From b0da0ffb3ea1dd3f0782f56a7bfd1d179aa3afac Mon Sep 17 00:00:00 2001 From: Alexander Zobnin Date: Sun, 25 Jun 2017 18:51:55 +0300 Subject: [PATCH] move timeseries processing into 'timeseries' module --- dist/datasource-zabbix/dataProcessor.js | 248 ++++------------- dist/datasource-zabbix/dataProcessor.js.map | 2 +- dist/datasource-zabbix/timeseries.js | 248 +++++++++++++++++ dist/datasource-zabbix/timeseries.js.map | 1 + dist/test/datasource-zabbix/dataProcessor.js | 232 ++-------------- dist/test/datasource-zabbix/timeseries.js | 270 +++++++++++++++++++ src/datasource-zabbix/dataProcessor.js | 234 +--------------- src/datasource-zabbix/timeseries.js | 260 ++++++++++++++++++ 8 files changed, 852 insertions(+), 643 deletions(-) create mode 100644 dist/datasource-zabbix/timeseries.js create mode 100644 dist/datasource-zabbix/timeseries.js.map create mode 100644 dist/test/datasource-zabbix/timeseries.js create mode 100644 src/datasource-zabbix/timeseries.js diff --git a/dist/datasource-zabbix/dataProcessor.js b/dist/datasource-zabbix/dataProcessor.js index 2003ea4..f5e28a2 100644 --- a/dist/datasource-zabbix/dataProcessor.js +++ b/dist/datasource-zabbix/dataProcessor.js @@ -1,118 +1,11 @@ 'use strict'; -System.register(['lodash', './utils'], function (_export, _context) { +System.register(['lodash', './utils', './timeseries'], function (_export, _context) { "use strict"; - var _, utils, metricFunctions, aggregationFunctions; + var _, utils, ts, downsampleSeries, groupBy, sumSeries, scale, delta, SUM, COUNT, AVERAGE, MIN, MAX, MEDIAN, metricFunctions, aggregationFunctions; - /** - * 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 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: [[, ], ...] - */ - function groupBy(interval, groupByCallback, datapoints) { - var ms_interval = utils.parseInterval(interval); - - // Calculate frame timestamps - var frames = _.groupBy(datapoints, function (point) { - // Calculate time for group of points - return Math.floor(point[1] / ms_interval) * ms_interval; - }); - - // frame: { '': [[, ], ...] } - // return [{ '': }, { '': }, ...] - var grouped = _.mapValues(frames, function (frame) { - var points = _.map(frame, function (point) { - return point[0]; - }); - return groupByCallback(points); - }); - - // Convert points to Grafana format - return sortByTime(_.map(grouped, function (value, timestamp) { - return [Number(value), Number(timestamp)]; - })); - }function sumSeries(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 limit(order, n, orderByFunc, timeseries) { + function limit(order, n, orderByFunc, timeseries) { var orderByCallback = aggregationFunctions[orderByFunc]; var sortByIteratee = function sortByIteratee(ts) { var values = _.map(ts.datapoints, function (point) { @@ -126,31 +19,14 @@ System.register(['lodash', './utils'], function (_export, _context) { } else { return sortedTimeseries.slice(-n); } - }function SUM(values) { - var sum = 0; - _.each(values, function (value) { - sum += value; - }); - return sum; - }function COUNT(values) { - return values.length; - }function AVERAGE(values) { - var sum = 0; - _.each(values, function (value) { - sum += value; - }); - return sum / values.length; - }function MIN(values) { - return _.min(values); - }function MAX(values) { - return _.max(values); - }function MEDIAN(values) { - var sorted = _.sortBy(values); - return sorted[Math.floor(sorted.length / 2)]; - }function setAlias(alias, timeseries) { + } + + function setAlias(alias, timeseries) { timeseries.target = alias; return timeseries; - }function replaceAlias(regexp, newAlias, timeseries) { + } + + function replaceAlias(regexp, newAlias, timeseries) { var pattern = void 0; if (utils.isRegex(regexp)) { pattern = utils.buildRegex(regexp); @@ -161,105 +37,71 @@ System.register(['lodash', './utils'], function (_export, _context) { var alias = timeseries.target.replace(pattern, newAlias); timeseries.target = alias; return timeseries; - }function setAliasByRegex(alias, timeseries) { + } + + function setAliasByRegex(alias, timeseries) { timeseries.target = extractText(timeseries.target, alias); return timeseries; - }function extractText(str, pattern) { + } + + function extractText(str, pattern) { var extractPattern = new RegExp(pattern); var extractedValue = extractPattern.exec(str); extractedValue = extractedValue[0]; return extractedValue; - }function scale(factor, datapoints) { - return _.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) { + } + + function groupByWrapper(interval, groupFunc, datapoints) { var groupByCallback = aggregationFunctions[groupFunc]; return groupBy(interval, groupByCallback, datapoints); - }function aggregateByWrapper(interval, aggregateFunc, datapoints) { + } + + function aggregateByWrapper(interval, aggregateFunc, datapoints) { // Flatten all points in frame and then just use groupBy() var flattenedPoints = _.flatten(datapoints, true); var groupByCallback = aggregationFunctions[aggregateFunc]; return groupBy(interval, groupByCallback, flattenedPoints); - }function aggregateWrapper(groupByCallback, interval, datapoints) { - var flattenedPoints = _.flatten(datapoints, true); - return groupBy(interval, groupByCallback, flattenedPoints); - }function sortByTime(series) { - return _.sortBy(series, function (point) { - return point[1]; - }); } - /** - * Interpolate series with gaps - */ - function interpolateSeries(series) { - var left, right; + function aggregateWrapper(groupByCallback, interval, datapoints) { + var flattenedPoints = _.flatten(datapoints, true); + return groupBy(interval, groupByCallback, flattenedPoints); + } - // 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; - }function timeShift(interval, range) { + function timeShift(interval, range) { var shift = utils.parseTimeShiftInterval(interval) / 1000; return _.map(range, function (time) { return time - shift; }); - }function unShiftTimeSeries(interval, datapoints) { + } + + function unShiftTimeSeries(interval, datapoints) { var unshift = utils.parseTimeShiftInterval(interval); return _.map(datapoints, function (dp) { return [dp[0], dp[1] + unshift]; }); - }return { + } + + return { setters: [function (_lodash) { _ = _lodash.default; }, function (_utils) { utils = _utils; + }, function (_timeseries) { + ts = _timeseries.default; }], execute: function () { + downsampleSeries = ts.downsample; + groupBy = ts.groupBy; + sumSeries = ts.sumSeries; + scale = ts.scale; + delta = ts.delta; + SUM = ts.SUM; + COUNT = ts.COUNT; + AVERAGE = ts.AVERAGE; + MIN = ts.MIN; + MAX = ts.MAX; + MEDIAN = ts.MEDIAN; metricFunctions = { groupBy: groupByWrapper, scale: scale, diff --git a/dist/datasource-zabbix/dataProcessor.js.map b/dist/datasource-zabbix/dataProcessor.js.map index 5b9195b..bd825df 100644 --- a/dist/datasource-zabbix/dataProcessor.js.map +++ b/dist/datasource-zabbix/dataProcessor.js.map @@ -1 +1 @@ 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_ from 'lodash';\nimport * as utils from './utils';\n\n/**\n * Downsample datapoints series\n */\nfunction downsampleSeries(datapoints, time_to, ms_interval, func) {\n var downsampledSeries = [];\n var timeWindow = {\n from: time_to * 1000 - ms_interval,\n to: time_to * 1000\n };\n\n var points_sum = 0;\n var points_num = 0;\n var value_avg = 0;\n var frame = [];\n\n for (var i = datapoints.length - 1; i >= 0; i -= 1) {\n if (timeWindow.from < datapoints[i][1] && datapoints[i][1] <= timeWindow.to) {\n points_sum += datapoints[i][0];\n points_num++;\n frame.push(datapoints[i][0]);\n }\n else {\n value_avg = points_num ? points_sum / points_num : 0;\n\n if (func === \"max\") {\n downsampledSeries.push([_.max(frame), timeWindow.to]);\n }\n else if (func === \"min\") {\n downsampledSeries.push([_.min(frame), timeWindow.to]);\n }\n\n // avg by default\n else {\n downsampledSeries.push([value_avg, timeWindow.to]);\n }\n\n // Shift time window\n timeWindow.to = timeWindow.from;\n timeWindow.from -= ms_interval;\n\n points_sum = 0;\n points_num = 0;\n frame = [];\n\n // Process point again\n i++;\n }\n }\n return downsampledSeries.reverse();\n}\n\n/**\n * Group points by given time interval\n * datapoints: [[, ], ...]\n */\nfunction groupBy(interval, groupByCallback, datapoints) {\n var ms_interval = utils.parseInterval(interval);\n\n // Calculate frame timestamps\n var frames = _.groupBy(datapoints, function(point) {\n // Calculate time for group of points\n return Math.floor(point[1] / ms_interval) * ms_interval;\n });\n\n // frame: { '': [[, ], ...] }\n // return [{ '': }, { '': }, ...]\n var grouped = _.mapValues(frames, function(frame) {\n var points = _.map(frame, function(point) {\n return point[0];\n });\n return groupByCallback(points);\n });\n\n // Convert points to Grafana format\n return sortByTime(_.map(grouped, function(value, timestamp) {\n return [Number(value), Number(timestamp)];\n }));\n}\n\nfunction sumSeries(timeseries) {\n\n // Calculate new points for interpolation\n var new_timestamps = _.uniq(_.map(_.flatten(timeseries, true), function(point) {\n return point[1];\n }));\n new_timestamps = _.sortBy(new_timestamps);\n\n var interpolated_timeseries = _.map(timeseries, function(series) {\n var timestamps = _.map(series, function(point) {\n return point[1];\n });\n var new_points = _.map(_.difference(new_timestamps, timestamps), function(timestamp) {\n return [null, timestamp];\n });\n var new_series = series.concat(new_points);\n return sortByTime(new_series);\n });\n\n _.each(interpolated_timeseries, interpolateSeries);\n\n var new_timeseries = [];\n var sum;\n for (var i = new_timestamps.length - 1; i >= 0; i--) {\n sum = 0;\n for (var j = interpolated_timeseries.length - 1; j >= 0; j--) {\n sum += interpolated_timeseries[j][i][0];\n }\n new_timeseries.push([sum, new_timestamps[i]]);\n }\n\n return sortByTime(new_timeseries);\n}\n\nfunction limit(order, n, orderByFunc, timeseries) {\n let orderByCallback = aggregationFunctions[orderByFunc];\n let sortByIteratee = (ts) => {\n let values = _.map(ts.datapoints, (point) => {\n return point[0];\n });\n return orderByCallback(values);\n };\n let sortedTimeseries = _.sortBy(timeseries, sortByIteratee);\n if (order === 'bottom') {\n return sortedTimeseries.slice(0, n);\n } else {\n return sortedTimeseries.slice(-n);\n }\n}\n\nfunction SUM(values) {\n var sum = 0;\n _.each(values, function(value) {\n sum += value;\n });\n return sum;\n}\n\nfunction COUNT(values) {\n return values.length;\n}\n\nfunction AVERAGE(values) {\n var sum = 0;\n _.each(values, function(value) {\n sum += value;\n });\n return sum / values.length;\n}\n\nfunction MIN(values) {\n return _.min(values);\n}\n\nfunction MAX(values) {\n return _.max(values);\n}\n\nfunction MEDIAN(values) {\n var sorted = _.sortBy(values);\n return sorted[Math.floor(sorted.length / 2)];\n}\n\nfunction setAlias(alias, timeseries) {\n timeseries.target = alias;\n return timeseries;\n}\n\nfunction 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newSeries;\n}\n\nfunction groupByWrapper(interval, groupFunc, datapoints) {\n var groupByCallback = aggregationFunctions[groupFunc];\n return groupBy(interval, groupByCallback, datapoints);\n}\n\nfunction