TimeSeriesTest.cpp

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/*
 * Copyright 2012-present Facebook, Inc.
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *   http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

#include <folly/stats/BucketedTimeSeries-defs.h>
#include <folly/stats/BucketedTimeSeries.h>
#include <folly/stats/MultiLevelTimeSeries-defs.h>
#include <folly/stats/MultiLevelTimeSeries.h>
#include <folly/stats/detail/Bucket.h>

#include <array>

#include <glog/logging.h>

#include <folly/container/Foreach.h>
#include <folly/portability/GTest.h>

using folly::BucketedTimeSeries;
using std::string;
using std::vector;
using std::chrono::seconds;

using Bucket = folly::detail::Bucket<int64_t>;
using StatsClock = folly::LegacyStatsClock<std::chrono::seconds>;
using TimePoint = StatsClock::time_point;
using Duration = StatsClock::duration;

/*
 * Helper functions to allow us to directly log time points and duration
 */
namespace std {
std::ostream& operator<<(std::ostream& os, std::chrono::seconds s) {
  os << s.count();
  return os;
}
std::ostream& operator<<(std::ostream& os, TimePoint tp) {
  os << tp.time_since_epoch().count();
  return os;
}
} // namespace std

namespace {
TimePoint mkTimePoint(int value) {
  return TimePoint(StatsClock::duration(value));
}

struct TestData {
  TestData(int d, int b, std::initializer_list<int> starts)
      : duration(d), numBuckets(b) {
    bucketStarts.reserve(starts.size());
    for (int s : starts) {
      bucketStarts.push_back(mkTimePoint(s));
    }
  }
  seconds duration;
  size_t numBuckets;
  vector<TimePoint> bucketStarts;
};
vector<TestData> testData = {
    // 71 seconds x 4 buckets
    {71, 4, {0, 18, 36, 54}},
    // 100 seconds x 10 buckets
    {100, 10, {0, 10, 20, 30, 40, 50, 60, 70, 80, 90}},
    // 10 seconds x 10 buckets
    {10, 10, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}},
    // 10 seconds x 1 buckets
    {10, 1, {0}},
    // 1 second x 1 buckets
    {1, 1, {0}},
};
} // namespace

TEST(BucketedTimeSeries, getBucketInfo) {
  for (const auto& data : testData) {
    BucketedTimeSeries<int64_t> ts(data.numBuckets, data.duration);

    for (uint32_t n = 0; n < 10000; n += 1234) {
      seconds offset(n * data.duration);

      for (uint32_t idx = 0; idx < data.numBuckets; ++idx) {
        auto bucketStart = data.bucketStarts[idx];
        TimePoint nextBucketStart;
        if (idx + 1 < data.numBuckets) {
          nextBucketStart = data.bucketStarts[idx + 1];
        } else {
          nextBucketStart = TimePoint(data.duration);
        }

        TimePoint expectedStart = offset + bucketStart;
        TimePoint expectedNextStart = offset + nextBucketStart;
        TimePoint midpoint =
            expectedStart + (expectedNextStart - expectedStart) / 2;

        vector<std::pair<string, TimePoint>> timePoints = {
            {"expectedStart", expectedStart},
            {"midpoint", midpoint},
            {"expectedEnd", expectedNextStart - seconds(1)},
        };

        for (const auto& point : timePoints) {
          // Check that getBucketIdx() returns the expected index
          EXPECT_EQ(idx, ts.getBucketIdx(point.second))
              << data.duration << "x" << data.numBuckets << ": " << point.first
              << "=" << point.second;

          // Check the data returned by getBucketInfo()
          size_t returnedIdx;
          TimePoint returnedStart;
          TimePoint returnedNextStart;
          ts.getBucketInfo(
              expectedStart, &returnedIdx, &returnedStart, &returnedNextStart);
          EXPECT_EQ(idx, returnedIdx)
              << data.duration << "x" << data.numBuckets << ": " << point.first
              << "=" << point.second;
          EXPECT_EQ(expectedStart, returnedStart)
              << data.duration << "x" << data.numBuckets << ": " << point.first
              << "=" << point.second;
          EXPECT_EQ(expectedNextStart, returnedNextStart)
              << data.duration << "x" << data.numBuckets << ": " << point.first
              << "=" << point.second;
        }
      }
    }
  }
}

void testUpdate100x10(size_t offset) {
  // This test code only works when offset is a multiple of the bucket width
  CHECK_EQ(0, offset % 10);

  // Create a 100 second timeseries, with 10 buckets
  BucketedTimeSeries<int64_t> ts(10, seconds(100));

  auto setup = [&] {
    ts.clear();
    // Add 1 value to each bucket
    for (int n = 5; n <= 95; n += 10) {
      ts.addValue(seconds(n + offset), 6);
    }

    EXPECT_EQ(10, ts.count());
    EXPECT_EQ(60, ts.sum());
    EXPECT_EQ(6, ts.avg());
  };

  // Update 2 buckets forwards.  This should throw away 2 data points.
  setup();
  ts.update(seconds(110 + offset));
  EXPECT_EQ(8, ts.count());
  EXPECT_EQ(48, ts.sum());
  EXPECT_EQ(6, ts.avg());

  // The last time we added was 95.
  // Try updating to 189.  This should clear everything but the last bucket.
  setup();
  ts.update(seconds(151 + offset));
  EXPECT_EQ(4, ts.count());
  // EXPECT_EQ(6, ts.sum());
  EXPECT_EQ(6, ts.avg());

  // The last time we added was 95.
  // Try updating to 193: This is nearly one full loop around,
  // back to the same bucket.  update() needs to clear everything
  setup();
  ts.update(seconds(193 + offset));
  EXPECT_EQ(0, ts.count());
  EXPECT_EQ(0, ts.sum());
  EXPECT_EQ(0, ts.avg());

  // The last time we added was 95.
  // Try updating to 197: This is slightly over one full loop around,
  // back to the same bucket.  update() needs to clear everything
  setup();
  ts.update(seconds(197 + offset));
  EXPECT_EQ(0, ts.count());
  EXPECT_EQ(0, ts.sum());
  EXPECT_EQ(0, ts.avg());

  // The last time we added was 95.
  // Try updating to 230: This is well over one full loop around,
  // and everything should be cleared.
  setup();
  ts.update(seconds(230 + offset));
  EXPECT_EQ(0, ts.count());
  EXPECT_EQ(0, ts.sum());
  EXPECT_EQ(0, ts.avg());
}

TEST(BucketedTimeSeries, update100x10) {
  // Run testUpdate100x10() multiple times, with various offsets.
  // This makes sure the update code works regardless of which bucket it starts
  // at in the modulo arithmetic.
  testUpdate100x10(0);
  testUpdate100x10(50);
  testUpdate100x10(370);
  testUpdate100x10(1937090);
}

