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===

package implements a fast bloom filter with real 'bitset' and JSONMarshal/JSONUnmarshal to store/reload the Bloom filter.

NOTE: the package uses unsafe.Pointer to set and read the bits from the bitset. If you're uncomfortable with using the unsafe package, please consider using my bloom filter package at github.com/AndreasBriese/bloom

===

changelog 11/2015: new thread safe methods AddTS(), HasTS(), AddIfNotHasTS() following a suggestion from Srdjan Marinovic (github @a-little-srdjan), who used this to code a bloomfilter cache.

This bloom filter was developed to strengthen a website-log database and was tested and optimized for this log-entry mask: "2014/%02i/%02i %02i:%02i:%02i /info.html". Nonetheless bbloom should work with any other form of entries.

Hash function is a modified Berkeley DB sdbm hash (to optimize for smaller strings). sdbm http://www.cse.yorku.ca/~oz/hash.html

Found sipHash (SipHash-2-4, a fast short-input PRF created by Jean-Philippe Aumasson and Daniel J. Bernstein.) to be about as fast. sipHash had been ported by Dimtry Chestnyk to Go (github.com/dchest/siphash )

Minimum hashset size is: 512 ([4]uint64; will be set automatically).

###install

go get github.com/AndreasBriese/bbloom

###test

• change to folder ../bbloom
• create wordlist in file "words.txt" (you might use python permut.py)
• run 'go test -bench=.' within the folder

go test -bench=.

If you've installed the GOCONVEY TDD-framework http://goconvey.co/ you can run the tests automatically.

using go's testing framework now (have in mind that the op timing is related to 65536 operations of Add, Has, AddIfNotHas respectively)

after installation add

import (
	...
	"github.com/AndreasBriese/bbloom"
	...
	)

at your header. In the program use

// create a bloom filter for 65536 items and 1 % wrong-positive ratio 
bf := bbloom.New(float64(1<<16), float64(0.01))

// or 
// create a bloom filter with 650000 for 65536 items and 7 locs per hash explicitly
// bf = bbloom.New(float64(650000), float64(7))
// or
bf = bbloom.New(650000.0, 7.0)

// add one item
bf.Add([]byte("butter"))

// Number of elements added is exposed now 
// Note: ElemNum will not be included in JSON export (for compatability to older version)
nOfElementsInFilter := bf.ElemNum

// check if item is in the filter
isIn := bf.Has([]byte("butter"))    // should be true
isNotIn := bf.Has([]byte("Butter")) // should be false

// 'add only if item is new' to the bloomfilter
added := bf.AddIfNotHas([]byte("butter"))    // should be false because 'butter' is already in the set
added = bf.AddIfNotHas([]byte("buTTer"))    // should be true because 'buTTer' is new

// thread safe versions for concurrent use: AddTS, HasTS, AddIfNotHasTS
// add one item
bf.AddTS([]byte("peanutbutter"))
// check if item is in the filter
isIn = bf.HasTS([]byte("peanutbutter"))    // should be true
isNotIn = bf.HasTS([]byte("peanutButter")) // should be false
// 'add only if item is new' to the bloomfilter
added = bf.AddIfNotHasTS([]byte("butter"))    // should be false because 'peanutbutter' is already in the set
added = bf.AddIfNotHasTS([]byte("peanutbuTTer"))    // should be true because 'penutbuTTer' is new

// convert to JSON ([]byte) 
Json := bf.JSONMarshal()

// bloomfilters Mutex is exposed for external un-/locking
// i.e. mutex lock while doing JSON conversion
bf.Mtx.Lock()
Json = bf.JSONMarshal()
bf.Mtx.Unlock()

// restore a bloom filter from storage 
bfNew := bbloom.JSONUnmarshal(Json)

isInNew := bfNew.Has([]byte("butter"))    // should be true
isNotInNew := bfNew.Has([]byte("Butter")) // should be false

to work with the bloom filter.

It's about 3 times faster than William Fitzgeralds bitset bloom filter https://github.com/willf/bloom . And it is about so fast as my []bool set variant for Boom filters (see https://github.com/AndreasBriese/bloom ) but having a 8times smaller memory footprint:

Bloom filter (filter size 524288, 7 hashlocs)
github.com/AndreasBriese/bbloom 'Add' 65536 items (10 repetitions): 6595800 ns (100 ns/op)
github.com/AndreasBriese/bbloom 'Has' 65536 items (10 repetitions): 5986600 ns (91 ns/op)
github.com/AndreasBriese/bloom 'Add' 65536 items (10 repetitions): 6304684 ns (96 ns/op)
github.com/AndreasBriese/bloom 'Has' 65536 items (10 repetitions): 6568663 ns (100 ns/op)

github.com/willf/bloom 'Add' 65536 items (10 repetitions): 24367224 ns (371 ns/op)
github.com/willf/bloom 'Test' 65536 items (10 repetitions): 21881142 ns (333 ns/op)
github.com/dataence/bloom/standard 'Add' 65536 items (10 repetitions): 23041644 ns (351 ns/op)
github.com/dataence/bloom/standard 'Check' 65536 items (10 repetitions): 19153133 ns (292 ns/op)
github.com/cabello/bloom 'Add' 65536 items (10 repetitions): 131921507 ns (2012 ns/op)
github.com/cabello/bloom 'Contains' 65536 items (10 repetitions): 131108962 ns (2000 ns/op)

(on MBPro15 OSX10.8.5 i7 4Core 2.4Ghz)

With 32bit bloom filters (bloom32) using modified sdbm, bloom32 does hashing with only 2 bit shifts, one xor and one substraction per byte. smdb is about as fast as fnv64a but gives less collisions with the dataset (see mask above). bloom.New(float64(10 * 1<<16),float64(7)) populated with 1<<16 random items from the dataset (see above) and tested against the rest results in less than 0.05% collisions.