move timeseries processing into 'timeseries' module
This commit is contained in:
Alexander Zobnin
248
dist/datasource-zabbix/dataProcessor.js
vendored
248
dist/datasource-zabbix/dataProcessor.js
vendored
@@ -1,118 +1,11 @@
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'use strict';
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System.register(['lodash', './utils'], function (_export, _context) {
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System.register(['lodash', './utils', './timeseries'], function (_export, _context) {
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"use strict";
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var _, utils, metricFunctions, aggregationFunctions;
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var _, utils, ts, downsampleSeries, groupBy, sumSeries, scale, delta, SUM, COUNT, AVERAGE, MIN, MAX, MEDIAN, metricFunctions, aggregationFunctions;
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/**
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* Downsample datapoints series
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*/
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function downsampleSeries(datapoints, time_to, ms_interval, func) {
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var downsampledSeries = [];
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var timeWindow = {
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from: time_to * 1000 - ms_interval,
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to: time_to * 1000
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};
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var points_sum = 0;
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var points_num = 0;
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var value_avg = 0;
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var frame = [];
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for (var i = datapoints.length - 1; i >= 0; i -= 1) {
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if (timeWindow.from < datapoints[i][1] && datapoints[i][1] <= timeWindow.to) {
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points_sum += datapoints[i][0];
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points_num++;
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frame.push(datapoints[i][0]);
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} else {
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value_avg = points_num ? points_sum / points_num : 0;
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if (func === "max") {
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downsampledSeries.push([_.max(frame), timeWindow.to]);
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} else if (func === "min") {
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downsampledSeries.push([_.min(frame), timeWindow.to]);
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}
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// avg by default
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else {
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downsampledSeries.push([value_avg, timeWindow.to]);
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}
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// Shift time window
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timeWindow.to = timeWindow.from;
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timeWindow.from -= ms_interval;
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points_sum = 0;
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points_num = 0;
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frame = [];
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// Process point again
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i++;
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}
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}
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return downsampledSeries.reverse();
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}
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/**
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* Group points by given time interval
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* datapoints: [[<value>, <unixtime>], ...]
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*/
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function groupBy(interval, groupByCallback, datapoints) {
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var ms_interval = utils.parseInterval(interval);
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// Calculate frame timestamps
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var frames = _.groupBy(datapoints, function (point) {
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// Calculate time for group of points
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return Math.floor(point[1] / ms_interval) * ms_interval;
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});
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// frame: { '<unixtime>': [[<value>, <unixtime>], ...] }
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// return [{ '<unixtime>': <value> }, { '<unixtime>': <value> }, ...]
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var grouped = _.mapValues(frames, function (frame) {
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var points = _.map(frame, function (point) {
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return point[0];
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});
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return groupByCallback(points);
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});
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// Convert points to Grafana format
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return sortByTime(_.map(grouped, function (value, timestamp) {
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return [Number(value), Number(timestamp)];
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}));
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}function sumSeries(timeseries) {
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// Calculate new points for interpolation
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var new_timestamps = _.uniq(_.map(_.flatten(timeseries, true), function (point) {
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return point[1];
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}));
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new_timestamps = _.sortBy(new_timestamps);
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var interpolated_timeseries = _.map(timeseries, function (series) {
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var timestamps = _.map(series, function (point) {
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return point[1];
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});
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var new_points = _.map(_.difference(new_timestamps, timestamps), function (timestamp) {
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return [null, timestamp];
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});
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var new_series = series.concat(new_points);
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return sortByTime(new_series);
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});
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_.each(interpolated_timeseries, interpolateSeries);
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var new_timeseries = [];
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var sum;
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for (var i = new_timestamps.length - 1; i >= 0; i--) {
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sum = 0;
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for (var j = interpolated_timeseries.length - 1; j >= 0; j--) {
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sum += interpolated_timeseries[j][i][0];
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}
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new_timeseries.push([sum, new_timestamps[i]]);
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}
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return sortByTime(new_timeseries);
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}function limit(order, n, orderByFunc, timeseries) {
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function limit(order, n, orderByFunc, timeseries) {
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var orderByCallback = aggregationFunctions[orderByFunc];
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var sortByIteratee = function sortByIteratee(ts) {
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var values = _.map(ts.datapoints, function (point) {
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@@ -126,31 +19,14 @@ System.register(['lodash', './utils'], function (_export, _context) {