TEST(BucketedTimeSeries, update71x5) {
  // Create a 71 second timeseries, with 5 buckets
  // This tests when the number of buckets does not divide evenly into the
  // duration.
  BucketedTimeSeries<int64_t> ts(5, seconds(71));

  auto setup = [&] {
    ts.clear();
    // Add 1 value to each bucket
    ts.addValue(seconds(11), 6);
    ts.addValue(seconds(24), 6);
    ts.addValue(seconds(42), 6);
    ts.addValue(seconds(43), 6);
    ts.addValue(seconds(66), 6);

    EXPECT_EQ(5, ts.count());
    EXPECT_EQ(30, ts.sum());
    EXPECT_EQ(6, ts.avg());
  };

  // Update 2 buckets forwards.  This should throw away 2 data points.
  setup();
  ts.update(seconds(99));
  EXPECT_EQ(3, ts.count());
  EXPECT_EQ(18, ts.sum());
  EXPECT_EQ(6, ts.avg());

  // Update 3 buckets forwards.  This should throw away 3 data points.
  setup();
  ts.update(seconds(100));
  EXPECT_EQ(2, ts.count());
  EXPECT_EQ(12, ts.sum());
  EXPECT_EQ(6, ts.avg());

  // Update 4 buckets forwards, just under the wrap limit.
  // This should throw everything but the last bucket away.
  setup();
  ts.update(seconds(127));
  EXPECT_EQ(1, ts.count());
  EXPECT_EQ(6, ts.sum());
  EXPECT_EQ(6, ts.avg());

  // Update 5 buckets forwards, exactly at the wrap limit.
  // This should throw everything away.
  setup();
  ts.update(seconds(128));
  EXPECT_EQ(0, ts.count());
  EXPECT_EQ(0, ts.sum());
  EXPECT_EQ(0, ts.avg());

  // Update very far forwards, wrapping multiple times.
  // This should throw everything away.
  setup();
  ts.update(seconds(1234));
  EXPECT_EQ(0, ts.count());
  EXPECT_EQ(0, ts.sum());
  EXPECT_EQ(0, ts.avg());
}

TEST(BucketedTimeSeries, elapsed) {
  BucketedTimeSeries<int64_t> ts(60, seconds(600));

  // elapsed() is 0 when no data points have been added
  EXPECT_EQ(0, ts.elapsed().count());

  // With exactly 1 data point, elapsed() should report 1 second of data
  seconds start(239218);
  ts.addValue(start + seconds(0), 200);
  EXPECT_EQ(1, ts.elapsed().count());
  // Adding a data point 10 seconds later should result in an elapsed time of
  // 11 seconds (the time range is [0, 10], inclusive).
  ts.addValue(start + seconds(10), 200);
  EXPECT_EQ(11, ts.elapsed().count());

  // elapsed() returns to 0 after clear()
  ts.clear();
  EXPECT_EQ(0, ts.elapsed().count());

  // Restart, with the starting point on an easier number to work with
  ts.addValue(seconds(10), 200);
  EXPECT_EQ(1, ts.elapsed().count());
  ts.addValue(seconds(580), 200);
  EXPECT_EQ(571, ts.elapsed().count());
  ts.addValue(seconds(590), 200);
  EXPECT_EQ(581, ts.elapsed().count());
  ts.addValue(seconds(598), 200);
  EXPECT_EQ(589, ts.elapsed().count());
  ts.addValue(seconds(599), 200);
  EXPECT_EQ(590, ts.elapsed().count());
  ts.addValue(seconds(600), 200);
  EXPECT_EQ(591, ts.elapsed().count());
  ts.addValue(seconds(608), 200);
  EXPECT_EQ(599, ts.elapsed().count());
  ts.addValue(seconds(609), 200);
  EXPECT_EQ(600, ts.elapsed().count());
  // Once we reach 600 seconds worth of data, when we move on to the next
  // second a full bucket will get thrown out.  Now we drop back down to 591
  // seconds worth of data
  ts.addValue(seconds(610), 200);
  EXPECT_EQ(591, ts.elapsed().count());
  ts.addValue(seconds(618), 200);
  EXPECT_EQ(599, ts.elapsed().count());
  ts.addValue(seconds(619), 200);
  EXPECT_EQ(600, ts.elapsed().count());
  ts.addValue(seconds(620), 200);
  EXPECT_EQ(591, ts.elapsed().count());
  ts.addValue(seconds(123419), 200);
  EXPECT_EQ(600, ts.elapsed().count());
  ts.addValue(seconds(123420), 200);
  EXPECT_EQ(591, ts.elapsed().count());
  ts.addValue(seconds(123425), 200);
  EXPECT_EQ(596, ts.elapsed().count());

  // Time never moves backwards.
  // Calling update with an old timestamp will just be ignored.
  ts.update(seconds(29));
  EXPECT_EQ(596, ts.elapsed().count());
}

TEST(BucketedTimeSeries, rate) {
  BucketedTimeSeries<int64_t> ts(60, seconds(600));

  // Add 3 values every 2 seconds, until fill up the buckets
  for (size_t n = 0; n < 600; n += 2) {
    ts.addValue(seconds(n), 200, 3);
  }

  EXPECT_EQ(900, ts.count());
  EXPECT_EQ(180000, ts.sum());
  EXPECT_EQ(200, ts.avg());

  // Really we only entered 599 seconds worth of data: [0, 598] (inclusive)
  EXPECT_EQ(599, ts.elapsed().count());
  EXPECT_NEAR(300.5, ts.rate(), 0.005);
  EXPECT_NEAR(1.5, ts.countRate(), 0.005);

  // If we add 1 more second, now we will have 600 seconds worth of data
  ts.update(seconds(599));
  EXPECT_EQ(600, ts.elapsed().count());
  EXPECT_NEAR(300, ts.rate(), 0.005);
  EXPECT_EQ(300, ts.rate<int>());
  EXPECT_NEAR(1.5, ts.countRate(), 0.005);

  // However, 1 more second after that and we will have filled up all the
  // buckets, and have to drop one.
  ts.update(seconds(600));
  EXPECT_EQ(591, ts.elapsed().count());
  EXPECT_NEAR(299.5, ts.rate(), 0.01);
  EXPECT_EQ(299, ts.rate<int>());
  EXPECT_NEAR(1.5, ts.countRate(), 0.005);
}

TEST(BucketedTimeSeries, avgTypeConversion) {
  // Make sure the computed average values are accurate regardless
  // of the input type and return type.

