, 1472502343], [1.0, 1472502344], [0.0, 1472502345], [1.0, 1472502346], [1.0, 1472502347], [1.0, 1472502348], [1.0, 1472502349], [1.0, 1472502350], [0.0, 1472502351], [0.0, 1472502352], [1.0, 1472502353], [1.0, 1472502354], [0.0, 1472502355], [1.0, 1472502356], [1.0, 1472502357], [0.0, 1472502358], [0.0, 1472502359], [1.0, 1472502360], [1.0, 1472502361], [1.0, 1472502362], [1.0, 1472502363], [1.0, 1472502364], [0.0, 1472502365], [0.0, 1472502366], [1.0, 1472502367], [0.0, 1472502368], [0.0, 1472502369], [0.0, 1472502370], [0.0, 1472502371], [1.0, 1472502372], [1.0, 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[1.0, 1472503108], [0.0, 1472503109], [1.0, 1472503110], [0.0, 1472503111], [0.0, 1472503112], [1.0, 1472503113], [0.0, 1472503114], [0.0, 1472503115], [1.0, 1472503116], [0.0, 1472503117], [0.0, 1472503118], [1.0, 1472503119], [1.0, 1472503120], [0.0, 1472503121], [1.0, 1472503122], [1.0, 1472503123], [0.0, 1472503124], [0.0, 1472503125], [1.0, 1472503126], [1.0, 1472503127], [0.0, 1472503128], [0.0, 1472503129], [1.0, 1472503130], [1.0, 1472503131], [0.0, 1472503132], [1.0, 1472503133], [0.0, 1472503134], [1.0, 1472503135], [1.0, 1472503136], [1.0, 1472503137], [1.0, 1472503138], [0.0, 1472503139], [1.0, 1472503140], [0.0, 1472503141], [0.0, 1472503142], [0.0, 1472503143], [0.0, 1472503144], [0.0, 1472503145], [1.0, 1472503146], [0.0, 1472503147], [1.0, 1472503148], [1.0, 1472503149], [0.0, 1472503150], [1.0, 1472503151], [0.0, 1472503152], [0.0, 1472503153], [0.0, 1472503154], [0.0, 1472503155], [1.0, 1472503156], [1.0, 1472503157], [1.0, 1472503158], [0.0, 1472503159], [1.0, 1472503160], [1.0, 1472503161], [1.0, 1472503162], [1.0, 1472503163], [1.0, 1472503164], [0.0, 1472503165], [1.0, 1472503166], [0.0, 1472503167], [0.0, 1472503168], [1.0, 1472503169], [0.0, 1472503170], [0.0, 1472503171], [0.0, 1472503172], [1.0, 1472503173], [0.0, 1472503174], [1.0, 1472503175], [1.0, 1472503176], [1.0, 1472503177], [1.0, 1472503178], [0.0, 1472503179], [1.0, 1472503180], [1.0, 1472503181], [0.0, 1472503182], [0.0, 1472503183], [1.0, 1472503184], [1.0, 1472503185], [0.0, 1472503186], [1.0, 1472503187], [0.0, 1472503188], [1.0, 1472503189], [1.0, 1472503190], [1.0, 1472503191], [0.0, 1472503192], [0.0, 1472503193], [0.0, 1472503194], [1.0, 1472503195], [0.0, 1472503196], [0.0, 1472503197], [0.0, 1472503198], [0.0, 1472503199], [0.0, 1472503200], [1.0, 1472503201], [0.0, 1472503202], [0.0, 1472503203], [1.0, 1472503204], [0.0, 1472503205], [1.0, 1472503206], [0.0, 1472503207], [1.0, 1472503208], [0.0, 1472503209], [0.0, 1472503210], [0.0, 1472503211], [0.0, 1472503212], [0.0, 1472503213], [0.0, 1472503214], [0.0, 1472503215], [1.0, 1472503216], [0.0, 1472503217], [1.0, 1472503218], [0.0, 1472503219], [0.0, 1472503220], [0.0, 1472503221], [1.0, 1472503222], [1.0, 1472503223]]}]
共有900分（15*60）。同样，我们可以看到数据点是零或只有一个。
因此，有一些平均算法在图形上显示数据，显示的点不是零或一。
我很想知道当我将时间 window 从 1 分钟更改为更宽的范围时，石墨会通过什么计算来绘制图形。
在source code之后，这里是负责计算每个像素点数的部分（最好的情况是1对1）：
def consolidateDataPoints(self):
numberOfPixels = self.graphWidth = self.area['xmax'] - self.area['xmin'] - (self.lineWidth + 1)
for series in self.data:
numberOfDataPoints = self.timeRange/series.step
minXStep = float( self.params.get('minXStep',1.0) )
divisor = self.timeRange / series.step
bestXStep = numberOfPixels / divisor
if bestXStep < minXStep:
drawableDataPoints = int( numberOfPixels / minXStep )
pointsPerPixel = math.ceil( float(numberOfDataPoints) / float(drawableDataPoints) )
series.consolidate(pointsPerPixel)
series.xStep = (numberOfPixels * pointsPerPixel) / numberOfDataPoints
else:
series.xStep = bestXStep
您可能会注意到 - 将 minXStep=NUMBER 添加到查询字符串可以扩展合并 window。 default consolidation method 是平均值。
我每秒都在保存数据。
这里是碳时间序列的定义：
cat /etc/carbon/storage-schemas.conf
[requests]
pattern = ^requests\.
