2 * Copyright 2017 Facebook, Inc.
4 * Licensed under the Apache License, Version 2.0 (the "License");
5 * you may not use this file except in compliance with the License.
6 * You may obtain a copy of the License at
8 * http://www.apache.org/licenses/LICENSE-2.0
10 * Unless required by applicable law or agreed to in writing, software
11 * distributed under the License is distributed on an "AS IS" BASIS,
12 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13 * See the License for the specific language governing permissions and
14 * limitations under the License.
17 #include <folly/detail/Stats.h>
18 #include <folly/stats/BucketedTimeSeries-defs.h>
19 #include <folly/stats/BucketedTimeSeries.h>
20 #include <folly/stats/MultiLevelTimeSeries-defs.h>
21 #include <folly/stats/MultiLevelTimeSeries.h>
25 #include <glog/logging.h>
27 #include <folly/Foreach.h>
28 #include <folly/portability/GTest.h>
30 using std::chrono::seconds;
33 using folly::BucketedTimeSeries;
35 using Bucket = folly::detail::Bucket<int64_t>;
36 using StatsClock = folly::LegacyStatsClock<std::chrono::seconds>;
37 using TimePoint = StatsClock::time_point;
38 using Duration = StatsClock::duration;
41 * Helper functions to allow us to directly log time points and duration
44 std::ostream& operator<<(std::ostream& os, std::chrono::seconds s) {
48 std::ostream& operator<<(std::ostream& os, TimePoint tp) {
49 os << tp.time_since_epoch().count();
55 TimePoint mkTimePoint(int value) {
56 return TimePoint(StatsClock::duration(value));
60 TestData(int d, int b, std::initializer_list<int> starts)
61 : duration(d), numBuckets(b) {
62 bucketStarts.reserve(starts.size());
63 for (int s : starts) {
64 bucketStarts.push_back(mkTimePoint(s));
69 vector<TimePoint> bucketStarts;
71 vector<TestData> testData = {
72 // 71 seconds x 4 buckets
73 { 71, 4, {0, 18, 36, 54}},
74 // 100 seconds x 10 buckets
75 { 100, 10, {0, 10, 20, 30, 40, 50, 60, 70, 80, 90}},
76 // 10 seconds x 10 buckets
77 { 10, 10, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}},
78 // 10 seconds x 1 buckets
80 // 1 second x 1 buckets
85 TEST(BucketedTimeSeries, getBucketInfo) {
86 for (const auto& data : testData) {
87 BucketedTimeSeries<int64_t> ts(data.numBuckets, data.duration);
89 for (uint32_t n = 0; n < 10000; n += 1234) {
90 seconds offset(n * data.duration);
92 for (uint32_t idx = 0; idx < data.numBuckets; ++idx) {
93 auto bucketStart = data.bucketStarts[idx];
94 TimePoint nextBucketStart;
95 if (idx + 1 < data.numBuckets) {
96 nextBucketStart = data.bucketStarts[idx + 1];
98 nextBucketStart = TimePoint(data.duration);
101 TimePoint expectedStart = offset + bucketStart;
102 TimePoint expectedNextStart = offset + nextBucketStart;
104 expectedStart + (expectedNextStart - expectedStart) / 2;
106 vector<std::pair<string, TimePoint>> timePoints = {
107 {"expectedStart", expectedStart},
108 {"midpoint", midpoint},
109 {"expectedEnd", expectedNextStart - seconds(1)},
112 for (const auto& point : timePoints) {
113 // Check that getBucketIdx() returns the expected index
114 EXPECT_EQ(idx, ts.getBucketIdx(point.second))
115 << data.duration << "x" << data.numBuckets << ": " << point.first
116 << "=" << point.second;
118 // Check the data returned by getBucketInfo()
120 TimePoint returnedStart;
121 TimePoint returnedNextStart;
122 ts.getBucketInfo(expectedStart, &returnedIdx,
123 &returnedStart, &returnedNextStart);
124 EXPECT_EQ(idx, returnedIdx) << data.duration << "x" << data.numBuckets
125 << ": " << point.first << "="
127 EXPECT_EQ(expectedStart, returnedStart)
128 << data.duration << "x" << data.numBuckets << ": " << point.first
129 << "=" << point.second;
130 EXPECT_EQ(expectedNextStart, returnedNextStart)
131 << data.duration << "x" << data.numBuckets << ": " << point.first
132 << "=" << point.second;
139 void testUpdate100x10(size_t offset) {
140 // This test code only works when offset is a multiple of the bucket width
141 CHECK_EQ(0, offset % 10);
143 // Create a 100 second timeseries, with 10 buckets
144 BucketedTimeSeries<int64_t> ts(10, seconds(100));
148 // Add 1 value to each bucket
149 for (int n = 5; n <= 95; n += 10) {
150 ts.addValue(seconds(n + offset), 6);
153 EXPECT_EQ(10, ts.count());
154 EXPECT_EQ(60, ts.sum());
155 EXPECT_EQ(6, ts.avg());
158 // Update 2 buckets forwards. This should throw away 2 data points.
160 ts.update(seconds(110 + offset));
161 EXPECT_EQ(8, ts.count());
162 EXPECT_EQ(48, ts.sum());
163 EXPECT_EQ(6, ts.avg());
165 // The last time we added was 95.
166 // Try updating to 189. This should clear everything but the last bucket.
168 ts.update(seconds(151 + offset));
169 EXPECT_EQ(4, ts.count());
170 //EXPECT_EQ(6, ts.sum());
171 EXPECT_EQ(6, ts.avg());
173 // The last time we added was 95.
174 // Try updating to 193: This is nearly one full loop around,
175 // back to the same bucket. update() needs to clear everything
177 ts.update(seconds(193 + offset));
178 EXPECT_EQ(0, ts.count());
179 EXPECT_EQ(0, ts.sum());
180 EXPECT_EQ(0, ts.avg());
182 // The last time we added was 95.
183 // Try updating to 197: This is slightly over one full loop around,
184 // back to the same bucket. update() needs to clear everything
186 ts.update(seconds(197 + offset));
187 EXPECT_EQ(0, ts.count());
188 EXPECT_EQ(0, ts.sum());
189 EXPECT_EQ(0, ts.avg());
191 // The last time we added was 95.
192 // Try updating to 230: This is well over one full loop around,
193 // and everything should be cleared.
195 ts.update(seconds(230 + offset));
196 EXPECT_EQ(0, ts.count());
197 EXPECT_EQ(0, ts.sum());
198 EXPECT_EQ(0, ts.avg());
201 TEST(BucketedTimeSeries, update100x10) {
202 // Run testUpdate100x10() multiple times, with various offsets.
203 // This makes sure the update code works regardless of which bucket it starts
204 // at in the modulo arithmetic.
