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https://github.com/ClickHouse/ClickHouse.git
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452 lines
13 KiB
C++
452 lines
13 KiB
C++
#include "ColumnVector.h"
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#include <cstring>
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#include <cmath>
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#include <common/unaligned.h>
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#include <Common/Exception.h>
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#include <Common/Arena.h>
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#include <Common/SipHash.h>
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#include <Common/NaNUtils.h>
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#include <Common/RadixSort.h>
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#include <Common/assert_cast.h>
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#include <Common/WeakHash.h>
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#include <Common/HashTable/Hash.h>
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#include <IO/WriteBuffer.h>
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#include <IO/WriteHelpers.h>
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#include <Columns/ColumnsCommon.h>
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#include <DataStreams/ColumnGathererStream.h>
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#include <ext/bit_cast.h>
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#include <pdqsort.h>
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#ifdef __SSE2__
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#include <emmintrin.h>
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#endif
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namespace DB
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{
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namespace ErrorCodes
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{
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extern const int PARAMETER_OUT_OF_BOUND;
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extern const int SIZES_OF_COLUMNS_DOESNT_MATCH;
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extern const int LOGICAL_ERROR;
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}
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template <typename T>
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StringRef ColumnVector<T>::serializeValueIntoArena(size_t n, Arena & arena, char const *& begin) const
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{
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auto pos = arena.allocContinue(sizeof(T), begin);
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unalignedStore<T>(pos, data[n]);
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return StringRef(pos, sizeof(T));
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}
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template <typename T>
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const char * ColumnVector<T>::deserializeAndInsertFromArena(const char * pos)
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{
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data.push_back(unalignedLoad<T>(pos));
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return pos + sizeof(T);
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}
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template <typename T>
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void ColumnVector<T>::updateHashWithValue(size_t n, SipHash & hash) const
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{
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hash.update(data[n]);
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}
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template <typename T>
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void ColumnVector<T>::updateWeakHash32(WeakHash32 & hash) const
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{
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auto s = data.size();
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if (hash.getData().size() != s)
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throw Exception("Size of WeakHash32 does not match size of column: column size is " + std::to_string(s) +
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", hash size is " + std::to_string(hash.getData().size()), ErrorCodes::LOGICAL_ERROR);
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const T * begin = data.data();
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const T * end = begin + s;
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UInt32 * hash_data = hash.getData().data();
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while (begin < end)
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{
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*hash_data = intHashCRC32(*begin, *hash_data);
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++begin;
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++hash_data;
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}
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}
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template <typename T>
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struct ColumnVector<T>::less
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{
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const Self & parent;
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int nan_direction_hint;
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less(const Self & parent_, int nan_direction_hint_) : parent(parent_), nan_direction_hint(nan_direction_hint_) {}
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bool operator()(size_t lhs, size_t rhs) const { return CompareHelper<T>::less(parent.data[lhs], parent.data[rhs], nan_direction_hint); }
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};
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template <typename T>
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struct ColumnVector<T>::greater
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{
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const Self & parent;
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int nan_direction_hint;
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greater(const Self & parent_, int nan_direction_hint_) : parent(parent_), nan_direction_hint(nan_direction_hint_) {}
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bool operator()(size_t lhs, size_t rhs) const { return CompareHelper<T>::greater(parent.data[lhs], parent.data[rhs], nan_direction_hint); }
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};
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namespace
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{
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template <typename T>
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struct ValueWithIndex
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{
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T value;
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UInt32 index;
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};
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template <typename T>
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struct RadixSortTraits : RadixSortNumTraits<T>
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{
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using Element = ValueWithIndex<T>;
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static T & extractKey(Element & elem) { return elem.value; }
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};
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}
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template <typename T>
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void ColumnVector<T>::getPermutation(bool reverse, size_t limit, int nan_direction_hint, IColumn::Permutation & res) const
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{
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size_t s = data.size();
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res.resize(s);
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if (s == 0)
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return;
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if (limit >= s)
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limit = 0;
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if (limit)
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{
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for (size_t i = 0; i < s; ++i)
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res[i] = i;
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if (reverse)
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std::partial_sort(res.begin(), res.begin() + limit, res.end(), greater(*this, nan_direction_hint));
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else
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std::partial_sort(res.begin(), res.begin() + limit, res.end(), less(*this, nan_direction_hint));
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}
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else
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{
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/// A case for radix sort
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if constexpr (is_arithmetic_v<T> && !std::is_same_v<T, UInt128>)
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{
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/// Thresholds on size. Lower threshold is arbitrary. Upper threshold is chosen by the type for histogram counters.
