Chapter 26. Advanced library design: building a Bloom filter 

Introducing the Bloom filter 

A Bloom filter is a set-like data structure that is highly efficient in its use of space. It only supports two operations: insertion and membership querying. Unlike a normal set data structure, a Bloom filter can give incorrect answers. If we query it to see whether an element that we have inserted is present, it will answer affirmatively. If we query for an element that we have not inserted, it might incorrectly claim that the element is present.

For many applications, a low rate of false positives is tolerable. For instance, the job of a network traffic shaper is to throttle bulk transfers (e.g. BitTorrent) so that interactive sessions (such as ssh sessions or games) see good response times. A traffic shaper might use a Bloom filter to determine whether a packet belonging to a particular session is bulk or interactive. If it misidentifies one in ten thousand bulk packets as interactive and fails to throttle it, nobody will notice.

The attraction of a Bloom filter is its space efficiency. If we want to build a spell checker, and have a dictionary of half a million words, a set data structure might consume 20 megabytes of space. A Bloom filter, in contrast, would consume about half a megabyte, at the cost of missing perhaps 1% of misspelled words.

Behind the scenes, a Bloom filter is remarkably simple. It consists of a bit array and a handful of hash functions. We’ll use k for the number of hash functions. If we want to insert a value into the Bloom filter, we compute k hashes of the value, and turn on those bits in the bit array. If we want to see whether a value is present, we compute k hashes, and check all of those bits in the array to see if they are turned on.

To see how this works, let’s say we want to insert the strings "foo" and "bar" into a Bloom filter that is 8 bits wide, and we have two hash functions.

1. Compute the two hashes of "foo", and get the values 1 and

6. 2. Set bits 1 and 6 in the bit array. 3. Compute the two hashes of "bar", and get the values 6 and 3. 4. Set bits 6 and 3 in the bit array.

This example should make it clear why we cannot remove an element from a Bloom filter: both "foo" and "bar" resulted in bit 6 being set.

Suppose we now want to query the Bloom filter, to see whether the values "quux" and "baz" are present.

1. Compute the two hashes of "quux", and get the values 4 and

0. 2. Check bit 4 in the bit array. It is not set, so "quux" cannot be present. We do not need to check bit 0. 3. Compute the two hashes of "baz", and get the values 1 and 3. 4. Check bit 1 in the bit array. It is set, as is bit 3, so we say that "baz" is present even though it is not. We have reported a false positive.

For a survey of some of the uses of Bloom filters in networking, see [Broder02].

Use cases and package layout 

Not all users of Bloom filters have the same needs. In some cases, it suffices to create a Bloom filter in one pass, and only query it afterwards. For other applications, we may need to continue to update the Bloom filter after we create it. To accommodate these needs, we will design our library with mutable and immutable APIs.

We will segregate the mutable and immutable APIs that we publish by placing them in different modules: BloomFilter for the immutable code, and BloomFilter.Mutable for the mutable code.

In addition, we will create several “helper” modules that won’t provide parts of the public API, but will keep the internal code cleaner.

Finally, we will ask the user of our API to provide a function that can generate a number of hashes of an element. This function will have the type a -> [Word32]. We will use all of the hashes that this function returns, so the list must not be infinite!

Basic design 

The data structure that we use for our Haskell Bloom filter is a direct translation of the simple description we gave earlier: a bit array and a function that computes hashes.

module BloomFilter.Internal
    (
      Bloom(..)
    , MutBloom(..)
    ) where

import Data.Array.ST (STUArray)
import Data.Array.Unboxed (UArray)
import Data.Word (Word32)

data Bloom a = B {
      blmHash  :: (a -> [Word32])
    , blmArray :: UArray Word32 Bool
    }

When we create our Cabal package, we will not be exporting this BoomFilter.Internal module. It exists purely to let us control the visibility of names. We will import BoomFilter.Internal into both the mutable and immutable modules, but we will re-export from each module only the type that is relevant to that module’s API.

Unboxing, lifting, and bottom 

Unlike other Haskell arrays, a UArray contains unboxed values.

For a normal Haskell type, a value can be either fully evaluated, an unevaluated thunk, or the special value ⊥, pronounced (and sometimes written) “bottom”. The value ⊥ is a placeholder for a computation that does not succeed. Such a computation could take any of several forms. It could be an infinite loop; an application of error; or the special value undefined.

A type that can contain ⊥ is referred to as lifted. All normal Haskell types are lifted. In practice, this means that we can always write error "eek!" or undefined in place of a normal expression.

This ability to store thunks or ⊥ comes with a performance cost: it adds an extra layer of indirection. To see why we need this indirection, consider the Word32 type. A value of this type is a full 32 bits wide, so on a 32-bit system, there is no way to directly encode the value ⊥ within 32 bits. The runtime system has to maintain, and check, some extra data to track whether the value is ⊥ or not.

An unboxed value does away with this indirection. In doing so, it gains performance, but sacrifices the ability to represent a thunk or ⊥. Since it can be denser than a normal Haskell array, an array of unboxed values is an excellent choice for numeric data and bits.

GHC implements a UArray of Bool values by packing eight array elements into each byte, so this type is perfect for our needs.

The ST monad 

Back in the section called “Modifying array elements”, we mentioned that modifying an immutable array is prohibitively expensive, as it requires copying the entire array. Using a UArray does not change this, so what can we do to reduce the cost to bearable levels?

In an imperative language, we would simply modify the elements of the array in place; this will be our approach in Haskell, too.