206 testUpdate100x10(50);
207 testUpdate100x10(370);
208 testUpdate100x10(1937090);
211 TEST(BucketedTimeSeries, update71x5) {
212 // Create a 71 second timeseries, with 5 buckets
213 // This tests when the number of buckets does not divide evenly into the
215 BucketedTimeSeries<int64_t> ts(5, seconds(71));
219 // Add 1 value to each bucket
220 ts.addValue(seconds(11), 6);
221 ts.addValue(seconds(24), 6);
222 ts.addValue(seconds(42), 6);
223 ts.addValue(seconds(43), 6);
224 ts.addValue(seconds(66), 6);
226 EXPECT_EQ(5, ts.count());
227 EXPECT_EQ(30, ts.sum());
228 EXPECT_EQ(6, ts.avg());
231 // Update 2 buckets forwards. This should throw away 2 data points.
233 ts.update(seconds(99));
234 EXPECT_EQ(3, ts.count());
235 EXPECT_EQ(18, ts.sum());
236 EXPECT_EQ(6, ts.avg());
238 // Update 3 buckets forwards. This should throw away 3 data points.
240 ts.update(seconds(100));
241 EXPECT_EQ(2, ts.count());
242 EXPECT_EQ(12, ts.sum());
243 EXPECT_EQ(6, ts.avg());
245 // Update 4 buckets forwards, just under the wrap limit.
246 // This should throw everything but the last bucket away.
248 ts.update(seconds(127));
249 EXPECT_EQ(1, ts.count());
250 EXPECT_EQ(6, ts.sum());
251 EXPECT_EQ(6, ts.avg());
253 // Update 5 buckets forwards, exactly at the wrap limit.
254 // This should throw everything away.
256 ts.update(seconds(128));
257 EXPECT_EQ(0, ts.count());
258 EXPECT_EQ(0, ts.sum());
259 EXPECT_EQ(0, ts.avg());
261 // Update very far forwards, wrapping multiple times.
262 // This should throw everything away.
264 ts.update(seconds(1234));
265 EXPECT_EQ(0, ts.count());
266 EXPECT_EQ(0, ts.sum());
267 EXPECT_EQ(0, ts.avg());
270 TEST(BucketedTimeSeries, elapsed) {
271 BucketedTimeSeries<int64_t> ts(60, seconds(600));
273 // elapsed() is 0 when no data points have been added
274 EXPECT_EQ(0, ts.elapsed().count());
276 // With exactly 1 data point, elapsed() should report 1 second of data
277 seconds start(239218);
278 ts.addValue(start + seconds(0), 200);
279 EXPECT_EQ(1, ts.elapsed().count());
280 // Adding a data point 10 seconds later should result in an elapsed time of
281 // 11 seconds (the time range is [0, 10], inclusive).
282 ts.addValue(start + seconds(10), 200);
283 EXPECT_EQ(11, ts.elapsed().count());
285 // elapsed() returns to 0 after clear()
287 EXPECT_EQ(0, ts.elapsed().count());
289 // Restart, with the starting point on an easier number to work with
290 ts.addValue(seconds(10), 200);
291 EXPECT_EQ(1, ts.elapsed().count());
292 ts.addValue(seconds(580), 200);
293 EXPECT_EQ(571, ts.elapsed().count());
294 ts.addValue(seconds(590), 200);
295 EXPECT_EQ(581, ts.elapsed().count());
296 ts.addValue(seconds(598), 200);
297 EXPECT_EQ(589, ts.elapsed().count());
298 ts.addValue(seconds(599), 200);
299 EXPECT_EQ(590, ts.elapsed().count());
300 ts.addValue(seconds(600), 200);
301 EXPECT_EQ(591, ts.elapsed().count());
302 ts.addValue(seconds(608), 200);
303 EXPECT_EQ(599, ts.elapsed().count());
304 ts.addValue(seconds(609), 200);
305 EXPECT_EQ(600, ts.elapsed().count());
306 // Once we reach 600 seconds worth of data, when we move on to the next
307 // second a full bucket will get thrown out. Now we drop back down to 591
308 // seconds worth of data
309 ts.addValue(seconds(610), 200);
310 EXPECT_EQ(591, ts.elapsed().count());
311 ts.addValue(seconds(618), 200);
312 EXPECT_EQ(599, ts.elapsed().count());
313 ts.addValue(seconds(619), 200);
314 EXPECT_EQ(600, ts.elapsed().count());
315 ts.addValue(seconds(620), 200);
316 EXPECT_EQ(591, ts.elapsed().count());
317 ts.addValue(seconds(123419), 200);
318 EXPECT_EQ(600, ts.elapsed().count());
319 ts.addValue(seconds(123420), 200);
320 EXPECT_EQ(591, ts.elapsed().count());
321 ts.addValue(seconds(123425), 200);
322 EXPECT_EQ(596, ts.elapsed().count());
324 // Time never moves backwards.
325 // Calling update with an old timestamp will just be ignored.
326 ts.update(seconds(29));
327 EXPECT_EQ(596, ts.elapsed().count());
330 TEST(BucketedTimeSeries, rate) {
331 BucketedTimeSeries<int64_t> ts(60, seconds(600));
333 // Add 3 values every 2 seconds, until fill up the buckets
334 for (size_t n = 0; n < 600; n += 2) {
335 ts.addValue(seconds(n), 200, 3);
338 EXPECT_EQ(900, ts.count());
339 EXPECT_EQ(180000, ts.sum());
340 EXPECT_EQ(200, ts.avg());
342 // Really we only entered 599 seconds worth of data: [0, 598] (inclusive)
343 EXPECT_EQ(599, ts.elapsed().count());
344 EXPECT_NEAR(300.5, ts.rate(), 0.005);
345 EXPECT_NEAR(1.5, ts.countRate(), 0.005);
347 // If we add 1 more second, now we will have 600 seconds worth of data
348 ts.update(seconds(599));
349 EXPECT_EQ(600, ts.elapsed().count());
350 EXPECT_NEAR(300, ts.rate(), 0.005);
351 EXPECT_EQ(300, ts.rate<int>());
352 EXPECT_NEAR(1.5, ts.countRate(), 0.005);
354 // However, 1 more second after that and we will have filled up all the
355 // buckets, and have to drop one.
356 ts.update(seconds(600));
357 EXPECT_EQ(591, ts.elapsed().count());
358 EXPECT_NEAR(299.5, ts.rate(), 0.01);
359 EXPECT_EQ(299, ts.rate<int>());
360 EXPECT_NEAR(1.5, ts.countRate(), 0.005);
363 TEST(BucketedTimeSeries, avgTypeConversion) {
364 // Make sure the computed average values are accurate regardless
365 // of the input type and return type.
