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dd23e84cad
Summary: GetApproximateMemTableStats() could return some bad results with the standard skip list memtable. See this new db_bench test showing the dismal distribution of results when the actual number of entries in range is 1000: ``` $ ./db_bench --benchmarks=filluniquerandom,approximatememtablestats,readrandom --value_size=1 --num=1000000 --batch_size=1000 ... filluniquerandom : 1.391 micros/op 718915 ops/sec 1.391 seconds 1000000 operations; 11.7 MB/s approximatememtablestats : 3.711 micros/op 269492 ops/sec 3.711 seconds 1000000 operations; Reported entry count stats (expected 1000): Count: 1000000 Average: 2344.1611 StdDev: 26587.27 Min: 0 Median: 965.8555 Max: 835273 Percentiles: P50: 965.86 P75: 1610.77 P99: 12618.01 P99.9: 74991.58 P99.99: 830970.97 ------------------------------------------------------ [ 0, 1 ] 131344 13.134% 13.134% ### ( 1, 2 ] 115 0.011% 13.146% ( 2, 3 ] 106 0.011% 13.157% ( 3, 4 ] 190 0.019% 13.176% ( 4, 6 ] 214 0.021% 13.197% ( 6, 10 ] 522 0.052% 13.249% ( 10, 15 ] 748 0.075% 13.324% ( 15, 22 ] 1002 0.100% 13.424% ( 22, 34 ] 1948 0.195% 13.619% ( 34, 51 ] 3067 0.307% 13.926% ( 51, 76 ] 4213 0.421% 14.347% ( 76, 110 ] 5721 0.572% 14.919% ( 110, 170 ] 11375 1.137% 16.056% ( 170, 250 ] 17928 1.793% 17.849% ( 250, 380 ] 36597 3.660% 21.509% # ( 380, 580 ] 77882 7.788% 29.297% ## ( 580, 870 ] 160193 16.019% 45.317% ### ( 870, 1300 ] 210098 21.010% 66.326% #### ( 1300, 1900 ] 167461 16.746% 83.072% ### ( 1900, 2900 ] 78678 7.868% 90.940% ## ( 2900, 4400 ] 47743 4.774% 95.715% # ( 4400, 6600 ] 17650 1.765% 97.480% ( 6600, 9900 ] 11895 1.190% 98.669% ( 9900, 14000 ] 4993 0.499% 99.168% ( 14000, 22000 ] 2384 0.238% 99.407% ( 22000, 33000 ] 1966 0.197% 99.603% ( 50000, 75000 ] 2968 0.297% 99.900% ( 570000, 860000 ] 999 0.100% 100.000% readrandom : 1.967 micros/op 508487 ops/sec 1.967 seconds 1000000 operations; 8.2 MB/s (1000000 of 1000000 found) ``` Perhaps the only good thing to say about the old implementation was that it was fast, though apparently not that fast. I've implemented a much more robust and reasonably fast new version of the function. It's still logarithmic but with some larger constant factors. The standard deviation from true count is around 20% or less, and roughly the CPU cost of two memtable point look-ups. See code comments for detail. ``` $ ./db_bench --benchmarks=filluniquerandom,approximatememtablestats,readrandom --value_size=1 --num=1000000 --batch_size=1000 ... filluniquerandom : 1.478 micros/op 676434 ops/sec 1.478 seconds 1000000 operations; 11.0 MB/s approximatememtablestats : 2.694 micros/op 371157 ops/sec 2.694 seconds 1000000 operations; Reported entry count stats (expected 1000): Count: 1000000 Average: 1073.5158 StdDev: 197.80 Min: 608 Median: 1079.9506 Max: 2176 Percentiles: P50: 1079.95 P75: 1223.69 P99: 1852.36 P99.9: 1898.70 P99.99: 2176.00 ------------------------------------------------------ ( 580, 870 ] 134848 13.485% 13.485% ### ( 870, 1300 ] 747868 74.787% 88.272% ############### ( 1300, 1900 ] 116536 11.654% 99.925% ## ( 1900, 2900 ] 748 0.075% 100.000% readrandom : 1.997 micros/op 500654 ops/sec 1.997 seconds 1000000 operations; 8.1 MB/s (1000000 of 1000000 found) ``` We can already see that the distribution of results is dramatically better and wonderfully normal-looking, with relative standard deviation around 20%. The function is also FASTER, at least with these parameters. Let's look how this behavior generalizes, first *much* larger range: ``` $ ./db_bench --benchmarks=filluniquerandom,approximatememtablestats,readrandom --value_size=1 --num=1000000 --batch_size=30000 filluniquerandom : 1.390 micros/op 719654 ops/sec 1.376 seconds 990000 operations; 11.7 MB/s approximatememtablestats : 1.129 micros/op 885649 ops/sec 1.129 seconds 1000000 operations; Reported entry count stats (expected 30000): Count: 1000000 Average: 31098.8795 StdDev: 3601.47 Min: 21504 Median: 29333.9303 Max: 43008 Percentiles: P50: 29333.93 P75: 33018.00 P99: 43008.00 P99.9: 43008.00 P99.99: 43008.00 ------------------------------------------------------ ( 14000, 22000 ] 408 0.041% 0.041% ( 22000, 33000 ] 749327 74.933% 74.974% ############### ( 33000, 50000 ] 250265 25.027% 100.000% ##### readrandom : 1.894 micros/op 528083 ops/sec 1.894 seconds 1000000 operations; 8.5 