retentions = 1s:7d
出于测试目的，我向 Graphite 服务器发送了 step 函数的值：
setInterval(function() {
graphite.put('step', Math.round(Math.random()) );
}, 1000);
该函数以随机方式生成 1 或 0，并将其放在 requests.beryllium.step 目标上。
这是显示 1 分钟的石墨图：
/render?width=400&from=-2minutes&until=-1minute&height=250&target=requests.beryllium.step&_uniq=0.06224050732088515&title=requests.beryllium.step
图表上有预期的 60 个数据点。我通过获取 json 数据进行了检查：
/render?width=400&from=-2minutes&until=-1minute&height=250&target=requests.beryllium.step&_uniq=0.06224050732088515&title=requests.beryllium.step&format=json
结果（数组中的 60 个点，选中）：
[{"target": "requests.beryllium.step", "datapoints": [[1.0, 1472502764], [0.0, 1472502765], [1.0, 1472502766], [0.0, 1472502767], [1.0, 1472502768], [0.0, 1472502769], [1.0, 1472502770], [0.0, 1472502771], [1.0, 1472502772], [1.0, 1472502773], [0.0, 1472502774], [1.0, 1472502775], [0.0, 1472502776], [1.0, 1472502777], [1.0, 1472502778], [0.0, 1472502779], [1.0, 1472502780], [0.0, 1472502781], [1.0, 1472502782], [1.0, 1472502783], [0.0, 1472502784], [1.0, 1472502785], [0.0, 1472502786], [1.0, 1472502787], [0.0, 1472502788], [0.0, 1472502789], [1.0, 1472502790], [1.0, 1472502791], [0.0, 1472502792], [1.0, 1472502793], [0.0, 1472502794], [1.0, 1472502795], [0.0, 1472502796], [1.0, 1472502797], [0.0, 1472502798], [1.0, 1472502799], [1.0, 1472502800], [0.0, 1472502801], [0.0, 1472502802], [0.0, 1472502803], [1.0, 1472502804], [1.0, 1472502805], [1.0, 1472502806], [1.0, 1472502807], [1.0, 1472502808], [0.0, 1472502809], [0.0, 1472502810], [1.0, 1472502811], [1.0, 1472502812], [1.0, 1472502813], [0.0, 1472502814], [1.0, 1472502815], [1.0, 1472502816], [0.0, 1472502817], [0.0, 1472502818], [0.0, 1472502819], [0.0, 1472502820], [0.0, 1472502821], [0.0, 1472502822], [1.0, 1472502823]]}]
所有数据点都是零或一，这也是我们在图表上看到的。至此，一切正常。
现在，我用 15 分钟画了一张图：
/render?width=400&from=-16minutes&until=-1minute&height=250&target=requests.beryllium.step&_uniq=0.06224050732088515&title=requests.beryllium.step&format=png
在图表上我们可以看到不是 1 或 0 的点。让我们检查点数组中的内容：
/render?width=400&from=-16minutes&until=-1minute&height=250&target=requests.beryllium.step&_uniq=0.06224050732088515&title=requests.beryllium.step&format=json
结果：