  {
    // Simple sanity tests for small positive integer values
    BucketedTimeSeries<int64_t> ts(60, seconds(600));
    ts.addValue(seconds(0), 4, 100);
    ts.addValue(seconds(0), 10, 200);
    ts.addValue(seconds(0), 16, 100);

    EXPECT_DOUBLE_EQ(10.0, ts.avg());
    EXPECT_DOUBLE_EQ(10.0, ts.avg<float>());
    EXPECT_EQ(10, ts.avg<uint64_t>());
    EXPECT_EQ(10, ts.avg<int64_t>());
    EXPECT_EQ(10, ts.avg<int32_t>());
    EXPECT_EQ(10, ts.avg<int16_t>());
    EXPECT_EQ(10, ts.avg<int8_t>());
    EXPECT_EQ(10, ts.avg<uint8_t>());
  }

  {
    // Test signed integer types with negative values
    BucketedTimeSeries<int64_t> ts(60, seconds(600));
    ts.addValue(seconds(0), -100);
    ts.addValue(seconds(0), -200);
    ts.addValue(seconds(0), -300);
    ts.addValue(seconds(0), -200, 65535);

    EXPECT_DOUBLE_EQ(-200.0, ts.avg());
    EXPECT_DOUBLE_EQ(-200.0, ts.avg<float>());
    EXPECT_EQ(-200, ts.avg<int64_t>());
    EXPECT_EQ(-200, ts.avg<int32_t>());
    EXPECT_EQ(-200, ts.avg<int16_t>());
  }

  {
    // Test uint64_t values that would overflow int64_t
    BucketedTimeSeries<uint64_t> ts(60, seconds(600));
    ts.addValueAggregated(
        seconds(0),
        std::numeric_limits<uint64_t>::max(),
        std::numeric_limits<uint64_t>::max());

    EXPECT_DOUBLE_EQ(1.0, ts.avg());
    EXPECT_DOUBLE_EQ(1.0, ts.avg<float>());
    EXPECT_EQ(1, ts.avg<uint64_t>());
    EXPECT_EQ(1, ts.avg<int64_t>());
    EXPECT_EQ(1, ts.avg<int8_t>());
  }

  {
    // Test doubles with small-ish values that will fit in integer types
    BucketedTimeSeries<double> ts(60, seconds(600));
    ts.addValue(seconds(0), 4.0, 100);
    ts.addValue(seconds(0), 10.0, 200);
    ts.addValue(seconds(0), 16.0, 100);

    EXPECT_DOUBLE_EQ(10.0, ts.avg());
    EXPECT_DOUBLE_EQ(10.0, ts.avg<float>());
    EXPECT_EQ(10, ts.avg<uint64_t>());
    EXPECT_EQ(10, ts.avg<int64_t>());
    EXPECT_EQ(10, ts.avg<int32_t>());
    EXPECT_EQ(10, ts.avg<int16_t>());
    EXPECT_EQ(10, ts.avg<int8_t>());
    EXPECT_EQ(10, ts.avg<uint8_t>());
  }

  {
    // Test doubles with huge values
    BucketedTimeSeries<double> ts(60, seconds(600));
    ts.addValue(seconds(0), 1e19, 100);
    ts.addValue(seconds(0), 2e19, 200);
    ts.addValue(seconds(0), 3e19, 100);

    EXPECT_DOUBLE_EQ(ts.avg(), 2e19);
    EXPECT_NEAR(ts.avg<float>(), 2e19, 1e11);
  }

  {
    // Test doubles where the sum adds up larger than a uint64_t,
    // but the average fits in an int64_t
    BucketedTimeSeries<double> ts(60, seconds(600));
    uint64_t value = 0x3fffffffffffffff;
    FOR_EACH_RANGE (i, 0, 16) { ts.addValue(seconds(0), value); }

    EXPECT_DOUBLE_EQ(value, ts.avg());
    EXPECT_DOUBLE_EQ(value, ts.avg<float>());
    // Some precision is lost here due to the huge sum, so the
    // integer average returned is off by one.
    EXPECT_NEAR(value, ts.avg<uint64_t>(), 1);
    EXPECT_NEAR(value, ts.avg<int64_t>(), 1);
  }

  {
    // Test BucketedTimeSeries with a smaller integer type
    BucketedTimeSeries<int16_t> ts(60, seconds(600));
    FOR_EACH_RANGE (i, 0, 101) { ts.addValue(seconds(0), i); }

    EXPECT_DOUBLE_EQ(50.0, ts.avg());
    EXPECT_DOUBLE_EQ(50.0, ts.avg<float>());
    EXPECT_EQ(50, ts.avg<uint64_t>());
    EXPECT_EQ(50, ts.avg<int64_t>());
    EXPECT_EQ(50, ts.avg<int16_t>());
    EXPECT_EQ(50, ts.avg<int8_t>());
  }

  {
    // Test BucketedTimeSeries with long double input
    BucketedTimeSeries<long double> ts(60, seconds(600));
    ts.addValueAggregated(seconds(0), 1000.0L, 7);

    long double expected = 1000.0L / 7.0L;
    EXPECT_DOUBLE_EQ(static_cast<double>(expected), ts.avg());
    EXPECT_DOUBLE_EQ(static_cast<float>(expected), ts.avg<float>());
    EXPECT_DOUBLE_EQ(expected, ts.avg<long double>());
    EXPECT_EQ(static_cast<uint64_t>(expected), ts.avg<uint64_t>());
    EXPECT_EQ(static_cast<int64_t>(expected), ts.avg<int64_t>());
  }

  {
    // Test BucketedTimeSeries with int64_t values,
    // but using an average that requires a fair amount of precision.
    BucketedTimeSeries<int64_t> ts(60, seconds(600));
    ts.addValueAggregated(seconds(0), 1000, 7);

    long double expected = 1000.0L / 7.0L;
    EXPECT_DOUBLE_EQ(static_cast<double>(expected), ts.avg());
    EXPECT_DOUBLE_EQ(static_cast<float>(expected), ts.avg<float>());
    EXPECT_DOUBLE_EQ(expected, ts.avg<long double>());
    EXPECT_EQ(static_cast<uint64_t>(expected), ts.avg<uint64_t>());
    EXPECT_EQ(static_cast<int64_t>(expected), ts.avg<int64_t>());
  }
}

TEST(BucketedTimeSeries, forEachBucket) {
  typedef BucketedTimeSeries<int64_t>::Bucket BucketSeries;
  struct BucketInfo {
    BucketInfo(const BucketSeries* b, TimePoint s, TimePoint ns)
        : bucket(b), start(s), nextStart(ns) {}

    const BucketSeries* bucket;
    TimePoint start;
    TimePoint nextStart;
  };

  for (const auto& data : testData) {
    BucketedTimeSeries<int64_t> ts(data.numBuckets, seconds(data.duration));

    vector<BucketInfo> info;
    auto fn = [&](const BucketSeries& bucket,
                  TimePoint bucketStart,
                  TimePoint bucketEnd) -> bool {
      info.emplace_back(&bucket, bucketStart, bucketEnd);
      return true;
    };

    // If we haven't yet added any data, the current bucket will start at 0,
    // and all data previous buckets will have negative times.
    ts.forEachBucket(fn);

    CHECK_EQ(data.numBuckets, info.size());

