ef5b384729
1, const qualifiers on return types make no sense and will trigger a compile warning: warning: type qualifiers ignored on function return type [-Wignored-qualifiers] 2, class HistogramImpl has virtual functions and thus should have a virtual destructor 3, with some toolchain, the macro __STDC_FORMAT_MACROS is predefined and thus should be checked before define Change-Id: I69747a03bfae88671bfbb2637c80d17600159c99 Signed-off-by: liuhuahang <liuhuahang@zerus.co>
199 lines
6.9 KiB
C++
199 lines
6.9 KiB
C++
// Copyright (c) 2013, Facebook, Inc. All rights reserved.
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// This source code is licensed under the BSD-style license found in the
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// LICENSE file in the root directory of this source tree. An additional grant
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// of patent rights can be found in the PATENTS file in the same directory.
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//
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// Copyright (c) 2011 The LevelDB Authors. All rights reserved.
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// Use of this source code is governed by a BSD-style license that can be
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// found in the LICENSE file. See the AUTHORS file for names of contributors.
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#include "util/histogram.h"
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#include <cassert>
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#include <math.h>
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#include <stdio.h>
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#include "port/port.h"
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namespace rocksdb {
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HistogramBucketMapper::HistogramBucketMapper()
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:
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// Add newer bucket index here.
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// Should be alwyas added in sorted order.
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// If you change this, you also need to change
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// size of array buckets_ in HistogramImpl
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bucketValues_(
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{1, 2, 3, 4, 5, 6,
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7, 8, 9, 10, 12, 14,
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16, 18, 20, 25, 30, 35,
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40, 45, 50, 60, 70, 80,
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90, 100, 120, 140, 160, 180,
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200, 250, 300, 350, 400, 450,
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500, 600, 700, 800, 900, 1000,
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1200, 1400, 1600, 1800, 2000, 2500,
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3000, 3500, 4000, 4500, 5000, 6000,
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7000, 8000, 9000, 10000, 12000, 14000,
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16000, 18000, 20000, 25000, 30000, 35000,
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40000, 45000, 50000, 60000, 70000, 80000,
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90000, 100000, 120000, 140000, 160000, 180000,
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200000, 250000, 300000, 350000, 400000, 450000,
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500000, 600000, 700000, 800000, 900000, 1000000,
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1200000, 1400000, 1600000, 1800000, 2000000, 2500000,
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3000000, 3500000, 4000000, 4500000, 5000000, 6000000,
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7000000, 8000000, 9000000, 10000000, 12000000, 14000000,
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16000000, 18000000, 20000000, 25000000, 30000000, 35000000,
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40000000, 45000000, 50000000, 60000000, 70000000, 80000000,
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90000000, 100000000, 120000000, 140000000, 160000000, 180000000,
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200000000, 250000000, 300000000, 350000000, 400000000, 450000000,
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500000000, 600000000, 700000000, 800000000, 900000000, 1000000000}),
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maxBucketValue_(bucketValues_.back()),
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minBucketValue_(bucketValues_.front()) {
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for (size_t i =0; i < bucketValues_.size(); ++i) {
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valueIndexMap_[bucketValues_[i]] = i;
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}
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}
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size_t HistogramBucketMapper::IndexForValue(const uint64_t value) const {
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if (value >= maxBucketValue_) {
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return bucketValues_.size() - 1;
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} else if ( value >= minBucketValue_ ) {
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std::map<uint64_t, uint64_t>::const_iterator lowerBound =
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valueIndexMap_.lower_bound(value);
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if (lowerBound != valueIndexMap_.end()) {
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return lowerBound->second;
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} else {
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return 0;
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}
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} else {
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return 0;
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}
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}
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namespace {
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const HistogramBucketMapper bucketMapper;
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}
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void HistogramImpl::Clear() {
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min_ = bucketMapper.LastValue();
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max_ = 0;
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num_ = 0;
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sum_ = 0;
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sum_squares_ = 0;
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memset(buckets_, 0, sizeof buckets_);
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}
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bool HistogramImpl::Empty() { return sum_squares_ == 0; }
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void HistogramImpl::Add(uint64_t value) {
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const size_t index = bucketMapper.IndexForValue(value);
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buckets_[index] += 1;
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if (min_ > value) min_ = value;
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if (max_ < value) max_ = value;
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num_++;
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sum_ += value;
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sum_squares_ += (value * value);
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}