aggregateByWrapper(interval, aggregateFunc, datapoints) {\n // Flatten all points in frame and then just use groupBy()\n var flattenedPoints = _.flatten(datapoints, true);\n var groupByCallback = aggregationFunctions[aggregateFunc];\n return groupBy(interval, groupByCallback, flattenedPoints);\n}\n\nfunction aggregateWrapper(groupByCallback, interval, datapoints) {\n var flattenedPoints = _.flatten(datapoints, true);\n return groupBy(interval, groupByCallback, flattenedPoints);\n}\n\nfunction sortByTime(series) {\n return _.sortBy(series, function(point) {\n return point[1];\n });\n}\n\n/**\n * Interpolate series with gaps\n */\nfunction interpolateSeries(series) {\n var left, right;\n\n // Interpolate series\n for (var i = series.length - 1; i >= 0; i--) {\n if (!series[i][0]) {\n 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_ from 'lodash';\nimport * as utils from './utils';\nimport ts from './timeseries';\n\nlet downsampleSeries = ts.downsample;\nlet groupBy = ts.groupBy;\nlet sumSeries = ts.sumSeries;\nlet scale = ts.scale;\nlet delta = ts.delta;\n\nlet SUM = ts.SUM;\nlet COUNT = ts.COUNT;\nlet AVERAGE = ts.AVERAGE;\nlet MIN = ts.MIN;\nlet MAX = ts.MAX;\nlet MEDIAN = ts.MEDIAN;\n\nfunction limit(order, n, orderByFunc, timeseries) {\n let orderByCallback = aggregationFunctions[orderByFunc];\n let sortByIteratee = (ts) => {\n let values = _.map(ts.datapoints, (point) => {\n return point[0];\n });\n return orderByCallback(values);\n };\n let sortedTimeseries = _.sortBy(timeseries, sortByIteratee);\n if (order === 'bottom') {\n return sortedTimeseries.slice(0, n);\n } else {\n return sortedTimeseries.slice(-n);\n }\n}\n\nfunction setAlias(alias, timeseries) {\n timeseries.target = alias;\n return timeseries;\n}\n\nfunction replaceAlias(regexp, newAlias, timeseries) {\n let pattern;\n if 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groupByCallback, flattenedPoints);\n}\n\nfunction aggregateWrapper(groupByCallback, interval, datapoints) {\n var flattenedPoints = _.flatten(datapoints, true);\n return groupBy(interval, groupByCallback, flattenedPoints);\n}\n\nfunction timeShift(interval, range) {\n let shift = utils.parseTimeShiftInterval(interval) / 1000;\n return _.map(range, time => {\n return time - shift;\n });\n}\n\nfunction unShiftTimeSeries(interval, datapoints) {\n let unshift = utils.parseTimeShiftInterval(interval);\n return _.map(datapoints, dp => {\n return [\n dp[0],\n dp[1] + unshift\n ];\n });\n}\n\nlet metricFunctions = {\n groupBy: groupByWrapper,\n scale: scale,\n delta: delta,\n aggregateBy: aggregateByWrapper,\n average: _.partial(aggregateWrapper, AVERAGE),\n min: _.partial(aggregateWrapper, MIN),\n max: _.partial(aggregateWrapper, MAX),\n median: _.partial(aggregateWrapper, MEDIAN),\n sum: _.partial(aggregateWrapper, SUM),\n count: _.partial(aggregateWrapper, COUNT),\n sumSeries: sumSeries,\n top: _.partial(limit, 'top'),\n bottom: _.partial(limit, 'bottom'),\n timeShift: timeShift,\n setAlias: setAlias,\n setAliasByRegex: setAliasByRegex,\n replaceAlias: replaceAlias\n};\n\nlet aggregationFunctions = {\n avg: AVERAGE,\n min: MIN,\n max: MAX,\n median: MEDIAN,\n sum: SUM,\n count: COUNT\n};\n\nexport default {\n downsampleSeries: downsampleSeries,\n groupBy: groupBy,\n AVERAGE: AVERAGE,\n MIN: MIN,\n MAX: MAX,\n MEDIAN: MEDIAN,\n SUM: SUM,\n COUNT: COUNT,\n unShiftTimeSeries: unShiftTimeSeries,\n\n get aggregationFunctions() {\n return aggregationFunctions;\n },\n\n get metricFunctions() {\n return metricFunctions;\n }\n};\n"]} \ No newline at end of file diff --git a/dist/datasource-zabbix/timeseries.js b/dist/datasource-zabbix/timeseries.js new file mode 100644 index 0000000..61c8818 --- /dev/null +++ b/dist/datasource-zabbix/timeseries.js @@ -0,0 +1,248 @@ +'use strict'; + +System.register(['lodash', './utils'], function (_export, _context) { + "use strict"; + + var _, utils, exportedFunctions; + + /** + * 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([_.