membership

Type Members

1. case class StableBloomFilter[T](funnel: Funnel[T], m: Long, d: Int, FPR: Double) extends Product with Serializable

   

   Implements the stable bloom filter from the paper by F.

   Implements the stable bloom filter from the paper by F. Deng and D. Rafiei. Approximately detecting duplicates for streaming data using stable bloom filters. In SIGMOD, pages 25–36, 2006.

   We use heap storage (an array of Longs). This implies M=m*d can be set as high as about 125 giga-bits.

   T

   the type of funnel used

   funnel

   a Guava funnel for taking input

   m

   number of cells (see the paper, m is a Long but m/floor(63/d) must fit in a 32-bit Int)

   d

   bits per cell (see the paper, should lie in [1,63] but often set to 1, 2 or 3)

   FPR

   expected false positive rate (should lie in (0,1))

2. case class StableBloomFilterBuilder[T](funnel: Funnel[T], numCells: Long = 1000000, bitsPerCell: Int = 3, falsePositiveRate: Double = 0.01) extends Product with Serializable

   

   A builder interface for creating StableBloomFilter instances.

   A builder interface for creating StableBloomFilter instances.

   T

   the type of item inserted into the filter

   funnel

   a guava funnel

   numCells

   number of cells in the filter

   bitsPerCell

   number of bits per cell in the filter

   falsePositiveRate

   desired maximum false positive rate of the filter