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Peter Dillinger e4f1e64c30 Add prefetching (batched MultiGet) for experimental Ribbon filter (#7889)
Summary:
Adds support for prefetching data in Ribbon queries,
which especially optimizes batched Ribbon queries for MultiGet
(~222ns/key to ~97ns/key) but also single key queries on cold memory
(~333ns to ~226ns) because many queries span more than one cache line.

This required some refactoring of the query algorithm, and there
does not appear to be a noticeable regression in "hot memory" query
times (perhaps from 48ns to 50ns).

Pull Request resolved: https://github.com/facebook/rocksdb/pull/7889

Test Plan:
existing unit tests, plus performance validation with
filter_bench:

Each data point is the best of two runs. I saturated the machine
CPUs with other filter_bench runs in the background.

Before:

    $ ./filter_bench -impl=3 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50
    WARNING: Assertions are enabled; benchmarks unnecessarily slow
    Building...
    Build avg ns/key: 125.86
    Number of filters: 1993
    Total size (MB): 168.166
    Reported total allocated memory (MB): 183.211
    Reported internal fragmentation: 8.94626%
    Bits/key stored: 7.05341
    Prelim FP rate %: 0.951827
    ----------------------------
    Mixed inside/outside queries...
      Single filter net ns/op: 48.0111
      Batched, prepared net ns/op: 222.384
      Batched, unprepared net ns/op: 343.908
      Skewed 50% in 1% net ns/op: 252.916
      Skewed 80% in 20% net ns/op: 320.579
      Random filter net ns/op: 332.957

After:

    $ ./filter_bench -impl=3 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50
    WARNING: Assertions are enabled; benchmarks unnecessarily slow
    Building...
    Build avg ns/key: 128.117
    Number of filters: 1993
    Total size (MB): 168.166
    Reported total allocated memory (MB): 183.211
    Reported internal fragmentation: 8.94626%
    Bits/key stored: 7.05341
    Prelim FP rate %: 0.951827
    ----------------------------
    Mixed inside/outside queries...
      Single filter net ns/op: 49.8812
      Batched, prepared net ns/op: 97.1514
      Batched, unprepared net ns/op: 222.025
      Skewed 50% in 1% net ns/op: 197.48
      Skewed 80% in 20% net ns/op: 212.457
      Random filter net ns/op: 226.464

Bloom comparison, for reference:

    $ ./filter_bench -impl=2 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50
    WARNING: Assertions are enabled; benchmarks unnecessarily slow
    Building...
    Build avg ns/key: 35.3042
    Number of filters: 1993
    Total size (MB): 238.488
    Reported total allocated memory (MB): 262.875
    Reported internal fragmentation: 10.2255%
    Bits/key stored: 10.0029
    Prelim FP rate %: 0.965327
    ----------------------------
    Mixed inside/outside queries...
      Single filter net ns/op: 9.09931
      Batched, prepared net ns/op: 34.21
      Batched, unprepared net ns/op: 88.8564
      Skewed 50% in 1% net ns/op: 139.75
      Skewed 80% in 20% net ns/op: 181.264
      Random filter net ns/op: 173.88

Reviewed By: jay-zhuang

Differential Revision: D26378710

Pulled By: pdillinger

fbshipit-source-id: 058428967c55ed763698284cd3b4bbe3351b6e69
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README.md

RocksDB: A Persistent Key-Value Store for Flash and RAM Storage

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RocksDB is developed and maintained by Facebook Database Engineering Team. It is built on earlier work on LevelDB by Sanjay Ghemawat (sanjay@google.com) and Jeff Dean (jeff@google.com)

This code is a library that forms the core building block for a fast key-value server, especially suited for storing data on flash drives. It has a Log-Structured-Merge-Database (LSM) design with flexible tradeoffs between Write-Amplification-Factor (WAF), Read-Amplification-Factor (RAF) and Space-Amplification-Factor (SAF). It has multi-threaded compactions, making it especially suitable for storing multiple terabytes of data in a single database.

Start with example usage here: https://github.com/facebook/rocksdb/tree/master/examples

See the github wiki for more explanation.

The public interface is in include/. Callers should not include or rely on the details of any other header files in this package. Those internal APIs may be changed without warning.

Design discussions are conducted in https://www.facebook.com/groups/rocksdb.dev/ and https://rocksdb.slack.com/

License

RocksDB is dual-licensed under both the GPLv2 (found in the COPYING file in the root directory) and Apache 2.0 License (found in the LICENSE.Apache file in the root directory). You may select, at your option, one of the above-listed licenses.