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// Copyright (c) 2014, 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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#pragma once
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#ifndef ROCKSDB_LITE
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#include <string>
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#include <memory>
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#include <utility>
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#include <vector>
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#include "db/dbformat.h"
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#include "rocksdb/env.h"
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#include "table/table_reader.h"
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namespace rocksdb {
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class Arena;
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class TableReader;
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class CuckooTableReader: public TableReader {
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public:
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CuckooTableReader(
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const Options& options,
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std::unique_ptr<RandomAccessFile>&& file,
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uint64_t file_size,
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uint64_t (*get_slice_hash)(const Slice&, uint32_t, uint64_t));
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~CuckooTableReader() {}
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std::shared_ptr<const TableProperties> GetTableProperties() const override {
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return table_props_;
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}
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Status status() const { return status_; }
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Status Get(
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const ReadOptions& readOptions, const Slice& key, void* handle_context,
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bool (*result_handler)(void* arg, const ParsedInternalKey& k,
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const Slice& v),
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void (*mark_key_may_exist_handler)(void* handle_context) = nullptr)
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override;
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Iterator* NewIterator(const ReadOptions&, Arena* arena = nullptr) override;
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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
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void Prepare(const Slice& target) override;
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// Report an approximation of how much memory has been used.
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size_t ApproximateMemoryUsage() const override;
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// Following methods are not implemented for Cuckoo Table Reader
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uint64_t ApproximateOffsetOf(const Slice& key) override { return 0; }
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void SetupForCompaction() override {}
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// End of methods not implemented.
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private:
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friend class CuckooTableIterator;
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void LoadAllKeys(std::vector<std::pair<Slice, uint32_t>>* key_to_bucket_id);
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std::unique_ptr<RandomAccessFile> file_;
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Slice file_data_;
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bool is_last_level_;
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std::shared_ptr<const TableProperties> table_props_;
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Status status_;
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uint32_t num_hash_fun_;
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std::string unused_key_;
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uint32_t key_length_;
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uint32_t value_length_;
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uint32_t bucket_length_;
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uint64_t num_buckets_;
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uint64_t (*get_slice_hash_)(const Slice& s, uint32_t index,
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uint64_t max_num_buckets);
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};
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} // namespace rocksdb
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#endif // ROCKSDB_LITE
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