Haskell provides a special monad, named ST¹, which lets us work safely with mutable state. Compared to the State monad, it has some powerful added capabilities.

• We can thaw an immutable array to give a mutable array; modify the mutable array in place; and freeze a new immutable array when we are done.
• We have the ability to use mutable references. This lets us implement data structures that we can modify after construction, as in an imperative language. This ability is vital for some imperative data structures and algorithms, for which similarly efficient purely functional alternatives have not yet been discovered.

The IO monad also provides these capabilities. The major difference between the two is that the ST monad is intentionally designed so that we can escape from it back into pure Haskell code. We enter the ST monad via the execution function runST, in the same way as for most other Haskell monads (except IO, of course), and we escape by returning from runST.

When we apply a monad’s execution function, we expect it to behave repeatably: given the same body and arguments, we must get the same results every time. This also applies to runST. To achieve this repeatability, the ST monad is more restrictive than the IO monad. We cannot read or write files, create global variables, or fork threads. Indeed, although we can create and work with mutable references and arrays, the type system prevents them from escaping to the caller of runST. A mutable array must be frozen into an immutable array before we can return it, and a mutable reference cannot escape at all.

Designing an API for qualified import 

The public interfaces that we provide for working with Bloom filters are worth a little discussion.

module BloomFilter.Mutable
    (
      MutBloom
    , elem
    , notElem
    , insert
    , length
    , new
    ) where

import Control.Monad (liftM)
import Control.Monad.ST (ST)
import Data.Array.MArray (getBounds, newArray, readArray, writeArray)
import Data.Word (Word32)
import Prelude hiding (elem, length, notElem)

import BloomFilter.Internal (MutBloom(..))

We export several names that clash with names exported by the Prelude. This is deliberate: we expect users of our modules to import them with qualified names. This reduces the burden on the memory of our users, as they should already be familiar with the Prelude’s elem, notElem, and length functions.

When we use a module written in this style, we might often import it with a single-letter prefix, for instance as import qualified BloomFilter.Mutable as M. This would allow us to write M.length, which stays compact and readable.

Alternatively, we could import the module unqualified, and import the Prelude while hiding the clashing names with import Prelude hiding (length). This is much less useful, as it gives a reader skimming the code no local cue that they are not actually seeing the Prelude’s length.

Of course, we seem to be violating this precept in our own module’s header: we import the Prelude, and hide some of the names it exports. There is a practical reason for this. We define a function named length. If we export this from our module without first hiding the Prelude’s length, the compiler will complain that it cannot tell whether to export our version of length or the Prelude’s.

While we could export the fully qualified name BloomFilter.Mutable.length to eliminate the ambiguity, that seems uglier in this case. This decision has no consequences for someone using our module, just for ourselves as the authors of what ought to be a “black box”, so there is little chance of confusion here.

Creating a mutable Bloom filter 

We put type declaration for our mutable Bloom filter in the BoomFilter.Internal module, along with the immutable Bloom type.

data MutBloom s a = MB {
      mutHash :: (a -> [Word32])
    , mutArray :: STUArray s Word32 Bool
    }

The STUArray type gives us a mutable unboxed array that we can work with in the ST monad. To create an STUArray, we use the newArray function. The new function belongs in the BloomFilter.Mutable function.

new :: (a -> [Word32]) -> Word32 -> ST s (MutBloom s a)
new hash numBits = MB hash `liftM` newArray (0,numBits-1) False

Most of the methods of STUArray are actually implementations of the MArray type class, which is defined in the Data.Array.MArray module.

Our length function is slightly complicated by two factors. We are relying on our bit array’s record of its own bounds, and an MArray instance’s getBounds function has a monadic type. We also have to add one to the answer, as the upper bound of the array is one less than its actual length.

length :: MutBloom s a -> ST s Word32
length filt = (succ . snd) `liftM` getBounds (mutArray filt)

To add an element to the Bloom filter, we set all of the bits indicated by the hash function. We use the mod function to ensure that all of the hashes stay within the bounds of our array, and isolate our code that computes offsets into the bit array in one function.

insert :: MutBloom s a -> a -> ST s ()
insert filt elt = indices filt elt >>=
                  mapM_ (\bit -> writeArray (mutArray filt) bit True)

indices :: MutBloom s a -> a -> ST s [Word32]
indices filt elt = do
  modulus <- length filt
  return $ map (`mod` modulus) (mutHash filt elt)

Testing for membership is no more difficult. If every bit indicated by the hash function is set, we consider an element to be present in the Bloom filter.

elem, notElem :: a -> MutBloom s a -> ST s Bool

elem elt filt = indices filt elt >>=
                allM (readArray (mutArray filt))

notElem elt filt = not `liftM` elem elt filt

We need to write a small supporting function: a monadic version of all, which we will call allM.

allM :: Monad m => (a -> m Bool) -> [a] -> m Bool
allM p (x:xs) = do
  ok <- p x
  if ok
    then allM p xs
    else return False
allM _ [] = return True

The immutable API 

Our interface to the immutable Bloom filter has the same structure as the mutable API.

module BloomFilter
    (
      Bloom
    , length
    , elem
    , notElem
    , fromList
    ) where

import BloomFilter.Internal
import BloomFilter.Mutable (insert, new)
import Data.Array.ST (runSTUArray)
import Data.Array.IArray ((!), bounds)
import Data.Word (Word32)
import Prelude hiding (elem, length, notElem)

length :: Bloom a -> Int
length = fromIntegral . len

len :: Bloom a -> Word32
len = succ . snd . bounds . blmArray

elem :: a -> Bloom a -> Bool
elt `elem` filt   = all test (blmHash filt elt)
  where test hash = blmArray filt ! (hash `mod` len filt)

notElem :: a -> Bloom a -> Bool
elt `notElem` filt = not (elt `elem` filt)

