@netdata/charts
Version:
Netdata frontend SDK and chart utilities
318 lines (317 loc) • 16 kB
JavaScript
;
var _testUtilities = require("@jest/testUtilities");
var _columns = require("./columns");
var _useDimensionValueCache = require("./useDimensionValueCache");
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function _toConsumableArray(r) { return _arrayWithoutHoles(r) || _iterableToArray(r) || _unsupportedIterableToArray(r) || _nonIterableSpread(); }
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function _iterableToArray(r) { if ("undefined" != typeof Symbol && null != r[Symbol.iterator] || null != r["@@iterator"]) return Array.from(r); }
function _arrayWithoutHoles(r) { if (Array.isArray(r)) return _arrayLikeToArray(r); }
function _arrayLikeToArray(r, a) { (null == a || a > r.length) && (a = r.length); for (var e = 0, n = Array(a); e < a; e++) n[e] = r[e]; return n; }
function _typeof(o) { "@babel/helpers - typeof"; return _typeof = "function" == typeof Symbol && "symbol" == typeof Symbol.iterator ? function (o) { return typeof o; } : function (o) { return o && "function" == typeof Symbol && o.constructor === Symbol && o !== Symbol.prototype ? "symbol" : typeof o; }, _typeof(o); }
var getCellValue = function getCellValue(cell) {
return cell !== null && _typeof(cell) === "object" ? cell[0] : cell;
};
var makeStats = function makeStats(ids, rows) {
return ids.reduce(function (stats, id, index) {
var values = rows.map(function (row) {
return getCellValue(row[index + 1]);
});
stats.min.push(Math.min.apply(Math, _toConsumableArray(values)));
stats.avg.push(values.reduce(function (sum, value) {
return sum + value;
}, 0) / values.length);
stats.max.push(Math.max.apply(Math, _toConsumableArray(values)));
stats.arp.push(0);
return stats;
}, {
min: [],
avg: [],
max: [],
arp: []
});
};
var makePayload = function makePayload(ids, rows) {
var _ref = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : {},
_ref$stats = _ref.stats,
stats = _ref$stats === void 0 ? makeStats(ids, rows) : _ref$stats;
return {
summary: {
contexts: [{
id: "test.context",
sts: {
min: 0,
max: 100
}
}]
},
db: {
update_every: 1,
dimensions: {
ids: ids,
names: ids,
units: ids.map(function () {
return "requests";
})
},
units: "requests"
},
view: {
title: "Test chart",
units: "requests",
chart_type: "line",
dimensions: {
ids: ids,
names: ids,
units: ids.map(function () {
return "requests";
}),
sts: stats
}
},
result: {
labels: ["time"].concat(_toConsumableArray(ids)),
point: {
value: 0,
arp: 1,
pa: 2
},
data: rows
}
};
};
var value = function value(number) {
return [number, 0, 0];
};
var loadChart = /*#__PURE__*/function () {
var _ref2 = _asyncToGenerator(/*#__PURE__*/_regenerator().m(function _callee(payload) {
var _makeTestChart, chart, loaded;
return _regenerator().w(function (_context) {
while (1) switch (_context.n) {
case 0:
_makeTestChart = (0, _testUtilities.makeTestChart)({
mockData: payload
}), chart = _makeTestChart.chart;
loaded = new Promise(function (resolve) {
return chart.on("successFetch", resolve);
});
chart.doneFetch(payload);
_context.n = 1;
return loaded;
case 1:
return _context.a(2, chart);
}
}, _callee);
}));
return function loadChart(_x) {
return _ref2.apply(this, arguments);
};
}();
describe("dimension value cache", function () {
it("calculates latest values and window stats from the chart payload", /*#__PURE__*/_asyncToGenerator(/*#__PURE__*/_regenerator().m(function _callee2() {
var chart, cache;
return _regenerator().w(function (_context2) {
while (1) switch (_context2.n) {
case 0:
_context2.n = 1;
return loadChart(makePayload(["low", "mid", "high"], [[1000, value(1), value(2), value(10)], [2000, value(1), value(4), value(20)], [3000, value(1), value(6), value(30)]]));
case 1:
chart = _context2.v;
cache = (0, _useDimensionValueCache.createDimensionValueCache)(chart, {
period: "window",
includeAdvancedStats: true
});
expect(cache.get("mid", "value", {
period: "latest"
})).toBe(6);
expect(cache.get("mid", "min", {
period: "window"
})).toBe(2);
expect(cache.get("mid", "avg", {
period: "window"
})).toBe(4);
expect(cache.get("mid", "max", {
period: "window"
})).toBe(6);
expect(cache.get("mid", "median", {
period: "window"
})).toBe(4);
expect(cache.get("mid", "range", {
period: "window"
})).toBe(4);
expect(cache.get("mid", "volume", {
period: "window"
})).toBe(12);
case 2:
return _context2.a(2);
}
}, _callee2);
})));
it("sorts by cached stat values without rescanning dimensions on every comparison", /*#__PURE__*/_asyncToGenerator(/*#__PURE__*/_regenerator().m(function _callee3() {
