From 2f6da443113bf3bfee0470a1e1766b551e25d419 Mon Sep 17 00:00:00 2001 From: Alexander Zobnin Date: Sun, 25 Jun 2017 22:51:27 +0300 Subject: [PATCH] Implement groupBy with for() and measure performance about 8 times faster --- .../benchmarks/timeseries_bench.js | 50 ++++++++-- dist/datasource-zabbix/dataProcessor.js | 2 +- dist/datasource-zabbix/dataProcessor.js.map | 2 +- dist/datasource-zabbix/timeseries.js | 99 ++++++++++++++----- dist/datasource-zabbix/timeseries.js.map | 2 +- .../benchmarks/timeseries_bench.js | 10 ++ dist/test/datasource-zabbix/dataProcessor.js | 2 +- dist/test/datasource-zabbix/timeseries.js | 50 +++++++++- .../benchmarks/timeseries_bench.js | 11 +++ src/datasource-zabbix/dataProcessor.js | 2 +- src/datasource-zabbix/timeseries.js | 44 +++++++++ 11 files changed, 236 insertions(+), 38 deletions(-) diff --git a/dist/datasource-zabbix/benchmarks/timeseries_bench.js b/dist/datasource-zabbix/benchmarks/timeseries_bench.js index e678a2e..f336783 100644 --- a/dist/datasource-zabbix/benchmarks/timeseries_bench.js +++ b/dist/datasource-zabbix/benchmarks/timeseries_bench.js @@ -1,12 +1,46 @@ +import _ from 'lodash'; import ts from '../timeseries'; -let datapoints = [[]]; +let datapoints = [[10.7104, 1498409636085], [10.578, 1498409651011], [10.5985, 1498409666628], [10.6877, 1498409681525], [10.5495, 1498409696586], [10.5981, 1498409711009], [10.5076, 1498409726949], [11.4807, 1498409741853], [11.6165, 1498409756165], [11.8575, 1498409771018], [11.9936, 1498409786056], [10.7566, 1498409801942], [10.7484, 1498409816010], [10.6038, 1498409831018], [10.2932, 1498409846010], [10.4912, 1498409861946], [10.4151, 1498409876871], [10.2401, 1498409891710], [10.4921, 1498409906143], [10.4413, 1498409921477], [10.6318, 1498409936147], [10.5277, 1498409951915], [10.6333, 1498409966052], [10.6417, 1498409981944], [10.4505, 1498409996867], [10.5812, 1498410011770], [10.4934, 1498410026573], [10.5731, 1498410041317], [10.5, 1498410056213], [10.6505, 1498410071013], [9.4035, 1498410086387]]; -var fibonacci = function (n) { - return n < 2 ? n : fibonacci(n - 1) + fibonacci(n - 2); -}; +let series_set = [ + [[1.0247, 1498409631773], [0.9988, 1498409646697], [0.9817, 1498409661239], [0.9569, 1498409676045], [1.0331, 1498409691922], [1.0755, 1498409706546], [1.1862, 1498409721525], [1.2984, 1498409736175], [1.2389, 1498409751817], [1.1452, 1498409766783], [1.102, 1498409781699], [0.9647, 1498409796664], [1.0063, 1498409811627], [1.0318, 1498409826887], [1.065, 1498409841645], [1.0907, 1498409856647], [1.0229, 1498409871521], [1.0654, 1498409886031], [1.0568, 1498409901544], [1.0818, 1498409916194], [1.1335, 1498409931672], [1.057, 1498409946673], [1.0243, 1498409961669], [1.0329, 1498409976637], [1.1428, 1498409991563], [1.2198, 1498410006441], [1.2192, 1498410021230], [1.2615, 1498410036027], [1.1765, 1498410051907], [1.2352, 1498410066109], [1.0557, 1498410081043]], + [[10.7104, 1498409636085], [10.578, 1498409651011], [10.5985, 1498409666628], [10.6877, 1498409681525], [10.5495, 1498409696586], [10.5981, 1498409711009], [10.5076, 1498409726949], [11.4807, 1498409741853], [11.6165, 1498409756165], [11.8575, 1498409771018], [11.9936, 1498409786056], [10.7566, 1498409801942], [10.7484, 1498409816010], [10.6038, 1498409831018], [10.2932, 1498409846010], [10.4912, 1498409861946], [10.4151, 1498409876871], [10.2401, 1498409891710], [10.4921, 1498409906143], [10.4413, 1498409921477], [10.6318, 1498409936147], [10.5277, 1498409951915], [10.6333, 1498409966052], [10.6417, 1498409981944], [10.4505, 