Implement groupBy with for() and measure performance

about 8 times faster

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);
+    }
+  }
 }];

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) {

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,