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: [[, ], ...] + */ + function groupBy(interval, groupByCallback, datapoints) { + var ms_interval = utils.parseInterval(interval); + + // Calculate frame timestamps + var frames = _.groupBy(datapoints, function (point) { + // Calculate time for group of points + return Math.floor(point[1] / ms_interval) * ms_interval; + }); + + // frame: { '': [[, ], ...] } + // return [{ '': }, { '': }, ...] + var grouped = _.mapValues(frames, function (frame) { + var points = _.map(frame, function (point) { + return point[0]; + }); + return groupByCallback(points); + }); + + // Convert points to Grafana format + return sortByTime(_.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 = _.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 scale(factor, datapoints) { + return _.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; + _.each(values, function (value) { + sum += value; + }); + return sum; + }function COUNT(values) { + return values.length; + }function AVERAGE(values) { + var sum = 0; + _.each(values, function (value) { + sum += value; + }); + return sum / values.length; + }function MIN(values) { + return _.min(values); + }function MAX(values) { + return _.max(values); + }function MEDIAN(values) { + var sorted = _.sortBy(values); + return sorted[Math.floor(sorted.length / 2)]; + } + + /////////////////////// + // Utility functions // + /////////////////////// + + 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] !== 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 diff --git a/dist/datasource-zabbix/timeseries.js.map b/dist/datasource-zabbix/timeseries.js.map new file mode 100644 index 0000000..7ff82aa --- /dev/null +++ b/dist/datasource-zabbix/timeseries.js.map @@ -0,0 +1 @@ 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interpolated_timeseries[j][i][0];\n }\n new_timeseries.push([sum, new_timestamps[i]]);\n }\n\n return sortByTime(new_timeseries);\n}\n\nfunction scale(factor, datapoints) {\n return _.map(datapoints, point => {\n return [\n point[0] * factor,\n point[1]\n ];\n });\n}\n\nfunction delta(datapoints) {\n let newSeries = [];\n let deltaValue;\n for (var i = 1; i < datapoints.length; i++) {\n deltaValue = datapoints[i][0] - datapoints[i - 1][0];\n newSeries.push([deltaValue, datapoints[i][1]]);\n }\n return newSeries;\n}\n\nfunction SUM(values) {\n var sum = 0;\n _.each(values, function (value) {\n sum += value;\n });\n return sum;\n}\n\nfunction COUNT(values) {\n return values.length;\n}\n\nfunction AVERAGE(values) {\n var sum = 0;\n _.each(values, function (value) {\n sum += value;\n });\n return sum / values.length;\n}\n\nfunction MIN(values) {\n return _.min(values);\n}\n\nfunction MAX(values) {\n return _.max(values);\n}\n\nfunction MEDIAN(values) {\n var sorted = _.sortBy(values);\n return sorted[Math.floor(sorted.length / 2)];\n}\n\n///////////////////////\n// Utility functions //\n///////////////////////\n\nfunction sortByTime(series) {\n return _.sortBy(series, function (point) {\n return point[1];\n });\n}\n\n/**\n * Interpolate series with gaps\n */\nfunction interpolateSeries(series) {\n var left, right;\n\n // Interpolate series\n for (var i = series.length - 1; i >= 0; i--) {\n if (!series[i][0]) {\n left = findNearestLeft(series, series[i]);\n right = findNearestRight(series, series[i]);\n if (!left) {\n left = right;\n }\n if (!right) {\n right = left;\n }\n series[i][0] = linearInterpolation(series[i][1], left, right);\n }\n }\n return series;\n}\n\nfunction linearInterpolation(timestamp, left, right) {\n if (left[1] === right[1]) {\n return (left[0] + right[0]) / 2;\n } else {\n return (left[0] + (right[0] - left[0]) / (right[1] - left[1]) * (timestamp - left[1]));\n }\n}\n\nfunction findNearestRight(series, point) {\n var point_index = _.indexOf(series, point);\n var nearestRight;\n for (var i = point_index; i < series.length; i++) {\n if (series[i][0] !