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rocksdb/table/cuckoo_table_reader.cc

313 lines
10 KiB

// Copyright (c) 2014, Facebook, Inc. All rights reserved.
// This source code is licensed under the BSD-style license found in the
// LICENSE file in the root directory of this source tree. An additional grant
// of patent rights can be found in the PATENTS file in the same directory.
//
// Copyright (c) 2011 The LevelDB Authors. All rights reserved.
// Use of this source code is governed by a BSD-style license that can be
// found in the LICENSE file. See the AUTHORS file for names of contributors.
#ifndef ROCKSDB_LITE
#include "table/cuckoo_table_reader.h"
#include <algorithm>
#include <limits>
#include <string>
#include <utility>
#include <vector>
#include "rocksdb/iterator.h"
#include "table/meta_blocks.h"
#include "util/arena.h"
#include "util/coding.h"
namespace rocksdb {
extern const uint64_t kCuckooTableMagicNumber;
CuckooTableReader::CuckooTableReader(
const Options& options,
std::unique_ptr<RandomAccessFile>&& file,
uint64_t file_size,
const Comparator* comparator,
uint64_t (*get_slice_hash)(const Slice&, uint32_t, uint64_t))
: file_(std::move(file)),
ucomp_(comparator),
get_slice_hash_(get_slice_hash) {
if (!options.allow_mmap_reads) {
status_ = Status::InvalidArgument("File is not mmaped");
}
TableProperties* props = nullptr;
status_ = ReadTableProperties(file_.get(), file_size, kCuckooTableMagicNumber,
options.env, options.info_log.get(), &props);
if (!status_.ok()) {
return;
}
table_props_.reset(props);
auto& user_props = props->user_collected_properties;
auto hash_funs = user_props.find(CuckooTablePropertyNames::kNumHashTable);
if (hash_funs == user_props.end()) {
status_ = Status::InvalidArgument("Number of hash functions not found");
return;
}
num_hash_fun_ = *reinterpret_cast<const uint32_t*>(hash_funs->second.data());
auto unused_key = user_props.find(CuckooTablePropertyNames::kEmptyKey);
if (unused_key == user_props.end()) {
status_ = Status::InvalidArgument("Empty bucket value not found");
return;
}
unused_key_ = unused_key->second;
key_length_ = props->fixed_key_len;
auto value_length = user_props.find(CuckooTablePropertyNames::kValueLength);
if (value_length == user_props.end()) {
status_ = Status::InvalidArgument("Value length not found");
return;
}
value_length_ = *reinterpret_cast<const uint32_t*>(
value_length->second.data());
bucket_length_ = key_length_ + value_length_;
auto num_buckets = user_props.find(CuckooTablePropertyNames::kMaxNumBuckets);
if (num_buckets == user_props.end()) {
status_ = Status::InvalidArgument("Num buckets not found");
return;
}
num_buckets_ = *reinterpret_cast<const uint64_t*>(num_buckets->second.data());
auto is_last_level = user_props.find(CuckooTablePropertyNames::kIsLastLevel);
if (is_last_level == user_props.end()) {
status_ = Status::InvalidArgument("Is last level not found");
return;
}
is_last_level_ = *reinterpret_cast<const bool*>(is_last_level->second.data());
status_ = file_->Read(0, file_size, &file_data_, nullptr);
}
Status CuckooTableReader::Get(
const ReadOptions& readOptions, const Slice& key, void* handle_context,
bool (*result_handler)(void* arg, const ParsedInternalKey& k,
const Slice& v),
void (*mark_key_may_exist_handler)(void* handle_context)) {
assert(key.size() == key_length_ + (is_last_level_ ? 8 : 0));
Slice user_key = ExtractUserKey(key);
for (uint32_t hash_cnt = 0; hash_cnt < num_hash_fun_; ++hash_cnt) {
uint64_t hash_val = get_slice_hash_(user_key, hash_cnt, num_buckets_);
assert(hash_val < num_buckets_);
