https://github.com/kseo/bloom_filter

A stand-alone Bloom filter implementation written in Dart
https://github.com/kseo/bloom_filter

Last synced: 26 days ago
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A stand-alone Bloom filter implementation written in Dart

• Host: GitHub
• URL: https://github.com/kseo/bloom_filter
• Owner: kseo
• License: bsd-3-clause
• Created: 2016-10-11T10:22:21.000Z (about 8 years ago)
• Default Branch: master
• Last Pushed: 2016-11-05T15:55:43.000Z (about 8 years ago)
• Last Synced: 2023-08-20T21:28:40.921Z (about 1 year ago)
• Language: Dart
• Homepage: https://pub.dartlang.org/packages/bloom_filter
• Size: 11.7 KB
• Stars: 2
• Watchers: 2
• Forks: 3
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • License: LICENSE

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README

        
# bloom_filter

[![Build Status](https://travis-ci.org/kseo/bloom_filter.svg?branch=master)](https://travis-ci.org/kseo/bloom_filter)

A stand-alone Bloom filter implementation written in Dart inspired by

[Java-BloomFilter][Java-BloomFilter].

[Java-BloomFilter]: https://github.com/magnuss/java-bloomfilter

## Bloom filters

Bloom filters are used for set membership tests. They are fast and

space-efficient at the cost of accuracy. Although there is a certain probability

of error, Bloom filters never produce false negatives.

## Examples

To create an empty Bloom filter, just call the constructor with the required

false positive probability and the number of elements you expect to add to the

Bloom filter.

```dart

double falsePositiveProbability = 0.1;

int expectedNumberOfElements = 100;

BloomFilter bloomFilter = new

BloomFilter(falsePositiveProbability, expectedNumberOfElements);

```

The constructor chooses a length and number of hash functions which will provide

the given false positive probability (approximately). Note that if you insert

more elements than the number of expected elements you specify, the actual false

positive probability will rapidly increase.

After the Bloom filter has been created, new elements may be added using the

`add` method.

```dart

bloomFilter.add("foo");

```

To check whether an element has been stored in the Bloom filter, use the

`mightContain` method.

```dart

bloomFilter.mightContain("foo"); // returns true

```

Keep in mind that the accuracy of this method depends on the false positive

probability. It will always return true for elements which have been added to

the Bloom filter, but it may also return true for elements which have not been

added. The accuracy can be estimated using the

`expectedFalsePositiveProbability` getter.

Put together, here is the full example.

```dart

import 'package:bloom_filter/bloom_filter.dart';

main() {

  double falsePositiveProbability = 0.1;

  int expectedSize = 100;

  BloomFilter bloomFilter =

      new BloomFilter(falsePositiveProbability, expectedSize);

  bloomFilter.add("foo");

  if (bloomFilter.mightContain("foo")) {

    // Always returns true

    print("BloomFilter contains foo!");

    print(

        "Probability of a false positive: ${bloomFilter.expectedFalsePositiveProbability}");

  }

  if (bloomFilter.mightContain("bar")) {

    // Should return false, but could return true

    print("There was a false positive.");

  }

}

```