9d3f1dcf44
The original logic is to first deserialize the ColumnString into a HashSet (insert the deserialized elements into the hashset), and then traverse all the HashSet elements into the target HashSet during the merge phase. After optimization, when deserializing, elements are directly inserted into the target HashSet, thereby reducing unnecessary hashset insert overhead. In one of our internal query tests, 30 hashsets were merged in second phase aggregation(the average cardinality is 1,400,000), and the cardinality after merging is 42,000,000. After optimization, the MergeTime dropped from 5s965ms to 3s375ms.
232 lines
8.0 KiB
C++
232 lines
8.0 KiB
C++
// Licensed to the Apache Software Foundation (ASF) under one
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// or more contributor license agreements. See the NOTICE file
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// distributed with this work for additional information
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// regarding copyright ownership. The ASF licenses this file
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// to you under the Apache License, Version 2.0 (the
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// "License"); you may not use this file except in compliance
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// with the License. You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing,
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// software distributed under the License is distributed on an
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// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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// KIND, either express or implied. See the License for the
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// specific language governing permissions and limitations
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// under the License.
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// This file is copied from
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// https://github.com/ClickHouse/ClickHouse/blob/master/src/AggregateFunctions/AggregateFunctionUniq.h
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// and modified by Doris
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#pragma once
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#include <stddef.h>
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#include <algorithm>
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#include <boost/iterator/iterator_facade.hpp>
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#include <memory>
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#include <type_traits>
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#include <vector>
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// IWYU pragma: no_include <opentelemetry/common/threadlocal.h>
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#include "common/compiler_util.h" // IWYU pragma: keep
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#include "vec/aggregate_functions/aggregate_function.h"
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#include "vec/columns/column.h"
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#include "vec/columns/column_vector.h"
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#include "vec/columns/columns_number.h"
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#include "vec/common/assert_cast.h"
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#include "vec/common/hash_table/hash.h"
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#include "vec/common/hash_table/phmap_fwd_decl.h"
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#include "vec/common/sip_hash.h"
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#include "vec/common/string_ref.h"
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#include "vec/common/uint128.h"
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#include "vec/core/types.h"
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#include "vec/data_types/data_type_number.h"
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#include "vec/io/io_helper.h"
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#include "vec/io/var_int.h"
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namespace doris {
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namespace vectorized {
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class Arena;
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class BufferReadable;
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class BufferWritable;
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template <typename T>
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class ColumnDecimal;
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} // namespace vectorized
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} // namespace doris
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template <typename T>
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struct HashCRC32;
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namespace doris::vectorized {
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/// uniqExact
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template <typename T>
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struct AggregateFunctionUniqExactData {
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static constexpr bool is_string_key = std::is_same_v<T, String>;
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using Key = std::conditional_t<is_string_key, UInt128, T>;
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using Hash = std::conditional_t<is_string_key, UInt128TrivialHash, HashCRC32<Key>>;
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using Set = flat_hash_set<Key, Hash>;
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static UInt128 ALWAYS_INLINE get_key(const StringRef& value) {
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UInt128 key;
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SipHash hash;
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hash.update(value.data, value.size);
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hash.get128(key.low, key.high);
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return key;
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}
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Set set;
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static String get_name() { return "uniqExact"; }
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};
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namespace detail {
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/** The structure for the delegation work to add one element to the `uniq` aggregate functions.
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* Used for partial specialization to add strings.
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*/
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template <typename T, typename Data>
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struct OneAdder {
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static void ALWAYS_INLINE add(Data& data, const IColumn& column, size_t row_num) {
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if constexpr (std::is_same_v<T, String>) {
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StringRef value = column.get_data_at(row_num);
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data.set.insert(Data::get_key(value));
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} else if constexpr (IsDecimalNumber<T>) {
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data.set.insert(assert_cast<const ColumnDecimal<T>&>(column).get_data()[row_num]);
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} else {
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data.set.insert(assert_cast<const ColumnVector<T>&>(column).get_data()[row_num]);
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}
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}
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};
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} // namespace detail
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/// Calculates the number of different values approximately or exactly.