We provide an easy-to-use means to create an immutable Bloom filter, via a fromList function. This hides the ST monad from our users, so that they only see the immutable type.

fromList :: (a -> [Word32])    -- family of hash functions to use
         -> Word32             -- number of bits in filter
         -> [a]                -- values to populate with
         -> Bloom a
fromList hash numBits values =
    B hash . runSTUArray $
      do mb <- new hash numBits
         mapM_ (insert mb) values
         return (mutArray mb)

The key to this function is runSTUArray. We mentioned earlier that in order to return an immutable array from the ST monad, we must freeze a mutable array. The runSTUArray function combines execution with freezing. Given an action that returns an STUArray, it executes the action using runST; freezes the STUArray that it returns; and returns that as a UArray.

The MArray type class provides a freeze function that we could use instead, but runSTUArray is both more convenient and more efficient. The efficiency lies in the fact that freeze must copy the underlying data from the STUArray to the new UArray, to ensure that subsequent modifications of the STUArray cannot affect the contents of the UArray. Thanks to the type system, runSTUArray can guarantee that an STUArray is no longer accessible when it uses it to create a UArray. It can thus share the underlying contents between the two arrays, avoiding the copy.

Creating a friendly interface 

Although our immutable Bloom filter API is straightforward to use once we have created a Bloom value, the fromList function leaves some important decisions unresolved. We still have to choose a function that can generate many hash values, and determine what the capacity of a Bloom filter should be.

easyList :: (Hashable a)
         => Double        -- false positive rate (between 0 and 1)
         -> [a]           -- values to populate the filter with
         -> Either String (B.Bloom a)

Here is a possible “friendlier” way to create a Bloom filter. It leaves responsibility for hashing values in the hands of a type class, Hashable. It lets us configure the Bloom filter based on a parameter that is easier to understand, namely the rate of false positives that we are willing to tolerate. And it chooses the size of the filter for us, based on the desired false positive rate and the number of elements in the input list.

This function will of course not always be usable: for example, it will fail if the length of the input list is too long. However, its simplicity rounds out the other interfaces we provide. It lets us provide our users with a range of control over creation, from entirely imperative to completely declarative.

Re-exporting names for convenience 

In the export list for our module, we re-export some names from the base BloomFilter module. This allows casual users to import only the BloomFilter.Easy module, and have access to all of the types and functions they are likely to need.

If we import both BloomFilter.Easy and BloomFilter, you might wonder what will happen if we try to use a name exported by both. We already know that if we import BloomFilter unqualified and try to use length, GHC will issue an error about ambiguity, because the Prelude also makes the name length available.

The Haskell standard requires an implementation to be able to tell when several names refer to the same “thing”. For instance, the Bloom type is exported by BloomFilter and BloomFilter.Easy. If we import both modules and try to use Bloom, GHC will be able to see that the Bloom re-exported from BloomFilter.Easy is the same as the one exported from BloomFilter, and it will not report an ambiguity.

Hashing values 

A Bloom filter depends on fast, high-quality hashes for good performance and a low false positive rate. It is surprisingly difficult to write a general purpose hash function that has both of these properties.

Luckily for us, a fellow named Bob Jenkins developed some hash functions that have exactly these properties, and he placed the code in the public domain at http://burtleburtle.net/bob/hash/doobs.html². He wrote his hash functions in C, so we can easily use the FFI to create bindings to them. The specific source file that we need from that site is named lookup3.c. We create a cbits directory and download it to there.

There remains one hitch: we will frequently need seven or even ten hash functions. We really don’t want to scrape together that many different functions, and fortunately we do not need to: in most cases, we can get away with just two. We will see how shortly. The Jenkins hash library includes two functions, hashword2 and hashlittle2, that compute two hash values. Here is a C header file that describes the APIs of these two functions. We save this to cbits/lookup3.h.

/* save this file as lookup3.h */

#ifndef _lookup3_h
#define _lookup3_h

#include <stdint.h>
#include <sys/types.h>

/* only accepts uint32_t aligned arrays of uint32_t */
void hashword2(const uint32_t *key,  /* array of uint32_t */
           size_t length,        /* number of uint32_t values */
           uint32_t *pc,         /* in: seed1, out: hash1 */
           uint32_t *pb);        /* in: seed2, out: hash2 */

/* handles arbitrarily aligned arrays of bytes */
void hashlittle2(const void *key,   /* array of bytes */
         size_t length,     /* number of bytes */
         uint32_t *pc,      /* in: seed1, out: hash1 */
         uint32_t *pb);     /* in: seed2, out: hash2 */

#endif /* _lookup3_h */

A “salt” is a value that perturbs the hash value that the function computes. If we hash the same value with two different salts, we will get two different hashes. Since these functions compute two hashes, they accept two salts.

Here are our Haskell bindings to these functions.