== null) {\n return series[i];\n }\n }\n return nearestRight;\n}\n\nfunction findNearestLeft(series, point) {\n var point_index = _.indexOf(series, point);\n var nearestLeft;\n for (var i = point_index; i > 0; i--) {\n if (series[i][0] !== null) {\n return series[i];\n }\n }\n return nearestLeft;\n}\n\n////////////\n// Export //\n////////////\n\nconst exportedFunctions = {\n downsample,\n groupBy,\n sumSeries,\n scale,\n delta,\n SUM,\n COUNT,\n AVERAGE,\n MIN,\n MAX,\n MEDIAN\n};\n\nexport default exportedFunctions;\n"]} \ No newline at end of file diff --git a/dist/test/datasource-zabbix/dataProcessor.js b/dist/test/datasource-zabbix/dataProcessor.js index d33c7fc..316e7eb 100644 --- a/dist/test/datasource-zabbix/dataProcessor.js +++ b/dist/test/datasource-zabbix/dataProcessor.js @@ -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: [[, ], ...] - */ -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: { '': [[, ], ...] } - // return [{ '': }, { '': }, ...] - 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) { diff --git a/dist/test/datasource-zabbix/timeseries.js b/dist/test/datasource-zabbix/timeseries.js new file mode 100644 index 0000000..040eb19 --- /dev/null +++ b/dist/test/datasource-zabbix/timeseries.js @@ -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: [[, ], ...] + */ +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: { '': [[, ], ...] } + // return [{ '': }, { '': }, ...] + 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; diff --git a/src/datasource-zabbix/dataProcessor.js b/src/datasource-zabbix/dataProcessor.js index f98fc8f..79d500b 100644 --- a/src/datasource-zabbix/dataProcessor.js +++ b/src/datasource-zabbix/dataProcessor.js @@ -1,118 +1,19 @@ import _ from 'lodash'; import * as utils from './utils'; +import ts from './timeseries'; -/** - * 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 - }; +let downsampleSeries = ts.downsample; +let groupBy = ts.groupBy; +let sumSeries = ts.sumSeries; +let scale = ts.scale; +let delta = ts.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([_.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: [[, ], ...] - */ -function groupBy(interval, groupByCallback, datapoints) { - var ms_interval = utils.parseInterval(interval); - - // Calculate frame timestamps - var frames = _.groupBy(datapoints, function(point) { - // Calculate time for group of points - return Math.floor(point[1] / ms_interval) * ms_interval; - }); - - // frame: { '': [[, ], ...] } - // return [{ '': }, { '': }, ...] - var grouped = _.mapValues(frames, function(frame) { - var points = _.map(frame, function(point) { - return point[0]; - }); - return groupByCallback(points); - }); - - // Convert points to Grafana format - return sortByTime(_.map(grouped, function(value, timestamp) { - return [Number(value), Number(timestamp)]; - })); -} - -function sumSeries(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); -} +let SUM = ts.SUM; +let COUNT = ts.COUNT; +let AVERAGE = ts.AVERAGE; +let MIN = ts.MIN; +let MAX = ts.MAX; +let MEDIAN = ts.MEDIAN; function limit(order, n, orderByFunc, timeseries) { let orderByCallback = aggregationFunctions[orderByFunc]; @@ -130,39 +31,6 @@ function limit(order, n, orderByFunc, timeseries) { } } -function SUM(values) { - var sum = 