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template <typename T, typename Data>
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class AggregateFunctionUniq final
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: public IAggregateFunctionDataHelper<Data, AggregateFunctionUniq<T, Data>> {
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public:
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using KeyType = std::conditional_t<std::is_same_v<T, String>, UInt128, T>;
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AggregateFunctionUniq(const DataTypes& argument_types_)
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: IAggregateFunctionDataHelper<Data, AggregateFunctionUniq<T, Data>>(argument_types_) {}
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String get_name() const override { return Data::get_name(); }
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DataTypePtr get_return_type() const override { return std::make_shared<DataTypeInt64>(); }
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void add(AggregateDataPtr __restrict place, const IColumn** columns, size_t row_num,
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Arena*) const override {
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detail::OneAdder<T, Data>::add(this->data(place), *columns[0], row_num);
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}
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static ALWAYS_INLINE const KeyType* get_keys(std::vector<KeyType>& keys_container,
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const IColumn& column, size_t batch_size) {
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if constexpr (std::is_same_v<T, String>) {
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keys_container.resize(batch_size);
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for (size_t i = 0; i != batch_size; ++i) {
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StringRef value = column.get_data_at(i);
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keys_container[i] = Data::get_key(value);
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}
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return keys_container.data();
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} else {
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using ColumnType =
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std::conditional_t<IsDecimalNumber<T>, ColumnDecimal<T>, ColumnVector<T>>;
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return assert_cast<const ColumnType&>(column).get_data().data();
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}
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}
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void add_batch(size_t batch_size, AggregateDataPtr* places, size_t place_offset,
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const IColumn** columns, Arena* arena, bool /*agg_many*/) const override {
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std::vector<KeyType> keys_container;
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const KeyType* keys = get_keys(keys_container, *columns[0], batch_size);
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std::vector<typename Data::Set*> array_of_data_set(batch_size);
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for (size_t i = 0; i != batch_size; ++i) {
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array_of_data_set[i] = &(this->data(places[i] + place_offset).set);
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}
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for (size_t i = 0; i != batch_size; ++i) {
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if (i + HASH_MAP_PREFETCH_DIST < batch_size) {
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array_of_data_set[i + HASH_MAP_PREFETCH_DIST]->prefetch(
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keys[i + HASH_MAP_PREFETCH_DIST]);
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}
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array_of_data_set[i]->insert(keys[i]);
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}
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}
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void merge(AggregateDataPtr __restrict place, ConstAggregateDataPtr rhs,
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Arena*) const override {
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auto& rhs_set = this->data(rhs).set;
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if (rhs_set.size() == 0) return;
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auto& set = this->data(place).set;
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set.rehash(set.size() + rhs_set.size());
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for (auto elem : rhs_set) {
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set.insert(elem);
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}
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}
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void add_batch_single_place(size_t batch_size, AggregateDataPtr place, const IColumn** columns,
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Arena* arena) const override {
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std::vector<KeyType> keys_container;
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const KeyType* keys = get_keys(keys_container, *columns[0], batch_size);
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auto& set = this->data(place).set;
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for (size_t i = 0; i != batch_size; ++i) {
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if (i + HASH_MAP_PREFETCH_DIST < batch_size) {
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set.prefetch(keys[i + HASH_MAP_PREFETCH_DIST]);
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}
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set.insert(keys[i]);
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}
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}
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void serialize(ConstAggregateDataPtr __restrict place, BufferWritable& buf) const override {
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auto& set = this->data(place).set;
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write_var_uint(set.size(), buf);
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for (const auto& elem : set) {
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write_pod_binary(elem, buf);
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}
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}
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void deserialize_and_merge(AggregateDataPtr __restrict place, AggregateDataPtr __restrict rhs,
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BufferReadable& buf, Arena* arena) const override {
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auto& set = this->data(place).set;
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UInt64 size;
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read_var_uint(size, buf);
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set.rehash(size + set.size());
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for (size_t i = 0; i < size; ++i) {
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KeyType ref;
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read_pod_binary(ref, buf);
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set.insert(ref);
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}
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}
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void deserialize(AggregateDataPtr __restrict place, BufferReadable& buf,
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Arena* arena) const override {
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auto& set = this->data(place).set;
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UInt64 size;
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read_var_uint(size, buf);
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set.rehash(size + set.size());
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for (size_t i = 0; i < size; ++i) {
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KeyType ref;
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read_pod_binary(ref, buf);
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set.insert(ref);
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}
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}
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void insert_result_into(ConstAggregateDataPtr __restrict place, IColumn& to) const override {
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assert_cast<ColumnInt64&>(to).get_data().push_back(this->data(place).set.size());
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}
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};
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} // namespace doris::vectorized
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