MB/s (989989 of 1000000 found) ``` This is *even faster* and relatively *more accurate*, with relative standard deviation closer to 10%. Code comments explain why. Now let's look at smaller ranges. Implementation quirks or conveniences: * When actual number in range is >= 40, the minimum return value is 40. * When the actual is <= 10, it is guaranteed to return that actual number. ``` $ ./db_bench --benchmarks=filluniquerandom,approximatememtablestats,readrandom --value_size=1 --num=1000000 --batch_size=75 ... filluniquerandom : 1.417 micros/op 705668 ops/sec 1.417 seconds 999975 operations; 11.4 MB/s approximatememtablestats : 3.342 micros/op 299197 ops/sec 3.342 seconds 1000000 operations; Reported entry count stats (expected 75): Count: 1000000 Average: 75.1210 StdDev: 15.02 Min: 40 Median: 71.9395 Max: 256 Percentiles: P50: 71.94 P75: 89.69 P99: 119.12 P99.9: 166.68 P99.99: 229.78 ------------------------------------------------------ ( 34, 51 ] 38867 3.887% 3.887% # ( 51, 76 ] 550554 55.055% 58.942% ########### ( 76, 110 ] 398854 39.885% 98.828% ######## ( 110, 170 ] 11353 1.135% 99.963% ( 170, 250 ] 364 0.036% 99.999% ( 250, 380 ] 8 0.001% 100.000% readrandom : 1.861 micros/op 537224 ops/sec 1.861 seconds 1000000 operations; 8.7 MB/s (999974 of 1000000 found) $ ./db_bench --benchmarks=filluniquerandom,approximatememtablestats,readrandom --value_size=1 --num=1000000 --batch_size=25 ... filluniquerandom : 1.501 micros/op 666283 ops/sec 1.501 seconds 1000000 operations; 10.8 MB/s approximatememtablestats : 5.118 micros/op 195401 ops/sec 5.118 seconds 1000000 operations; Reported entry count stats (expected 25): Count: 1000000 Average: 26.2392 StdDev: 4.58 Min: 25 Median: 28.4590 Max: 72 Percentiles: P50: 28.46 P75: 31.69 P99: 49.27 P99.9: 67.95 P99.99: 72.00 ------------------------------------------------------ ( 22, 34 ] 928936 92.894% 92.894% ################### ( 34, 51 ] 67960 6.796% 99.690% # ( 51, 76 ] 3104 0.310% 100.000% readrandom : 1.892 micros/op 528595 ops/sec 1.892 seconds 1000000 operations; 8.6 MB/s (1000000 of 1000000 found) $ ./db_bench --benchmarks=filluniquerandom,approximatememtablestats,readrandom --value_size=1 --num=1000000 --batch_size=10 ... filluniquerandom : 1.642 micros/op 608916 ops/sec 1.642 seconds 1000000 operations; 9.9 MB/s approximatememtablestats : 3.042 micros/op 328721 ops/sec 3.042 seconds 1000000 operations; Reported entry count stats (expected 10): Count: 1000000 Average: 10.0000 StdDev: 0.00 Min: 10 Median: 10.0000 Max: 10 Percentiles: P50: 10.00 P75: 10.00 P99: 10.00 P99.9: 10.00 P99.99: 10.00 ------------------------------------------------------ ( 6, 10 ] 1000000 100.000% 100.000% #################### readrandom : 1.805 micros/op 554126 ops/sec 1.805 seconds 1000000 operations; 9.0 MB/s (1000000 of 1000000 found) ``` Remarkably consistent. Pull Request resolved: https://github.com/facebook/rocksdb/pull/13047 Test Plan: new db_bench test for both performance and accuracy (see above); added to crash test; unit test updated. Reviewed By: cbi42 Differential Revision: D63722003 Pulled By: pdillinger fbshipit-source-id: cfc8613c085e87c17ecec22d82601aac2a5a1b26
408 lines
14 KiB
C++
408 lines
14 KiB
C++
// Copyright (c) 2011-present, Facebook, Inc. All rights reserved.
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// This source code is licensed under both the GPLv2 (found in the
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// COPYING file in the root directory) and Apache 2.0 License
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// (found in the LICENSE.Apache file in the root directory).
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//
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#include <random>
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#include "db/memtable.h"
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#include "memory/arena.h"
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#include "memtable/inlineskiplist.h"
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#include "rocksdb/memtablerep.h"
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#include "rocksdb/utilities/options_type.h"
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#include "util/string_util.h"
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namespace ROCKSDB_NAMESPACE {