Implement Prepare method in CuckooTableReader Summary: - Implement Prepare method - Rewrite performance tests in cuckoo_table_reader_test to write new file only if one doesn't already exist. - Add performance tests for batch lookup along with prefetching. Test Plan: ./cuckoo_table_reader_test --enable_perf Results (We get better results if we used int64 comparator instead of string comparator (TBD in future diffs)): With 100000000 items and hash table ratio 0.500000, number of hash functions used: 2. Time taken per op is 0.208us (4.8 Mqps) with batch size of 0 With 100000000 items and hash table ratio 0.500000, number of hash functions used: 2. Time taken per op is 0.182us (5.5 Mqps) with batch size of 10 With 100000000 items and hash table ratio 0.500000, number of hash functions used: 2. Time taken per op is 0.161us (6.2 Mqps) with batch size of 25 With 100000000 items and hash table ratio 0.500000, number of hash functions used: 2. Time taken per op is 0.161us (6.2 Mqps) with batch size of 50 With 100000000 items and hash table ratio 0.500000, number of hash functions used: 2. Time taken per op is 0.163us (6.1 Mqps) with batch size of 100 With 100000000 items and hash table ratio 0.600000, number of hash functions used: 3. Time taken per op is 0.252us (4.0 Mqps) with batch size of 0 With 100000000 items and hash table ratio 0.600000, number of hash functions used: 3. Time taken per op is 0.192us (5.2 Mqps) with batch size of 10 With 100000000 items and hash table ratio 0.600000, number of hash functions used: 3. Time taken per op is 0.195us (5.1 Mqps) with batch size of 25 With 100000000 items and hash table ratio 0.600000, number of hash functions used: 3. Time taken per op is 0.191us (5.2 Mqps) with batch size of 50 With 100000000 items and hash table ratio 0.600000, number of hash functions used: 3. Time taken per op is 0.194us (5.1 Mqps) with batch size of 100 With 100000000 items and hash table ratio 0.750000, number of hash functions used: 3. Time taken per op is 0.228us (4.4 Mqps) with batch size of 0 With 100000000 items and hash table ratio 0.750000, number of hash functions used: 3. Time taken per op is 0.185us (5.4 Mqps) with batch size of 10 With 100000000 items and hash table ratio 0.750000, number of hash functions used: 3. Time taken per op is 0.186us (5.4 Mqps) with batch size of 25 With 100000000 items and hash table ratio 0.750000, number of hash functions used: 3. Time taken per op is 0.189us (5.3 Mqps) with batch size of 50 With 100000000 items and hash table ratio 0.750000, number of hash functions used: 3. Time taken per op is 0.188us (5.3 Mqps) with batch size of 100 With 100000000 items and hash table ratio 0.900000, number of hash functions used: 3. Time taken per op is 0.325us (3.1 Mqps) with batch size of 0 With 100000000 items and hash table ratio 0.900000, number of hash functions used: 3. Time taken per op is 0.196us (5.1 Mqps) with batch size of 10 With 100000000 items and hash table ratio 0.900000, number of hash functions used: 3. Time taken per op is 0.199us (5.0 Mqps) with batch size of 25 With 100000000 items and hash table ratio 0.900000, number of hash functions used: 3. Time taken per op is 0.196us (5.1 Mqps) with batch size of 50 With 100000000 items and hash table ratio 0.900000, number of hash functions used: 3. Time taken per op is 0.209us (4.8 Mqps) with batch size of 100 Reviewers: sdong, yhchiang, igor, ljin Reviewed By: ljin Subscribers: leveldb Differential Revision: https://reviews.facebook.net/D22167
10 years ago
const char* bucket = &file_data_.data()[hash_val * bucket_length_];
if (ucomp_->Compare(Slice(unused_key_.data(), user_key.size()),
Slice(bucket, user_key.size())) == 0) {
return Status::OK();
}
// Here, we compare only the user key part as we support only one entry
// per user key and we don't support sanpshot.
if (ucomp_->Compare(user_key, Slice(bucket, user_key.size())) == 0) {
Slice value = Slice(&bucket[key_length_], value_length_);
if (is_last_level_) {
ParsedInternalKey found_ikey(Slice(bucket, key_length_), 0, kTypeValue);
result_handler(handle_context, found_ikey, value);
} else {
Slice full_key(bucket, key_length_);
ParsedInternalKey found_ikey;
ParseInternalKey(full_key, &found_ikey);
result_handler(handle_context, found_ikey, value);
}
// We don't support merge operations. So, we return here.