368 // Simple sanity tests for small positive integer values
369 BucketedTimeSeries<int64_t> ts(60, seconds(600));
370 ts.addValue(seconds(0), 4, 100);
371 ts.addValue(seconds(0), 10, 200);
372 ts.addValue(seconds(0), 16, 100);
374 EXPECT_DOUBLE_EQ(10.0, ts.avg());
375 EXPECT_DOUBLE_EQ(10.0, ts.avg<float>());
376 EXPECT_EQ(10, ts.avg<uint64_t>());
377 EXPECT_EQ(10, ts.avg<int64_t>());
378 EXPECT_EQ(10, ts.avg<int32_t>());
379 EXPECT_EQ(10, ts.avg<int16_t>());
380 EXPECT_EQ(10, ts.avg<int8_t>());
381 EXPECT_EQ(10, ts.avg<uint8_t>());
385 // Test signed integer types with negative values
386 BucketedTimeSeries<int64_t> ts(60, seconds(600));
387 ts.addValue(seconds(0), -100);
388 ts.addValue(seconds(0), -200);
389 ts.addValue(seconds(0), -300);
390 ts.addValue(seconds(0), -200, 65535);
392 EXPECT_DOUBLE_EQ(-200.0, ts.avg());
393 EXPECT_DOUBLE_EQ(-200.0, ts.avg<float>());
394 EXPECT_EQ(-200, ts.avg<int64_t>());
395 EXPECT_EQ(-200, ts.avg<int32_t>());
396 EXPECT_EQ(-200, ts.avg<int16_t>());
400 // Test uint64_t values that would overflow int64_t
401 BucketedTimeSeries<uint64_t> ts(60, seconds(600));
402 ts.addValueAggregated(seconds(0),
403 std::numeric_limits<uint64_t>::max(),
404 std::numeric_limits<uint64_t>::max());
406 EXPECT_DOUBLE_EQ(1.0, ts.avg());
407 EXPECT_DOUBLE_EQ(1.0, ts.avg<float>());
408 EXPECT_EQ(1, ts.avg<uint64_t>());
409 EXPECT_EQ(1, ts.avg<int64_t>());
410 EXPECT_EQ(1, ts.avg<int8_t>());
414 // Test doubles with small-ish values that will fit in integer types
415 BucketedTimeSeries<double> ts(60, seconds(600));
416 ts.addValue(seconds(0), 4.0, 100);
417 ts.addValue(seconds(0), 10.0, 200);
418 ts.addValue(seconds(0), 16.0, 100);
420 EXPECT_DOUBLE_EQ(10.0, ts.avg());
421 EXPECT_DOUBLE_EQ(10.0, ts.avg<float>());
422 EXPECT_EQ(10, ts.avg<uint64_t>());
423 EXPECT_EQ(10, ts.avg<int64_t>());
424 EXPECT_EQ(10, ts.avg<int32_t>());
425 EXPECT_EQ(10, ts.avg<int16_t>());
426 EXPECT_EQ(10, ts.avg<int8_t>());
427 EXPECT_EQ(10, ts.avg<uint8_t>());
431 // Test doubles with huge values
432 BucketedTimeSeries<double> ts(60, seconds(600));
433 ts.addValue(seconds(0), 1e19, 100);
434 ts.addValue(seconds(0), 2e19, 200);
435 ts.addValue(seconds(0), 3e19, 100);
437 EXPECT_DOUBLE_EQ(ts.avg(), 2e19);
438 EXPECT_NEAR(ts.avg<float>(), 2e19, 1e11);
442 // Test doubles where the sum adds up larger than a uint64_t,
443 // but the average fits in an int64_t
444 BucketedTimeSeries<double> ts(60, seconds(600));
445 uint64_t value = 0x3fffffffffffffff;
446 FOR_EACH_RANGE(i, 0, 16) {
447 ts.addValue(seconds(0), value);
450 EXPECT_DOUBLE_EQ(value, ts.avg());
451 EXPECT_DOUBLE_EQ(value, ts.avg<float>());
452 // Some precision is lost here due to the huge sum, so the
453 // integer average returned is off by one.
454 EXPECT_NEAR(value, ts.avg<uint64_t>(), 1);
455 EXPECT_NEAR(value, ts.avg<int64_t>(), 1);
459 // Test BucketedTimeSeries with a smaller integer type
460 BucketedTimeSeries<int16_t> ts(60, seconds(600));
461 FOR_EACH_RANGE(i, 0, 101) {
462 ts.addValue(seconds(0), i);
465 EXPECT_DOUBLE_EQ(50.0, ts.avg());
466 EXPECT_DOUBLE_EQ(50.0, ts.avg<float>());
467 EXPECT_EQ(50, ts.avg<uint64_t>());
468 EXPECT_EQ(50, ts.avg<int64_t>());
469 EXPECT_EQ(50, ts.avg<int16_t>());
470 EXPECT_EQ(50, ts.avg<int8_t>());
474 // Test BucketedTimeSeries with long double input
475 BucketedTimeSeries<long double> ts(60, seconds(600));
476 ts.addValueAggregated(seconds(0), 1000.0L, 7);
478 long double expected = 1000.0L / 7.0L;
479 EXPECT_DOUBLE_EQ(static_cast<double>(expected), ts.avg());
480 EXPECT_DOUBLE_EQ(static_cast<float>(expected), ts.avg<float>());
481 EXPECT_DOUBLE_EQ(expected, ts.avg<long double>());
482 EXPECT_EQ(static_cast<uint64_t>(expected), ts.avg<uint64_t>());
483 EXPECT_EQ(static_cast<int64_t>(expected), ts.avg<int64_t>());
487 // Test BucketedTimeSeries with int64_t values,
488 // but using an average that requires a fair amount of precision.
489 BucketedTimeSeries<int64_t> ts(60, seconds(600));
490 ts.addValueAggregated(seconds(0), 1000, 7);
492 long double expected = 1000.0L / 7.0L;
493 EXPECT_DOUBLE_EQ(static_cast<double>(expected), ts.avg());
494 EXPECT_DOUBLE_EQ(static_cast<float>(expected), ts.avg<float>());
495 EXPECT_DOUBLE_EQ(expected, ts.avg<long double>());
496 EXPECT_EQ(static_cast<uint64_t>(expected), ts.avg<uint64_t>());
497 EXPECT_EQ(static_cast<int64_t>(expected), ts.avg<int64_t>());
501 TEST(BucketedTimeSeries, forEachBucket) {
502 typedef BucketedTimeSeries<int64_t>::Bucket Bucket;
504 BucketInfo(const Bucket* b, TimePoint s, TimePoint ns)
505 : bucket(b), start(s), nextStart(ns) {}
507 const Bucket* bucket;
512 for (const auto& data : testData) {
513 BucketedTimeSeries<int64_t> ts(data.numBuckets, seconds(data.duration));
515 vector<BucketInfo> info;
517 const Bucket& bucket,
518 TimePoint bucketStart,
519 TimePoint bucketEnd) -> bool {
520 info.emplace_back(&bucket, bucketStart, bucketEnd);
524 // If we haven't yet added any data, the current bucket will start at 0,
525 // and all data previous buckets will have negative times.
