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Remove unnecessary QuantileTDigest layer

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This commit is contained in:
Pavel Kruglov 2020-11-17 14:01:51 +03:00
parent 1a02ec85a5
commit 7ecd207eac
1 changed files with 17 additions and 65 deletions
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82
src/AggregateFunctions/QuantileTDigest.h
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@ -37,7 +37,7 @@ namespace ErrorCodes
* uses asin, which slows down the algorithm a bit.
*/
template <typename T>
class TDigest
class QuantileTDigest
{
using Value = Float32;
using Count = Float32;
@ -95,6 +95,14 @@ class TDigest
BetterFloat count = 0;
size_t unmerged = 0;
/** Linear interpolation at the point x on the line (x1, y1)..(x2, y2)
*/
static Value interpolate(Value x, Value x1, Value y1, Value x2, Value y2)
{
double k = (x - x1) / (x2 - x1);
return y1 + k * (y2 - y1);
}
struct RadixSortTraits
{
using Element = Centroid;
@ -124,7 +132,8 @@ class TDigest
if (unmerged > params.max_unmerged)
compress();
}
void compressBrute() {
void compressBrute()
{
if (centroids.size() <= params.max_centroids)
return;
const size_t batch_size = (centroids.size() + params.max_centroids - 1) / params.max_centroids; // at least 2
@ -256,7 +265,7 @@ public:
addCentroid(Centroid{vx, static_cast<Count>(cnt)});
}
void merge(const TDigest & other)
void merge(const QuantileTDigest & other)
{
for (const auto & c : other.centroids)
addCentroid(c);
@ -293,77 +302,21 @@ public:
compress(); // Allows reading/writing TDigests with different epsilon/max_centroids params
}
Count getCount()
{
return count;
}
const Centroids & getCentroids() const
{
return centroids;
}
void reset()
{
centroids.resize(0);
count = 0;
unmerged = 0;
}
};
template <typename T>
class QuantileTDigest
{
using Value = Float32;
using Count = Float32;
TDigest<T> main_tdigest;
/** Linear interpolation at the point x on the line (x1, y1)..(x2, y2)
*/
static Value interpolate(Value x, Value x1, Value y1, Value x2, Value y2)
{
double k = (x - x1) / (x2 - x1);
return y1 + k * (y2 - y1);
}
public:
void add(T x, UInt64 cnt = 1)
{
main_tdigest.add(x, cnt);
}
void merge(const QuantileTDigest & other)
{
main_tdigest.merge(other.main_tdigest);
}
void serialize(WriteBuffer & buf)
{
main_tdigest.serialize(buf);
}
void deserialize(ReadBuffer & buf)
{
main_tdigest.deserialize(buf);
}
/** Calculates the quantile q [0, 1] based on the digest.
* For an empty digest returns NaN.
*/
template <typename ResultType>
ResultType getImpl(Float64 level)
{
auto & centroids = main_tdigest.getCentroids();
if (centroids.empty())
return std::is_floating_point_v<ResultType> ? NAN : 0;
main_tdigest.compress();
compress();
if (centroids.size() == 1)
return centroids.front().mean;
Float64 x = level * main_tdigest.getCount();
Float64 x = level * count;
Float64 prev_x = 0;
Count sum = 0;
Value prev_mean = centroids.front().mean;
@ -391,7 +344,6 @@ public:
template <typename ResultType>
void getManyImpl(const Float64 * levels, const size_t * levels_permutation, size_t size, ResultType * result)
{
auto & centroids = main_tdigest.getCentroids();
if (centroids.empty())
{
for (size_t result_num = 0; result_num < size; ++result_num)
@ -399,7 +351,7 @@ public:
return;
}
main_tdigest.compress();
compress();
if (centroids.size() == 1)
{
@ -408,7 +360,7 @@ public:
return;
}
Float64 x = levels[levels_permutation[0]] * main_tdigest.getCount();
Float64 x = levels[levels_permutation[0]] * count;
Float64 prev_x = 0;
Count sum = 0;
Value prev_mean = centroids.front().mean;
@ -426,7 +378,7 @@ public:
if (result_num >= size)
return;
x = levels[levels_permutation[result_num]] * main_tdigest.getCount();
x = levels[levels_permutation[result_num]] * count;
}
sum += c.count;