move timeseries processing into 'timeseries' module
This commit is contained in:
Alexander Zobnin
232
dist/test/datasource-zabbix/dataProcessor.js
vendored
232
dist/test/datasource-zabbix/dataProcessor.js
vendored
@@ -12,120 +12,26 @@ var _utils = require('./utils');
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var utils = _interopRequireWildcard(_utils);
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var _timeseries = require('./timeseries');
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var _timeseries2 = _interopRequireDefault(_timeseries);
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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; } }
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function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; }
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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 downsampleSeries = _timeseries2.default.downsample;
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var groupBy = _timeseries2.default.groupBy;
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var sumSeries = _timeseries2.default.sumSeries;
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var scale = _timeseries2.default.scale;
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var delta = _timeseries2.default.delta;
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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([_lodash2.default.max(frame), timeWindow.to]);
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} else if (func === "min") {
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downsampledSeries.push([_lodash2.default.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 = _lodash2.default.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 = _lodash2.default.mapValues(frames, function (frame) {
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var points = _lodash2.default.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(_lodash2.default.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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function sumSeries(timeseries) {
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// Calculate new points for interpolation
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var new_timestamps = _lodash2.default.uniq(_lodash2.default.map(_lodash2.default.flatten(timeseries, true), function (point) {
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return point[1];
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}));
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new_timestamps = _lodash2.default.sortBy(new_timestamps);
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var interpolated_timeseries = _lodash2.default.map(timeseries, function (series) {
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var timestamps = _lodash2.default.map(series, function (point) {
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return point[1];
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});
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var new_points = _lodash2.default.map(_lodash2.default.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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_lodash2.default.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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}
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var SUM = _timeseries2.default.SUM;
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var COUNT = _timeseries2.default.COUNT;
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var AVERAGE = _timeseries2.default.AVERAGE;
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var MIN = _timeseries2.default.MIN;
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var MAX = _timeseries2.default.MAX;
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var MEDIAN = _timeseries2.default.MEDIAN;
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function limit(order, n, orderByFunc, timeseries) {
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var orderByCallback = aggregationFunctions[orderByFunc];
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@@ -143,39 +49,6 @@ function limit(order, n, orderByFunc, timeseries) {
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}
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}
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function SUM(values) {
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var sum = 0;
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_lodash2.default.each(values, function (value) {
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sum += value;
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});
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return sum;
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}
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function COUNT(values) {
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return values.length;
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}
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function AVERAGE(values) {
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var sum = 0;
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_lodash2.default.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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}
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function MIN(values) {
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return _lodash2.default.min(values);
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}
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function MAX(values) {
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return _lodash2.default.max(values);
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}
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function MEDIAN(values) {
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var sorted = _lodash2.default.sortBy(values);
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return sorted[Math.floor(sorted.length / 2)];
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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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@@ -206,22 +79,6 @@ function extractText(str, pattern) {
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return extractedValue;
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}
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function scale(factor, datapoints) {
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return _lodash2.default.map(datapoints, function (point) {
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return [point[0] * factor, point[1]];
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});
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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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}
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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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@@ -239,65 +96,6 @@ function aggregateWrapper(groupByCallback, interval, datapoints) {
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return groupBy(interval, groupByCallback, flattenedPoints);
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}
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function sortByTime(series) {
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return _lodash2.default.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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// 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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}
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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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}
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function findNearestRight(series, point) {
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var point_index = _lodash2.default.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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}
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function findNearestLeft(series, point) {
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var point_index = _lodash2.default.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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}
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function timeShift(interval, range) {
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var shift = utils.parseTimeShiftInterval(interval) / 1000;
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return _lodash2.default.map(range, function (time) {
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