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https://github.com/mavam/libbf

:dart: Bloom filters for C++11
https://github.com/mavam/libbf

bloom-filter synopsis

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:dart: Bloom filters for C++11

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README 

        
⚠️  **Maintainer needed**: unfortunately I lack the cycles to maintain this

project. I would gladly support anyone who is willing to step in.



**libbf** is a C++11 library which implements [various Bloom

filters][blog-post], including:



- Basic

- Counting

- Spectral MI

- Spectral RM

- Bitwise

- A^2

- Stable



[blog-post]: http://matthias.vallentin.net/blog/2011/06/a-garden-variety-of-bloom-filters/



Synopsis

========



    #include 

    #include 



    int main()

    {

      bf::basic_bloom_filter b(0.8, 100);



      // Add two elements.

      b.add("foo");

      b.add(42);



      // Test set membership

      std::cout << b.lookup("foo") << std::endl;  // 1

      std::cout << b.lookup("bar") << std::endl;  // 0

      std::cout << b.lookup(42) << std::endl;     // 1



      // Remove all elements.

      b.clear();

      std::cout << b.lookup("foo") << std::endl;  // 0

      std::cout << b.lookup(42) << std::endl;     // 0



      return 0;

    }



Requirements

============



- A C++11 compiler (GCC >= 4.7 or Clang >= 3.2)

- CMake (>= 2.8)



Installation

============



The build process uses CMake, wrapped in autotools-like scripts. The configure

script honors the `CXX` environment variable to select a specific C++compiler.

For example, the following steps compile libbf with Clang and install it under

`PREFIX`:



    CXX=clang++ ./configure --prefix=PREFIX

    make

    make test

    make install



Documentation

=============



The most recent version of the Doxygen API documentation exists at

. Alternatively, you can build the

documentation locally via `make doc` and then browse to

`doc/gh-pages/api/index.html`.



Usage

=====



After having installed libbf, you can use it in your application by including

the header file `bf.h` and linking against the library. All data structures

reside in the namespace `bf` and the following examples assume:



    using namespace bf;



Each Bloom filter inherits from the abstract base class `bloom_filter`, which

provides addition and lookup via the virtual functions `add` and `lookup`.

These functions take an *object* as argument, which serves a light-weight view

over sequential data for hashing.



For example, if you can create a basic Bloom filter with a desired

false-positive probability and capacity as follows:



    // Construction.

    bloom_filter* bf = new basic_bloom_filter(0.8, 100);



    // Addition.

    bf->add("foo");

    bf->add(42);



    // Lookup.

    assert(bf->lookup("foo") == 1);

    assert(bf->lookup(42) == 1);



    // Remove all elements from the Bloom filter.

    bf->clear();



In this case, libbf computes the optimal number of hash functions needed to

achieve the desired false-positive rate which holds until the capacity has been

reached (80% and 100 distinct elements, in the above example). Alternatively,

you can construct a basic Bloom filter by specifying the number of hash

functions and the number of cells in the underlying bit vector:



    bloom_filter* bf = new basic_bloom_filter(make_hasher(3), 1024);



Since not all Bloom filter implementations come with closed-form solutions

based on false-positive probabilities, most constructors use this latter form

of explicit resource provisioning.



In the above example, the free function `make_hasher` constructs a *hasher*-an

abstraction for hashing objects *k* times. There exist currently two different

hasher, a `default_hasher` and a

[`double_hasher`](http://www.eecs.harvard.edu/~kirsch/pubs/bbbf/rsa.pdf). The

latter uses a linear combination of two pairwise-independent, universal hash

functions to produce the *k* digests, whereas the former merely hashes the

object *k* times.



Evaluation

----------



libbf also ships with a small Bloom filter tool `bf` in the test directory.

This program supports evaluation the accuracy of the different Bloom filter

flavors with respect to their false-positive and false-negative rates. Have a

look at the console help (`-h` or `--help`) for detailed usage instructions.



The tool operates in two phases:



1. Read input from a file and insert it into a Bloom filter

2. Query the Bloom filter and compare the result to the ground truth



For example, consider the following input file:



    foo

    bar

    baz

    baz

    foo



From this input file, you can generate the real ground truth file as follows:



    sort input.txt | uniq -c | tee query.txt

       1 bar

       2 baz

       2 foo



The tool `bf` will compute false-positive and false-negative counts for each

element, based on the ground truth given. In the case of a simple counting

Bloom filter, an invocation may look like this:



    bf -t counting -m 2 -k 3 -i input.txt -q query.txt | column -t



Yielding the following output:



    TN  TP  FP  FN  G  C  E

    0   1   0   0   1  1  bar

    0   1   0   1   2  1  baz

    0   1   0   2   2  1  foo



The column headings denote true negatives (`TN`), true positives (`TP`), false

positives (`FP`), false negatives (`FN`), ground truth count (`G`), actual

count (`C`), and the queried element. The counts are cumulative to support

incremental evaluation.



Versioning

==========

We follow [Semantic Versioning](http://semver.org/spec/v1.0.0.html). The version X.Y.Z indicates:



* X is the major version (backward-incompatible),

* Y is the minor version (backward-compatible), and

* Z is the patch version (backward-compatible bug fix).



License

========



libbf comes with a BSD-style license (see [COPYING](COPYING) for details).