    // Check the data passed in to the function
    size_t infoIdx = 0;
    size_t bucketIdx = 1;
    seconds offset = -data.duration;
    for (size_t n = 0; n < data.numBuckets; ++n) {
      if (bucketIdx >= data.numBuckets) {
        bucketIdx = 0;
        offset += data.duration;
      }

      EXPECT_EQ(data.bucketStarts[bucketIdx] + offset, info[infoIdx].start)
          << data.duration << "x" << data.numBuckets
          << ": bucketIdx=" << bucketIdx << ", infoIdx=" << infoIdx;

      size_t nextBucketIdx = bucketIdx + 1;
      seconds nextOffset = offset;
      if (nextBucketIdx >= data.numBuckets) {
        nextBucketIdx = 0;
        nextOffset += data.duration;
      }
      EXPECT_EQ(
          data.bucketStarts[nextBucketIdx] + nextOffset,
          info[infoIdx].nextStart)
          << data.duration << "x" << data.numBuckets
          << ": bucketIdx=" << bucketIdx << ", infoIdx=" << infoIdx;

      EXPECT_EQ(&ts.getBucketByIndex(bucketIdx), info[infoIdx].bucket);

      ++bucketIdx;
      ++infoIdx;
    }
  }
}

TEST(BucketedTimeSeries, queryByIntervalSimple) {
  BucketedTimeSeries<int> a(3, seconds(12));
  for (int i = 0; i < 8; i++) {
    a.addValue(seconds(i), 1);
  }
  // We added 1 at each second from 0..7
  // Query from the time period 0..2.
  // This is entirely in the first bucket, which has a sum of 4.
  // The code knows only part of the bucket is covered, and correctly
  // estimates the desired sum as 3.
  EXPECT_EQ(2, a.sum(mkTimePoint(0), mkTimePoint(2)));
}

TEST(BucketedTimeSeries, queryByInterval) {
  // Set up a BucketedTimeSeries tracking 6 seconds in 3 buckets
  const int kNumBuckets = 3;
  const int kDuration = 6;
  BucketedTimeSeries<double> b(kNumBuckets, seconds(kDuration));

  for (unsigned int i = 0; i < kDuration; ++i) {
    // add value 'i' at time 'i'
    b.addValue(mkTimePoint(i), i);
  }

  // Current bucket state:
  // 0: time=[0, 2): values=(0, 1), sum=1, count=2
  // 1: time=[2, 4): values=(2, 3), sum=5, count=1
  // 2: time=[4, 6): values=(4, 5), sum=9, count=2
  // clang-format off
  double expectedSums1[kDuration + 1][kDuration + 1] = {
      {0,  4.5,   9, 11.5,  14, 14.5,  15},
      {0,  4.5,   7,  9.5,  10, 10.5,  -1},
      {0,  2.5,   5,  5.5,   6,   -1,  -1},
      {0,  2.5,   3,  3.5,  -1,   -1,  -1},
      {0,  0.5,   1,   -1,  -1,   -1,  -1},
      {0,  0.5,  -1,   -1,  -1,   -1,  -1},
      {0,   -1,  -1,   -1,  -1,   -1,  -1},
  };
  int expectedCounts1[kDuration + 1][kDuration + 1] = {
      {0,  1,  2,  3,  4,  5,  6},
      {0,  1,  2,  3,  4,  5, -1},
      {0,  1,  2,  3,  4, -1, -1},
      {0,  1,  2,  3, -1, -1, -1},
      {0,  1,  2, -1, -1, -1, -1},
      {0,  1, -1, -1, -1, -1, -1},
      {0, -1, -1, -1, -1, -1, -1},
  };
  // clang-format on

  TimePoint currentTime = b.getLatestTime() + seconds(1);
  for (int i = 0; i <= kDuration + 1; i++) {
    for (int j = 0; j <= kDuration - i; j++) {
      TimePoint start = currentTime - seconds(i + j);
      TimePoint end = currentTime - seconds(i);
      double expectedSum = expectedSums1[i][j];
      EXPECT_EQ(expectedSum, b.sum(start, end))
          << "i=" << i << ", j=" << j << ", interval=[" << start << ", " << end
          << ")";

      uint64_t expectedCount = expectedCounts1[i][j];
      EXPECT_EQ(expectedCount, b.count(start, end))
          << "i=" << i << ", j=" << j << ", interval=[" << start << ", " << end
          << ")";

      double expectedAvg = expectedCount ? expectedSum / expectedCount : 0;
      EXPECT_EQ(expectedAvg, b.avg(start, end))
          << "i=" << i << ", j=" << j << ", interval=[" << start << ", " << end
          << ")";

      double expectedRate = j ? expectedSum / j : 0;
      EXPECT_EQ(expectedRate, b.rate(start, end))
          << "i=" << i << ", j=" << j << ", interval=[" << start << ", " << end
          << ")";
    }
  }

  // Add 3 more values.
  // This will overwrite 1 full bucket, and put us halfway through the next.
  for (unsigned int i = kDuration; i < kDuration + 3; ++i) {
    b.addValue(mkTimePoint(i), i);
  }
  EXPECT_EQ(mkTimePoint(4), b.getEarliestTime());

  // Current bucket state:
  // 0: time=[6,  8): values=(6, 7), sum=13, count=2
  // 1: time=[8, 10): values=(8),    sum=8, count=1
  // 2: time=[4,  6): values=(4, 5), sum=9, count=2
  // clang-format off
  double expectedSums2[kDuration + 1][kDuration + 1] = {
      {0,    8, 14.5,   21, 25.5,  30,  30},
      {0,  6.5,   13, 17.5,   22,  22,  -1},
      {0,  6.5,   11, 15.5, 15.5,  -1,  -1},
      {0,  4.5,    9,    9,   -1,  -1,  -1},
      {0,  4.5,  4.5,   -1,   -1,  -1,  -1},
      {0,    0,   -1,   -1,   -1,  -1,  -1},
      {0,   -1,   -1,   -1,   -1,  -1,  -1},
  };
  int expectedCounts2[kDuration + 1][kDuration + 1] = {
      {0,  1,  2,  3,  4,  5,  5},
      {0,  1,  2,  3,  4,  4, -1},
      {0,  1,  2,  3,  3, -1, -1},
      {0,  1,  2,  2, -1, -1, -1},
      {0,  1,  1, -1, -1, -1, -1},
      {0,  0, -1, -1, -1, -1, -1},
      {0, -1, -1, -1, -1, -1, -1},
  };
  // clang-format on

  currentTime = b.getLatestTime() + seconds(1);
  for (int i = 0; i <= kDuration + 1; i++) {
    for (int j = 0; j <= kDuration - i; j++) {
      TimePoint start = currentTime - seconds(i + j);
      TimePoint end = currentTime - seconds(i);
      double expectedSum = expectedSums2[i][j];
      EXPECT_EQ(expectedSum, b.sum(start, end))
          << "i=" << i << ", j=" << j << ", interval=[" << start << ", " << end
          << ")";

      uint64_t expectedCount = expectedCounts2[i][j];
      EXPECT_EQ(expectedCount, b.count(start, end))
          << "i=" << i << ", j=" << j << ", interval=[" << start << ", " << end
          << ")";

      double expectedAvg = expectedCount ? expectedSum / expectedCount : 0;
      EXPECT_EQ(expectedAvg, b.avg(start, end))
          << "i=" << i << ", j=" << j << ", interval=[" << start << ", " << end
          << ")";

