Files
grafana-zabbix/plugins/datasource-zabbix/dataProcessingService.js
2016-01-29 17:00:22 +03:00

252 lines
6.9 KiB
JavaScript

define([
'angular',
'lodash',
'moment',
'./utils'
],
function (angular, _, moment, utils) {
'use strict';
var module = angular.module('grafana.services');
module.service('DataProcessingService', function() {
var self = this;
/**
* Downsample datapoints series
*/
this.downsampleSeries = function(datapoints, time_to, ms_interval, func) {
var downsampledSeries = [];
var timeWindow = {
from: time_to * 1000 - ms_interval,
to: time_to * 1000
};
var points_sum = 0;
var points_num = 0;
var value_avg = 0;
var frame = [];
for (var i = datapoints.length - 1; i >= 0; i -= 1) {
if (timeWindow.from < datapoints[i][1] && datapoints[i][1] <= timeWindow.to) {
points_sum += datapoints[i][0];
points_num++;
frame.push(datapoints[i][0]);
}
else {
value_avg = points_num ? points_sum / points_num : 0;
if (func === "max") {
downsampledSeries.push([_.max(frame), timeWindow.to]);
}
else if (func === "min") {
downsampledSeries.push([_.min(frame), timeWindow.to]);
}
// avg by default
else {
downsampledSeries.push([value_avg, timeWindow.to]);
}
// Shift time window
timeWindow.to = timeWindow.from;
timeWindow.from -= ms_interval;
points_sum = 0;
points_num = 0;
frame = [];
// Process point again
i++;
}
}
return downsampledSeries.reverse();
};
/**
* Group points by given time interval
* datapoints: [[<value>, <unixtime>], ...]
*/
this.groupBy = function(interval, groupByCallback, datapoints) {
var ms_interval = utils.parseInterval(interval);
// Calculate frame timestamps
var min_timestamp = datapoints[0][1];
var frames = _.groupBy(datapoints, function(point) {
var group_time = Math.floor(point[1] / ms_interval) * ms_interval;
// Prevent points outside of time range
if (group_time < min_timestamp) {
group_time = min_timestamp;
}
return group_time;
});
// frame: { '<unixtime>': [[<value>, <unixtime>], ...] }
// return [{ '<unixtime>': <value> }, { '<unixtime>': <value> }, ...]
var grouped = _.mapValues(frames, function(frame) {
var points = _.map(frame, function(point) {
return point[0];
});
return groupByCallback(points);
});
// Convert points to Grafana format
return _.map(grouped, function(value, timestamp) {
return [Number(value), Number(timestamp)];
});
};
this.sumSeries = function(timeseries) {
// Calculate new points for interpolation
var new_timestamps = _.uniq(_.map(_.flatten(timeseries, true), function(point) {
return point[1];
}));
new_timestamps = _.sortBy(new_timestamps);
var interpolated_timeseries = _.map(timeseries, function(series) {
var timestamps = _.map(series, function(point) {
return point[1];
});
var new_points = _.map(_.difference(new_timestamps, timestamps), function(timestamp) {
return [null, timestamp];
});
var new_series = series.concat(new_points);
return sortByTime(new_series);
});
_.each(interpolated_timeseries, interpolateSeries);
var new_timeseries = [];
var sum;
for (var i = new_timestamps.length - 1; i >= 0; i--) {
sum = 0;
for (var j = interpolated_timeseries.length - 1; j >= 0; j--) {
sum += interpolated_timeseries[j][i][0];
}
new_timeseries.push([sum, new_timestamps[i]]);
}
return sortByTime(new_timeseries);
};
function sortByTime(series) {
return _.sortBy(series, function(point) {
return point[1];
});
}
/**
* Interpolate series with gaps
*/
function interpolateSeries(series) {
var left, right;
// Interpolate series
for (var i = series.length - 1; i >= 0; i--) {
if (!series[i][0]) {
left = findNearestLeft(series, series[i]);
right = findNearestRight(series, series[i]);
if (!left) {
left = right;
}
if (!right) {
right = left;
}
series[i][0] = linearInterpolation(series[i][1], left, right);
}
}
return series;
}
function linearInterpolation(timestamp, left, right) {
if (left[1] === right[1]) {
return (left[0] + right[0]) / 2;
} else {
return (left[0] + (right[0] - left[0]) / (right[1] - left[1]) * (timestamp - left[1]));
}
}
function findNearestRight(series, point) {
var point_index = _.indexOf(series, point);
var nearestRight;
for (var i = point_index; i < series.length; i++) {
if (series[i][0]) {
return series[i];
}
}
return nearestRight;
}
function findNearestLeft(series, point) {
var point_index = _.indexOf(series, point);
var nearestLeft;
for (var i = point_index; i > 0; i--) {
if (series[i][0]) {
return series[i];
}
}
return nearestLeft;
}
this.medianBy = function(interval, timeseries) {
var flatten_series = _.flatten(timeseries, true);
return self.groupBy(interval, self.MEDIAN, flatten_series);
};
this.AVERAGE = function(values) {
var sum = 0;
_.each(values, function(value) {
sum += value;
});
return sum / values.length;
};
this.MIN = function(values) {
return _.min(values);
};
this.MAX = function(values) {
return _.max(values);
};
this.MEDIAN = function(values) {
var sorted = _.sortBy(values);
return sorted[Math.floor(sorted.length / 2)];
};
this.setAlias = function(alias, timeseries) {
timeseries.target = alias;
return timeseries;
};
this.aggregationFunctions = {
avg: this.AVERAGE,
min: this.MIN,
max: this.MAX,
median: this.MEDIAN
};
this.groupByWrapper = function(interval, groupFunc, datapoints) {
var groupByCallback = self.aggregationFunctions[groupFunc];
return self.groupBy(interval, groupByCallback, datapoints);
};
this.aggregateWrapper = function(groupByCallback, interval, datapoints) {
var flattenedPoints = _.flatten(datapoints, true);
return self.groupBy(interval, groupByCallback, flattenedPoints);
};
this.metricFunctions = {
groupBy: this.groupByWrapper,
average: _.partial(this.aggregateWrapper, this.AVERAGE),
min: _.partial(this.aggregateWrapper, this.MIN),
max: _.partial(this.aggregateWrapper, this.MAX),
sumSeries: this.sumSeries,
medianBy: this.medianBy,
setAlias: this.setAlias,
};
});
});