526 ts.forEachBucket(fn);
528 CHECK_EQ(data.numBuckets, info.size());
530 // Check the data passed in to the function
532 size_t bucketIdx = 1;
533 seconds offset = -data.duration;
534 for (size_t n = 0; n < data.numBuckets; ++n) {
535 if (bucketIdx >= data.numBuckets) {
537 offset += data.duration;
540 EXPECT_EQ(data.bucketStarts[bucketIdx] + offset, info[infoIdx].start)
541 << data.duration << "x" << data.numBuckets
542 << ": bucketIdx=" << bucketIdx << ", infoIdx=" << infoIdx;
544 size_t nextBucketIdx = bucketIdx + 1;
545 seconds nextOffset = offset;
546 if (nextBucketIdx >= data.numBuckets) {
548 nextOffset += data.duration;
551 data.bucketStarts[nextBucketIdx] + nextOffset,
552 info[infoIdx].nextStart)
553 << data.duration << "x" << data.numBuckets
554 << ": bucketIdx=" << bucketIdx << ", infoIdx=" << infoIdx;
556 EXPECT_EQ(&ts.getBucketByIndex(bucketIdx), info[infoIdx].bucket);
564 TEST(BucketedTimeSeries, queryByIntervalSimple) {
565 BucketedTimeSeries<int> a(3, seconds(12));
566 for (int i = 0; i < 8; i++) {
567 a.addValue(seconds(i), 1);
569 // We added 1 at each second from 0..7
570 // Query from the time period 0..2.
571 // This is entirely in the first bucket, which has a sum of 4.
572 // The code knows only part of the bucket is covered, and correctly
573 // estimates the desired sum as 3.
574 EXPECT_EQ(2, a.sum(mkTimePoint(0), mkTimePoint(2)));
577 TEST(BucketedTimeSeries, queryByInterval) {
578 // Set up a BucketedTimeSeries tracking 6 seconds in 3 buckets
579 const int kNumBuckets = 3;
580 const int kDuration = 6;
581 BucketedTimeSeries<double> b(kNumBuckets, seconds(kDuration));
583 for (unsigned int i = 0; i < kDuration; ++i) {
584 // add value 'i' at time 'i'
585 b.addValue(mkTimePoint(i), i);
588 // Current bucket state:
589 // 0: time=[0, 2): values=(0, 1), sum=1, count=2
590 // 1: time=[2, 4): values=(2, 3), sum=5, count=1
591 // 2: time=[4, 6): values=(4, 5), sum=9, count=2
592 double expectedSums1[kDuration + 1][kDuration + 1] = {
593 {0, 4.5, 9, 11.5, 14, 14.5, 15},
594 {0, 4.5, 7, 9.5, 10, 10.5, -1},
595 {0, 2.5, 5, 5.5, 6, -1, -1},
596 {0, 2.5, 3, 3.5, -1, -1, -1},
597 {0, 0.5, 1, -1, -1, -1, -1},
598 {0, 0.5, -1, -1, -1, -1, -1},
599 {0, -1, -1, -1, -1, -1, -1}
601 int expectedCounts1[kDuration + 1][kDuration + 1] = {
602 {0, 1, 2, 3, 4, 5, 6},
603 {0, 1, 2, 3, 4, 5, -1},
604 {0, 1, 2, 3, 4, -1, -1},
605 {0, 1, 2, 3, -1, -1, -1},
606 {0, 1, 2, -1, -1, -1, -1},
607 {0, 1, -1, -1, -1, -1, -1},
608 {0, -1, -1, -1, -1, -1, -1}
611 TimePoint currentTime = b.getLatestTime() + seconds(1);
612 for (int i = 0; i <= kDuration + 1; i++) {
613 for (int j = 0; j <= kDuration - i; j++) {
614 TimePoint start = currentTime - seconds(i + j);
615 TimePoint end = currentTime - seconds(i);
616 double expectedSum = expectedSums1[i][j];
617 EXPECT_EQ(expectedSum, b.sum(start, end))
618 << "i=" << i << ", j=" << j << ", interval=[" << start << ", " << end
621 uint64_t expectedCount = expectedCounts1[i][j];
622 EXPECT_EQ(expectedCount, b.count(start, end))
623 << "i=" << i << ", j=" << j << ", interval=[" << start << ", " << end
626 double expectedAvg = expectedCount ? expectedSum / expectedCount : 0;
627 EXPECT_EQ(expectedAvg, b.avg(start, end))
628 << "i=" << i << ", j=" << j << ", interval=[" << start << ", " << end
631 double expectedRate = j ? expectedSum / j : 0;
632 EXPECT_EQ(expectedRate, b.rate(start, end))
633 << "i=" << i << ", j=" << j << ", interval=[" << start << ", " << end
638 // Add 3 more values.
639 // This will overwrite 1 full bucket, and put us halfway through the next.
640 for (unsigned int i = kDuration; i < kDuration + 3; ++i) {
641 b.addValue(mkTimePoint(i), i);
643 EXPECT_EQ(mkTimePoint(4), b.getEarliestTime());
645 // Current bucket state:
646 // 0: time=[6, 8): values=(6, 7), sum=13, count=2
647 // 1: time=[8, 10): values=(8), sum=8, count=1
648 // 2: time=[4, 6): values=(4, 5), sum=9, count=2
649 double expectedSums2[kDuration + 1][kDuration + 1] = {
650 {0, 8, 14.5, 21, 25.5, 30, 30},
651 {0, 6.5, 13, 17.5, 22, 22, -1},
652 {0, 6.5, 11, 15.5, 15.5, -1, -1},
653 {0, 4.5, 9, 9, -1, -1, -1},
654 {0, 4.5, 4.5, -1, -1, -1, -1},
655 {0, 0, -1, -1, -1, -1, -1},
656 {0, -1, -1, -1, -1, -1, -1}
658 int expectedCounts2[kDuration + 1][kDuration + 1] = {
659 {0, 1, 2, 3, 4, 5, 5},
660 {0, 1, 2, 3, 4, 4, -1},
661 {0, 1, 2, 3, 3, -1, -1},
662 {0, 1, 2, 2, -1, -1, -1},
663 {0, 1, 1, -1, -1, -1, -1},
664 {0, 0, -1, -1, -1, -1, -1},
665 {0, -1, -1, -1, -1, -1, -1}
668 currentTime = b.getLatestTime() + seconds(1);
669 for (int i = 0; i <= kDuration + 1; i++) {
670 for (int j = 0; j <= kDuration - i; j++) {
671 TimePoint start = currentTime - seconds(i + j);
672 TimePoint end = currentTime - seconds(i);
673 double expectedSum = expectedSums2[i][j];
674 EXPECT_EQ(expectedSum, b.sum(start, end))
675 << "i=" << i << ", j=" << j << ", interval=[" << start << ", " << end
678 uint64_t expectedCount = expectedCounts2[i][j];
679 EXPECT_EQ(expectedCount, b.count(start, end))
680 << "i=" << i << ", j=" << j << ", interval=[" << start << ", " << end
683 double expectedAvg = expectedCount ? expectedSum / expectedCount : 0;
684 EXPECT_EQ(expectedAvg, b.avg(start, end))
685 << "i=" << i << ", j=" << j << ", interval=[" << start << ", " << end
688 TimePoint dataStart = std::max(start, b.getEarliestTime());
689 TimePoint dataEnd = std::max(end, dataStart);
690 seconds expectedInterval = dataEnd - dataStart;
691 EXPECT_EQ(expectedInterval, b.elapsed(start, end))
692 << "i=" << i << ", j=" << j << ", interval=[" << start << ", " << end
695 double expectedRate = expectedInterval.count() ?