{-# LANGUAGE BangPatterns, ForeignFunctionInterface #-}
module BloomFilter.Hash
    (
      Hashable(..)
    , hash
    , doubleHash
    ) where

import Data.Bits ((.&.), shiftR)
import Foreign.Marshal.Array (withArrayLen)
import Control.Monad (foldM)
import Data.Word (Word32, Word64)
import Foreign.C.Types (CSize)
import Foreign.Marshal.Utils (with)
import Foreign.Ptr (Ptr, castPtr, plusPtr)
import Foreign.Storable (Storable, peek, sizeOf)
import qualified Data.ByteString as Strict
import qualified Data.ByteString.Lazy as Lazy
import System.IO.Unsafe (unsafePerformIO)

foreign import ccall unsafe "lookup3.h hashword2" hashWord2

foreign import ccall unsafe "lookup3.h hashlittle2" hashLittle2

We have specified that the definitions of the functions can be found in the lookup3.h header file that we just created.

For convenience and efficiency, we will combine the 32-bit salts consumed, and the hash values computed, by the Jenkins hash functions into a single 64-bit value.

hashIO :: Ptr a    -- value to hash
       -> CSize    -- number of bytes
       -> Word64   -- salt
       -> IO Word64
hashIO ptr bytes salt =
    with (fromIntegral salt) $ \sp -> do
      let p1 = castPtr sp
          p2 = castPtr sp `plusPtr` 4
      go p1 p2
      peek sp
  where go p1 p2
          | bytes .&. 3 == 0 = hashWord2 (castPtr ptr) words p1 p2
          | otherwise        = hashLittle2 ptr bytes p1 p2
        words = bytes `div` 4

Without explicit types around to describe what is happening, the above code is not completely obvious. The with function allocates room for the salt on the C stack, and stores the current salt value in there, so sp is a Ptr Word64. The pointers p1 and p2 are Ptr Word32; p1 points at the low word of sp, and p2 at the high word. This is how we chop the single Word64 salt into two Ptr Word32 parameters.

Because all of our data pointers are coming from the Haskell heap, we know that they will be aligned on an address that is safe to pass to either hashWord2 (which only accepts 32-bit-aligned addresses) or hashLittle2. Since hashWord32 is the faster of the two hashing functions, we call it if our data is a multiple of 4 bytes in size, otherwise hashLittle2.

Since the C hash function will write the computed hashes into p1 and p2, we only need to peek the pointer sp to retrieve the computed hash.

We don’t want clients of this module to be stuck fiddling with low-level details, so we use a type class to provide a clean, high-level interface.

class Hashable a where
    hashSalt :: Word64        -- ^ salt
             -> a             -- ^ value to hash
             -> Word64

hash :: Hashable a => a -> Word64
hash = hashSalt 0x106fc397cf62f64d3

We also provide a number of useful implementations of this type class. To hash basic types, we must write a little boilerplate code.

hashStorable :: Storable a => Word64 -> a -> Word64
hashStorable salt k = unsafePerformIO . with k $ \ptr ->
                      hashIO ptr (fromIntegral (sizeOf k)) salt

instance Hashable Char   where hashSalt = hashStorable
instance Hashable Int    where hashSalt = hashStorable
instance Hashable Double where hashSalt = hashStorable

We might prefer to use the Storable type class to write just one declaration, as follows:

instance Storable a => Hashable a where
    hashSalt = hashStorable

Unfortunately, Haskell does not permit us to write instances of this form, as allowing them would make the type system undecidable: they can cause the compiler’s type checker to loop infinitely. This restriction on undecidable types forces us to write out individual declarations. It does not, however, pose a problem for a definition such as this one.

hashList :: (Storable a) => Word64 -> [a] -> IO Word64
hashList salt xs =
    withArrayLen xs $ \len ptr ->
      hashIO ptr (fromIntegral (len * sizeOf x)) salt
  where x = head xs

instance (Storable a) => Hashable [a] where
    hashSalt salt xs = unsafePerformIO $ hashList salt xs

The compiler will accept this instance, so we gain the ability to hash values of many list types³. Most importantly, since Char is an instance of Storable, we can now hash String values.

For tuple types, we take advantage of function composition. We take a salt in at one end of the composition pipeline, and use the result of hashing each tuple element as the salt for the next element.

hash2 :: (Hashable a) => a -> Word64 -> Word64
hash2 k salt = hashSalt salt k

instance (Hashable a, Hashable b) => Hashable (a,b) where
    hashSalt salt (a,b) = hash2 b . hash2 a $ salt

instance (Hashable a, Hashable b, Hashable c) => Hashable (a,b,c) where
    hashSalt salt (a,b,c) = hash2 c . hash2 b . hash2 a $ salt

To hash ByteString types, we write special instances that plug straight into the internals of the ByteString types. This gives us excellent hashing performance.

hashByteString :: Word64 -> Strict.ByteString -> IO Word64
hashByteString salt bs = Strict.useAsCStringLen bs $ \(ptr, len) ->
                         hashIO ptr (fromIntegral len) salt

instance Hashable Strict.ByteString where
    hashSalt salt bs = unsafePerformIO $ hashByteString salt bs

rechunk :: Lazy.ByteString -> [Strict.ByteString]
rechunk s
    | Lazy.null s = []
    | otherwise   = let (pre,suf) = Lazy.splitAt chunkSize s
                    in  repack pre : rechunk suf
    where repack    = Strict.concat . Lazy.toChunks
          chunkSize = 64 * 1024

instance Hashable Lazy.ByteString where
    hashSalt salt bs = unsafePerformIO $
                       foldM hashByteString salt (rechunk bs)

Since a lazy ByteString is represented as a series of chunks, we must be careful with the boundaries between those chunks. The string "foobar" can be represented in five different ways, for example ["fo","obar"] or ["foob","ar"]. This is invisible to most users of the type, but not to us since we use the underlying chunks directly. Our rechunk function ensures that the chunks we pass to the C hashing code are a uniform 64KB in size, so that we will give consistent hash values no matter where the original chunk boundaries lie.