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} else {
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return sortedTimeseries.slice(-n);
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}
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}function SUM(values) {
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var sum = 0;
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_.each(values, function (value) {
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sum += value;
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});
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return sum;
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}function COUNT(values) {
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return values.length;
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}function AVERAGE(values) {
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var sum = 0;
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_.each(values, function (value) {
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sum += value;
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});
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return sum / values.length;
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}function MIN(values) {
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return _.min(values);
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}function MAX(values) {
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return _.max(values);
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}function MEDIAN(values) {
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var sorted = _.sortBy(values);
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return sorted[Math.floor(sorted.length / 2)];
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}function setAlias(alias, timeseries) {
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}
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function setAlias(alias, timeseries) {
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timeseries.target = alias;
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return timeseries;
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}function replaceAlias(regexp, newAlias, timeseries) {
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}
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function replaceAlias(regexp, newAlias, timeseries) {
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var pattern = void 0;
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if (utils.isRegex(regexp)) {
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pattern = utils.buildRegex(regexp);
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@@ -161,105 +37,71 @@ System.register(['lodash', './utils'], function (_export, _context) {
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var alias = timeseries.target.replace(pattern, newAlias);
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timeseries.target = alias;
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return timeseries;
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}function setAliasByRegex(alias, timeseries) {
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}
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function setAliasByRegex(alias, timeseries) {
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timeseries.target = extractText(timeseries.target, alias);
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return timeseries;
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}function extractText(str, pattern) {
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}
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function extractText(str, pattern) {
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var extractPattern = new RegExp(pattern);
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var extractedValue = extractPattern.exec(str);
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extractedValue = extractedValue[0];
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return extractedValue;
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}function scale(factor, datapoints) {
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return _.map(datapoints, function (point) {
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return [point[0] * factor, point[1]];
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});
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}function delta(datapoints) {
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var newSeries = [];
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var deltaValue = void 0;
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for (var i = 1; i < datapoints.length; i++) {
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deltaValue = datapoints[i][0] - datapoints[i - 1][0];
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newSeries.push([deltaValue, datapoints[i][1]]);
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}
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return newSeries;
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}function groupByWrapper(interval, groupFunc, datapoints) {
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}
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function groupByWrapper(interval, groupFunc, datapoints) {
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var groupByCallback = aggregationFunctions[groupFunc];
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return groupBy(interval, groupByCallback, datapoints);
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}function aggregateByWrapper(interval, aggregateFunc, datapoints) {
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}
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function aggregateByWrapper(interval, aggregateFunc, datapoints) {
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// Flatten all points in frame and then just use groupBy()
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var flattenedPoints = _.flatten(datapoints, true);
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var groupByCallback = aggregationFunctions[aggregateFunc];
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return groupBy(interval, groupByCallback, flattenedPoints);
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}function aggregateWrapper(groupByCallback, interval, datapoints) {
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var flattenedPoints = _.flatten(datapoints, true);
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return groupBy(interval, groupByCallback, flattenedPoints);
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}function sortByTime(series) {
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return _.sortBy(series, function (point) {
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return point[1];
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});
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}
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/**
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* Interpolate series with gaps
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*/
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function interpolateSeries(series) {
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var left, right;
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function aggregateWrapper(groupByCallback, interval, datapoints) {
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var flattenedPoints = _.flatten(datapoints, true);
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return groupBy(interval, groupByCallback, flattenedPoints);
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}
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// Interpolate series
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for (var i = series.length - 1; i >= 0; i--) {
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if (!series[i][0]) {
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left = findNearestLeft(series, series[i]);
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right = findNearestRight(series, series[i]);
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if (!left) {
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left = right;
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}
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if (!right) {
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right = left;
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}
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series[i][0] = linearInterpolation(series[i][1], left, right);
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}
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}
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return series;
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}function linearInterpolation(timestamp, left, right) {
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if (left[1] === right[1]) {