0; - _.each(values, function(value) { - sum += value; - }); - return sum; -} - -function COUNT(values) { - return values.length; -} - -function AVERAGE(values) { - var sum = 0; - _.each(values, function(value) { - sum += value; - }); - return sum / values.length; -} - -function MIN(values) { - return _.min(values); -} - -function MAX(values) { - return _.max(values); -} - -function MEDIAN(values) { - var sorted = _.sortBy(values); - return sorted[Math.floor(sorted.length / 2)]; -} - function setAlias(alias, timeseries) { timeseries.target = alias; return timeseries; @@ -193,25 +61,6 @@ function extractText(str, pattern) { return extractedValue; } -function scale(factor, datapoints) { - return _.map(datapoints, point => { - return [ - point[0] * factor, - point[1] - ]; - }); -} - -function delta(datapoints) { - let newSeries = []; - let deltaValue; - 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); @@ -229,65 +78,6 @@ function aggregateWrapper(groupByCallback, interval, datapoints) { return groupBy(interval, groupByCallback, flattenedPoints); } -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] !== 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; -} - function timeShift(interval, range) { let shift = utils.parseTimeShiftInterval(interval) / 1000; return _.map(range, time => { diff --git a/src/datasource-zabbix/timeseries.js b/src/datasource-zabbix/timeseries.js new file mode 100644 index 0000000..7bae320 --- /dev/null +++ b/src/datasource-zabbix/timeseries.js @@ -0,0 +1,260 @@ +/** + * timeseries.js + * + * This module contains functions for working with time series. + */ + +import _ from 'lodash'; +import * as utils from './utils'; + +/** + * Downsample time series by using given function (avg, min, max). + */ +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([_.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: [[, ], ...] + */ +function groupBy(interval, groupByCallback, datapoints) { + var ms_interval = utils.parseInterval(interval); + + // Calculate frame timestamps + var frames = _.groupBy(datapoints, function (point) { + // Calculate time for group of points + return Math.floor(point[1] / ms_interval) * ms_interval; + }); + + // frame: { '': [[, ], ...] } + // return [{ '': }, { '': }, ...] + var grouped = _.mapValues(frames, function (frame) { + var points = _.map(frame, function (point) { + return point[0]; + }); + return groupByCallback(points); + }); + + // Convert points to Grafana format + return sortByTime(_.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 = _.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 scale(factor, datapoints) { + return _.map(datapoints, point => { + return [ + point[0] * factor, + point[1] + ]; + }); +} + +function delta(datapoints) { + let newSeries = []; + let deltaValue; + 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; + _.each(values, function (value) { + sum += value; + }); + return sum; +} + +function COUNT(values) { + return values.length; +} + +function AVERAGE(values) { + var sum = 0; + _.each(values, function (value) { + sum += value; + }); + return sum / values.length; +} + +function MIN(values) { + return _.min(values); +} + +function MAX(values) { + return _.max(values); +} + +function MEDIAN(values) { + var sorted = _.sortBy(values); + return sorted[Math.floor(sorted.length / 2)]; +} + +/////////////////////// +// Utility functions // +/////////////////////// + +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] !== 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 // +//////////// + +const exportedFunctions = { + downsample, + groupBy, + sumSeries, + scale, + delta, + SUM, + COUNT, + AVERAGE, + MIN, + MAX, + MEDIAN +}; + +export default exportedFunctions;