1498409996867], [10.5812, 1498410011770], [10.4934, 1498410026573], [10.5731, 1498410041317], [10.5, 1498410056213], [10.6505, 1498410071013], [9.4035, 1498410086387]] +]; -module.exports = function () { - fibonacci(10); - fibonacci(8); -}; +module.exports = [ + { + name: 'groupBy', + tests: { + 'groupBy(AVERAGE)': () => { + ts.groupBy(datapoints, '5m', ts.AVERAGE); + }, + 'groupBy(MAX)': () => { + ts.groupBy(datapoints, '5m', ts.COUNT); + } + } + }, + { + name: 'sumSeries', + tests: { + 'sumSeries()': () => { + ts.sumSeries(series_set); + }, + 'groupBy(MAX)->sumSeries()': () => { + let prepeared_series = _.map(series_set, datapoints => ts.groupBy(datapoints, '5m', ts.MAX)); + ts.sumSeries(prepeared_series); + } + } + }, + { + name: 'groupBy vs groupBy_perf', + tests: { + 'groupBy()': () => { + ts.groupBy(datapoints, '5m', ts.AVERAGE); + }, + 'groupBy_perf()': () => { + ts.groupBy_perf(datapoints, '5m', ts.AVERAGE); + } + } + } +]; diff --git a/dist/datasource-zabbix/dataProcessor.js b/dist/datasource-zabbix/dataProcessor.js index 0828868..73ddd46 100644 --- a/dist/datasource-zabbix/dataProcessor.js +++ b/dist/datasource-zabbix/dataProcessor.js @@ -92,7 +92,7 @@ System.register(['lodash', './utils', './timeseries'], function (_export, _conte }], execute: function () { downsampleSeries = ts.downsample; - groupBy = ts.groupBy; + groupBy = ts.groupBy_perf; sumSeries = ts.sumSeries; delta = ts.delta; diff --git a/dist/datasource-zabbix/dataProcessor.js.map b/dist/datasource-zabbix/dataProcessor.js.map index 654ddaf..00034f2 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';\nimport ts from './timeseries';\n\nlet downsampleSeries = ts.downsample;\nlet groupBy = ts.groupBy;\nlet sumSeries = ts.sumSeries;\nlet delta = ts.delta;\nlet scale = (factor, datapoints) => ts.scale(datapoints, factor);\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 (utils.isRegex(regexp)) {\n pattern = utils.buildRegex(regexp);\n } else {\n pattern = regexp;\n }\n\n let alias = timeseries.target.replace(pattern, newAlias);\n timeseries.target = alias;\n return timeseries;\n}\n\nfunction setAliasByRegex(alias, timeseries) {\n timeseries.target = extractText(timeseries.target, alias);\n return timeseries;\n}\n\nfunction extractText(str, pattern) {\n var extractPattern = new RegExp(pattern);\n var extractedValue = extractPattern.exec(str);\n extractedValue = extractedValue[0];\n return extractedValue;\n}\n\nfunction groupByWrapper(interval, groupFunc, datapoints) {\n var groupByCallback = aggregationFunctions[groupFunc];\n return groupBy(datapoints, interval, groupByCallback);\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(flattenedPoints, interval, groupByCallback);\n}\n\nfunction aggregateWrapper(groupByCallback, interval, datapoints) {\n var flattenedPoints = _.flatten(datapoints, true);\n return groupBy(flattenedPoints, interval, groupByCallback);\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 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_ from 'lodash';\nimport * as utils from './utils';\nimport ts from './timeseries';\n\nlet downsampleSeries = ts.downsample;\nlet groupBy = ts.groupBy_perf;\nlet sumSeries = ts.sumSeries;\nlet delta = ts.delta;\nlet scale = (factor, datapoints) => ts.scale(datapoints, factor);\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 (utils.isRegex(regexp)) {\n pattern = utils.buildRegex(regexp);\n } else {\n pattern = regexp;\n }\n\n let alias = timeseries.target.replace(pattern, newAlias);\n timeseries.target = alias;\n return timeseries;\n}\n\nfunction setAliasByRegex(alias, timeseries) {\n