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return (left[0] + right[0]) / 2;
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} else {
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return left[0] + (right[0] - left[0]) / (right[1] - left[1]) * (timestamp - left[1]);
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}
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}function findNearestRight(series, point) {
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var point_index = _.indexOf(series, point);
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var nearestRight;
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for (var i = point_index; i < series.length; i++) {
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if (series[i][0] !== null) {
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return series[i];
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}
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}
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return nearestRight;
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}function findNearestLeft(series, point) {
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var point_index = _.indexOf(series, point);
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var nearestLeft;
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for (var i = point_index; i > 0; i--) {
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if (series[i][0] !== null) {
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return series[i];
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}
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}
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return nearestLeft;
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}function timeShift(interval, range) {
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function timeShift(interval, range) {
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var shift = utils.parseTimeShiftInterval(interval) / 1000;
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return _.map(range, function (time) {
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return time - shift;
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});
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}function unShiftTimeSeries(interval, datapoints) {
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}
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function unShiftTimeSeries(interval, datapoints) {
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var unshift = utils.parseTimeShiftInterval(interval);
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return _.map(datapoints, function (dp) {
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return [dp[0], dp[1] + unshift];
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});
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}return {
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}
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return {
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setters: [function (_lodash) {
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_ = _lodash.default;
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}, function (_utils) {
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utils = _utils;
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}, function (_timeseries) {
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ts = _timeseries.default;
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}],
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execute: function () {
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downsampleSeries = ts.downsample;
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groupBy = ts.groupBy;
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sumSeries = ts.sumSeries;
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scale = ts.scale;
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delta = ts.delta;
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SUM = ts.SUM;
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COUNT = ts.COUNT;
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AVERAGE = ts.AVERAGE;
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MIN = ts.MIN;
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MAX = ts.MAX;
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MEDIAN = ts.MEDIAN;
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metricFunctions = {
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groupBy: groupByWrapper,
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scale: scale,
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2
dist/datasource-zabbix/dataProcessor.js.map
vendored
2
dist/datasource-zabbix/dataProcessor.js.map
vendored
File diff suppressed because one or more lines are too long
248
dist/datasource-zabbix/timeseries.js
vendored
Normal file
248
dist/datasource-zabbix/timeseries.js
vendored
Normal file
@@ -0,0 +1,248 @@
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'use strict';
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System.register(['lodash', './utils'], function (_export, _context) {
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"use strict";
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var _, utils, exportedFunctions;
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/**
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* Downsample time series by using given function (avg, min, max).
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*/
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/**
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* timeseries.js
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*
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* This module contains functions for working with time series.
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*/
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function downsample(datapoints, time_to, ms_interval, func) {
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var downsampledSeries = [];
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var timeWindow = {
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from: time_to * 1000 - ms_interval,
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to: time_to * 1000
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};
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var points_sum = 0;
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var points_num = 0;
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var value_avg = 0;
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var frame = [];
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for (var i = datapoints.length - 1; i >= 0; i -= 1) {
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if (timeWindow.from < datapoints[i][1] && datapoints[i][1] <= timeWindow.to) {
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points_sum += datapoints[i][0];
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points_num++;
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frame.push(datapoints[i][0]);
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} else {
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value_avg = points_num ? points_sum / points_num : 0;
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if (func === "max") {
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downsampledSeries.push([_.max(frame), timeWindow.to]);
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} else if (func === "min") {
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downsampledSeries.push([_.min(frame), timeWindow.to]);
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}
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// avg by default
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else {
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downsampledSeries.push([value_avg, timeWindow.to]);
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}
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// Shift time window
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timeWindow.to = timeWindow.from;
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timeWindow.from -= ms_interval;
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points_sum = 0;
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points_num = 0;
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frame = [];
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// Process point again
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i++;
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}
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}
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return downsampledSeries.reverse();
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}
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/**
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* Group points by given time interval
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* datapoints: [[<value>, <unixtime>], ...]