Turning two hashes into many 

As we mentioned earlier, we need many more than two hashes to make effective use of a Bloom filter. We can use a technique called double hashing to combine the two values computed by the Jenkins hash functions, yielding many more hashes. The resulting hashes are of good enough quality for our needs, and far cheaper than computing many distinct hashes.

doubleHash :: Hashable a => Int -> a -> [Word32]
doubleHash numHashes value = [h1 + h2 * i | i <- [0..num]]
    where h   = hashSalt 0x9150a946c4a8966e value
          h1  = fromIntegral (h `shiftR` 32) .&. maxBound
          h2  = fromIntegral h
          num = fromIntegral numHashes

Implementing the easy creation function 

In the BloomFilter.Easy module, we use our new doubleHash function to define the easyList function whose type we defined earlier.

module BloomFilter.Easy
    (
      suggestSizing
    , sizings
    , easyList

    -- re-export useful names from BloomFilter
    , B.Bloom
    , B.length
    , B.elem
    , B.notElem
    ) where

import BloomFilter.Hash (Hashable, doubleHash)
import Data.List (genericLength)
import Data.Maybe (catMaybes)
import Data.Word (Word32)
import qualified BloomFilter as B

easyList errRate values =
    case suggestSizing (genericLength values) errRate of
      Left err            -> Left err
      Right (bits,hashes) -> Right filt
        where filt = B.fromList (doubleHash hashes) bits values

This depends on a suggestSizing function that estimates the best combination of filter size and number of hashes to compute, based on our desired false positive rate and the maximum number of elements that we expect the filter to contain.

suggestSizing

    -> Double        -- desired false positive rate
    -> Either String (Word32,Int) -- (filter size, number of hashes)
suggestSizing capacity errRate
    | capacity <= 0                = Left "capacity too small"
    | errRate <= 0 || errRate >= 1 = Left "invalid error rate"
    | null saneSizes               = Left "capacity too large"
    | otherwise                    = Right (minimum saneSizes)
  where saneSizes = catMaybes . map sanitize $ sizings capacity errRate
        sanitize (bits,hashes)
          | bits > maxWord32 - 1 = Nothing
          | otherwise            = Just (ceiling bits, truncate hashes)
          where maxWord32 = fromIntegral (maxBound :: Word32)

sizings :: Integer -> Double -> [(Double, Double)]
sizings capacity errRate =
    [(((-k) * cap / log (1 - (errRate ** (1 / k)))), k) | k <- [1..50]]
  where cap = fromIntegral capacity

We perform some rather paranoid checking. For instance, the sizings function suggests pairs of array size and hash count, but it does not validate its suggestions. Since we use 32-bit hashes, we must filter out suggested array sizes that are too large.

In our suggestSizing function, we attempt to minimise only the size of the bit array, without regard for the number of hashes. To see why, let us interactively explore the relationship between array size and number of hashes.

Suppose we want to insert 10 million elements into a Bloom filter, with a false positive rate of 0.1%.

ghci> let kbytes (bits,hashes) = (ceiling bits `div` 8192, hashes)
ghci> :m +BloomFilter.Easy Data.List
Could not find module `BloomFilter.Easy':
  Use -v to see a list of the files searched for.
ghci> mapM_ (print . kbytes) . take 10 . sort $ sizings 10000000 0.001

<interactive>:1:35: Not in scope: `sort'

<interactive>:1:42: Not in scope: `sizings'

We achieve the most compact table (just over 17KB) by computing 10 hashes. If we really were hashing the data repeatedly, we could reduce the number of hashes to 7 at a cost of 5% in space. Since we are using Jenkins’s hash functions which compute two hashes in a single pass, and double hashing the results to produce additional hashes, the cost to us of computing extra those hashes is tiny, so we will choose the smallest table size.

If we increase our tolerance for false positives tenfold, to 1%, the amount of space and the number of hashes we need drop, though not by easily predictable amounts.

ghci> mapM_ (print . kbytes) . take 10 . sort $ sizings 10000000 0.01

<interactive>:1:35: Not in scope: `sort'

<interactive>:1:42: Not in scope: `sizings'

Creating a Cabal package 

We have created a moderately complicated library, with four public modules and one internal module. To turn this into a package that we can easily redistribute, we create a rwh-bloomfilter.cabal file.

Cabal allows us to describe several libraries in a single package. A .cabal file begins with information that is common to all of the libraries, which is followed by a distinct section for each library.

Name:               rwh-bloomfilter
Version:            0.1
License:            BSD3
License-File:       License.txt
Category:           Data
Stability:          experimental
Build-Type:         Simple

As we are bundling some C code with our library, we tell Cabal about our C source files.

Extra-Source-Files: cbits/lookup3.c cbits/lookup3.h

The extra-source-files directive has no effect on a build: it directs Cabal to bundle some extra files if we run runhaskell Setup sdist to create a source tarball for redistribution.

Dealing with different build setups 

Prior to 2007, the standard Haskell libraries were organised in a handful of large packages, of which the biggest was named base. This organisation tied many unrelated libraries together, so the Haskell community split the base package up into a number of more modular libraries. For instance, the array types migrated from base into a package named array.