return Status::OK();
}
}
return Status::OK();
}
Implement Prepare method in CuckooTableReader Summary: - Implement Prepare method - Rewrite performance tests in cuckoo_table_reader_test to write new file only if one doesn't already exist. - Add performance tests for batch lookup along with prefetching. Test Plan: ./cuckoo_table_reader_test --enable_perf Results (We get better results if we used int64 comparator instead of string comparator (TBD in future diffs)): With 100000000 items and hash table ratio 0.500000, number of hash functions used: 2. Time taken per op is 0.208us (4.8 Mqps) with batch size of 0 With 100000000 items and hash table ratio 0.500000, number of hash functions used: 2. Time taken per op is 0.182us (5.5 Mqps) with batch size of 10 With 100000000 items and hash table ratio 0.500000, number of hash functions used: 2. Time taken per op is 0.161us (6.2 Mqps) with batch size of 25 With 100000000 items and hash table ratio 0.500000, number of hash functions used: 2. Time taken per op is 0.161us (6.2 Mqps) with batch size of 50 With 100000000 items and hash table ratio 0.500000, number of hash functions used: 2. Time taken per op is 0.163us (6.1 Mqps) with batch size of 100 With 100000000 items and hash table ratio 0.600000, number of hash functions used: 3. Time taken per op is 0.252us (4.0 Mqps) with batch size of 0 With 100000000 items and hash table ratio 0.600000, number of hash functions used: 3. Time taken per op is 0.192us (5.2 Mqps) with batch size of 10 With 100000000 items and hash table ratio 0.600000, number of hash functions used: 3. Time taken per op is 0.195us (5.1 Mqps) with batch size of 25 With 100000000 items and hash table ratio 0.600000, number of hash functions used: 3. Time taken per op is 0.191us (5.2 Mqps) with batch size of 50 With 100000000 items and hash table ratio 0.600000, number of hash functions used: 3. Time taken per op is 0.194us (5.1 Mqps) with batch size of 100 With 100000000 items and hash table ratio 0.750000, number of hash functions used: 3. Time taken per op is 0.228us (4.4 Mqps) with batch size of 0 With 100000000 items and hash table ratio 0.750000, number of hash functions used: 3. Time taken per op is 0.185us (5.4 Mqps) with batch size of 10 With 100000000 items and hash table ratio 0.750000, number of hash functions used: 3. Time taken per op is 0.186us (5.4 Mqps) with batch size of 25 With 100000000 items and hash table ratio 0.750000, number of hash functions used: 3. Time taken per op is 0.189us (5.3 Mqps) with batch size of 50 With 100000000 items and hash table ratio 0.750000, number of hash functions used: 3. Time taken per op is 0.188us (5.3 Mqps) with batch size of 100 With 100000000 items and hash table ratio 0.900000, number of hash functions used: 3. Time taken per op is 0.325us (3.1 Mqps) with batch size of 0 With 100000000 items and hash table ratio 0.900000, number of hash functions used: 3. Time taken per op is 0.196us (5.1 Mqps) with batch size of 10 With 100000000 items and hash table ratio 0.900000, number of hash functions used: 3. Time taken per op is 0.199us (5.0 Mqps) with batch size of 25 With 100000000 items and hash table ratio 0.900000, number of hash functions used: 3. Time taken per op is 0.196us (5.1 Mqps) with batch size of 50 With 100000000 items and hash table ratio 0.900000, number of hash functions used: 3. Time taken per op is 0.209us (4.8 Mqps) with batch size of 100 Reviewers: sdong, yhchiang, igor, ljin Reviewed By: ljin Subscribers: leveldb Differential Revision: https://reviews.facebook.net/D22167
10 years ago
void CuckooTableReader::Prepare(const Slice& key) {
Slice user_key = ExtractUserKey(key);