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*/
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function groupBy(interval, groupByCallback, datapoints) {
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var ms_interval = utils.parseInterval(interval);
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// Calculate frame timestamps
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var frames = _.groupBy(datapoints, function (point) {
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// Calculate time for group of points
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return Math.floor(point[1] / ms_interval) * ms_interval;
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});
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// frame: { '<unixtime>': [[<value>, <unixtime>], ...] }
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// return [{ '<unixtime>': <value> }, { '<unixtime>': <value> }, ...]
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var grouped = _.mapValues(frames, function (frame) {
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var points = _.map(frame, function (point) {
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return point[0];
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});
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return groupByCallback(points);
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});
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// Convert points to Grafana format
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return sortByTime(_.map(grouped, function (value, timestamp) {
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return [Number(value), Number(timestamp)];
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}));
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}
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/**
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* Summarize set of time series into one.
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* @param {object[]} timeseries
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*/
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function sumSeries(timeseries) {
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// Calculate new points for interpolation
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var new_timestamps = _.uniq(_.map(_.flatten(timeseries, true), function (point) {
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return point[1];
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}));
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new_timestamps = _.sortBy(new_timestamps);
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var interpolated_timeseries = _.map(timeseries, function (series) {
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var timestamps = _.map(series, function (point) {
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return point[1];
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});
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var new_points = _.map(_.difference(new_timestamps, timestamps), function (timestamp) {
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return [null, timestamp];
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});
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var new_series = series.concat(new_points);
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return sortByTime(new_series);
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});
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_.each(interpolated_timeseries, interpolateSeries);
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var new_timeseries = [];
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var sum;
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for (var i = new_timestamps.length - 1; i >= 0; i--) {
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sum = 0;
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for (var j = interpolated_timeseries.length - 1; j >= 0; j--) {
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sum += interpolated_timeseries[j][i][0];
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}
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new_timeseries.push([sum, new_timestamps[i]]);
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}
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return sortByTime(new_timeseries);
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}function scale(factor, datapoints) {
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return _.map(datapoints, function (point) {
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return [point[0] * factor, point[1]];
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});
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}function delta(datapoints) {
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var newSeries = [];
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var deltaValue = void 0;
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for (var i = 1; i < datapoints.length; i++) {
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deltaValue = datapoints[i][0] - datapoints[i - 1][0];
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newSeries.push([deltaValue, datapoints[i][1]]);
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}
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return newSeries;