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namespace {
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class SkipListRep : public MemTableRep {
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InlineSkipList<const MemTableRep::KeyComparator&> skip_list_;
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const MemTableRep::KeyComparator& cmp_;
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const SliceTransform* transform_;
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const size_t lookahead_;
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friend class LookaheadIterator;
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public:
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explicit SkipListRep(const MemTableRep::KeyComparator& compare,
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Allocator* allocator, const SliceTransform* transform,
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const size_t lookahead)
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: MemTableRep(allocator),
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skip_list_(compare, allocator),
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cmp_(compare),
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transform_(transform),
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lookahead_(lookahead) {}
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KeyHandle Allocate(const size_t len, char** buf) override {
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*buf = skip_list_.AllocateKey(len);
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return static_cast<KeyHandle>(*buf);
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}
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// Insert key into the list.
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// REQUIRES: nothing that compares equal to key is currently in the list.
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void Insert(KeyHandle handle) override {
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skip_list_.Insert(static_cast<char*>(handle));
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}
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bool InsertKey(KeyHandle handle) override {
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return skip_list_.Insert(static_cast<char*>(handle));
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}
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void InsertWithHint(KeyHandle handle, void** hint) override {
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skip_list_.InsertWithHint(static_cast<char*>(handle), hint);
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}
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bool InsertKeyWithHint(KeyHandle handle, void** hint) override {
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return skip_list_.InsertWithHint(static_cast<char*>(handle), hint);
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}
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void InsertWithHintConcurrently(KeyHandle handle, void** hint) override {
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skip_list_.InsertWithHintConcurrently(static_cast<char*>(handle), hint);
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}
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bool InsertKeyWithHintConcurrently(KeyHandle handle, void** hint) override {
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return skip_list_.InsertWithHintConcurrently(static_cast<char*>(handle),
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hint);
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}
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void InsertConcurrently(KeyHandle handle) override {
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skip_list_.InsertConcurrently(static_cast<char*>(handle));
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}
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bool InsertKeyConcurrently(KeyHandle handle) override {
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return skip_list_.InsertConcurrently(static_cast<char*>(handle));
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}
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// Returns true iff an entry that compares equal to key is in the list.