Implement Prepare method in CuckooTableReader Summary: - Implement Prepare method - Rewrite performance tests in cuckoo_table_reader_test to write new file only if one doesn't already exist. - Add performance tests for batch lookup along with prefetching. Test Plan: ./cuckoo_table_reader_test --enable_perf Results (We get better results if we used int64 comparator instead of string comparator (TBD in future diffs)): With 100000000 items and hash table ratio 0.500000, number of hash functions used: 2. Time taken per op is 0.208us (4.8 Mqps) with batch size of 0 With 100000000 items and hash table ratio 0.500000, number of hash functions used: 2. Time taken per op is 0.182us (5.5 Mqps) with batch size of 10 With 100000000 items and hash table ratio 0.500000, number of hash functions used: 2. Time taken per op is 0.161us (6.2 Mqps) with batch size of 25 With 100000000 items and hash table ratio 0.500000, number of hash functions used: 2. Time taken per op is 0.161us (6.2 Mqps) with batch size of 50 With 100000000 items and hash table ratio 0.500000, number of hash functions used: 2. Time taken per op is 0.163us (6.1 Mqps) with batch size of 100 With 100000000 items and hash table ratio 0.600000, number of hash functions used: 3. Time taken per op is 0.252us (4.0 Mqps) with batch size of 0 With 100000000 items and hash table ratio 0.600000, number of hash functions used: 3. Time taken per op is 0.192us (5.2 Mqps) with batch size of 10 With 100000000 items and hash table ratio 0.600000, number of hash functions used: 3. Time taken per op is 0.195us (5.1 Mqps) with batch size of 25 With 100000000 items and hash table ratio 0.600000, number of hash functions used: 3. Time taken per op is 0.191us (5.2 Mqps) with batch size of 50 With 100000000 items and hash table ratio 0.600000, number of hash functions used: 3. Time taken per op is 0.194us (5.1 Mqps) with batch size of 100 With 100000000 items and hash table ratio 0.750000, number of hash functions used: 3. Time taken per op is 0.228us (4.4 Mqps) with batch size of 0 With 100000000 items and hash table ratio 0.750000, number of hash functions used: 3. Time taken per op is 0.185us (5.4 Mqps) with batch size of 10 With 100000000 items and hash table ratio 0.750000, number of hash functions used: 3. Time taken per op is 0.186us (5.4 Mqps) with batch size of 25 With 100000000 items and hash table ratio 0.750000, number of hash functions used: 3. Time taken per op is 0.189us (5.3 Mqps) with batch size of 50 With 100000000 items and hash table ratio 0.750000, number of hash functions used: 3. Time taken per op is 0.188us (5.3 Mqps) with batch size of 100 With 100000000 items and hash table ratio 0.900000, number of hash functions used: 3. Time taken per op is 0.325us (3.1 Mqps) with batch size of 0 With 100000000 items and hash table ratio 0.900000, number of hash functions used: 3. Time taken per op is 0.196us (5.1 Mqps) with batch size of 10 With 100000000 items and hash table ratio 0.900000, number of hash functions used: 3. Time taken per op is 0.199us (5.0 Mqps) with batch size of 25 With 100000000 items and hash table ratio 0.900000, number of hash functions used: 3. Time taken per op is 0.196us (5.1 Mqps) with batch size of 50 With 100000000 items and hash table ratio 0.900000, number of hash functions used: 3. Time taken per op is 0.209us (4.8 Mqps) with batch size of 100 Reviewers: sdong, yhchiang, igor, ljin Reviewed By: ljin Subscribers: leveldb Differential Revision: https://reviews.facebook.net/D22167
10 years ago
// Prefetching first location also helps improve Get performance.
for (uint32_t hash_cnt = 0; hash_cnt < num_hash_fun_; ++hash_cnt) {
uint64_t hash_val = get_slice_hash_(user_key, hash_cnt, num_buckets_);
Implement Prepare method in CuckooTableReader Summary: - Implement Prepare method - Rewrite performance tests in cuckoo_table_reader_test to write new file only if one doesn't already exist. - Add performance tests for batch lookup along with prefetching. Test Plan: ./cuckoo_table_reader_test --enable_perf Results (We get better results if we used int64 comparator instead of string comparator (TBD in future diffs)): With 100000000 items and hash table ratio 0.500000, number of hash functions used: 2. Time taken per op is 0.208us (4.8 Mqps) with batch size of 0 With 100000000 items and hash table ratio 0.500000, number of hash functions used: 2. Time taken per op is 0.182us (5.5 Mqps) with batch size of 10 With 100000000 items and hash