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}function SUM(values) {
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var sum = 0;
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_.each(values, function (value) {
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sum += value;
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});
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return sum;
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}function COUNT(values) {
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return values.length;
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}function AVERAGE(values) {
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var sum = 0;
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_.each(values, function (value) {
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sum += value;
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});
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return sum / values.length;
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}function MIN(values) {
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return _.min(values);
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}function MAX(values) {
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return _.max(values);
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}function MEDIAN(values) {
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var sorted = _.sortBy(values);
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return sorted[Math.floor(sorted.length / 2)];
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}
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///////////////////////
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// Utility functions //
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///////////////////////
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function sortByTime(series) {
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return _.sortBy(series, function (point) {
|
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return point[1];
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});
|
||||
}
|
||||
|
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/**
|
||||
* Interpolate series with gaps
|
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*/
|
||||
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] !== null) {
|
||||
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] !== null) {
|
||||
return series[i];
|
||||
}
|
||||
}
|
||||
return nearestLeft;
|
||||
}
|
||||
|
||||
////////////
|
||||
// Export //
|
||||
////////////
|
||||
|
||||
return {
|
||||
setters: [function (_lodash) {
|
||||
_ = _lodash.default;
|
||||
}, function (_utils) {
|
||||
utils = _utils;
|
||||
}],
|
||||
execute: function () {
|
||||
exportedFunctions = {
|
||||
downsample: downsample,
|
||||
groupBy: groupBy,
|
||||
sumSeries: sumSeries,
|
||||
scale: scale,
|
||||
delta: delta,
|
||||
SUM: SUM,
|
||||
COUNT: COUNT,
|
||||
AVERAGE: AVERAGE,
|
||||
MIN: MIN,
|
||||
MAX: MAX,
|
||||
MEDIAN: MEDIAN
|
||||
};
|
||||
|
||||
_export('default', exportedFunctions);
|
||||
}
|
||||
};
|
||||
});
|
||||
//# sourceMappingURL=timeseries.js.map
|
||||
1
dist/datasource-zabbix/timeseries.js.map
vendored
Normal file
1
dist/datasource-zabbix/timeseries.js.map
vendored
Normal file
File diff suppressed because one or more lines are too long
232
dist/test/datasource-zabbix/dataProcessor.js
vendored
232
dist/test/datasource-zabbix/dataProcessor.js
vendored
@@ -12,120 +12,26 @@ var _utils = require('./utils');
|
||||
|
||||
var utils = _interopRequireWildcard(_utils);
|
||||
|
||||
var _timeseries = require('./timeseries');
|
||||
|
||||
var _timeseries2 = _interopRequireDefault(_timeseries);
|
||||
|
||||
function _interopRequireWildcard(obj) { if (obj && obj.__esModule) { return obj; } else { var newObj = {}; if (obj != null) { for (var key in obj) { if (Object.prototype.hasOwnProperty.call(obj, key)) newObj[key] = obj[key]; } } newObj.default = obj; return newObj; } }
|
||||
|
||||
function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; }
|
||||
|
||||
/**
|
||||
* Downsample datapoints series
|
||||
*/
|
||||
function downsampleSeries(datapoints, time_to, ms_interval, func) {
|
||||
var downsampledSeries = [];
|
||||
var timeWindow = {
|
||||
from: time_to * 1000 - ms_interval,
|
||||
to: time_to * 1000
|
||||
};
|
||||
var downsampleSeries = _timeseries2.default.downsample;
|
||||
var groupBy = _timeseries2.default.groupBy;
|
||||
var sumSeries = _timeseries2.default.sumSeries;
|
||||
var scale = _timeseries2.default.scale;
|
||||
var delta = _timeseries2.default.delta;
|
||||
|
||||
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([_lodash2.default.max(frame), timeWindow.to]);
|
||||
} else if (func === "min") {
|
||||
downsampledSeries.push([_lodash2.default.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>], ...]
|
||||
*/
|
||||
function groupBy(interval, groupByCallback, datapoints) {
|
||||
var ms_interval = utils.parseInterval(interval);
|
||||
|
||||
// Calculate frame timestamps
|
||||
var frames = _lodash2.default.groupBy(datapoints, function (point) {
|
||||
// Calculate time for group of points
|
||||
return Math.floor(point[1] / ms_interval) * ms_interval;
|
||||
});
|
||||
|
||||
// frame: { '<unixtime>': [[<value>, <unixtime>], ...] }
|
||||
// return [{ '<unixtime>': <value> }, { '<unixtime>': <value> }, ...]