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bool Contains(const char* key) const override {
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return skip_list_.Contains(key);
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}
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size_t ApproximateMemoryUsage() override {
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// All memory is allocated through allocator; nothing to report here
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return 0;
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}
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void Get(const LookupKey& k, void* callback_args,
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bool (*callback_func)(void* arg, const char* entry)) override {
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SkipListRep::Iterator iter(&skip_list_);
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Slice dummy_slice;
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for (iter.Seek(dummy_slice, k.memtable_key().data());
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iter.Valid() && callback_func(callback_args, iter.key());
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iter.Next()) {
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}
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}
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Status GetAndValidate(const LookupKey& k, void* callback_args,
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bool (*callback_func)(void* arg, const char* entry),
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bool allow_data_in_errors) override {
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SkipListRep::Iterator iter(&skip_list_);
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Slice dummy_slice;
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Status status = iter.SeekAndValidate(dummy_slice, k.memtable_key().data(),
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allow_data_in_errors);
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for (; iter.Valid() && status.ok() &&
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callback_func(callback_args, iter.key());
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status = iter.NextAndValidate(allow_data_in_errors)) {
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}
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return status;
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}
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uint64_t ApproximateNumEntries(const Slice& start_ikey,
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const Slice& end_ikey) override {
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return skip_list_.ApproximateNumEntries(start_ikey, end_ikey);
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}
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void UniqueRandomSample(const uint64_t num_entries,
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const uint64_t target_sample_size,
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std::unordered_set<const char*>* entries) override {
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entries->clear();
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// Avoid divide-by-0.
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assert(target_sample_size > 0);
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assert(num_entries > 0);
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// NOTE: the size of entries is not enforced to be exactly
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// target_sample_size at the end of this function, it might be slightly
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// greater or smaller.
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SkipListRep::Iterator iter(&skip_list_);
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// There are two methods to create the subset of samples (size m)
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// from the table containing N elements:
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// 1-Iterate linearly through the N memtable entries. For each entry i,
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// add it to the sample set with a probability
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// (target_sample_size - entries.size() ) / (N-i).
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//
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// 2-Pick m random elements without repetition.
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// We pick Option 2 when m<sqrt(N) and
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// Option 1 when m > sqrt(N).
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if (target_sample_size >
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static_cast<uint64_t>(std::sqrt(1.0 * num_entries))) {
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Random* rnd = Random::GetTLSInstance();
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iter.SeekToFirst();
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uint64_t counter = 0, num_samples_left = target_sample_size;
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for (; iter.Valid() && (num_samples_left > 0); iter.Next(), counter++) {
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// Add entry to sample set with probability
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// num_samples_left/(num_entries - counter).
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if (rnd->Next() % (num_entries - counter) < num_samples_left) {
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entries->insert(iter.key());
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num_samples_left--;
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}
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}
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} else {
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// Option 2: pick m random elements with no duplicates.
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// If Option 2 is picked, then target_sample_size<sqrt(N)
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// Using a set spares the need to check for duplicates.
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for (uint64_t i = 0; i < target_sample_size; i++) {
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// We give it 5 attempts to find a non-duplicate
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// With 5 attempts, the chances of returning `entries` set
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// of size target_sample_size is:
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// PROD_{i=1}^{target_sample_size-1} [1-(i/N)^5]
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// which is monotonically increasing with N in the worse case
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// of target_sample_size=sqrt(N), and is always >99.9% for N>4.
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// At worst, for the final pick , when m=sqrt(N) there is
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// a probability of p= 1/sqrt(N) chances to find a duplicate.
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for (uint64_t j = 0; j < 5; j++) {
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iter.RandomSeek();
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// unordered_set::insert returns pair<iterator, bool>.
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// The second element is true if an insert successfully happened.