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if (s >= 256 && s <= std::numeric_limits<UInt32>::max())
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{
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PaddedPODArray<ValueWithIndex<T>> pairs(s);
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for (UInt32 i = 0; i < UInt32(s); ++i)
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pairs[i] = {data[i], i};
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RadixSort<RadixSortTraits<T>>::executeLSD(pairs.data(), s);
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/// Radix sort treats all NaNs to be greater than all numbers.
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/// If the user needs the opposite, we must move them accordingly.
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size_t nans_to_move = 0;
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if (std::is_floating_point_v<T> && nan_direction_hint < 0)
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{
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for (ssize_t i = s - 1; i >= 0; --i)
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{
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if (isNaN(pairs[i].value))
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++nans_to_move;
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else
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break;
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}
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}
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if (reverse)
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{
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if (nans_to_move)
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{
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for (size_t i = 0; i < s - nans_to_move; ++i)
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res[i] = pairs[s - nans_to_move - 1 - i].index;
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for (size_t i = s - nans_to_move; i < s; ++i)
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res[i] = pairs[s - 1 - (i - (s - nans_to_move))].index;
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}
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else
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{
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for (size_t i = 0; i < s; ++i)
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res[s - 1 - i] = pairs[i].index;
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}
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}
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else
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{
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if (nans_to_move)
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{
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for (size_t i = 0; i < nans_to_move; ++i)
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res[i] = pairs[i + s - nans_to_move].index;
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for (size_t i = nans_to_move; i < s; ++i)
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res[i] = pairs[i - nans_to_move].index;
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}
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else
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{
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for (size_t i = 0; i < s; ++i)
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res[i] = pairs[i].index;
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}
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}
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return;
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}
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}
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/// Default sorting algorithm.
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for (size_t i = 0; i < s; ++i)
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res[i] = i;
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if (reverse)
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pdqsort(res.begin(), res.end(), greater(*this, nan_direction_hint));
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else
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pdqsort(res.begin(), res.end(), less(*this, nan_direction_hint));
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}
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}
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template <typename T>
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const char * ColumnVector<T>::getFamilyName() const
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{
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return TypeName<T>::get();
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}
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template <typename T>
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MutableColumnPtr ColumnVector<T>::cloneResized(size_t size) const
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{
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auto res = this->create();
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if (size > 0)
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{
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auto & new_col = static_cast<Self &>(*res);
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new_col.data.resize(size);
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size_t count = std::min(this->size(), size);
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memcpy(new_col.data.data(), data.data(), count * sizeof(data[0]));
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if (size > count)
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memset(static_cast<void *>(&new_col.data[count]), static_cast<int>(ValueType()), (size - count) * sizeof(ValueType));
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}
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return res;
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}
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template <typename T>
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UInt64 ColumnVector<T>::get64(size_t n) const
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{
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return ext::bit_cast<UInt64>(data[n]);
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}
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template <typename T>
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inline Float64 ColumnVector<T>::getFloat64(size_t n) const
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{
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return static_cast<Float64>(data[n]);
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}
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template <typename T>
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Float32 ColumnVector<T>::getFloat32(size_t n) const
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{
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return static_cast<Float32>(data[n]);
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}
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template <typename T>
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void ColumnVector<T>::insertRangeFrom(const IColumn & src, size_t start, size_t length)
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{
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const ColumnVector & src_vec = assert_cast<const ColumnVector &>(src);
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if (start + length > src_vec.data.size())
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throw Exception("Parameters start = "
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+ toString(start) + ", length = "
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+ toString(length) + " are out of bound in ColumnVector<T>::insertRangeFrom method"
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" (data.size() = " + toString(src_vec.data.size()) + ").",
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ErrorCodes::PARAMETER_OUT_OF_BOUND);
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size_t old_size = data.size();
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data.resize(old_size + length);
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memcpy(data.data() + old_size, &src_vec.data[start], length * sizeof(data[0]));
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}
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template <typename T>
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ColumnPtr ColumnVector<T>::filter(const IColumn::Filter & filt, ssize_t result_size_hint) const
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{
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size_t size = data.size();
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if (size != filt.size())
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throw Exception("Size of filter doesn't match size of column.", ErrorCodes::SIZES_OF_COLUMNS_DOESNT_MATCH);
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auto res = this->create();
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Container & res_data = res->getData();
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if (result_size_hint)
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res_data.reserve(result_size_hint > 0 ? result_size_hint : size);
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const UInt8 * filt_pos = filt.data();
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const UInt8 * filt_end = filt_pos + size;
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const T * data_pos = data.data();
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#ifdef __SSE2__
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/** A slightly more optimized version.
