#define FOLLY_DETAIL_STATS_H_
#include <cstdint>
+#include <type_traits>
namespace folly { namespace detail {
/*
- * Helper functions for how to perform division based on the desired
+ * Helper function to compute the average, given a specified input type and
* return type.
*/
-// For floating point input types, do floating point division
-template <typename ReturnType, typename ValueType>
-typename std::enable_if<std::is_floating_point<ValueType>::value,
- ReturnType>::type
-avgHelper(ValueType sum, uint64_t count) {
- if (count == 0) { return ReturnType(0); }
- return static_cast<ReturnType>(sum / count);
-}
-
-// For floating point return types, do floating point division
-template <typename ReturnType, typename ValueType>
-typename std::enable_if<std::is_floating_point<ReturnType>::value &&
- !std::is_floating_point<ValueType>::value,
- ReturnType>::type
-avgHelper(ValueType sum, uint64_t count) {
- if (count == 0) { return ReturnType(0); }
- return static_cast<ReturnType>(sum) / count;
-}
-
-// For signed integer input types, do signed division
-template <typename ReturnType, typename ValueType>
-typename std::enable_if<!std::is_floating_point<ReturnType>::value &&
- !std::is_floating_point<ValueType>::value &&
- std::is_signed<ValueType>::value,
- ReturnType>::type
-avgHelper(ValueType sum, uint64_t count) {
+// If the input is long double, divide using long double to avoid losing
+// precision.
+template <typename ReturnType>
+ReturnType avgHelper(long double sum, uint64_t count) {
if (count == 0) { return ReturnType(0); }
- return sum / static_cast<int64_t>(count);
+ const long double countf = count;
+ return static_cast<ReturnType>(sum / countf);
}
-// For unsigned integer input types, do unsigned division
+// In all other cases divide using double precision.
+// This should be relatively fast, and accurate enough for most use cases.
template <typename ReturnType, typename ValueType>
-typename std::enable_if<!std::is_floating_point<ReturnType>::value &&
- !std::is_floating_point<ValueType>::value &&
- std::is_unsigned<ValueType>::value,
+typename std::enable_if<!std::is_same<typename std::remove_cv<ValueType>::type,
+ long double>::value,
ReturnType>::type
avgHelper(ValueType sum, uint64_t count) {
if (count == 0) { return ReturnType(0); }
- return sum / count;
+ const double sumf = sum;
+ const double countf = count;
+ return static_cast<ReturnType>(sumf / countf);
}
}
TEST(BucketedTimeSeries, avgTypeConversion) {
- // The average code has many different code paths to decide what type of
- // division to perform (floating point, signed integer, unsigned integer).
- // Test the various code paths.
+ // Make sure the computed average values are accurate regardless
+ // of the input type and return type.
{
// Simple sanity tests for small positive integer values
ts.addValue(seconds(0), 10, 200);
ts.addValue(seconds(0), 16, 100);
- EXPECT_DOUBLE_EQ(ts.avg(), 10.0);
- EXPECT_DOUBLE_EQ(ts.avg<float>(), 10.0);
- EXPECT_EQ(ts.avg<uint64_t>(), 10);
- EXPECT_EQ(ts.avg<int64_t>(), 10);
- EXPECT_EQ(ts.avg<int32_t>(), 10);
- EXPECT_EQ(ts.avg<int16_t>(), 10);
- EXPECT_EQ(ts.avg<int8_t>(), 10);
- EXPECT_EQ(ts.avg<uint8_t>(), 10);
+ 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>());
}
{
ts.addValue(seconds(0), -300);
ts.addValue(seconds(0), -200, 65535);
- EXPECT_DOUBLE_EQ(ts.avg(), -200.0);
- EXPECT_DOUBLE_EQ(ts.avg<float>(), -200.0);
- EXPECT_EQ(ts.avg<int64_t>(), -200);
- EXPECT_EQ(ts.avg<int32_t>(), -200);
- EXPECT_EQ(ts.avg<int16_t>(), -200);
+ 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>());
}
{
std::numeric_limits<uint64_t>::max(),
std::numeric_limits<uint64_t>::max());
- EXPECT_DOUBLE_EQ(ts.avg(), 1.0);
- EXPECT_DOUBLE_EQ(ts.avg<float>(), 1.0);
- EXPECT_EQ(ts.avg<uint64_t>(), 1);
- EXPECT_EQ(ts.avg<int64_t>(), 1);
- EXPECT_EQ(ts.avg<int8_t>(), 1);
+ 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>());
}
{
ts.addValue(seconds(0), 10.0, 200);
ts.addValue(seconds(0), 16.0, 100);
- EXPECT_DOUBLE_EQ(ts.avg(), 10.0);
- EXPECT_DOUBLE_EQ(ts.avg<float>(), 10.0);
- EXPECT_EQ(ts.avg<uint64_t>(), 10);
- EXPECT_EQ(ts.avg<int64_t>(), 10);
- EXPECT_EQ(ts.avg<int32_t>(), 10);
- EXPECT_EQ(ts.avg<int16_t>(), 10);
- EXPECT_EQ(ts.avg<int8_t>(), 10);
- EXPECT_EQ(ts.avg<uint8_t>(), 10);
+ 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>());
}
{
ts.addValue(seconds(0), value);
}
- EXPECT_DOUBLE_EQ(ts.avg(), value);
- EXPECT_DOUBLE_EQ(ts.avg<float>(), value);
- EXPECT_DOUBLE_EQ(ts.avg<uint64_t>(), value);
- EXPECT_DOUBLE_EQ(ts.avg<int64_t>(), 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);
}
{
ts.addValue(seconds(0), i);
}
- EXPECT_DOUBLE_EQ(ts.avg(), 50.0);
- EXPECT_DOUBLE_EQ(ts.avg<float>(), 50.0);
- EXPECT_DOUBLE_EQ(ts.avg<uint64_t>(), 50);
- EXPECT_DOUBLE_EQ(ts.avg<int64_t>(), 50);
- EXPECT_DOUBLE_EQ(ts.avg<int16_t>(), 50);
- EXPECT_DOUBLE_EQ(ts.avg<int8_t>(), 50);
+ 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>());
}
}