timeseries.target = extractText(timeseries.target, alias);\n return timeseries;\n}\n\nfunction extractText(str, pattern) {\n var extractPattern = new RegExp(pattern);\n var extractedValue = extractPattern.exec(str);\n extractedValue = extractedValue[0];\n return extractedValue;\n}\n\nfunction groupByWrapper(interval, groupFunc, datapoints) {\n var groupByCallback = aggregationFunctions[groupFunc];\n return groupBy(datapoints, interval, groupByCallback);\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(flattenedPoints, interval, groupByCallback);\n}\n\nfunction aggregateWrapper(groupByCallback, interval, datapoints) {\n var flattenedPoints = _.flatten(datapoints, true);\n return groupBy(flattenedPoints, interval, groupByCallback);\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 index 3601454..f917ea9 100644 --- a/dist/datasource-zabbix/timeseries.js +++ b/dist/datasource-zabbix/timeseries.js @@ -3,21 +3,11 @@ System.register(['lodash', './utils'], function (_export, _context) { "use strict"; - var _, utils, exportedFunctions; + var _, utils, POINT_VALUE, POINT_TIMESTAMP, exportedFunctions; /** * Downsample time series by using given function (avg, min, max). */ - /** - * timeseries.js - * - * This module contains functions for working with time series. - * - * datapoints - array of points where point is [value, timestamp]. In almost all cases (if other wasn't - * explicitly said) we assume datapoints are sorted by timestamp. - * - */ - function downsample(datapoints, time_to, ms_interval, func) { var downsampledSeries = []; var timeWindow = { @@ -92,6 +82,34 @@ System.register(['lodash', './utils'], function (_export, _context) { })); } + function groupBy_perf(datapoints, interval, groupByCallback) { + var ms_interval = utils.parseInterval(interval); + var grouped_series = []; + var frame_values = []; + var frame_value = void 0; + var frame_ts = datapoints.length ? getPointTimeFrame(datapoints[0][POINT_TIMESTAMP], ms_interval) : 0; + var point_frame_ts = frame_ts; + var point = void 0; + + for (var i = 0; i < datapoints.length; i++) { + point = datapoints[i]; + point_frame_ts = getPointTimeFrame(point[POINT_TIMESTAMP], ms_interval); + if (point_frame_ts === frame_ts) { + frame_values.push(point[POINT_VALUE]); + } else { + frame_value = groupByCallback(frame_values); + grouped_series.push([frame_value, frame_ts]); + frame_ts = point_frame_ts; + frame_values = [point[POINT_VALUE]]; + } + } + + frame_value = groupByCallback(frame_values); + grouped_series.push([frame_value, frame_ts]); + + return grouped_series; + } + /** * Summarize set of time series into one. * @param {datapoints[]} timeseries array of time series @@ -128,11 +146,15 @@ System.register(['lodash', './utils'], function (_export, _context) { } return sortByTime(new_timeseries); - }function scale(datapoints, factor) { + } + + function scale(datapoints, factor) { return _.map(datapoints, function (point) { return [point[0] * factor, point[1]]; }); - }function delta(datapoints) { + } + + function delta(datapoints) { var newSeries = []; var deltaValue = void 0; for (var i = 1; i < datapoints.length; i++) { @@ -140,25 +162,37 @@ System.register(['lodash', './utils'], function (_export, _context) { newSeries.push([deltaValue, datapoints[i][1]]); } return newSeries; - }function SUM(values) { + } + + function SUM(values) { var sum = 0; _.each(values, function (value) { sum += value; }); return sum; - }function COUNT(values) { + } + + function COUNT(values) { return values.length; - }function AVERAGE(values) { + } + + function