table ratio 0.500000, number of hash functions used: 2. Time taken per op is 0.161us (6.2 Mqps) with batch size of 25 With 100000000 items and hash table ratio 0.500000, number of hash functions used: 2. Time taken per op is 0.161us (6.2 Mqps) with batch size of 50 With 100000000 items and hash table ratio 0.500000, number of hash functions used: 2. Time taken per op is 0.163us (6.1 Mqps) with batch size of 100 With 100000000 items and hash table ratio 0.600000, number of hash functions used: 3. Time taken per op is 0.252us (4.0 Mqps) with batch size of 0 With 100000000 items and hash table ratio 0.600000, number of hash functions used: 3. Time taken per op is 0.192us (5.2 Mqps) with batch size of 10 With 100000000 items and hash table ratio 0.600000, number of hash functions used: 3. Time taken per op is 0.195us (5.1 Mqps) with batch size of 25 With 100000000 items and hash table ratio 0.600000, number of hash functions used: 3. Time taken per op is 0.191us (5.2 Mqps) with batch size of 50 With 100000000 items and hash table ratio 0.600000, number of hash functions used: 3. Time taken per op is 0.194us (5.1 Mqps) with batch size of 100 With 100000000 items and hash table ratio 0.750000, number of hash functions used: 3. Time taken per op is 0.228us (4.4 Mqps) with batch size of 0 With 100000000 items and hash table ratio 0.750000, number of hash functions used: 3. Time taken per op is 0.185us (5.4 Mqps) with batch size of 10 With 100000000 items and hash table ratio 0.750000, number of hash functions used: 3. Time taken per op is 0.186us (5.4 Mqps) with batch size of 25 With 100000000 items and hash table ratio 0.750000, number of hash functions used: 3. Time taken per op is 0.189us (5.3 Mqps) with batch size of 50 With 100000000 items and hash table ratio 0.750000, number of hash functions used: 3. Time taken per op is 0.188us (5.3 Mqps) with batch size of 100 With 100000000 items and hash table ratio 0.900000, number of hash functions used: 3. Time taken per op is 0.325us (3.1 Mqps) with batch size of 0 With 100000000 items and hash table ratio 0.900000, number of hash functions used: 3. Time taken per op is 0.196us (5.1 Mqps) with batch size of 10 With 100000000 items and hash table ratio 0.900000, number of hash functions used: 3. Time taken per op is 0.199us (5.0 Mqps) with batch size of 25 With 100000000 items and hash table ratio 0.900000, number of hash functions used: 3. Time taken per op is 0.196us (5.1 Mqps) with batch size of 50 With 100000000 items and hash table ratio 0.900000, number of hash functions used: 3. Time taken per op is 0.209us (4.8 Mqps) with batch size of 100 Reviewers: sdong, yhchiang, igor, ljin Reviewed By: ljin Subscribers: leveldb Differential Revision: https://reviews.facebook.net/D22167
10 years ago
PREFETCH(&file_data_.data()[hash_val * bucket_length_], 0, 3);
}
}
class CuckooTableIterator : public Iterator {
public:
explicit CuckooTableIterator(CuckooTableReader* reader);
~CuckooTableIterator() {}
bool Valid() const override;
void SeekToFirst() override;
void SeekToLast() override;
void Seek(const Slice& target) override;
void Next() override;
void Prev() override;
Slice key() const override;
Slice value() const override;
Status status() const override { return status_; }
void LoadKeysFromReader();
private:
struct CompareKeys {
CompareKeys(const Comparator* ucomp, const bool last_level)
: ucomp_(ucomp),
is_last_level_(last_level) {}
bool operator()(const std::pair<Slice, uint32_t>& first,
const std::pair<Slice, uint32_t>& second) const {
if (is_last_level_) {
return ucomp_->Compare(first.first, second.first) < 0;
} else {
return ucomp_->Compare(ExtractUserKey(first.first),
ExtractUserKey(second.first)) < 0;
}
}
private:
const Comparator* ucomp_;
const bool is_last_level_;
};
const CompareKeys comparator_;
void PrepareKVAtCurrIdx();
CuckooTableReader* reader_;
Status status_;
// Contains a map of keys to bucket_id sorted in key order.
std::vector<std::pair<Slice, uint32_t>> key_to_bucket_id_;
// We assume that the number of items can be stored in uint32 (4 Billion).