A Cabal package needs to specify the other packages that it needs to have present in order to build. This makes it possible for Cabal’s command line interface automatically download and build a package’s dependencies, if necessary. We would like our code to work with as many versions of GHC as possible, regardless of whether they have the modern layout of base and numerous other packages. We thus need to be able to specify that we depend on the array package if it is present, and base alone otherwise.

Cabal provides a generic configurations feature, which we can use to selectively enable parts of a .cabal file. A build configuration is controlled by a Boolean-valued flag. If it is True, the text following an if flag directive is used, otherwise the text following the associated else is used.

Cabal-Version:      >= 1.2

Flag split-base
  Description: Has the base package been split up?
  Default: True

Flag bytestring-in-base
  Description: Is ByteString in the base or bytestring package?
  Default: False

• The configurations feature was introduced in version 1.2 of Cabal, so we specify that our package cannot be built with an older version.
• The meaning of the split-base flag should be self-explanatory.
• The bytestring-in-base flag deals with a more torturous history. When the bytestring package was first created, it was bundled with GHC 6.4, and kept separate from the base package. In GHC 6.6, it was incorporated into the base package, but it became independent again when the base package was split before the release of GHC 6.8.1.

These flags are usually invisible to people building a package, because Cabal handles them automatically. Before we explain what happens, it will help to see the beginning of the Library section of our .cabal file.

Library
  if flag(bytestring-in-base)
    -- bytestring was in base-2.0 and 2.1.1
    Build-Depends: base >= 2.0 && < 2.2
  else
    -- in base 1.0 and 3.0, bytestring is a separate package
    Build-Depends: base < 2.0 || >= 3, bytestring >= 0.9

  if flag(split-base)
    Build-Depends: base >= 3.0, array
  else
    Build-Depends: base < 3.0

Cabal creates a package description with the default values of the flags (a missing default is assumed to be True). If that configuration can be built (e.g. because all of the needed package versions are available), it will be used. Otherwise, Cabal tries different combinations of flags until it either finds a configuration that it can build or exhausts the alternatives.

For example, if we were to begin with both split-base and bytestring-in-base set to True, Cabal would select the following package dependencies.

Build-Depends: base >= 2.0 && < 2.2
Build-Depends: base >= 3.0, array

The base package cannot simultaneously be newer than 3.0 and older than 2.2, so Cabal would reject this configuration as inconsistent. For a modern version of GHC, after a few attempts it would discover this configuration that will indeed build.

-- in base 1.0 and 3.0, bytestring is a separate package
Build-Depends: base < 2.0 || >= 3, bytestring >= 0.9
Build-Depends: base >= 3.0, array

When we run runhaskell Setup configure, we can manually specify the values of flags via the --flag option, though we will rarely need to do so in practice.

Compilation options, and interfacing to C 

Continuing with our .cabal file, we fill out the remaining details of the Haskell side of our library. If we enable profiling when we build, we want all of our top-level functions to show up in any profiling output.

GHC-Prof-Options: -auto-all

The Other-Modules property lists Haskell modules that are private to the library. Such modules will be invisible to code that uses this package.

When we build this package with GHC, Cabal will pass the options from the GHC-Options property to the compiler.

The -O2 option makes GHC optimise our code aggressively. Code compiled without optimisation is very slow, so we should always use -O2 for production code.

To help ourselves to write cleaner code, we usually add the -Wall option, which enables all of GHC’s warnings. This will cause GHC to issue complaints if it encounters potential problems, such as overlapping patterns; function parameters that are not used; and a myriad of other potential stumbling blocks. While it is often safe to ignore these warnings, we generally prefer to fix up our code to eliminate them. The small added effort usually yields code that is easier to read and maintain.

When we compile with -fvia-C, GHC will generate C code and use the system’s C compiler to compile it, instead of going straight to assembly language as it usually does. This slows compilation down, but sometimes the C compiler can further improve GHC’s optimised code, so it can be worthwhile.

We include -fvia-C here mainly to show how to make compilation with it work.

C-Sources:        cbits/lookup3.c
CC-Options:       -O3
Include-Dirs:     cbits
Includes:         lookup3.h
Install-Includes: lookup3.h

For the C-Sources property, we only need to list files that must be compiled into our library. The CC-Options property contains options for the C compiler (-O3 specifies a high level of optimisation). Because our FFI bindings for the Jenkins hash functions refer to the lookup3.h header file, we need to tell Cabal where to find the header file. We must also tell it to install the header file (Install-Includes), as otherwise client code will fail to find the header file when we try to build it.

Testing with QuickCheck 

Before we pay any attention to performance, we want to establish that our Bloom filter behaves correctly. We can easily use QuickCheck to test some basic properties.

{-# LANGUAGE GeneralizedNewtypeDeriving #-}
module Main where

import BloomFilter.Hash (Hashable)
import Data.Word (Word8, Word32)
import System.Random (Random(..), RandomGen)
import Test.QuickCheck
import qualified BloomFilter.Easy as B
import qualified Data.ByteString as Strict
import qualified Data.ByteString.Lazy as Lazy

We will not use the normal quickCheck function to test our properties, as the 100 test inputs that it generates do not provide much coverage.

handyCheck :: Testable a => Int -> a -> IO ()
handyCheck limit = check defaultConfig {
                     configMaxTest = limit
                   , configEvery   = \_ _ -> ""
                   }

Our first task is to ensure that if we add a value to a Bloom filter, a subsequent membership test will always report it as present, no matter what the chosen false positive rate or input value is.