      TimePoint dataStart = std::max(start, b.getEarliestTime());
      TimePoint dataEnd = std::max(end, dataStart);
      seconds expectedInterval = dataEnd - dataStart;
      EXPECT_EQ(expectedInterval, b.elapsed(start, end))
          << "i=" << i << ", j=" << j << ", interval=[" << start << ", " << end
          << ")";

      double expectedRate =
          expectedInterval.count() ? expectedSum / expectedInterval.count() : 0;
      EXPECT_EQ(expectedRate, b.rate(start, end))
          << "i=" << i << ", j=" << j << ", interval=[" << start << ", " << end
          << ")";
    }
  }
}

TEST(BucketedTimeSeries, rateByInterval) {
  const int kNumBuckets = 5;
  const seconds kDuration(10);
  BucketedTimeSeries<double> b(kNumBuckets, kDuration);

  // Add data points at a constant rate of 10 per second.
  // Start adding data points at kDuration, and fill half of the buckets for
  // now.
  TimePoint start(kDuration);
  TimePoint end(kDuration + (kDuration / 2));
  const double kFixedRate = 10.0;
  for (TimePoint i = start; i < end; i += seconds(1)) {
    b.addValue(i, kFixedRate);
  }

  // Querying the rate should yield kFixedRate.
  EXPECT_EQ(kFixedRate, b.rate());
  EXPECT_EQ(kFixedRate, b.rate(start, end));
  EXPECT_EQ(kFixedRate, b.rate(start, start + kDuration));
  EXPECT_EQ(kFixedRate, b.rate(end - kDuration, end));
  EXPECT_EQ(kFixedRate, b.rate(end - seconds(1), end));
  // We have been adding 1 data point per second, so countRate()
  // should be 1.
  EXPECT_EQ(1.0, b.countRate());
  EXPECT_EQ(1.0, b.countRate(start, end));
  EXPECT_EQ(1.0, b.countRate(start, start + kDuration));
  EXPECT_EQ(1.0, b.countRate(end - kDuration, end));
  EXPECT_EQ(1.0, b.countRate(end - seconds(1), end));

  // We haven't added anything before time kDuration.
  // Querying data earlier than this should result in a rate of 0.
  EXPECT_EQ(0.0, b.rate(mkTimePoint(0), mkTimePoint(1)));
  EXPECT_EQ(0.0, b.countRate(mkTimePoint(0), mkTimePoint(1)));

  // Fill the remainder of the timeseries from kDuration to kDuration*2
  start = end;
  end = TimePoint(kDuration * 2);
  for (TimePoint i = start; i < end; i += seconds(1)) {
    b.addValue(i, kFixedRate);
  }

  EXPECT_EQ(kFixedRate, b.rate());
  EXPECT_EQ(kFixedRate, b.rate(TimePoint(kDuration), TimePoint(kDuration * 2)));
  EXPECT_EQ(kFixedRate, b.rate(TimePoint(), TimePoint(kDuration * 2)));
  EXPECT_EQ(kFixedRate, b.rate(TimePoint(), TimePoint(kDuration * 10)));
  EXPECT_EQ(1.0, b.countRate());
  EXPECT_EQ(1.0, b.countRate(TimePoint(kDuration), TimePoint(kDuration * 2)));
  EXPECT_EQ(1.0, b.countRate(TimePoint(), TimePoint(kDuration * 2)));
  EXPECT_EQ(1.0, b.countRate(TimePoint(), TimePoint(kDuration * 10)));
}

TEST(BucketedTimeSeries, addHistorical) {
  const int kNumBuckets = 5;
  const seconds kDuration(10);
  BucketedTimeSeries<double> b(kNumBuckets, kDuration);

  // Initially fill with a constant rate of data
  for (TimePoint i = mkTimePoint(0); i < mkTimePoint(10); i += seconds(1)) {
    b.addValue(i, 10.0);
  }

  EXPECT_EQ(10.0, b.rate());
  EXPECT_EQ(10.0, b.avg());
  EXPECT_EQ(10, b.count());

  // Add some more data points to the middle bucket
  b.addValue(mkTimePoint(4), 40.0);
  b.addValue(mkTimePoint(5), 40.0);
  EXPECT_EQ(15.0, b.avg());
  EXPECT_EQ(18.0, b.rate());
  EXPECT_EQ(12, b.count());

  // Now start adding more current data points, until we are about to roll over
  // the bucket where we added the extra historical data.
  for (TimePoint i = mkTimePoint(10); i < mkTimePoint(14); i += seconds(1)) {
    b.addValue(i, 10.0);
  }
  EXPECT_EQ(15.0, b.avg());
  EXPECT_EQ(18.0, b.rate());
  EXPECT_EQ(12, b.count());

  // Now roll over the middle bucket
  b.addValue(mkTimePoint(14), 10.0);
  b.addValue(mkTimePoint(15), 10.0);
  EXPECT_EQ(10.0, b.avg());
  EXPECT_EQ(10.0, b.rate());
  EXPECT_EQ(10, b.count());

  // Add more historical values past the bucket window.
  // These should be ignored.
  EXPECT_FALSE(b.addValue(mkTimePoint(4), 40.0));
  EXPECT_FALSE(b.addValue(mkTimePoint(5), 40.0));
  EXPECT_EQ(10.0, b.avg());
  EXPECT_EQ(10.0, b.rate());
  EXPECT_EQ(10, b.count());
}

TEST(BucketedTimeSeries, reConstructEmptyTimeSeries) {
  auto verify = [](auto timeSeries) {
    EXPECT_TRUE(timeSeries.empty());
    EXPECT_EQ(0, timeSeries.sum());
    EXPECT_EQ(0, timeSeries.count());
  };

  // Create a 100 second timeseries with 10 buckets_
  BucketedTimeSeries<int64_t> ts(10, seconds(100));

  verify(ts);

  auto firstTime = ts.firstTime();
  auto latestTime = ts.latestTime();
  auto duration = ts.duration();
  auto buckets = ts.buckets();

  // Reconstruct the timeseries
  BucketedTimeSeries<int64_t> newTs(firstTime, latestTime, duration, buckets);

  verify(newTs);
}

TEST(BucketedTimeSeries, reConstructWithValidData) {
  // Create a 100 second timeseries with 10 buckets_
  BucketedTimeSeries<int64_t> ts(10, seconds(100));

  auto setup = [&] {
    ts.clear();
    // Add 1 value to each bucket
    for (int n = 5; n <= 95; n += 10) {
      ts.addValue(seconds(n), 6);
    }