|
||||
var grouped = _lodash2.default.mapValues(frames, function (frame) {
|
||||
var points = _lodash2.default.map(frame, function (point) {
|
||||
return point[0];
|
||||
});
|
||||
return groupByCallback(points);
|
||||
});
|
||||
|
||||
// Convert points to Grafana format
|
||||
return sortByTime(_lodash2.default.map(grouped, function (value, timestamp) {
|
||||
return [Number(value), Number(timestamp)];
|
||||
}));
|
||||
}
|
||||
|
||||
function sumSeries(timeseries) {
|
||||
|
||||
// Calculate new points for interpolation
|
||||
var new_timestamps = _lodash2.default.uniq(_lodash2.default.map(_lodash2.default.flatten(timeseries, true), function (point) {
|
||||
return point[1];
|
||||
}));
|
||||
new_timestamps = _lodash2.default.sortBy(new_timestamps);
|
||||
|
||||
var interpolated_timeseries = _lodash2.default.map(timeseries, function (series) {
|
||||
var timestamps = _lodash2.default.map(series, function (point) {
|
||||
return point[1];
|
||||
});
|
||||
var new_points = _lodash2.default.map(_lodash2.default.difference(new_timestamps, timestamps), function (timestamp) {
|
||||
return [null, timestamp];
|
||||
});
|
||||
var new_series = series.concat(new_points);
|
||||
return sortByTime(new_series);
|
||||
});
|
||||
|
||||
_lodash2.default.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);
|
||||
}
|
||||
var SUM = _timeseries2.default.SUM;
|
||||
var COUNT = _timeseries2.default.COUNT;
|
||||
var AVERAGE = _timeseries2.default.AVERAGE;
|
||||
var MIN = _timeseries2.default.MIN;
|
||||
var MAX = _timeseries2.default.MAX;
|
||||
var MEDIAN = _timeseries2.default.MEDIAN;
|
||||
|
||||
function limit(order, n, orderByFunc, timeseries) {
|
||||
var orderByCallback = aggregationFunctions[orderByFunc];
|
||||
@@ -143,39 +49,6 @@ function limit(order, n, orderByFunc, timeseries) {
|
||||
}
|
||||
}
|
||||
|
||||
function SUM(values) {
|
||||
var sum = 0;
|
||||
_lodash2.default.each(values, function (value) {
|
||||
sum += value;
|
||||
});
|
||||
return sum;
|
||||
}
|
||||
|
||||
function COUNT(values) {
|
||||
return values.length;
|
||||
}
|
||||
|
||||
function AVERAGE(values) {
|
||||
var sum = 0;
|
||||
_lodash2.default.each(values, function (value) {
|
||||
sum += value;
|
||||
});
|
||||
return sum / values.length;
|
||||
}
|
||||
|
||||
function MIN(values) {
|
||||
return _lodash2.default.min(values);
|
||||
}
|
||||
|
||||
function MAX(values) {
|
||||
return _lodash2.default.max(values);
|
||||
}
|
||||
|
||||
function MEDIAN(values) {
|
||||
var sorted = _lodash2.default.sortBy(values);
|
||||
return sorted[Math.floor(sorted.length / 2)];
|
||||
}
|
||||
|
||||
function setAlias(alias, timeseries) {
|
||||
timeseries.target = alias;
|
||||
return timeseries;
|
||||
@@ -206,22 +79,6 @@ function extractText(str, pattern) {
|
||||
return extractedValue;
|
||||
}
|
||||
|
||||
function scale(factor, datapoints) {
|
||||
return _lodash2.default.map(datapoints, function (point) {
|
||||
return [point[0] * factor, point[1]];
|
||||
});
|
||||
}
|
||||
|
||||
function delta(datapoints) {
|
||||
var newSeries = [];
|
||||
var deltaValue = void 0;
|
||||
for (var i = 1; i < datapoints.length; i++) {
|
||||
deltaValue = datapoints[i][0] - datapoints[i - 1][0];
|
||||
newSeries.push([deltaValue, datapoints[i][1]]);
|
||||
}
|
||||
return newSeries;
|
||||
}
|
||||
|
||||
function groupByWrapper(interval, groupFunc, datapoints) {
|
||||
var groupByCallback = aggregationFunctions[groupFunc];