AVERAGE(values) { var sum = 0; _.each(values, function (value) { sum += value; }); return sum / values.length; - }function MIN(values) { + } + + function MIN(values) { return _.min(values); - }function MAX(values) { + } + + function MAX(values) { return _.max(values); - }function MEDIAN(values) { + } + + function MEDIAN(values) { var sorted = _.sortBy(values); return sorted[Math.floor(sorted.length / 2)]; } @@ -167,6 +201,18 @@ System.register(['lodash', './utils'], function (_export, _context) { // Utility functions // /////////////////////// + /** + * For given point calculate corresponding time frame. + * + * |__*_|_*__|___*| -> |*___|*___|*___| + * + * @param {*} timestamp + * @param {*} ms_interval + */ + function getPointTimeFrame(timestamp, ms_interval) { + return Math.floor(timestamp / ms_interval) * ms_interval; + } + function sortByTime(series) { return _.sortBy(series, function (point) { return point[1]; @@ -194,13 +240,17 @@ System.register(['lodash', './utils'], function (_export, _context) { } } return series; - }function linearInterpolation(timestamp, left, right) { + } + + 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) { + } + + function findNearestRight(series, point) { var point_index = _.indexOf(series, point); var nearestRight; for (var i = point_index; i < series.length; i++) { @@ -209,7 +259,9 @@ System.register(['lodash', './utils'], function (_export, _context) { } } return nearestRight; - }function findNearestLeft(series, point) { + } + + function findNearestLeft(series, point) { var point_index = _.indexOf(series, point); var nearestLeft; for (var i = point_index; i > 0; i--) { @@ -231,9 +283,12 @@ System.register(['lodash', './utils'], function (_export, _context) { utils = _utils; }], execute: function () { + POINT_VALUE = 0; + POINT_TIMESTAMP = 1; exportedFunctions = { downsample: downsample, groupBy: groupBy, + groupBy_perf: groupBy_perf, sumSeries: sumSeries, scale: scale, delta: delta, diff --git a/dist/datasource-zabbix/timeseries.js.map b/dist/datasource-zabbix/timeseries.js.map index cd0ed97..841092b 100644 --- a/dist/datasource-zabbix/timeseries.js.map +++ b/dist/datasource-zabbix/timeseries.js.map @@ -1 +1 @@ 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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 scale(datapoints, factor) {\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 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i < datapoints.length; i++) {\n point = datapoints[i];\n point_frame_ts = getPointTimeFrame(point[POINT_TIMESTAMP], ms_interval);\n if (point_frame_ts === frame_ts) {\n frame_values.push(point[POINT_VALUE]);\n } else {\n frame_value = groupByCallback(frame_values);\n grouped_series.push([frame_value, frame_ts]);\n frame_ts = point_frame_ts;\n frame_values = [point[POINT_VALUE]];\n }\n }\n\n frame_value = groupByCallback(frame_values);\n grouped_series.push([frame_value, frame_ts]);\n\n return grouped_series;\n}\n\n/**\n * Summarize set of time series into one.\n * @param {datapoints[]} timeseries array of time series\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 scale(datapoints, factor) {\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\n/**\n * For given point calculate corresponding time frame.