var ids, rows, chart, valueCache, originalGetRowDimensionValue, getRowDimensionValueCalls, sortingFn, tableRows, callsAfterFirstSort;
return _regenerator().w(function (_context3) {
while (1) switch (_context3.n) {
case 0:
ids = ["a", "b", "c", "d", "e", "f"];
rows = [[1000, value(5), value(1), value(3), value(4), value(2), value(6)], [2000, value(5), value(1), value(3), value(4), value(2), value(6)], [3000, value(5), value(1), value(3), value(4), value(2), value(6)]];
_context3.n = 1;
return loadChart(makePayload(ids, rows));
case 1:
chart = _context3.v;
valueCache = (0, _useDimensionValueCache.createDimensionValueCache)(chart, {
period: "window"
});
originalGetRowDimensionValue = chart.getRowDimensionValue;
getRowDimensionValueCalls = 0;
chart.getRowDimensionValue = function () {
getRowDimensionValueCalls++;
return originalGetRowDimensionValue.apply(void 0, arguments);
};
try {
sortingFn = (0, _columns.makeNumberSortingFn)(chart, {
key: "avg",
period: "window",
valueCache: valueCache
});
tableRows = chart.getDimensionIds().map(function (id) {
return {
original: id
};
});
tableRows.sort(sortingFn);
callsAfterFirstSort = getRowDimensionValueCalls;
expect(tableRows.map(function (row) {
return row.original;
})).toEqual(["b", "e", "c", "d", "a", "f"]);
expect(callsAfterFirstSort).toBe(0);
tableRows.reverse().sort(sortingFn);
expect(getRowDimensionValueCalls).toBe(callsAfterFirstSort);
} finally {
chart.getRowDimensionValue = originalGetRowDimensionValue;
}
case 2:
return _context3.a(2);
}
}, _callee3);
})));
it("uses response window stats instead of recalculating row values", /*#__PURE__*/_asyncToGenerator(/*#__PURE__*/_regenerator().m(function _callee4() {
var chart, originalGetRowDimensionValue, getRowDimensionValueCalls, cache;
return _regenerator().w(function (_context4) {
while (1) switch (_context4.n) {
case 0:
_context4.n = 1;
return loadChart(makePayload(["a", "b"], [[1000, value(100), value(1)], [2000, value(100), value(1)]], {
stats: {
min: [10, 20],
avg: [30, 40],
max: [50, 60],
arp: [1, 2]
}
}));
case 1:
chart = _context4.v;
originalGetRowDimensionValue = chart.getRowDimensionValue;
getRowDimensionValueCalls = 0;
chart.getRowDimensionValue = function () {
getRowDimensionValueCalls++;
return originalGetRowDimensionValue.apply(void 0, arguments);
};
try {
cache = (0, _useDimensionValueCache.createDimensionValueCache)(chart, {
period: "window"
});
expect(cache.get("a", "min", {
period: "window"
})).toBe(10);
expect(cache.get("a", "avg", {
period: "window"
})).toBe(30);
expect(cache.get("a", "max", {
period: "window"
})).toBe(50);
expect(cache.get("a", "arp", {
period: "window"
})).toBe(1);
expect(cache.get("a", "range", {
period: "window"
})).toBe(40);
expect(getRowDimensionValueCalls).toBe(0);
} finally {
chart.getRowDimensionValue = originalGetRowDimensionValue;
}
case 2:
return _context4.a(2);
}
}, _callee4);
})));
it("keeps selected-area stats scoped to highlighted rows", /*#__PURE__*/_asyncToGenerator(/*#__PURE__*/_regenerator().m(function _callee5() {
var chart, cache;
return _regenerator().w(function (_context5) {
while (1) switch (_context5.n) {
case 0:
_context5.n = 1;
return loadChart(makePayload(["a"], [[1000, value(1)], [2000, value(3)], [3000, value(100)]], {
stats: {
min: [1000],
avg: [1000],
max: [1000],
arp: [0]
}
}));
case 1:
chart = _context5.v;
chart.setAttribute("overlays", {
highlight: {
range: [1, 2]
}
});
cache = (0, _useDimensionValueCache.createDimensionValueCache)(chart, {
period: "highlight"
});
expect(cache.get("a", "min", {
period: "highlight"
})).toBe(1);
expect(cache.get("a", "avg", {
period: "highlight"
})).toBe(2);
expect(cache.get("a", "max", {
period: "highlight"
})).toBe(3);
case 2:
return _context5.a(2);
}
}, _callee5);
})));
it("keeps incremental heatmap bucket delta semantics", /*#__PURE__*/_asyncToGenerator(/*#__PURE__*/_regenerator().m(function _callee6() {
var chart, cache;
return _regenerator().w(function (_context6) {
while (1) switch (_context6.n) {
case 0:
_context6.n = 1;
return loadChart(makePayload(["bucket_0", "bucket_1", "bucket_2"], [[1000, value(10), value(15), value(25)], [2000, value(20), value(30), value(45)]]));
case 1:
chart = _context6.v;
chart.setAttribute("chartType", "heatmap");
chart.setAttribute("heatmapType", "incremental");
cache = (0, _useDimensionValueCache.createDimensionValueCache)(chart, {
period: "window"
});
expect(cache.get("bucket_1", "avg", {
period: "window"
})).toBe(7.5);
expect(cache.get("bucket_2", "avg", {
period: "window"
})).toBe(12.5);
expect(cache.get("bucket_1", "value", {
period: "latest"
})).toBe(10);
case 2:
return _context6.a(2);
}
}, _callee6);
})));
});