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// If element is already in the set, this bool will be false, and
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// true otherwise.
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if ((entries->insert(iter.key())).second) {
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break;
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}
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}
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}
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}
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}
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~SkipListRep() override = default;
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// Iteration over the contents of a skip list
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class Iterator : public MemTableRep::Iterator {
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InlineSkipList<const MemTableRep::KeyComparator&>::Iterator iter_;
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public:
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// Initialize an iterator over the specified list.
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// The returned iterator is not valid.
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explicit Iterator(
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const InlineSkipList<const MemTableRep::KeyComparator&>* list)
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: iter_(list) {}
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~Iterator() override = default;
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// Returns true iff the iterator is positioned at a valid node.
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bool Valid() const override { return iter_.Valid(); }
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// Returns the key at the current position.
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// REQUIRES: Valid()
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const char* key() const override {
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assert(Valid());
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return iter_.key();
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}
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// Advances to the next position.
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// REQUIRES: Valid()
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void Next() override {
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assert(Valid());
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iter_.Next();
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}
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// Advances to the previous position.
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// REQUIRES: Valid()
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void Prev() override {
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assert(Valid());
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iter_.Prev();
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}
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// Advance to the first entry with a key >= target
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void Seek(const Slice& user_key, const char* memtable_key) override {
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if (memtable_key != nullptr) {
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iter_.Seek(memtable_key);
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} else {
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iter_.Seek(EncodeKey(&tmp_, user_key));
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}
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}
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// Retreat to the last entry with a key <= target
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void SeekForPrev(const Slice& user_key, const char* memtable_key) override {
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if (memtable_key != nullptr) {
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iter_.SeekForPrev(memtable_key);
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} else {
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iter_.SeekForPrev(EncodeKey(&tmp_, user_key));
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}
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}
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void RandomSeek() override { iter_.RandomSeek(); }
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// Position at the first entry in list.
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// Final state of iterator is Valid() iff list is not empty.
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void SeekToFirst() override { iter_.SeekToFirst(); }
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// Position at the last entry in list.
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// Final state of iterator is Valid() iff list is not empty.
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void SeekToLast() override { iter_.SeekToLast(); }
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Status NextAndValidate(bool allow_data_in_errors) override {
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assert(Valid());
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return iter_.NextAndValidate(allow_data_in_errors);
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}
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Status SeekAndValidate(const Slice& user_key, const char* memtable_key,
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bool allow_data_in_errors) override {
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if (memtable_key != nullptr) {
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return iter_.SeekAndValidate(memtable_key, allow_data_in_errors);
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} else {
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return iter_.SeekAndValidate(EncodeKey(&tmp_, user_key),
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allow_data_in_errors);
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}
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}
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Status PrevAndValidate(bool allow_data_in_error) override {
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assert(Valid());
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return iter_.PrevAndValidate(allow_data_in_error);
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}
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protected:
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std::string tmp_; // For passing to EncodeKey
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};
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// Iterator over the contents of a skip list which also keeps track of the
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// previously visited node. In Seek(), it examines a few nodes after it
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// first, falling back to O(log n) search from the head of the list only if
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// the target key hasn't been found.