uint32_t curr_key_idx_;
Slice curr_value_;
IterKey curr_key_;
// No copying allowed
CuckooTableIterator(const CuckooTableIterator&) = delete;
void operator=(const Iterator&) = delete;
};
CuckooTableIterator::CuckooTableIterator(CuckooTableReader* reader)
: comparator_(reader->ucomp_, reader->is_last_level_),
reader_(reader),
curr_key_idx_(std::numeric_limits<int32_t>::max()) {
key_to_bucket_id_.clear();
curr_value_.clear();
curr_key_.Clear();
}
void CuckooTableIterator::LoadKeysFromReader() {
key_to_bucket_id_.reserve(reader_->GetTableProperties()->num_entries);
for (uint32_t bucket_id = 0; bucket_id < reader_->num_buckets_; bucket_id++) {
Slice read_key;
status_ = reader_->file_->Read(bucket_id * reader_->bucket_length_,
reader_->key_length_, &read_key, nullptr);
if (read_key != Slice(reader_->unused_key_)) {
key_to_bucket_id_.push_back(std::make_pair(read_key, bucket_id));
}
}
assert(key_to_bucket_id_.size() ==
reader_->GetTableProperties()->num_entries);
std::sort(key_to_bucket_id_.begin(), key_to_bucket_id_.end(), comparator_);
curr_key_idx_ = key_to_bucket_id_.size();
}
void CuckooTableIterator::SeekToFirst() {
curr_key_idx_ = 0;
PrepareKVAtCurrIdx();
}
void CuckooTableIterator::SeekToLast() {
curr_key_idx_ = key_to_bucket_id_.size() - 1;
PrepareKVAtCurrIdx();
}
void CuckooTableIterator::Seek(const Slice& target) {
// We assume that the target is an internal key. If this is last level file,
// we need to take only the user key part to seek.
Slice target_to_search = reader_->is_last_level_ ?
ExtractUserKey(target) : target;
auto seek_it = std::lower_bound(key_to_bucket_id_.begin(),
key_to_bucket_id_.end(),
std::make_pair(target_to_search, 0),
comparator_);
curr_key_idx_ = std::distance(key_to_bucket_id_.begin(), seek_it);
PrepareKVAtCurrIdx();
}
bool CuckooTableIterator::Valid() const {
return curr_key_idx_ < key_to_bucket_id_.size();
}
void CuckooTableIterator::PrepareKVAtCurrIdx() {
if (!Valid()) {
curr_value_.clear();
curr_key_.Clear();
return;
}
uint64_t offset = ((uint64_t) key_to_bucket_id_[curr_key_idx_].second
* reader_->bucket_length_) + reader_->key_length_;
status_ = reader_->file_->Read(offset, reader_->value_length_,
&curr_value_, nullptr);
if (reader_->is_last_level_) {
// Always return internal key.
curr_key_.SetInternalKey(
key_to_bucket_id_[curr_key_idx_].first, 0, kTypeValue);
}
}
void CuckooTableIterator::Next() {
if (!Valid()) {
curr_value_.clear();
curr_key_.Clear();
return;
}
++curr_key_idx_;
PrepareKVAtCurrIdx();
}
void CuckooTableIterator::Prev() {
if (curr_key_idx_ == 0) {
curr_key_idx_ = key_to_bucket_id_.size();
}
if (!Valid()) {
curr_value_.clear();
curr_key_.Clear();
return;
}
--curr_key_idx_;
PrepareKVAtCurrIdx();
}
Slice CuckooTableIterator::key() const {
assert(Valid());
if (reader_->is_last_level_) {
return curr_key_.GetKey();
} else {
return key_to_bucket_id_[curr_key_idx_].first;
}
}
Slice CuckooTableIterator::value() const {
assert(Valid());
return curr_value_;
}
extern Iterator* NewErrorIterator(const Status& status, Arena* arena);
Iterator* CuckooTableReader::NewIterator(
const ReadOptions& read_options, Arena* arena) {
if (!status().ok()) {
return NewErrorIterator(
Status::Corruption("CuckooTableReader status is not okay."), arena);
}
if (read_options.total_order_seek) {
return NewErrorIterator(
Status::InvalidArgument("total_order_seek is not supported."), arena);
}
CuckooTableIterator* iter;
if (arena == nullptr) {
iter = new CuckooTableIterator(this);
} else {
auto iter_mem = arena->AllocateAligned(sizeof(CuckooTableIterator));
iter = new (iter_mem) CuckooTableIterator(this);
}
if (iter->status().ok()) {
iter->LoadKeysFromReader();
}
return iter;
}
size_t CuckooTableReader::ApproximateMemoryUsage() const { return 0; }
} // namespace rocksdb
#endif