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* Based on the assumption that often pieces of consecutive values
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* completely pass or do not pass the filter.
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* Therefore, we will optimistically check the parts of `SIMD_BYTES` values.
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*/
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static constexpr size_t SIMD_BYTES = 16;
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const __m128i zero16 = _mm_setzero_si128();
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const UInt8 * filt_end_sse = filt_pos + size / SIMD_BYTES * SIMD_BYTES;
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while (filt_pos < filt_end_sse)
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{
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int mask = _mm_movemask_epi8(_mm_cmpgt_epi8(_mm_loadu_si128(reinterpret_cast<const __m128i *>(filt_pos)), zero16));
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if (0 == mask)
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{
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/// Nothing is inserted.
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}
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else if (0xFFFF == mask)
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{
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res_data.insert(data_pos, data_pos + SIMD_BYTES);
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}
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else
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{
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for (size_t i = 0; i < SIMD_BYTES; ++i)
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if (filt_pos[i])
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res_data.push_back(data_pos[i]);
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}
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filt_pos += SIMD_BYTES;
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data_pos += SIMD_BYTES;
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}
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#endif
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while (filt_pos < filt_end)
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{
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if (*filt_pos)
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res_data.push_back(*data_pos);
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++filt_pos;
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++data_pos;
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}
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return res;
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}
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template <typename T>
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ColumnPtr ColumnVector<T>::permute(const IColumn::Permutation & perm, size_t limit) const
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{
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size_t size = data.size();
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if (limit == 0)
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limit = size;
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else
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limit = std::min(size, limit);
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if (perm.size() < limit)
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throw Exception("Size of permutation is less than required.", ErrorCodes::SIZES_OF_COLUMNS_DOESNT_MATCH);
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auto res = this->create(limit);
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typename Self::Container & res_data = res->getData();
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for (size_t i = 0; i < limit; ++i)
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res_data[i] = data[perm[i]];
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return res;
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}
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template <typename T>
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ColumnPtr ColumnVector<T>::index(const IColumn & indexes, size_t limit) const
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{
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return selectIndexImpl(*this, indexes, limit);
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}
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template <typename T>
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ColumnPtr ColumnVector<T>::replicate(const IColumn::Offsets & offsets) const
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{
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const size_t size = data.size();
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if (size != offsets.size())
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throw Exception("Size of offsets doesn't match size of column.", ErrorCodes::SIZES_OF_COLUMNS_DOESNT_MATCH);
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if (0 == size)
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return this->create();
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auto res = this->create(offsets.back());
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auto it = res->getData().begin();
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for (size_t i = 0; i < size; ++i)
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{
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const auto span_end = res->getData().begin() + offsets[i];
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for (; it != span_end; ++it)
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*it = data[i];
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}
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return res;
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}
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template <typename T>
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void ColumnVector<T>::gather(ColumnGathererStream & gatherer)
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{
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gatherer.gather(*this);
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}
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template <typename T>
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void ColumnVector<T>::getExtremes(Field & min, Field & max) const
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{
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size_t size = data.size();
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if (size == 0)
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{
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min = T(0);
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max = T(0);
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return;
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}
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bool has_value = false;
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/** Skip all NaNs in extremes calculation.
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* If all values are NaNs, then return NaN.
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* NOTE: There exist many different NaNs.
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* Different NaN could be returned: not bit-exact value as one of NaNs from column.
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*/
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T cur_min = NaNOrZero<T>();
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T cur_max = NaNOrZero<T>();
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for (const T x : data)
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{
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if (isNaN(x))
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continue;
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if (!has_value)
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{
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cur_min = x;
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cur_max = x;
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has_value = true;
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continue;
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}
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if (x < cur_min)
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cur_min = x;
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else if (x > cur_max)
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cur_max = x;
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}
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min = NearestFieldType<T>(cur_min);
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max = NearestFieldType<T>(cur_max);
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}
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/// Explicit template instantiations - to avoid code bloat in headers.
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template class ColumnVector<UInt8>;
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template class ColumnVector<UInt16>;
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template class ColumnVector<UInt32>;
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template class ColumnVector<UInt64>;
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template class ColumnVector<UInt128>;
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template class ColumnVector<Int8>;
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template class ColumnVector<Int16>;
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template class ColumnVector<Int32>;
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template class ColumnVector<Int64>;
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template class ColumnVector<Int128>;
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template class ColumnVector<Float32>;
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template class ColumnVector<Float64>;
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}
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