[{"target": "requests.beryllium.step", "datapoints": [[1.0, 1472502324], [0.0, 1472502325], [0.0, 1472502326], [0.0, 1472502327], [1.0, 1472502328], [1.0, 1472502329], [1.0, 1472502330], [0.0, 1472502331], [1.0, 1472502332], [1.0, 1472502333], [0.0, 1472502334], [1.0, 1472502335], [0.0, 1472502336], [0.0, 1472502337], [0.0, 1472502338], [0.0, 1472502339], [0.0, 1472502340], [1.0, 1472502341], [1.0, 1472502342], [1.0, 1472502343], [1.0, 1472502344], [0.0, 1472502345], [1.0, 1472502346], [1.0, 1472502347], [1.0, 1472502348], [1.0, 1472502349], [1.0, 1472502350], [0.0, 1472502351], [0.0, 1472502352], [1.0, 1472502353], [1.0, 1472502354], [0.0, 1472502355], [1.0, 1472502356], [1.0, 1472502357], [0.0, 1472502358], [0.0, 1472502359], [1.0, 1472502360], [1.0, 1472502361], [1.0, 1472502362], [1.0, 1472502363], [1.0, 1472502364], [0.0, 1472502365], [0.0, 1472502366], [1.0, 1472502367], [0.0, 1472502368], [0.0, 1472502369], [0.0, 1472502370], [0.0, 1472502371], [1.0, 1472502372], [1.0, 1472502373], [0.0, 1472502374], [1.0, 1472502375], [1.0, 1472502376], [0.0, 1472502377], [0.0, 1472502378], [1.0, 1472502379], [0.0, 1472502380], [0.0, 1472502381], [1.0, 1472502382], [1.0, 1472502383], [1.0, 1472502384], [1.0, 1472502385], [0.0, 1472502386], [0.0, 1472502387], [1.0, 1472502388], [1.0, 1472502389], [0.0, 1472502390], [1.0, 1472502391], [0.0, 1472502392], [1.0, 1472502393], [0.0, 1472502394], [1.0, 1472502395], [1.0, 1472502396], [1.0, 1472502397], [0.0, 1472502398], [1.0, 1472502399], [1.0, 1472502400], [0.0, 1472502401], [0.0, 1472502402], [0.0, 1472502403], [1.0, 1472502404], [1.0, 1472502405], [0.0, 1472502406], [1.0, 1472502407], [0.0, 1472502408], [1.0, 1472502409], [1.0, 1472502410], [0.0, 1472502411], [0.0, 1472502412], [0.0, 1472502413], [null, 1472502414], [0.0, 1472502415], [0.0, 1472502416], [1.0, 1472502417], [1.0, 1472502418], [1.0, 1472502419], [1.0, 1472502420], [0.0, 1472502421], [1.0, 1472502422], [1.0, 1472502423], [0.0, 1472502424], [1.0, 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共有900分（15*60）。同样，我们可以看到数据点是零或只有一个。
因此，有一些平均算法在图形上显示数据，显示的点不是零或一。
我很想知道当我将时间 window 从 1 分钟更改为更宽的范围时，石墨会通过什么计算来绘制图形。
在source code之后，这里是负责计算每个像素点数的部分（最好的情况是1对1）：
def consolidateDataPoints(self):
numberOfPixels = self.graphWidth = self.area['xmax'] - self.area['xmin'] - (self.lineWidth + 1)
for series in self.data:
numberOfDataPoints = self.timeRange/series.step
minXStep = float( self.params.get('minXStep',1.0) )
divisor = self.timeRange / series.step
bestXStep = numberOfPixels / divisor
if bestXStep < minXStep:
drawableDataPoints = int( numberOfPixels / minXStep )
pointsPerPixel = math.ceil( float(numberOfDataPoints) / float(drawableDataPoints) )
series.consolidate(pointsPerPixel)
series.xStep = (numberOfPixels * pointsPerPixel) / numberOfDataPoints
else:
series.xStep = bestXStep
您可能会注意到 - 将 minXStep=NUMBER 添加到查询字符串可以扩展合并 window。 default consolidation method 是平均值。