696 expectedSum / expectedInterval.count() : 0;
697 EXPECT_EQ(expectedRate, b.rate(start, end))
698 << "i=" << i << ", j=" << j << ", interval=[" << start << ", " << end
704 TEST(BucketedTimeSeries, rateByInterval) {
705 const int kNumBuckets = 5;
706 const seconds kDuration(10);
707 BucketedTimeSeries<double> b(kNumBuckets, kDuration);
709 // Add data points at a constant rate of 10 per second.
710 // Start adding data points at kDuration, and fill half of the buckets for
712 TimePoint start(kDuration);
713 TimePoint end(kDuration + (kDuration / 2));
714 const double kFixedRate = 10.0;
715 for (TimePoint i = start; i < end; i += seconds(1)) {
716 b.addValue(i, kFixedRate);
719 // Querying the rate should yield kFixedRate.
720 EXPECT_EQ(kFixedRate, b.rate());
721 EXPECT_EQ(kFixedRate, b.rate(start, end));
722 EXPECT_EQ(kFixedRate, b.rate(start, start + kDuration));
723 EXPECT_EQ(kFixedRate, b.rate(end - kDuration, end));
724 EXPECT_EQ(kFixedRate, b.rate(end - seconds(1), end));
725 // We have been adding 1 data point per second, so countRate()
727 EXPECT_EQ(1.0, b.countRate());
728 EXPECT_EQ(1.0, b.countRate(start, end));
729 EXPECT_EQ(1.0, b.countRate(start, start + kDuration));
730 EXPECT_EQ(1.0, b.countRate(end - kDuration, end));
731 EXPECT_EQ(1.0, b.countRate(end - seconds(1), end));
733 // We haven't added anything before time kDuration.
734 // Querying data earlier than this should result in a rate of 0.
735 EXPECT_EQ(0.0, b.rate(mkTimePoint(0), mkTimePoint(1)));
736 EXPECT_EQ(0.0, b.countRate(mkTimePoint(0), mkTimePoint(1)));
738 // Fill the remainder of the timeseries from kDuration to kDuration*2
740 end = TimePoint(kDuration * 2);
741 for (TimePoint i = start; i < end; i += seconds(1)) {
742 b.addValue(i, kFixedRate);
745 EXPECT_EQ(kFixedRate, b.rate());
746 EXPECT_EQ(kFixedRate, b.rate(TimePoint(kDuration), TimePoint(kDuration * 2)));
747 EXPECT_EQ(kFixedRate, b.rate(TimePoint(), TimePoint(kDuration * 2)));
748 EXPECT_EQ(kFixedRate, b.rate(TimePoint(), TimePoint(kDuration * 10)));
749 EXPECT_EQ(1.0, b.countRate());
750 EXPECT_EQ(1.0, b.countRate(TimePoint(kDuration), TimePoint(kDuration * 2)));
751 EXPECT_EQ(1.0, b.countRate(TimePoint(), TimePoint(kDuration * 2)));
752 EXPECT_EQ(1.0, b.countRate(TimePoint(), TimePoint(kDuration * 10)));
755 TEST(BucketedTimeSeries, addHistorical) {
756 const int kNumBuckets = 5;
757 const seconds kDuration(10);
758 BucketedTimeSeries<double> b(kNumBuckets, kDuration);
760 // Initially fill with a constant rate of data
761 for (TimePoint i = mkTimePoint(0); i < mkTimePoint(10); i += seconds(1)) {
765 EXPECT_EQ(10.0, b.rate());
766 EXPECT_EQ(10.0, b.avg());
767 EXPECT_EQ(10, b.count());
769 // Add some more data points to the middle bucket
770 b.addValue(mkTimePoint(4), 40.0);
771 b.addValue(mkTimePoint(5), 40.0);
772 EXPECT_EQ(15.0, b.avg());
773 EXPECT_EQ(18.0, b.rate());
774 EXPECT_EQ(12, b.count());
776 // Now start adding more current data points, until we are about to roll over
777 // the bucket where we added the extra historical data.
778 for (TimePoint i = mkTimePoint(10); i < mkTimePoint(14); i += seconds(1)) {
781 EXPECT_EQ(15.0, b.avg());
782 EXPECT_EQ(18.0, b.rate());
783 EXPECT_EQ(12, b.count());
785 // Now roll over the middle bucket
786 b.addValue(mkTimePoint(14), 10.0);
787 b.addValue(mkTimePoint(15), 10.0);
788 EXPECT_EQ(10.0, b.avg());
789 EXPECT_EQ(10.0, b.rate());
790 EXPECT_EQ(10, b.count());
792 // Add more historical values past the bucket window.
793 // These should be ignored.