    EXPECT_EQ(10, ts.count());
    EXPECT_EQ(60, ts.sum());
    EXPECT_EQ(6, ts.avg());
  };

  setup();

  auto firstTime = ts.firstTime();
  auto latestTime = ts.latestTime();
  auto duration = ts.duration();
  auto buckets = ts.buckets();

  // Reconstruct the timeseries
  BucketedTimeSeries<int64_t> newTs(firstTime, latestTime, duration, buckets);

  auto compare = [&] {
    EXPECT_EQ(ts.firstTime(), newTs.firstTime());
    EXPECT_EQ(ts.latestTime(), newTs.latestTime());
    EXPECT_EQ(ts.duration(), newTs.duration());
    EXPECT_EQ(ts.buckets().size(), newTs.buckets().size());
    EXPECT_EQ(ts.sum(), newTs.sum());
    EXPECT_EQ(ts.count(), newTs.count());

    for (auto it1 = ts.buckets().begin(), it2 = newTs.buckets().begin();
         it1 != ts.buckets().end();
         it1++, it2++) {
      EXPECT_EQ(it1->sum, it2->sum);
      EXPECT_EQ(it1->count, it2->count);
    }
  };

  compare();
}

TEST(BucketedTimeSeries, reConstructWithCorruptedData) {
  // The total should have been 0 as firstTime > latestTime
  EXPECT_THROW(
      {
        std::vector<Bucket> buckets(10);
        buckets[0].sum = 1;
        buckets[0].count = 1;

        BucketedTimeSeries<int64_t> ts(
            mkTimePoint(1), mkTimePoint(0), Duration(10), buckets);
      },
      std::invalid_argument);

  // The duration should be no less than latestTime - firstTime
  EXPECT_THROW(
      BucketedTimeSeries<int64_t>(
          mkTimePoint(1),
          mkTimePoint(100),
          Duration(10),
          std::vector<Bucket>(10)),
      std::invalid_argument);
}

namespace IntMHTS {
enum Levels {
  MINUTE,
  HOUR,
  ALLTIME,
  NUM_LEVELS,
};

const seconds kMinuteHourDurations[] = {seconds(60), seconds(3600), seconds(0)};
} // namespace IntMHTS

TEST(MinuteHourTimeSeries, Basic) {
  folly::MultiLevelTimeSeries<int> mhts(
      60, IntMHTS::NUM_LEVELS, IntMHTS::kMinuteHourDurations);
  EXPECT_EQ(mhts.numLevels(), IntMHTS::NUM_LEVELS);
  EXPECT_EQ(mhts.numLevels(), 3);

  EXPECT_EQ(mhts.sum(IntMHTS::MINUTE), 0);
  EXPECT_EQ(mhts.sum(IntMHTS::HOUR), 0);
  EXPECT_EQ(mhts.sum(IntMHTS::ALLTIME), 0);

  EXPECT_EQ(mhts.avg(IntMHTS::MINUTE), 0);
  EXPECT_EQ(mhts.avg(IntMHTS::HOUR), 0);
  EXPECT_EQ(mhts.avg(IntMHTS::ALLTIME), 0);

  EXPECT_EQ(mhts.rate(IntMHTS::MINUTE), 0);
  EXPECT_EQ(mhts.rate(IntMHTS::HOUR), 0);
  EXPECT_EQ(mhts.rate(IntMHTS::ALLTIME), 0);

  EXPECT_EQ(mhts.getLevel(IntMHTS::MINUTE).elapsed().count(), 0);
  EXPECT_EQ(mhts.getLevel(IntMHTS::HOUR).elapsed().count(), 0);
  EXPECT_EQ(mhts.getLevel(IntMHTS::ALLTIME).elapsed().count(), 0);

  seconds cur_time(0);

  mhts.addValue(cur_time++, 10);
  mhts.flush();

  EXPECT_EQ(mhts.getLevel(IntMHTS::MINUTE).elapsed().count(), 1);
  EXPECT_EQ(mhts.getLevel(IntMHTS::HOUR).elapsed().count(), 1);
  EXPECT_EQ(mhts.getLevel(IntMHTS::ALLTIME).elapsed().count(), 1);

  for (int i = 0; i < 299; ++i) {
    mhts.addValue(cur_time++, 10);
  }
  mhts.flush();

  EXPECT_EQ(mhts.getLevel(IntMHTS::MINUTE).elapsed().count(), 60);
  EXPECT_EQ(mhts.getLevel(IntMHTS::HOUR).elapsed().count(), 300);
  EXPECT_EQ(mhts.getLevel(IntMHTS::ALLTIME).elapsed().count(), 300);

  EXPECT_EQ(mhts.sum(IntMHTS::MINUTE), 600);
  EXPECT_EQ(mhts.sum(IntMHTS::HOUR), 300 * 10);
  EXPECT_EQ(mhts.sum(IntMHTS::ALLTIME), 300 * 10);

  EXPECT_EQ(mhts.avg(IntMHTS::MINUTE), 10);
  EXPECT_EQ(mhts.avg(IntMHTS::HOUR), 10);
  EXPECT_EQ(mhts.avg(IntMHTS::ALLTIME), 10);

  EXPECT_EQ(mhts.rate(IntMHTS::MINUTE), 10);
  EXPECT_EQ(mhts.rate(IntMHTS::HOUR), 10);
  EXPECT_EQ(mhts.rate(IntMHTS::ALLTIME), 10);

  for (int i = 0; i < 3600 * 3 - 300; ++i) {
    mhts.addValue(cur_time++, 10);
  }
  mhts.flush();

  EXPECT_EQ(mhts.getLevel(IntMHTS::MINUTE).elapsed().count(), 60);
  EXPECT_EQ(mhts.getLevel(IntMHTS::HOUR).elapsed().count(), 3600);
  EXPECT_EQ(mhts.getLevel(IntMHTS::ALLTIME).elapsed().count(), 3600 * 3);

  EXPECT_EQ(mhts.sum(IntMHTS::MINUTE), 600);
  EXPECT_EQ(mhts.sum(IntMHTS::HOUR), 3600 * 10);
  EXPECT_EQ(mhts.sum(IntMHTS::ALLTIME), 3600 * 3 * 10);

  EXPECT_EQ(mhts.avg(IntMHTS::MINUTE), 10);
  EXPECT_EQ(mhts.avg(IntMHTS::HOUR), 10);
  EXPECT_EQ(mhts.avg(IntMHTS::ALLTIME), 10);

  EXPECT_EQ(mhts.rate(IntMHTS::MINUTE), 10);
  EXPECT_EQ(mhts.rate(IntMHTS::HOUR), 10);
  EXPECT_EQ(mhts.rate(IntMHTS::ALLTIME), 10);

  for (int i = 0; i < 3600; ++i) {
    mhts.addValue(cur_time++, 100);
  }
  mhts.flush();

