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// Licensed to the Apache Software Foundation (ASF) under one
// or more contributor license agreements. See the NOTICE file
// distributed with this work for additional information
// regarding copyright ownership. The ASF licenses this file
// to you under the Apache License, Version 2.0 (the
// "License"); you may not use this file except in compliancec
// 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 "contrib/libs/apache/arrow_next/cpp/src/parquet/size_statistics.h"
#include <algorithm>
#include <array>
#include <numeric>
#include <ostream>
#include <string_view>
#include <vector>
#include "contrib/libs/apache/arrow_next/cpp/src/arrow/util/logging.h"
#include "contrib/libs/apache/arrow_next/cpp/src/parquet/exception.h"
#include "contrib/libs/apache/arrow_next/cpp/src/parquet/schema.h"
namespace parquet20 {
namespace {
void MergeLevelHistogram(::arrow20::util::span<int64_t> histogram,
::arrow20::util::span<const int64_t> other) {
ARROW_DCHECK_EQ(histogram.size(), other.size());
std::transform(histogram.begin(), histogram.end(), other.begin(), histogram.begin(),
std::plus<>());
}
} // namespace
void SizeStatistics::Merge(const SizeStatistics& other) {
if (repetition_level_histogram.size() != other.repetition_level_histogram.size()) {
throw ParquetException("Repetition level histogram size mismatch");
}
if (definition_level_histogram.size() != other.definition_level_histogram.size()) {
throw ParquetException("Definition level histogram size mismatch");
}
if (unencoded_byte_array_data_bytes.has_value() !=
other.unencoded_byte_array_data_bytes.has_value()) {
throw ParquetException("Unencoded byte array data bytes are not consistent");
}
MergeLevelHistogram(repetition_level_histogram, other.repetition_level_histogram);
MergeLevelHistogram(definition_level_histogram, other.definition_level_histogram);
if (unencoded_byte_array_data_bytes.has_value()) {
unencoded_byte_array_data_bytes = unencoded_byte_array_data_bytes.value() +
other.unencoded_byte_array_data_bytes.value();
}
}
void SizeStatistics::IncrementUnencodedByteArrayDataBytes(int64_t value) {
ARROW_CHECK(unencoded_byte_array_data_bytes.has_value());
unencoded_byte_array_data_bytes = unencoded_byte_array_data_bytes.value() + value;
}
void SizeStatistics::Validate(const ColumnDescriptor* descr) const {
auto validate_histogram = [](const std::vector<int64_t>& histogram, int16_t max_level,
const std::string& name) {
if (histogram.empty()) {
// A levels histogram is always allowed to be missing.
return;
}
if (histogram.size() != static_cast<size_t>(max_level + 1)) {
std::stringstream ss;
ss << name << " level histogram size mismatch, size: " << histogram.size()
<< ", expected: " << (max_level + 1);
throw ParquetException(ss.str());
}
};
validate_histogram(repetition_level_histogram, descr->max_repetition_level(),
"Repetition");
validate_histogram(definition_level_histogram, descr->max_definition_level(),
"Definition");
if (unencoded_byte_array_data_bytes.has_value() &&
descr->physical_type() != Type::BYTE_ARRAY) {
throw ParquetException("Unencoded byte array data bytes does not support " +
TypeToString(descr->physical_type()));
}
}
void SizeStatistics::Reset() {
repetition_level_histogram.assign(repetition_level_histogram.size(), 0);
definition_level_histogram.assign(definition_level_histogram.size(), 0);
if (unencoded_byte_array_data_bytes.has_value()) {
unencoded_byte_array_data_bytes = 0;
}
}
std::unique_ptr<SizeStatistics> SizeStatistics::Make(const ColumnDescriptor* descr) {
auto size_stats = std::make_unique<SizeStatistics>();
// If the max level is 0, the level histogram can be omitted because it contains
// only single level (a.k.a. 0) and its count is equivalent to `num_values` of the
// column chunk or data page.
if (descr->max_repetition_level() != 0) {
size_stats->repetition_level_histogram.resize(descr->max_repetition_level() + 1, 0);
}
if (descr->max_definition_level() != 0) {
size_stats->definition_level_histogram.resize(descr->max_definition_level() + 1, 0);
}
if (descr->physical_type() == Type::BYTE_ARRAY) {
size_stats->unencoded_byte_array_data_bytes = 0;
}
return size_stats;
}
std::ostream& operator<<(std::ostream& os, const SizeStatistics& size_stats) {
constexpr std::string_view kComma = ", ";
os << "SizeStatistics{";
std::string_view sep = "";
if (size_stats.unencoded_byte_array_data_bytes.has_value()) {
os << "unencoded_byte_array_data_bytes="
<< *size_stats.unencoded_byte_array_data_bytes;
sep = kComma;
}
auto print_histogram = [&](std::string_view name,
const std::vector<int64_t>& histogram) {
if (!histogram.empty()) {
os << sep << name << "={";
sep = kComma;
std::string_view value_sep = "";
for (int64_t v : histogram) {
os << value_sep << v;
value_sep = kComma;
}
os << "}";
}
};
print_histogram("repetition_level_histogram", size_stats.repetition_level_histogram);
print_histogram("definition_level_histogram", size_stats.definition_level_histogram);
os << "}";
return os;
}
void UpdateLevelHistogram(::arrow20::util::span<const int16_t> levels,
::arrow20::util::span<int64_t> histogram) {
const int64_t num_levels = static_cast<int64_t>(levels.size());
DCHECK_GE(histogram.size(), 1);
const int16_t max_level = static_cast<int16_t>(histogram.size() - 1);
if (max_level == 0) {
histogram[0] += num_levels;
return;
}
#ifndef NDEBUG
for (auto level : levels) {
ARROW_DCHECK_LE(level, max_level);
}
#endif
if (max_level == 1) {
// Specialize the common case for non-repeated non-nested columns.
// Summing the levels gives us the number of 1s, and the number of 0s follows.
// We do repeated sums in the int16_t space, which the compiler is likely
// to vectorize efficiently.
constexpr int64_t kChunkSize = 1 << 14; // to avoid int16_t overflows
int64_t hist1 = 0;
auto it = levels.begin();
while (it != levels.end()) {
const auto chunk_size = std::min<int64_t>(levels.end() - it, kChunkSize);
hist1 += std::accumulate(levels.begin(), levels.begin() + chunk_size, int16_t{0});
it += chunk_size;
}
histogram[0] += num_levels - hist1;
histogram[1] += hist1;
return;
}
// The generic implementation issues a series of histogram load-stores.
// However, it limits store-to-load dependencies by interleaving partial histogram
// updates.
constexpr int kUnroll = 4;
std::array<std::vector<int64_t>, kUnroll> partial_hist;
for (auto& hist : partial_hist) {
hist.assign(histogram.size(), 0);
}
int64_t i = 0;
for (; i <= num_levels - kUnroll; i += kUnroll) {
for (int j = 0; j < kUnroll; ++j) {
++partial_hist[j][levels[i + j]];
}
}
for (; i < num_levels; ++i) {
++partial_hist[0][levels[i]];
}
for (const auto& hist : partial_hist) {
MergeLevelHistogram(histogram, hist);
}
}
} // namespace parquet20
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