\n *\n * |__*_|_*__|___*| -> |*___|*___|*___|\n *\n * @param {*} timestamp\n * @param {*} ms_interval\n */\nfunction getPointTimeFrame(timestamp, ms_interval) {\n return Math.floor(timestamp / ms_interval) * ms_interval;\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 groupBy_perf,\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/benchmarks/timeseries_bench.js b/dist/test/datasource-zabbix/benchmarks/timeseries_bench.js index 5cd69b7..732b18d 100644 --- a/dist/test/datasource-zabbix/benchmarks/timeseries_bench.js +++ b/dist/test/datasource-zabbix/benchmarks/timeseries_bench.js @@ -37,4 +37,14 @@ module.exports = [{ _timeseries2.default.sumSeries(prepeared_series); } } +}, { + name: 'groupBy vs groupBy_perf', + tests: { + 'groupBy()': function groupBy() { + _timeseries2.default.groupBy(datapoints, '5m', _timeseries2.default.AVERAGE); + }, + 'groupBy_perf()': function groupBy_perf() { + _timeseries2.default.groupBy_perf(datapoints, '5m', _timeseries2.default.AVERAGE); + } + } }]; diff --git a/dist/test/datasource-zabbix/dataProcessor.js b/dist/test/datasource-zabbix/dataProcessor.js index 5c79cf3..934bbbc 100644 --- a/dist/test/datasource-zabbix/dataProcessor.js +++ b/dist/test/datasource-zabbix/dataProcessor.js @@ -21,7 +21,7 @@ function _interopRequireWildcard(obj) { if (obj && obj.__esModule) { return obj; function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } var downsampleSeries = _timeseries2.default.downsample; -var groupBy = _timeseries2.default.groupBy; +var groupBy = _timeseries2.default.groupBy_perf; var sumSeries = _timeseries2.default.sumSeries; var delta = _timeseries2.default.delta; var scale = function scale(factor, datapoints) { diff --git a/dist/test/datasource-zabbix/timeseries.js b/dist/test/datasource-zabbix/timeseries.js index 3149377..ca8c772 100644 --- a/dist/test/datasource-zabbix/timeseries.js +++ b/dist/test/datasource-zabbix/timeseries.js @@ -16,9 +16,6 @@ function _interopRequireWildcard(obj) { if (obj && obj.__esModule) { return obj; function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } -/** - * Downsample time series by using given function (avg, min, max). - */ /** * timeseries.js * @@ -29,6 +26,12 @@ function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { de * */ +var POINT_VALUE = 0; +var POINT_TIMESTAMP = 1; + +/** + * Downsample time series by using given function (avg, min, max). + */ function downsample(datapoints, time_to, ms_interval, func) { var downsampledSeries = []; var timeWindow = { @@ -103,6 +106,34 @@ function groupBy(datapoints, interval, groupByCallback) { })); } +function groupBy_perf(datapoints, interval, groupByCallback) { + var ms_interval = utils.parseInterval(interval); + var grouped_series = []; + var frame_values = []; + var frame_value = void 0; + var frame_ts = datapoints.length ? getPointTimeFrame(datapoints[0][POINT_TIMESTAMP], ms_interval) : 0; + var point_frame_ts = frame_ts; + var point = void 0; + + for (var i = 0; i < datapoints.length; i++) { + point = datapoints[i]; + point_frame_ts = getPointTimeFrame(point[POINT_TIMESTAMP], ms_interval); + if (point_frame_ts === frame_ts) { + frame_values.push(point[POINT_VALUE]); + } else { + frame_value = groupByCallback(frame_values); + grouped_series.push([frame_value, frame_ts]); + frame_ts = point_frame_ts; + frame_values = [point[POINT_VALUE]]; + } + } + + frame_value = groupByCallback(frame_values); + grouped_series.push([frame_value, frame_ts]); + + return grouped_series; +} + /** * Summarize set of time series into one. * @param {datapoints[]} timeseries array of time series @@ -194,6 +225,18 @@ function MEDIAN(values) { // Utility functions // /////////////////////// +/** + * For given point calculate corresponding time frame. + * + * |__*_|_*__|___*| -> |*___|*___|*___| + * + * @param {*} timestamp + * @param {*} ms_interval + */ +function