|
||||
return groupBy(interval, groupByCallback, datapoints);
|
||||
@@ -239,65 +96,6 @@ function aggregateWrapper(groupByCallback, interval, datapoints) {
|
||||
return groupBy(interval, groupByCallback, flattenedPoints);
|
||||
}
|
||||
|
||||
function sortByTime(series) {
|
||||
return _lodash2.default.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 = _lodash2.default.indexOf(series, point);
|
||||
var nearestRight;
|
||||
for (var i = point_index; i < series.length; i++) {
|
||||
if (series[i][0] !== null) {
|
||||
return series[i];
|
||||
}
|
||||
}
|
||||
return nearestRight;
|
||||
}
|
||||
|
||||
function findNearestLeft(series, point) {
|
||||
var point_index = _lodash2.default.indexOf(series, point);
|
||||
var nearestLeft;
|
||||
for (var i = point_index; i > 0; i--) {
|
||||
if (series[i][0] !== null) {
|
||||
return series[i];
|
||||
}
|
||||
}
|
||||
return nearestLeft;
|
||||
}
|
||||
|
||||
function timeShift(interval, range) {
|
||||
var shift = utils.parseTimeShiftInterval(interval) / 1000;
|
||||
return _lodash2.default.map(range, function (time) {
|
||||
|
||||
270
dist/test/datasource-zabbix/timeseries.js
vendored
Normal file
270
dist/test/datasource-zabbix/timeseries.js
vendored
Normal file
@@ -0,0 +1,270 @@
|
||||
'use strict';
|
||||
|
||||
Object.defineProperty(exports, "__esModule", {
|
||||
value: true
|
||||
});
|
||||
|
||||
var _lodash = require('lodash');
|
||||
|
||||
var _lodash2 = _interopRequireDefault(_lodash);
|
||||
|
||||
var _utils = require('./utils');
|
||||
|
||||
var utils = _interopRequireWildcard(_utils);
|
||||
|
||||
function _interopRequireWildcard(obj) { if (obj && obj.__esModule) { return obj; } else { var newObj = {}; if (obj != null) { for (var key in obj) { if (Object.prototype.hasOwnProperty.call(obj, key)) newObj[key] = obj[key]; } } newObj.default = obj; return newObj; } }
|
||||
|
||||
function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; }
|
||||
|
||||
/**
|
||||
* Downsample time series by using given function (avg, min, max).
|
||||
*/
|
||||
/**
|
||||
* timeseries.js
|
||||
*
|
||||
* This module contains functions for working with time series.
|
||||
*/
|
||||
|
||||
function downsample(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([_lodash2.default.max(frame), timeWindow.to]);
|
||||
} else if (func === "min") {
|
||||
downsampledSeries.push([_lodash2.default.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>], ...]
|
||||
*/
|
||||
function groupBy(interval, groupByCallback, datapoints) {
|
||||
var ms_interval = utils.parseInterval(interval);
|
||||
|
||||
// Calculate frame timestamps
|
||||
var frames = _lodash2.default.groupBy(datapoints, function (point) {
|
||||
// Calculate time for group of points
|
||||
return Math.floor(point[1] / ms_interval) * ms_interval;
|
||||
});
|
||||
|
||||
// frame: { '<unixtime>': [[<value>, <unixtime>], ...] }
|
||||
// return [{ '<unixtime>': <value> }, { '<unixtime>': <value> }, ...]
|
||||
var grouped = _lodash2.default.mapValues(frames, function (frame) {
|
||||
var points = _lodash2.default.map(frame, function (point) {
|
||||
return point[0];
|
||||
});
|
||||
return groupByCallback(points);
|
||||
});
|
||||
|
||||
// Convert points to Grafana format
|
||||
return sortByTime(_lodash2.default.map(grouped, function (value, timestamp) {
|
||||
return [Number(value), Number(timestamp)];
|
||||
}));
|
||||
}
|
||||
|
||||
/**
|
||||
* Summarize set of time series into one.