  EXPECT_EQ(mhts.sum(IntMHTS::MINUTE), 60 * 100);
  EXPECT_EQ(mhts.sum(IntMHTS::HOUR), 3600 * 100);
  EXPECT_EQ(mhts.sum(IntMHTS::ALLTIME), 3600 * 3 * 10 + 3600 * 100);

  EXPECT_EQ(mhts.avg(IntMHTS::MINUTE), 100);
  EXPECT_EQ(mhts.avg(IntMHTS::HOUR), 100);
  EXPECT_EQ(mhts.avg(IntMHTS::ALLTIME), 32.5);
  EXPECT_EQ(mhts.avg<int>(IntMHTS::ALLTIME), 32);

  EXPECT_EQ(mhts.rate(IntMHTS::MINUTE), 100);
  EXPECT_EQ(mhts.rate(IntMHTS::HOUR), 100);
  EXPECT_EQ(mhts.rate(IntMHTS::ALLTIME), 32.5);
  EXPECT_EQ(mhts.rate<int>(IntMHTS::ALLTIME), 32);

  for (int i = 0; i < 1800; ++i) {
    mhts.addValue(cur_time++, 120);
  }
  mhts.flush();

  EXPECT_EQ(mhts.sum(IntMHTS::MINUTE), 60 * 120);
  EXPECT_EQ(mhts.sum(IntMHTS::HOUR), 1800 * 100 + 1800 * 120);
  EXPECT_EQ(
      mhts.sum(IntMHTS::ALLTIME), 3600 * 3 * 10 + 3600 * 100 + 1800 * 120);

  for (int i = 0; i < 60; ++i) {
    mhts.addValue(cur_time++, 1000);
  }
  mhts.flush();

  EXPECT_EQ(mhts.sum(IntMHTS::MINUTE), 60 * 1000);
  EXPECT_EQ(mhts.sum(IntMHTS::HOUR), 1740 * 100 + 1800 * 120 + 60 * 1000);
  EXPECT_EQ(
      mhts.sum(IntMHTS::ALLTIME),
      3600 * 3 * 10 + 3600 * 100 + 1800 * 120 + 60 * 1000);

  mhts.clear();
  EXPECT_EQ(mhts.sum(IntMHTS::ALLTIME), 0);
}

TEST(MinuteHourTimeSeries, QueryByInterval) {
  folly::MultiLevelTimeSeries<int> mhts(
      60, IntMHTS::NUM_LEVELS, IntMHTS::kMinuteHourDurations);

  TimePoint curTime;
  for (curTime = mkTimePoint(0); curTime < mkTimePoint(7200);
       curTime += seconds(1)) {
    mhts.addValue(curTime, 1);
  }
  for (curTime = mkTimePoint(7200); curTime < mkTimePoint(7200 + 3540);
       curTime += seconds(1)) {
    mhts.addValue(curTime, 10);
  }
  for (curTime = mkTimePoint(7200 + 3540); curTime < mkTimePoint(7200 + 3600);
       curTime += seconds(1)) {
    mhts.addValue(curTime, 100);
  }
  mhts.flush();

  struct TimeInterval {
    TimePoint start;
    TimePoint end;
  };
  TimeInterval intervals[12] = {
      {curTime - seconds(60), curTime},
      {curTime - seconds(3600), curTime},
      {curTime - seconds(7200), curTime},
      {curTime - seconds(3600), curTime - seconds(60)},
      {curTime - seconds(7200), curTime - seconds(60)},
      {curTime - seconds(7200), curTime - seconds(3600)},
      {curTime - seconds(50), curTime - seconds(20)},
      {curTime - seconds(3020), curTime - seconds(20)},
      {curTime - seconds(7200), curTime - seconds(20)},
      {curTime - seconds(3000), curTime - seconds(1000)},
      {curTime - seconds(7200), curTime - seconds(1000)},
      {curTime - seconds(7200), curTime - seconds(3600)},
  };

  int expectedSums[12] = {
      6000,
      41400,
      32400,
      35400,
      32130,
      16200,
      3000,
      33600,
      32310,
      20000,
      27900,
      16200,
  };

  int expectedCounts[12] = {
      60,
      3600,
      7200,
      3540,
      7140,
      3600,
      30,
      3000,
      7180,
      2000,
      6200,
      3600,
  };

  for (int i = 0; i < 12; ++i) {
    TimeInterval interval = intervals[i];

    int s = mhts.sum(interval.start, interval.end);
    EXPECT_EQ(expectedSums[i], s);

    int c = mhts.count(interval.start, interval.end);
    EXPECT_EQ(expectedCounts[i], c);

    int a = mhts.avg<int>(interval.start, interval.end);
    EXPECT_EQ(expectedCounts[i] ? (expectedSums[i] / expectedCounts[i]) : 0, a);

    int r = mhts.rate<int>(interval.start, interval.end);
    int expectedRate =
        expectedSums[i] / (interval.end - interval.start).count();
    EXPECT_EQ(expectedRate, r);
  }
}

TEST(MultiLevelTimeSeries, Basic) {
  // using constructor with initializer_list parameter
  folly::MultiLevelTimeSeries<int> mhts(
      60, {seconds(60), seconds(3600), seconds(0)});
  EXPECT_EQ(mhts.numLevels(), 3);

  EXPECT_EQ(mhts.sum(seconds(60)), 0);
  EXPECT_EQ(mhts.sum(seconds(3600)), 0);
  EXPECT_EQ(mhts.sum(seconds(0)), 0);

  EXPECT_EQ(mhts.avg(seconds(60)), 0);
  EXPECT_EQ(mhts.avg(seconds(3600)), 0);
  EXPECT_EQ(mhts.avg(seconds(0)), 0);

  EXPECT_EQ(mhts.rate(seconds(60)), 0);
  EXPECT_EQ(mhts.rate(seconds(3600)), 0);
  EXPECT_EQ(mhts.rate(seconds(0)), 0);

  EXPECT_EQ(mhts.getLevelByDuration(seconds(60)).elapsed().count(), 0);
  EXPECT_EQ(mhts.getLevelByDuration(seconds(3600)).elapsed().count(), 0);
  EXPECT_EQ(mhts.getLevelByDuration(seconds(0)).elapsed().count(), 0);

  seconds cur_time(0);

  mhts.addValue(cur_time++, 10);
  mhts.flush();

  EXPECT_EQ(mhts.getLevelByDuration(seconds(60)).elapsed().count(), 1);
  EXPECT_EQ(mhts.getLevelByDuration(seconds(3600)).elapsed().count(), 1);
  EXPECT_EQ(mhts.getLevelByDuration(seconds(0)).elapsed().count(), 1);

  for (int i = 0; i < 299; ++i) {
    mhts.addValue(cur_time++, 10);
  }
  mhts.flush();