getPointTimeFrame(timestamp, ms_interval) { + return Math.floor(timestamp / ms_interval) * ms_interval; +} + function sortByTime(series) { return _lodash2.default.sortBy(series, function (point) { return point[1]; @@ -260,6 +303,7 @@ function findNearestLeft(series, point) { var exportedFunctions = { downsample: downsample, groupBy: groupBy, + groupBy_perf: groupBy_perf, sumSeries: sumSeries, scale: scale, delta: delta, diff --git a/src/datasource-zabbix/benchmarks/timeseries_bench.js b/src/datasource-zabbix/benchmarks/timeseries_bench.js index 845b54f..f336783 100644 --- a/src/datasource-zabbix/benchmarks/timeseries_bench.js +++ b/src/datasource-zabbix/benchmarks/timeseries_bench.js @@ -31,5 +31,16 @@ module.exports = [ ts.sumSeries(prepeared_series); } } + }, + { + name: 'groupBy vs groupBy_perf', + tests: { + 'groupBy()': () => { + ts.groupBy(datapoints, '5m', ts.AVERAGE); + }, + 'groupBy_perf()': () => { + ts.groupBy_perf(datapoints, '5m', ts.AVERAGE); + } + } } ]; diff --git a/src/datasource-zabbix/dataProcessor.js b/src/datasource-zabbix/dataProcessor.js index a35b05a..621ca67 100644 --- a/src/datasource-zabbix/dataProcessor.js +++ b/src/datasource-zabbix/dataProcessor.js @@ -3,7 +3,7 @@ import * as utils from './utils'; import ts from './timeseries'; let downsampleSeries = ts.downsample; -let groupBy = ts.groupBy; +let groupBy = ts.groupBy_perf; let sumSeries = ts.sumSeries; let delta = ts.delta; let scale = (factor, datapoints) => ts.scale(datapoints, factor); diff --git a/src/datasource-zabbix/timeseries.js b/src/datasource-zabbix/timeseries.js index 8560beb..05bcd74 100644 --- a/src/datasource-zabbix/timeseries.js +++ b/src/datasource-zabbix/timeseries.js @@ -11,6 +11,9 @@ import _ from 'lodash'; import * as utils from './utils'; +const POINT_VALUE = 0; +const POINT_TIMESTAMP = 1; + /** * Downsample time series by using given function (avg, min, max). */ @@ -90,6 +93,34 @@ function groupBy(datapoints, interval, groupByCallback) { })); } +function groupBy_perf(datapoints, interval, groupByCallback) { + let ms_interval = utils.parseInterval(interval); + let grouped_series = []; + let frame_values = []; + let frame_value; + let frame_ts = datapoints.length ? getPointTimeFrame(datapoints[0][POINT_TIMESTAMP], ms_interval) : 0; + let point_frame_ts = frame_ts; + let point; + + for (let i=0; i < datapoints.length; i++) { + point = datapoints[i]; + point_frame_ts = getPointTimeFrame(point[POINT_TIMESTAMP], ms_interval); + if (point_frame_ts === frame_ts) { + frame_values.push(point[POINT_VALUE]); + } else { + frame_value = groupByCallback(frame_values); + grouped_series.push([frame_value, frame_ts]); + frame_ts = point_frame_ts; + frame_values = [point[POINT_VALUE]]; + } + } + + frame_value = groupByCallback(frame_values); + grouped_series.push([frame_value, frame_ts]); + + return grouped_series; +} + /** * Summarize set of time series into one. * @param {datapoints[]} timeseries array of time series @@ -184,6 +215,18 @@ function MEDIAN(values) { // Utility functions // /////////////////////// +/** + * For given point calculate corresponding time frame. + * + * |__*_|_*__|___*| -> |*___|*___|*___| + * + * @param {*} timestamp + * @param {*} ms_interval + */ +function getPointTimeFrame(timestamp, ms_interval) { + return Math.floor(timestamp / ms_interval) * ms_interval; +} + function sortByTime(series) { return _.sortBy(series, function (point) { return point[1]; @@ -250,6 +293,7 @@ function findNearestLeft(series, point) { const exportedFunctions = { downsample, groupBy, + groupBy_perf, sumSeries, scale, delta,