794 EXPECT_FALSE(b.addValue(mkTimePoint(4), 40.0));
795 EXPECT_FALSE(b.addValue(mkTimePoint(5), 40.0));
796 EXPECT_EQ(10.0, b.avg());
797 EXPECT_EQ(10.0, b.rate());
798 EXPECT_EQ(10, b.count());
801 TEST(BucketedTimeSeries, reConstructEmptyTimeSeries) {
802 auto verify = [](auto timeSeries) {
803 EXPECT_TRUE(timeSeries.empty());
804 EXPECT_EQ(0, timeSeries.sum());
805 EXPECT_EQ(0, timeSeries.count());
808 // Create a 100 second timeseries with 10 buckets_
809 BucketedTimeSeries<int64_t> ts(10, seconds(100));
813 auto firstTime = ts.firstTime();
814 auto latestTime = ts.latestTime();
815 auto duration = ts.duration();
816 auto buckets = ts.buckets();
818 // Reconstruct the timeseries
819 BucketedTimeSeries<int64_t> newTs(firstTime, latestTime, duration, buckets);
824 TEST(BucketedTimeSeries, reConstructWithValidData) {
825 // Create a 100 second timeseries with 10 buckets_
826 BucketedTimeSeries<int64_t> ts(10, seconds(100));
830 // Add 1 value to each bucket
831 for (int n = 5; n <= 95; n += 10) {
832 ts.addValue(seconds(n), 6);
835 EXPECT_EQ(10, ts.count());
836 EXPECT_EQ(60, ts.sum());
837 EXPECT_EQ(6, ts.avg());
842 auto firstTime = ts.firstTime();
843 auto latestTime = ts.latestTime();
844 auto duration = ts.duration();
845 auto buckets = ts.buckets();
847 // Reconstruct the timeseries
848 BucketedTimeSeries<int64_t> newTs(firstTime, latestTime, duration, buckets);
851 EXPECT_EQ(ts.firstTime(), newTs.firstTime());
852 EXPECT_EQ(ts.latestTime(), newTs.latestTime());
853 EXPECT_EQ(ts.duration(), newTs.duration());
854 EXPECT_EQ(ts.buckets().size(), newTs.buckets().size());
855 EXPECT_EQ(ts.sum(), newTs.sum());
856 EXPECT_EQ(ts.count(), newTs.count());
858 for (auto it1 = ts.buckets().begin(), it2 = newTs.buckets().begin();
859 it1 != ts.buckets().end();
861 EXPECT_EQ(it1->sum, it2->sum);
862 EXPECT_EQ(it1->count, it2->count);
869 TEST(BucketedTimeSeries, reConstructWithCorruptedData) {
870 // The total should have been 0 as firstTime > latestTime
873 std::vector<Bucket> buckets(10);
875 buckets[0].count = 1;
877 BucketedTimeSeries<int64_t> ts(
878 mkTimePoint(1), mkTimePoint(0), Duration(10), buckets);
880 std::invalid_argument);
882 // The duration should be no less than latestTime - firstTime
884 BucketedTimeSeries<int64_t>(
888 std::vector<Bucket>(10)),
889 std::invalid_argument);
900 const seconds kMinuteHourDurations[] = {
901 seconds(60), seconds(3600), seconds(0)
905 TEST(MinuteHourTimeSeries, Basic) {
906 folly::MultiLevelTimeSeries<int> mhts(60, IntMHTS::NUM_LEVELS,
907 IntMHTS::kMinuteHourDurations);
908 EXPECT_EQ(mhts.numLevels(), IntMHTS::NUM_LEVELS);
909 EXPECT_EQ(mhts.numLevels(), 3);
911 EXPECT_EQ(mhts.sum(IntMHTS::MINUTE), 0);
912 EXPECT_EQ(mhts.sum(IntMHTS::HOUR), 0);
913 EXPECT_EQ(mhts.sum(IntMHTS::ALLTIME), 0);
915 EXPECT_EQ(mhts.avg(IntMHTS::MINUTE), 0);
916 EXPECT_EQ(mhts.avg(IntMHTS::HOUR), 0);
917 EXPECT_EQ(mhts.avg(IntMHTS::ALLTIME), 0);
919 EXPECT_EQ(mhts.rate(IntMHTS::MINUTE), 0);
920 EXPECT_EQ(mhts.rate(IntMHTS::HOUR), 0);
921 EXPECT_EQ(mhts.rate(IntMHTS::ALLTIME), 0);
923 EXPECT_EQ(mhts.getLevel(IntMHTS::MINUTE).elapsed().count(), 0);
924 EXPECT_EQ(mhts.getLevel(IntMHTS::HOUR).elapsed().count(), 0);
925 EXPECT_EQ(mhts.getLevel(IntMHTS::ALLTIME).elapsed().count(), 0);
929 mhts.addValue(cur_time++, 10);
932 EXPECT_EQ(mhts.getLevel(IntMHTS::MINUTE).elapsed().count(), 1);
933 EXPECT_EQ(mhts.getLevel(IntMHTS::HOUR).elapsed().count(), 1);
934 EXPECT_EQ(mhts.getLevel(IntMHTS::ALLTIME).elapsed().count(), 1);
936 for (int i = 0; i < 299; ++i) {
937 mhts.addValue(cur_time++, 10);
941 EXPECT_EQ(mhts.getLevel(IntMHTS::MINUTE).elapsed().count(), 60);
942 EXPECT_EQ(mhts.getLevel(IntMHTS::HOUR).elapsed().count(), 300);
943 EXPECT_EQ(mhts.getLevel(IntMHTS::ALLTIME).elapsed().count(), 300);
945 EXPECT_EQ(mhts.sum(IntMHTS::MINUTE), 600);
946 EXPECT_EQ(mhts.sum(IntMHTS::HOUR), 300*10);
947 EXPECT_EQ(mhts.sum(IntMHTS::ALLTIME), 300*10);
949 EXPECT_EQ(mhts.avg(IntMHTS::MINUTE), 10);
950 EXPECT_EQ(mhts.avg(IntMHTS::HOUR), 10);
951 EXPECT_EQ(mhts.avg(IntMHTS::ALLTIME), 10);
953 EXPECT_EQ(mhts.rate(IntMHTS::MINUTE), 10);
954 EXPECT_EQ(mhts.rate(IntMHTS::HOUR), 10);
955 EXPECT_EQ(mhts.rate(IntMHTS::ALLTIME), 10);
957 for (int i = 0; i < 3600*3 - 300; ++i) {
958 mhts.addValue(cur_time++, 10);
962 EXPECT_EQ(mhts.getLevel(IntMHTS::MINUTE).elapsed().count(), 60);
963 EXPECT_EQ(mhts.getLevel(IntMHTS::HOUR).elapsed().count(), 3600);
964 EXPECT_EQ(mhts.getLevel(IntMHTS::ALLTIME).elapsed().count(), 3600*3);
966 EXPECT_EQ(mhts.sum(IntMHTS::MINUTE), 600);
967 EXPECT_EQ(mhts.sum(IntMHTS::HOUR), 3600*10);
968 EXPECT_EQ(mhts.sum(IntMHTS::ALLTIME), 3600*3*10);
970 EXPECT_EQ(mhts.avg(IntMHTS::MINUTE), 10);