We will use the easyList function to create a Bloom filter. The Random instance for Double generates numbers in the range zero to one, so QuickCheck can nearly supply us with arbitrary false positive rates.

However, we need to ensure that both zero and one are excluded from the false positives we test with. QuickCheck gives us two ways to do this.

• By construction: we specify the range of valid values to generate. QuickCheck provides a forAll combinator for this purpose.
• By elimination: when QuickCheck generates an arbitrary value for us, we filter out those that do not fit our criteria, using the (==>) operator. If we reject a value in this way, a test will appear to succeed.

If we can choose either method, it is always preferable to take the constructive approach. To see why, suppose that QuickCheck generates 1,000 arbitrary values for us, and we filter out 800 as unsuitable for some reason. We will appear to run 1,000 tests, but only 200 will actually do anything useful.

Following this idea, when we generate desired false positive rates, we could eliminate zeroes and ones from whatever QuickCheck gives us, but instead we construct values in an interval that will always be valid.

falsePositive :: Gen Double
falsePositive = choose (epsilon, 1 - epsilon)
    where epsilon = 1e-6

(=~>) :: Either a b -> (b -> Bool) -> Bool
k =~> f = either (const True) f k

prop_one_present _ elt =
    forAll falsePositive $ \errRate ->
      B.easyList errRate [elt] =~> \filt ->
        elt `B.elem` filt

Our small combinator, (=~>), lets us filter out failures of easyList: if it fails, the test automatically passes.

Polymorphic testing 

QuickCheck requires properties to be monomorphic. Since we have many different hashable types that we would like to test, we would very much like to avoid having to write the same test in many different ways.

Notice that although our prop_one_present function is polymorphic, it ignores its first argument. We use this to simulate monomorphic properties, as follows.

ghci> :load BloomCheck

BloomCheck.hs:9:17:
    Could not find module `BloomFilter.Easy':
      Use -v to see a list of the files searched for.
Failed, modules loaded: none.
ghci> :t prop_one_present

<interactive>:1:0: Not in scope: `prop_one_present'
ghci> :t prop_one_present (undefined :: Int)

<interactive>:1:0: Not in scope: `prop_one_present'

We can supply any value as the first argument to prop_one_present. All that matters is its type, as the same type will be used for the first element of the second argument.

ghci> handyCheck 5000 $ prop_one_present (undefined :: Int)

<interactive>:1:0: Not in scope: `handyCheck'

<interactive>:1:18: Not in scope: `prop_one_present'
ghci> handyCheck 5000 $ prop_one_present (undefined :: Double)

<interactive>:1:0: Not in scope: `handyCheck'

<interactive>:1:18: Not in scope: `prop_one_present'

If we populate a Bloom filter with many elements, they should all be present afterwards.

prop_all_present _ xs =
    forAll falsePositive $ \errRate ->
      B.easyList errRate xs =~> \filt ->
        all (`B.elem` filt) xs

This test also succeeds.

ghci> handyCheck 2000 $ prop_all_present (undefined :: Int)

<interactive>:1:0: Not in scope: `handyCheck'

<interactive>:1:18: Not in scope: `prop_all_present'

Writing Arbitrary instances for ByteStrings 

The QuickCheck library does not provide Arbitrary instances for ByteString types, so we must write our own. Rather than create a ByteString directly, we will use a pack function to create one from a [Word8].

instance Arbitrary Lazy.ByteString where
    arbitrary = Lazy.pack `fmap` arbitrary
    coarbitrary = coarbitrary . Lazy.unpack

instance Arbitrary Strict.ByteString where
    arbitrary = Strict.pack `fmap` arbitrary
    coarbitrary = coarbitrary . Strict.unpack

Also missing from QuickCheck are Arbitrary instances for the fixed-width types defined in Data.Word and Data.Int. We need to at least create an Arbitrary instance for Word8.

instance Random Word8 where
  randomR = integralRandomR
  random = randomR (minBound, maxBound)

instance Arbitrary Word8 where
    arbitrary = choose (minBound, maxBound)
    coarbitrary = integralCoarbitrary

We support these instances with a few common functions so that we can reuse them when writing instances for other integral types.

integralCoarbitrary n =
    variant $ if m >= 0 then 2*m else 2*(-m) + 1
  where m = fromIntegral n

integralRandomR (a,b) g = case randomR (c,d) g of
                            (x,h) -> (fromIntegral x, h)
    where (c,d) = (fromIntegral a :: Integer,
                   fromIntegral b :: Integer)

instance Random Word32 where
  randomR = integralRandomR
  random = randomR (minBound, maxBound)

instance Arbitrary Word32 where
    arbitrary = choose (minBound, maxBound)
    coarbitrary = integralCoarbitrary

With these Arbitrary instances created, we can try our existing properties on the ByteString types.

ghci> handyCheck 1000 $ prop_one_present (undefined :: Lazy.ByteString)

<interactive>:1:0: Not in scope: `handyCheck'

<interactive>:1:18: Not in scope: `prop_one_present'

<interactive>:1:49:
    Failed to load interface for `Lazy':
      Use -v to see a list of the files searched for.
ghci> handyCheck 1000 $ prop_all_present (undefined :: Strict.ByteString)

<interactive>:1:0: Not in scope: `handyCheck'

<interactive>:1:18: Not in scope: `prop_all_present'

<interactive>:1:49:
    Failed to load interface for `Strict':
      Use -v to see a list of the files searched for.

Are suggested sizes correct? 