|
||||
* @param {object[]} timeseries
|
||||
*/
|
||||
function sumSeries(timeseries) {
|
||||
|
||||
// Calculate new points for interpolation
|
||||
var new_timestamps = _lodash2.default.uniq(_lodash2.default.map(_lodash2.default.flatten(timeseries, true), function (point) {
|
||||
return point[1];
|
||||
}));
|
||||
new_timestamps = _lodash2.default.sortBy(new_timestamps);
|
||||
|
||||
var interpolated_timeseries = _lodash2.default.map(timeseries, function (series) {
|
||||
var timestamps = _lodash2.default.map(series, function (point) {
|
||||
return point[1];
|
||||
});
|
||||
var new_points = _lodash2.default.map(_lodash2.default.difference(new_timestamps, timestamps), function (timestamp) {
|
||||
return [null, timestamp];
|
||||
});
|
||||
var new_series = series.concat(new_points);
|
||||
return sortByTime(new_series);
|
||||
});
|
||||
|
||||
_lodash2.default.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 scale(factor, datapoints) {
|
||||
return _lodash2.default.map(datapoints, function (point) {
|
||||
return [point[0] * factor, point[1]];
|
||||
});
|
||||
}
|
||||
|
||||
function delta(datapoints) {
|
||||
var newSeries = [];
|
||||
var deltaValue = void 0;
|
||||
for (var i = 1; i < datapoints.length; i++) {
|
||||
deltaValue = datapoints[i][0] - datapoints[i - 1][0];
|
||||
newSeries.push([deltaValue, datapoints[i][1]]);
|
||||
}
|
||||
return newSeries;
|
||||
}
|
||||
|
||||
function SUM(values) {
|
||||
var sum = 0;
|
||||
_lodash2.default.each(values, function (value) {
|
||||
sum += value;
|
||||
});
|
||||
return sum;
|
||||
}
|
||||
|
||||
function COUNT(values) {
|
||||
return values.length;
|
||||
}
|
||||
|
||||
function AVERAGE(values) {
|
||||
var sum = 0;
|
||||
_lodash2.default.each(values, function (value) {
|
||||
sum += value;
|
||||
});
|
||||
return sum / values.length;
|
||||
}
|
||||
|
||||
function MIN(values) {
|
||||
return _lodash2.default.min(values);
|
||||
}
|
||||
|
||||
function MAX(values) {
|
||||
return _lodash2.default.max(values);
|
||||
}
|
||||
|
||||
function MEDIAN(values) {
|
||||
var sorted = _lodash2.default.sortBy(values);
|
||||
return sorted[Math.floor(sorted.length / 2)];
|
||||
}
|
||||
|
||||
///////////////////////
|
||||
// Utility functions //
|
||||
///////////////////////
|
||||
|
||||
function sortByTime(series) {
|
||||
return _lodash2.default.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 = _lodash2.default.indexOf(series, point);
|
||||
var nearestRight;
|
||||
for (var i = point_index; i < series.length; i++) {
|
||||
if (series[i][0] !== null) {
|
||||
return series[i];
|
||||
}
|
||||
}
|
||||
return nearestRight;
|
||||
}
|
||||
|
||||
function findNearestLeft(series, point) {
|
||||
var point_index = _lodash2.default.indexOf(series, point);
|
||||
var nearestLeft;
|
||||
for (var i = point_index; i > 0; i--) {
|
||||
if (series[i][0] !== null) {
|
||||
return series[i];
|
||||
}
|
||||
}
|
||||
return nearestLeft;
|
||||
}
|
||||
|
||||
////////////
|
||||
// Export //
|
||||
////////////
|
||||
|
||||
var exportedFunctions = {
|
||||
downsample: downsample,
|
||||
groupBy: groupBy,
|
||||
sumSeries: sumSeries,
|
||||
scale: scale,
|
||||
delta: delta,
|
||||
SUM: SUM,
|
||||
COUNT: COUNT,
|
||||
AVERAGE: AVERAGE,
|
||||
MIN: MIN,
|
||||
MAX: MAX,
|
||||
MEDIAN: MEDIAN
|
||||
};
|
||||
|
||||
exports.default = exportedFunctions;
|
||||
Reference in New Issue
Block a user