971 EXPECT_EQ(mhts.avg(IntMHTS::HOUR), 10);
972 EXPECT_EQ(mhts.avg(IntMHTS::ALLTIME), 10);
974 EXPECT_EQ(mhts.rate(IntMHTS::MINUTE), 10);
975 EXPECT_EQ(mhts.rate(IntMHTS::HOUR), 10);
976 EXPECT_EQ(mhts.rate(IntMHTS::ALLTIME), 10);
978 for (int i = 0; i < 3600; ++i) {
979 mhts.addValue(cur_time++, 100);
983 EXPECT_EQ(mhts.sum(IntMHTS::MINUTE), 60*100);
984 EXPECT_EQ(mhts.sum(IntMHTS::HOUR), 3600*100);
985 EXPECT_EQ(mhts.sum(IntMHTS::ALLTIME),
986 3600*3*10 + 3600*100);
988 EXPECT_EQ(mhts.avg(IntMHTS::MINUTE), 100);
989 EXPECT_EQ(mhts.avg(IntMHTS::HOUR), 100);
990 EXPECT_EQ(mhts.avg(IntMHTS::ALLTIME), 32.5);
991 EXPECT_EQ(mhts.avg<int>(IntMHTS::ALLTIME), 32);
993 EXPECT_EQ(mhts.rate(IntMHTS::MINUTE), 100);
994 EXPECT_EQ(mhts.rate(IntMHTS::HOUR), 100);
995 EXPECT_EQ(mhts.rate(IntMHTS::ALLTIME), 32.5);
996 EXPECT_EQ(mhts.rate<int>(IntMHTS::ALLTIME), 32);
998 for (int i = 0; i < 1800; ++i) {
999 mhts.addValue(cur_time++, 120);
1003 EXPECT_EQ(mhts.sum(IntMHTS::MINUTE), 60*120);
1004 EXPECT_EQ(mhts.sum(IntMHTS::HOUR),
1005 1800*100 + 1800*120);
1006 EXPECT_EQ(mhts.sum(IntMHTS::ALLTIME),
1007 3600*3*10 + 3600*100 + 1800*120);
1009 for (int i = 0; i < 60; ++i) {
1010 mhts.addValue(cur_time++, 1000);
1014 EXPECT_EQ(mhts.sum(IntMHTS::MINUTE), 60*1000);
1015 EXPECT_EQ(mhts.sum(IntMHTS::HOUR),
1016 1740*100 + 1800*120 + 60*1000);
1017 EXPECT_EQ(mhts.sum(IntMHTS::ALLTIME),
1018 3600*3*10 + 3600*100 + 1800*120 + 60*1000);
1021 EXPECT_EQ(mhts.sum(IntMHTS::ALLTIME), 0);
1024 TEST(MinuteHourTimeSeries, QueryByInterval) {
1025 folly::MultiLevelTimeSeries<int> mhts(60, IntMHTS::NUM_LEVELS,
1026 IntMHTS::kMinuteHourDurations);
1029 for (curTime = mkTimePoint(0); curTime < mkTimePoint(7200);
1030 curTime += seconds(1)) {
1031 mhts.addValue(curTime, 1);
1033 for (curTime = mkTimePoint(7200); curTime < mkTimePoint(7200 + 3540);
1034 curTime += seconds(1)) {
1035 mhts.addValue(curTime, 10);
1037 for (curTime = mkTimePoint(7200 + 3540); curTime < mkTimePoint(7200 + 3600);
1038 curTime += seconds(1)) {
1039 mhts.addValue(curTime, 100);
1043 struct TimeInterval {
1047 TimeInterval intervals[12] = {
1048 { curTime - seconds(60), curTime },
1049 { curTime - seconds(3600), curTime },
1050 { curTime - seconds(7200), curTime },
1051 { curTime - seconds(3600), curTime - seconds(60) },
1052 { curTime - seconds(7200), curTime - seconds(60) },
1053 { curTime - seconds(7200), curTime - seconds(3600) },
1054 { curTime - seconds(50), curTime - seconds(20) },
1055 { curTime - seconds(3020), curTime - seconds(20) },
1056 { curTime - seconds(7200), curTime - seconds(20) },
1057 { curTime - seconds(3000), curTime - seconds(1000) },
1058 { curTime - seconds(7200), curTime - seconds(1000) },
1059 { curTime - seconds(7200), curTime - seconds(3600) },
1062 int expectedSums[12] = {
1063 6000, 41400, 32400, 35400, 32130, 16200, 3000, 33600, 32310, 20000, 27900,
1067 int expectedCounts[12] = {
1068 60, 3600, 7200, 3540, 7140, 3600, 30, 3000, 7180, 2000, 6200, 3600
1071 for (int i = 0; i < 12; ++i) {
1072 TimeInterval interval = intervals[i];
1074 int s = mhts.sum(interval.start, interval.end);
1075 EXPECT_EQ(expectedSums[i], s);
1077 int c = mhts.count(interval.start, interval.end);
1078 EXPECT_EQ(expectedCounts[i], c);
1080 int a = mhts.avg<int>(interval.start, interval.end);
1081 EXPECT_EQ(expectedCounts[i] ?
1082 (expectedSums[i] / expectedCounts[i]) : 0,
1085 int r = mhts.rate<int>(interval.start, interval.end);
1087 expectedSums[i] / (interval.end - interval.start).count();
1088 EXPECT_EQ(expectedRate, r);
1092 TEST(MultiLevelTimeSeries, Basic) {
1093 // using constructor with initializer_list parameter
1094 folly::MultiLevelTimeSeries<int> mhts(
1095 60, {seconds(60), seconds(3600), seconds(0)});
1096 EXPECT_EQ(mhts.numLevels(), 3);
1098 EXPECT_EQ(mhts.sum(seconds(60)), 0);
1099 EXPECT_EQ(mhts.sum(seconds(3600)), 0);
1100 EXPECT_EQ(mhts.sum(seconds(0)), 0);
1102 EXPECT_EQ(mhts.avg(seconds(60)), 0);
1103 EXPECT_EQ(mhts.avg(seconds(3600)), 0);
1104 EXPECT_EQ(mhts.avg(seconds(0)), 0);
1106 EXPECT_EQ(mhts.rate(seconds(60)), 0);
1107 EXPECT_EQ(mhts.rate(seconds(3600)), 0);
1108 EXPECT_EQ(mhts.rate(seconds(0)), 0);
1110 EXPECT_EQ(mhts.getLevelByDuration(seconds(60)).elapsed().count(), 0);
1111 EXPECT_EQ(mhts.getLevelByDuration(seconds(3600)).elapsed().count(), 0);
1112 EXPECT_EQ(mhts.getLevelByDuration(seconds(0)).elapsed().count(), 0);
1114 seconds cur_time(0);
1116 mhts.addValue(cur_time++, 10);
1119 EXPECT_EQ(mhts.getLevelByDuration(seconds(60)).elapsed().count(), 1);
1120 EXPECT_EQ(mhts.getLevelByDuration(seconds(3600)).elapsed().count(), 1);
1121 EXPECT_EQ(mhts.getLevelByDuration(seconds(0)).elapsed().count(), 1);