  EXPECT_EQ(mhts.getLevelByDuration(seconds(60)).elapsed().count(), 60);
  EXPECT_EQ(mhts.getLevelByDuration(seconds(3600)).elapsed().count(), 300);
  EXPECT_EQ(mhts.getLevelByDuration(seconds(0)).elapsed().count(), 300);

  EXPECT_EQ(mhts.sum(seconds(60)), 600);
  EXPECT_EQ(mhts.sum(seconds(3600)), 300 * 10);
  EXPECT_EQ(mhts.sum(seconds(0)), 300 * 10);

  EXPECT_EQ(mhts.avg(seconds(60)), 10);
  EXPECT_EQ(mhts.avg(seconds(3600)), 10);
  EXPECT_EQ(mhts.avg(seconds(0)), 10);

  EXPECT_EQ(mhts.rate(seconds(60)), 10);
  EXPECT_EQ(mhts.rate(seconds(3600)), 10);
  EXPECT_EQ(mhts.rate(seconds(0)), 10);

  for (int i = 0; i < 3600 * 3 - 300; ++i) {
    mhts.addValue(cur_time++, 10);
  }
  mhts.flush();

  EXPECT_EQ(mhts.getLevelByDuration(seconds(60)).elapsed().count(), 60);
  EXPECT_EQ(mhts.getLevelByDuration(seconds(3600)).elapsed().count(), 3600);
  EXPECT_EQ(mhts.getLevelByDuration(seconds(0)).elapsed().count(), 3600 * 3);

  EXPECT_EQ(mhts.sum(seconds(60)), 600);
  EXPECT_EQ(mhts.sum(seconds(3600)), 3600 * 10);
  EXPECT_EQ(mhts.sum(seconds(0)), 3600 * 3 * 10);

  EXPECT_EQ(mhts.avg(seconds(60)), 10);
  EXPECT_EQ(mhts.avg(seconds(3600)), 10);
  EXPECT_EQ(mhts.avg(seconds(0)), 10);

  EXPECT_EQ(mhts.rate(seconds(60)), 10);
  EXPECT_EQ(mhts.rate(seconds(3600)), 10);
  EXPECT_EQ(mhts.rate(seconds(0)), 10);

  for (int i = 0; i < 3600; ++i) {
    mhts.addValue(cur_time++, 100);
  }
  mhts.flush();

  EXPECT_EQ(mhts.sum(seconds(60)), 60 * 100);
  EXPECT_EQ(mhts.sum(seconds(3600)), 3600 * 100);
  EXPECT_EQ(mhts.sum(seconds(0)), 3600 * 3 * 10 + 3600 * 100);

  EXPECT_EQ(mhts.avg(seconds(60)), 100);
  EXPECT_EQ(mhts.avg(seconds(3600)), 100);
  EXPECT_EQ(mhts.avg(seconds(0)), 32.5);
  EXPECT_EQ(mhts.avg<int>(seconds(0)), 32);

  EXPECT_EQ(mhts.rate(seconds(60)), 100);
  EXPECT_EQ(mhts.rate(seconds(3600)), 100);
  EXPECT_EQ(mhts.rate(seconds(0)), 32.5);
  EXPECT_EQ(mhts.rate<int>(seconds(0)), 32);

  for (int i = 0; i < 1800; ++i) {
    mhts.addValue(cur_time++, 120);
  }
  mhts.flush();

  EXPECT_EQ(mhts.sum(seconds(60)), 60 * 120);
  EXPECT_EQ(mhts.sum(seconds(3600)), 1800 * 100 + 1800 * 120);
  EXPECT_EQ(mhts.sum(seconds(0)), 3600 * 3 * 10 + 3600 * 100 + 1800 * 120);

  for (int i = 0; i < 60; ++i) {
    mhts.addValue(cur_time++, 1000);
  }
  mhts.flush();

  EXPECT_EQ(mhts.sum(seconds(60)), 60 * 1000);
  EXPECT_EQ(mhts.sum(seconds(3600)), 1740 * 100 + 1800 * 120 + 60 * 1000);
  EXPECT_EQ(
      mhts.sum(seconds(0)),
      3600 * 3 * 10 + 3600 * 100 + 1800 * 120 + 60 * 1000);

  mhts.clear();
  EXPECT_EQ(mhts.sum(seconds(0)), 0);
}

TEST(MultiLevelTimeSeries, QueryByInterval) {
  folly::MultiLevelTimeSeries<int> mhts(
      60, {seconds(60), seconds(3600), seconds(0)});

  TimePoint curTime;
  for (curTime = mkTimePoint(0); curTime < mkTimePoint(7200);
       curTime += seconds(1)) {
    mhts.addValue(curTime, 1);
  }
  for (curTime = mkTimePoint(7200); curTime < mkTimePoint(7200 + 3540);
       curTime += seconds(1)) {
    mhts.addValue(curTime, 10);
  }
  for (curTime = mkTimePoint(7200 + 3540); curTime < mkTimePoint(7200 + 3600);
       curTime += seconds(1)) {
    mhts.addValue(curTime, 100);
  }
  mhts.flush();

  struct TimeInterval {
    TimePoint start;
    TimePoint end;
  };

  std::array<TimeInterval, 12> intervals = {{
      {curTime - seconds(60), curTime},
      {curTime - seconds(3600), curTime},
      {curTime - seconds(7200), curTime},
      {curTime - seconds(3600), curTime - seconds(60)},
      {curTime - seconds(7200), curTime - seconds(60)},
      {curTime - seconds(7200), curTime - seconds(3600)},
      {curTime - seconds(50), curTime - seconds(20)},
      {curTime - seconds(3020), curTime - seconds(20)},
      {curTime - seconds(7200), curTime - seconds(20)},
      {curTime - seconds(3000), curTime - seconds(1000)},
      {curTime - seconds(7200), curTime - seconds(1000)},
      {curTime - seconds(7200), curTime - seconds(3600)},
  }};

  std::array<int, 12> expectedSums = {{6000,
                                       41400,
                                       32400,
                                       35400,
                                       32130,
                                       16200,
                                       3000,
                                       33600,
                                       32310,
                                       20000,
                                       27900,
                                       16200}};

  std::array<int, 12> expectedCounts = {
      {60, 3600, 7200, 3540, 7140, 3600, 30, 3000, 7180, 2000, 6200, 3600}};

  for (size_t i = 0; i < intervals.size(); ++i) {
    TimeInterval interval = intervals[i];

    int s = mhts.sum(interval.start, interval.end);
    EXPECT_EQ(expectedSums[i], s);

    int c = mhts.count(interval.start, interval.end);
    EXPECT_EQ(expectedCounts[i], c);

    int a = mhts.avg<int>(interval.start, interval.end);
    EXPECT_EQ(expectedCounts[i] ? (expectedSums[i] / expectedCounts[i]) : 0, a);

    int r = mhts.rate<int>(interval.start, interval.end);
    int expectedRate =
        expectedSums[i] / (interval.end - interval.start).count();
    EXPECT_EQ(expectedRate, r);
  }
}