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void HistogramImpl::Merge(const HistogramImpl& other) {
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if (other.min_ < min_) min_ = other.min_;
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if (other.max_ > max_) max_ = other.max_;
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num_ += other.num_;
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sum_ += other.sum_;
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sum_squares_ += other.sum_squares_;
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for (unsigned int b = 0; b < bucketMapper.BucketCount(); b++) {
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buckets_[b] += other.buckets_[b];
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}
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}
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double HistogramImpl::Median() const {
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return Percentile(50.0);
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}
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double HistogramImpl::Percentile(double p) const {
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double threshold = num_ * (p / 100.0);
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double sum = 0;
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for (unsigned int b = 0; b < bucketMapper.BucketCount(); b++) {
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sum += buckets_[b];
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if (sum >= threshold) {
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// Scale linearly within this bucket
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double left_point = (b == 0) ? 0 : bucketMapper.BucketLimit(b-1);
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double right_point = bucketMapper.BucketLimit(b);
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double left_sum = sum - buckets_[b];
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double right_sum = sum;
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double pos = 0;
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double right_left_diff = right_sum - left_sum;
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if (right_left_diff != 0) {
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pos = (threshold - left_sum) / (right_sum - left_sum);
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}
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double r = left_point + (right_point - left_point) * pos;
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if (r < min_) r = min_;
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if (r > max_) r = max_;
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return r;
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}
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}
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return max_;
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}
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double HistogramImpl::Average() const {
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if (num_ == 0.0) return 0;
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return sum_ / num_;
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}
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double HistogramImpl::StandardDeviation() const {
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if (num_ == 0.0) return 0;
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double variance = (sum_squares_ * num_ - sum_ * sum_) / (num_ * num_);
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return sqrt(variance);
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}
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std::string HistogramImpl::ToString() const {
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std::string r;
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char buf[200];
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snprintf(buf, sizeof(buf),
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"Count: %.0f Average: %.4f StdDev: %.2f\n",
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num_, Average(), StandardDeviation());
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r.append(buf);
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snprintf(buf, sizeof(buf),
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"Min: %.4f Median: %.4f Max: %.4f\n",
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(num_ == 0.0 ? 0.0 : min_), Median(), max_);
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r.append(buf);
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snprintf(buf, sizeof(buf),
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"Percentiles: "
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"P50: %.2f P75: %.2f P99: %.2f P99.9: %.2f P99.99: %.2f\n",
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Percentile(50), Percentile(75), Percentile(99), Percentile(99.9),
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Percentile(99.99));
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r.append(buf);
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r.append("------------------------------------------------------\n");
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const double mult = 100.0 / num_;
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double sum = 0;
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for (unsigned int b = 0; b < bucketMapper.BucketCount(); b++) {
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if (buckets_[b] <= 0.0) continue;
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sum += buckets_[b];
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snprintf(buf, sizeof(buf),
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"[ %7lu, %7lu ) %8lu %7.3f%% %7.3f%% ",
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// left
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(unsigned long)((b == 0) ? 0 : bucketMapper.BucketLimit(b-1)),
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(unsigned long)bucketMapper.BucketLimit(b), // right
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(unsigned long)buckets_[b], // count
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(mult * buckets_[b]), // percentage
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(mult * sum)); // cumulative percentage
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r.append(buf);
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// Add hash marks based on percentage; 20 marks for 100%.
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int marks = static_cast<int>(20*(buckets_[b] / num_) + 0.5);
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r.append(marks, '#');
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r.push_back('\n');
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}
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return r;
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}
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void HistogramImpl::Data(HistogramData * const data) const {
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assert(data);
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data->median = Median();
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data->percentile95 = Percentile(95);
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data->percentile99 = Percentile(99);
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data->average = Average();
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data->standard_deviation = StandardDeviation();
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}
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} // namespace levedb
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