1123 for (int i = 0; i < 299; ++i) {
1124 mhts.addValue(cur_time++, 10);
1128 EXPECT_EQ(mhts.getLevelByDuration(seconds(60)).elapsed().count(), 60);
1129 EXPECT_EQ(mhts.getLevelByDuration(seconds(3600)).elapsed().count(), 300);
1130 EXPECT_EQ(mhts.getLevelByDuration(seconds(0)).elapsed().count(), 300);
1132 EXPECT_EQ(mhts.sum(seconds(60)), 600);
1133 EXPECT_EQ(mhts.sum(seconds(3600)), 300 * 10);
1134 EXPECT_EQ(mhts.sum(seconds(0)), 300 * 10);
1136 EXPECT_EQ(mhts.avg(seconds(60)), 10);
1137 EXPECT_EQ(mhts.avg(seconds(3600)), 10);
1138 EXPECT_EQ(mhts.avg(seconds(0)), 10);
1140 EXPECT_EQ(mhts.rate(seconds(60)), 10);
1141 EXPECT_EQ(mhts.rate(seconds(3600)), 10);
1142 EXPECT_EQ(mhts.rate(seconds(0)), 10);
1144 for (int i = 0; i < 3600 * 3 - 300; ++i) {
1145 mhts.addValue(cur_time++, 10);
1149 EXPECT_EQ(mhts.getLevelByDuration(seconds(60)).elapsed().count(), 60);
1150 EXPECT_EQ(mhts.getLevelByDuration(seconds(3600)).elapsed().count(), 3600);
1151 EXPECT_EQ(mhts.getLevelByDuration(seconds(0)).elapsed().count(), 3600 * 3);
1153 EXPECT_EQ(mhts.sum(seconds(60)), 600);
1154 EXPECT_EQ(mhts.sum(seconds(3600)), 3600 * 10);
1155 EXPECT_EQ(mhts.sum(seconds(0)), 3600 * 3 * 10);
1157 EXPECT_EQ(mhts.avg(seconds(60)), 10);
1158 EXPECT_EQ(mhts.avg(seconds(3600)), 10);
1159 EXPECT_EQ(mhts.avg(seconds(0)), 10);
1161 EXPECT_EQ(mhts.rate(seconds(60)), 10);
1162 EXPECT_EQ(mhts.rate(seconds(3600)), 10);
1163 EXPECT_EQ(mhts.rate(seconds(0)), 10);
1165 for (int i = 0; i < 3600; ++i) {
1166 mhts.addValue(cur_time++, 100);
1170 EXPECT_EQ(mhts.sum(seconds(60)), 60 * 100);
1171 EXPECT_EQ(mhts.sum(seconds(3600)), 3600 * 100);
1172 EXPECT_EQ(mhts.sum(seconds(0)), 3600 * 3 * 10 + 3600 * 100);
1174 EXPECT_EQ(mhts.avg(seconds(60)), 100);
1175 EXPECT_EQ(mhts.avg(seconds(3600)), 100);
1176 EXPECT_EQ(mhts.avg(seconds(0)), 32.5);
1177 EXPECT_EQ(mhts.avg<int>(seconds(0)), 32);
1179 EXPECT_EQ(mhts.rate(seconds(60)), 100);
1180 EXPECT_EQ(mhts.rate(seconds(3600)), 100);
1181 EXPECT_EQ(mhts.rate(seconds(0)), 32.5);
1182 EXPECT_EQ(mhts.rate<int>(seconds(0)), 32);
1184 for (int i = 0; i < 1800; ++i) {
1185 mhts.addValue(cur_time++, 120);
1189 EXPECT_EQ(mhts.sum(seconds(60)), 60 * 120);
1190 EXPECT_EQ(mhts.sum(seconds(3600)), 1800 * 100 + 1800 * 120);
1191 EXPECT_EQ(mhts.sum(seconds(0)), 3600 * 3 * 10 + 3600 * 100 + 1800 * 120);
1193 for (int i = 0; i < 60; ++i) {
1194 mhts.addValue(cur_time++, 1000);
1198 EXPECT_EQ(mhts.sum(seconds(60)), 60 * 1000);
1199 EXPECT_EQ(mhts.sum(seconds(3600)), 1740 * 100 + 1800 * 120 + 60 * 1000);
1201 mhts.sum(seconds(0)),
1202 3600 * 3 * 10 + 3600 * 100 + 1800 * 120 + 60 * 1000);
1205 EXPECT_EQ(mhts.sum(seconds(0)), 0);
1208 TEST(MultiLevelTimeSeries, QueryByInterval) {
1209 folly::MultiLevelTimeSeries<int> mhts(
1210 60, {seconds(60), seconds(3600), seconds(0)});
1213 for (curTime = mkTimePoint(0); curTime < mkTimePoint(7200);
1214 curTime += seconds(1)) {
1215 mhts.addValue(curTime, 1);
1217 for (curTime = mkTimePoint(7200); curTime < mkTimePoint(7200 + 3540);
1218 curTime += seconds(1)) {
1219 mhts.addValue(curTime, 10);
1221 for (curTime = mkTimePoint(7200 + 3540); curTime < mkTimePoint(7200 + 3600);
1222 curTime += seconds(1)) {
1223 mhts.addValue(curTime, 100);
1227 struct TimeInterval {
1232 std::array<TimeInterval, 12> intervals = {{
1233 {curTime - seconds(60), curTime},
1234 {curTime - seconds(3600), curTime},
1235 {curTime - seconds(7200), curTime},
1236 {curTime - seconds(3600), curTime - seconds(60)},
1237 {curTime - seconds(7200), curTime - seconds(60)},
1238 {curTime - seconds(7200), curTime - seconds(3600)},
1239 {curTime - seconds(50), curTime - seconds(20)},
1240 {curTime - seconds(3020), curTime - seconds(20)},
1241 {curTime - seconds(7200), curTime - seconds(20)},
1242 {curTime - seconds(3000), curTime - seconds(1000)},
1243 {curTime - seconds(7200), curTime - seconds(1000)},
1244 {curTime - seconds(7200), curTime - seconds(3600)},
1247 std::array<int, 12> expectedSums = {{6000,
1260 std::array<int, 12> expectedCounts = {
1261 {60, 3600, 7200, 3540, 7140, 3600, 30, 3000, 7180, 2000, 6200, 3600}};
1263 for (size_t i = 0; i < intervals.size(); ++i) {
1264 TimeInterval interval = intervals[i];
1266 int s = mhts.sum(interval.start, interval.end);
1267 EXPECT_EQ(expectedSums[i], s);
1269 int c = mhts.count(interval.start, interval.end);
1270 EXPECT_EQ(expectedCounts[i], c);
1272 int a = mhts.avg<int>(interval.start, interval.end);
1273 EXPECT_EQ(expectedCounts[i] ? (expectedSums[i] / expectedCounts[i]) : 0, a);
1275 int r = mhts.rate<int>(interval.start, interval.end);
1277 expectedSums[i] / (interval.end - interval.start).count();
1278 EXPECT_EQ(expectedRate, r);