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class LookaheadIterator : public MemTableRep::Iterator {
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public:
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explicit LookaheadIterator(const SkipListRep& rep)
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: rep_(rep), iter_(&rep_.skip_list_), prev_(iter_) {}
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~LookaheadIterator() override = default;
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bool Valid() const override { return iter_.Valid(); }
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const char* key() const override {
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assert(Valid());
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return iter_.key();
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}
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void Next() override {
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assert(Valid());
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bool advance_prev = true;
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if (prev_.Valid()) {
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auto k1 = rep_.UserKey(prev_.key());
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auto k2 = rep_.UserKey(iter_.key());
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if (k1.compare(k2) == 0) {
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// same user key, don't move prev_
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advance_prev = false;
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} else if (rep_.transform_) {
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// only advance prev_ if it has the same prefix as iter_
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auto t1 = rep_.transform_->Transform(k1);
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auto t2 = rep_.transform_->Transform(k2);
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advance_prev = t1.compare(t2) == 0;
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}
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}
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if (advance_prev) {
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prev_ = iter_;
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}
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iter_.Next();
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}
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void Prev() override {
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assert(Valid());
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iter_.Prev();
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prev_ = iter_;
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}
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void Seek(const Slice& internal_key, const char* memtable_key) override {
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const char* encoded_key = (memtable_key != nullptr)
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? memtable_key
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: EncodeKey(&tmp_, internal_key);
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if (prev_.Valid() && rep_.cmp_(encoded_key, prev_.key()) >= 0) {
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// prev_.key() is smaller or equal to our target key; do a quick
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// linear search (at most lookahead_ steps) starting from prev_
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iter_ = prev_;
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size_t cur = 0;
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while (cur++ <= rep_.lookahead_ && iter_.Valid()) {
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if (rep_.cmp_(encoded_key, iter_.key()) <= 0) {
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return;
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}
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Next();
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}
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}
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iter_.Seek(encoded_key);
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prev_ = iter_;
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}
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void SeekForPrev(const Slice& internal_key,
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const char* memtable_key) override {
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const char* encoded_key = (memtable_key != nullptr)
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? memtable_key
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: EncodeKey(&tmp_, internal_key);
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iter_.SeekForPrev(encoded_key);
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prev_ = iter_;
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}
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void SeekToFirst() override {
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iter_.SeekToFirst();
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prev_ = iter_;
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}
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void SeekToLast() override {
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iter_.SeekToLast();
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prev_ = iter_;
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}
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protected:
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std::string tmp_; // For passing to EncodeKey
|
|
|
|
private:
|
|
const SkipListRep& rep_;
|
|
InlineSkipList<const MemTableRep::KeyComparator&>::Iterator iter_;
|
|
InlineSkipList<const MemTableRep::KeyComparator&>::Iterator prev_;
|
|
};
|
|
|
|
MemTableRep::Iterator* GetIterator(Arena* arena = nullptr) override {
|
|
if (lookahead_ > 0) {
|
|
void* mem =
|
|
arena ? arena->AllocateAligned(sizeof(SkipListRep::LookaheadIterator))
|
|
:
|
|
operator new(sizeof(SkipListRep::LookaheadIterator));
|
|
return new (mem) SkipListRep::LookaheadIterator(*this);
|
|
} else {
|
|
void* mem = arena ? arena->AllocateAligned(sizeof(SkipListRep::Iterator))
|
|
:
|
|
operator new(sizeof(SkipListRep::Iterator));
|
|
return new (mem) SkipListRep::Iterator(&skip_list_);
|
|
}
|
|
}
|
|
};
|
|
} // namespace
|
|
|
|
static std::unordered_map<std::string, OptionTypeInfo> skiplist_factory_info = {
|
|
{"lookahead",
|
|
{0, OptionType::kSizeT, OptionVerificationType::kNormal,
|
|
OptionTypeFlags::kDontSerialize /*Since it is part of the ID*/}},
|
|
};
|
|
|
|
SkipListFactory::SkipListFactory(size_t lookahead) : lookahead_(lookahead) {
|
|
RegisterOptions("SkipListFactoryOptions", &lookahead_,
|
|
&skiplist_factory_info);
|
|
}
|
|
|
|
std::string SkipListFactory::GetId() const {
|
|
std::string id = Name();
|
|
if (lookahead_ > 0) {
|
|
id.append(":").append(std::to_string(lookahead_));
|
|
}
|
|
return id;
|
|
}
|
|
|
|
MemTableRep* SkipListFactory::CreateMemTableRep(
|
|
const MemTableRep::KeyComparator& compare, Allocator* allocator,
|
|
const SliceTransform* transform, Logger* /*logger*/) {
|
|
return new SkipListRep(compare, allocator, transform, lookahead_);
|
|
}
|
|
|
|
} // namespace ROCKSDB_NAMESPACE
|