diff --git a/dist/datasource-zabbix/dataProcessor.js b/dist/datasource-zabbix/dataProcessor.js
index 73ddd46..ecc305f 100644
--- a/dist/datasource-zabbix/dataProcessor.js
+++ b/dist/datasource-zabbix/dataProcessor.js
@@ -3,7 +3,7 @@
 System.register(['lodash', './utils', './timeseries'], function (_export, _context) {
   "use strict";
 
-  var _, utils, ts, downsampleSeries, groupBy, sumSeries, delta, scale, SUM, COUNT, AVERAGE, MIN, MAX, MEDIAN, metricFunctions, aggregationFunctions;
+  var _, utils, ts, downsampleSeries, groupBy, groupBy_exported, sumSeries, delta, scale, SUM, COUNT, AVERAGE, MIN, MAX, MEDIAN, metricFunctions, aggregationFunctions;
 
   function limit(order, n, orderByFunc, timeseries) {
     var orderByCallback = aggregationFunctions[orderByFunc];
@@ -93,6 +93,11 @@ System.register(['lodash', './utils', './timeseries'], function (_export, _conte
     execute: function () {
       downsampleSeries = ts.downsample;
       groupBy = ts.groupBy_perf;
+
+      groupBy_exported = function groupBy_exported(interval, groupFunc, datapoints) {
+        return groupBy(datapoints, interval, groupFunc);
+      };
+
       sumSeries = ts.sumSeries;
       delta = ts.delta;
 
@@ -136,7 +141,7 @@ System.register(['lodash', './utils', './timeseries'], function (_export, _conte
 
       _export('default', {
         downsampleSeries: downsampleSeries,
-        groupBy: groupBy,
+        groupBy: groupBy_exported,
         AVERAGE: AVERAGE,
         MIN: MIN,
         MAX: MAX,
diff --git a/dist/datasource-zabbix/dataProcessor.js.map b/dist/datasource-zabbix/dataProcessor.js.map
index 00034f2..0c894c9 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_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, 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\ 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 groupBy_exported = (interval, groupFunc, datapoints) => groupBy(datapoints, interval, groupFunc);\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_exported,\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/test/benchmarks/timeseries_bench.js b/dist/test/benchmarks/timeseries_bench.js
new file mode 100644
index 0000000..732b18d
--- /dev/null
+++ b/dist/test/benchmarks/timeseries_bench.js
@@ -0,0 +1,50 @@
+'use strict';
+
+var _lodash = require('lodash');
+
+var _lodash2 = _interopRequireDefault(_lodash);
+
+var _timeseries = require('../timeseries');
+
+var _timeseries2 = _interopRequireDefault(_timeseries);
+
+function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; }
+
+var 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 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 = [{
+  name: 'groupBy',
+  tests: {
+    'groupBy(AVERAGE)': function groupByAVERAGE() {
+      _timeseries2.default.groupBy(datapoints, '5m', _timeseries2.default.AVERAGE);
+    },
+    'groupBy(MAX)': function groupByMAX() {
+      _timeseries2.default.groupBy(datapoints, '5m', _timeseries2.default.COUNT);
+    }
+  }
+}, {
+  name: 'sumSeries',
+  tests: {
+    'sumSeries()': function sumSeries() {
+      _timeseries2.default.sumSeries(series_set);
+    },
+    'groupBy(MAX)->sumSeries()': function groupByMAXSumSeries() {
+      var prepeared_series = _lodash2.default.map(series_set, function (datapoints) {
+        return _timeseries2.default.groupBy(datapoints, '5m', _timeseries2.default.MAX);
+      });
+      _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 934bbbc..a5ef8c7 100644
--- a/dist/test/datasource-zabbix/dataProcessor.js
+++ b/dist/test/datasource-zabbix/dataProcessor.js
@@ -22,6 +22,9 @@ function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { de
 
 var downsampleSeries = _timeseries2.default.downsample;
 var groupBy = _timeseries2.default.groupBy_perf;
+var groupBy_exported = function groupBy_exported(interval, groupFunc, datapoints) {
+  return groupBy(datapoints, interval, groupFunc);
+};
 var sumSeries = _timeseries2.default.sumSeries;
 var delta = _timeseries2.default.delta;
 var scale = function scale(factor, datapoints) {
@@ -143,7 +146,7 @@ var aggregationFunctions = {
 
 exports.default = {
   downsampleSeries: downsampleSeries,
-  groupBy: groupBy,
+  groupBy: groupBy_exported,
   AVERAGE: AVERAGE,
   MIN: MIN,
   MAX: MAX,
diff --git a/src/datasource-zabbix/dataProcessor.js b/src/datasource-zabbix/dataProcessor.js
index 621ca67..624b588 100644
--- a/src/datasource-zabbix/dataProcessor.js
+++ b/src/datasource-zabbix/dataProcessor.js
@@ -4,6 +4,7 @@ import ts from './timeseries';
 
 let downsampleSeries = ts.downsample;
 let groupBy = ts.groupBy_perf;
+let groupBy_exported = (interval, groupFunc, datapoints) => groupBy(datapoints, interval, groupFunc);
 let sumSeries = ts.sumSeries;
 let delta = ts.delta;
 let scale = (factor, datapoints) => ts.scale(datapoints, factor);
@@ -126,7 +127,7 @@ let aggregationFunctions = {
 
 export default {
   downsampleSeries: downsampleSeries,
-  groupBy: groupBy,
+  groupBy: groupBy_exported,
   AVERAGE: AVERAGE,
   MIN: MIN,
   MAX: MAX,