e3584f9c28
- Fix for issue 33 (non-null-terminated result from leveldb_property_value()) - Support for running multiple instances of a benchmark in parallel. - Reduce lock contention on Get(): (1) Do not hold the lock while searching memtables. (2) Shard block and table caches 16-ways. Benchmark for evaluating this change: $ db_bench --benchmarks=fillseq1,readrandom --threads=$n (fillseq1 is a small hack to make sure fillseq runs once regardless of number of threads specified on the command line). git-svn-id: https://leveldb.googlecode.com/svn/trunk@49 62dab493-f737-651d-591e-8d6aee1b9529
140 lines
4.6 KiB
C++
140 lines
4.6 KiB
C++
// 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 <math.h>
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#include <stdio.h>
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#include "port/port.h"
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#include "util/histogram.h"
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namespace leveldb {
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const double Histogram::kBucketLimit[kNumBuckets] = {
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1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 16, 18, 20, 25, 30, 35, 40, 45,
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50, 60, 70, 80, 90, 100, 120, 140, 160, 180, 200, 250, 300, 350, 400, 450,
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500, 600, 700, 800, 900, 1000, 1200, 1400, 1600, 1800, 2000, 2500, 3000,
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3500, 4000, 4500, 5000, 6000, 7000, 8000, 9000, 10000, 12000, 14000,
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16000, 18000, 20000, 25000, 30000, 35000, 40000, 45000, 50000, 60000,
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70000, 80000, 90000, 100000, 120000, 140000, 160000, 180000, 200000,
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250000, 300000, 350000, 400000, 450000, 500000, 600000, 700000, 800000,
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900000, 1000000, 1200000, 1400000, 1600000, 1800000, 2000000, 2500000,
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3000000, 3500000, 4000000, 4500000, 5000000, 6000000, 7000000, 8000000,
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9000000, 10000000, 12000000, 14000000, 16000000, 18000000, 20000000,
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25000000, 30000000, 35000000, 40000000, 45000000, 50000000, 60000000,
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70000000, 80000000, 90000000, 100000000, 120000000, 140000000, 160000000,
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180000000, 200000000, 250000000, 300000000, 350000000, 400000000,
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450000000, 500000000, 600000000, 700000000, 800000000, 900000000,
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1000000000, 1200000000, 1400000000, 1600000000, 1800000000, 2000000000,
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2500000000.0, 3000000000.0, 3500000000.0, 4000000000.0, 4500000000.0,
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5000000000.0, 6000000000.0, 7000000000.0, 8000000000.0, 9000000000.0,
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1e200,
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};
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void Histogram::Clear() {
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min_ = kBucketLimit[kNumBuckets-1];
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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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for (int i = 0; i < kNumBuckets; i++) {
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buckets_[i] = 0;
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}
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}
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void Histogram::Add(double value) {
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// Linear search is fast enough for our usage in db_bench
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int b = 0;
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while (b < kNumBuckets - 1 && kBucketLimit[b] <= value) {
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b++;
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}
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buckets_[b] += 1.0;
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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 Histogram::Merge(const Histogram& 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 (int b = 0; b < kNumBuckets; b++) {
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buckets_[b] += other.buckets_[b];
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}
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}
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double Histogram::Median() const {
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return Percentile(50.0);
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}
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double Histogram::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 (int b = 0; b < kNumBuckets; 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 : kBucketLimit[b-1];
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double right_point = kBucketLimit[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 = (threshold - left_sum) / (right_sum - left_sum);
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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 Histogram::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 Histogram::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 Histogram::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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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 (int b = 0; b < kNumBuckets; 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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"[ %7.0f, %7.0f ) %7.0f %7.3f%% %7.3f%% ",
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((b == 0) ? 0.0 : kBucketLimit[b-1]), // left
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kBucketLimit[b], // right
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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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}
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