The cost of testing properties of easyList increases rapidly as we increase the number of tests to run. We would still like to have some assurance that easyList will behave well on huge inputs. Since it is not practical to test this directly, we can use a proxy: will suggestSizing give a sensible array size and number of hashes even with extreme inputs?

This is a slightly tricky property to check. We need to vary both the desired false positive rate and the expected capacity. When we looked at some results from the sizings function, we saw that the relationship between these values is not easy to predict.

We can try to ignore the complexity.

prop_suggest_try1 =
  forAll falsePositive $ \errRate ->
    forAll (choose (1,maxBound :: Word32)) $ \cap ->
      case B.suggestSizing (fromIntegral cap) errRate of
        Left err -> False
        Right (bits,hashes) -> bits > 0 && bits < maxBound && hashes > 0

Not surprisingly, this gives us a test that is not actually useful.

ghci> handyCheck 1000 $ prop_suggest_try1

<interactive>:1:0: Not in scope: `handyCheck'

<interactive>:1:18: Not in scope: `prop_suggest_try1'
ghci> handyCheck 1000 $ prop_suggest_try1

<interactive>:1:0: Not in scope: `handyCheck'

<interactive>:1:18: Not in scope: `prop_suggest_try1'

When we plug the counterexamples that QuickCheck prints into suggestSizings, we can see that these inputs are rejected because they would result in a bit array that would be too large.

ghci> B.suggestSizing 1678125842 8.501133057303545e-3

<interactive>:1:0:
    Failed to load interface for `B':
      Use -v to see a list of the files searched for.

Since we can’t easily predict which combinations will cause this problem, we must resort to eliminating sizes and false positive rates before they bite us.

prop_suggest_try2 =
    forAll falsePositive $ \errRate ->
      forAll (choose (1,fromIntegral maxWord32)) $ \cap ->
        let bestSize = fst . minimum $ B.sizings cap errRate
        in bestSize < fromIntegral maxWord32 ==>
           either (const False) sane $ B.suggestSizing cap errRate
  where sane (bits,hashes) = bits > 0 && bits < maxBound && hashes > 0
        maxWord32 = maxBound :: Word32

If we try this with a small number of tests, it seems to work well.

ghci> handyCheck 1000 $ prop_suggest_try2

<interactive>:1:0: Not in scope: `handyCheck'

<interactive>:1:18: Not in scope: `prop_suggest_try2'

On a larger body of tests, we filter out too many combinations.

ghci> handyCheck 10000 $ prop_suggest_try2

<interactive>:1:0: Not in scope: `handyCheck'

<interactive>:1:19: Not in scope: `prop_suggest_try2'

To deal with this, we try to reduce the likelihood of generating inputs that we will subsequently reject.

prop_suggestions_sane =
    forAll falsePositive $ \errRate ->
      forAll (choose (1,fromIntegral maxWord32 `div` 8)) $ \cap ->
        let size = fst . minimum $ B.sizings cap errRate
        in size < fromIntegral maxWord32 ==>
           either (const False) sane $ B.suggestSizing cap errRate
  where sane (bits,hashes) = bits > 0 && bits < maxBound && hashes > 0
        maxWord32 = maxBound :: Word32

Finally, we have a robust looking property.

ghci> handyCheck 40000 $ prop_suggestions_sane

<interactive>:1:0: Not in scope: `handyCheck'

<interactive>:1:19: Not in scope: `prop_suggestions_sane'

Performance analysis and tuning 

We now have a correctness base line: our QuickCheck tests pass. When we start tweaking performance, we can rerun the tests at any time to ensure that we haven’t inadvertently broken anything.

Our first step is to write a small test application that we can use for timing.

module Main where

import Control.Parallel.Strategies (NFData(..))
import Control.Monad (forM_, mapM_)
import qualified BloomFilter.Easy as B
import qualified Data.ByteString.Char8 as BS
import Data.Time.Clock (diffUTCTime, getCurrentTime)
import System.Environment (getArgs)
import System.Exit (exitFailure)

timed :: (NFData a) => String -> IO a -> IO a
timed desc act = do
    start <- getCurrentTime
    ret <- act
    end <- rnf ret `seq` getCurrentTime
    putStrLn $ show (diffUTCTime end start) ++ " to " ++ desc
    return ret

instance NFData BS.ByteString where
    rnf _ = ()

instance NFData (B.Bloom a) where
    rnf filt = B.length filt `seq` ()

We borrow the rnf function that we introduced in the section called “Separating algorithm from evaluation” to develop a simple timing harness. Out timed action ensures that a value is evaluated to normal form in order to accurately capture the cost of evaluating it.

The application creates a Bloom filter from the contents of a file, treating each line as an element to add to the filter.

main = do
  args <- getArgs
  let files | null args = ["/usr/share/dict/words"]
            | otherwise = args
  forM_ files $ \file -> do

    words <- timed "read words" $
      BS.lines `fmap` BS.readFile file

    let len = length words
        errRate = 0.01

    putStrLn $ show len ++ " words"
    putStrLn $ "suggested sizings: " ++
               show (B.suggestSizing (fromIntegral len) errRate)

    filt <- timed "construct filter" $
      case B.easyList errRate words of
        Left errmsg -> do
          putStrLn $ "Error: " ++ errmsg
          exitFailure
        Right filt -> return filt

    timed "query every element" $
      mapM_ print $ filter (not . (`B.elem` filt)) words

We use timed to account for the costs of three distinct phases: reading and splitting the data into lines; populating the Bloom filter; and querying every element in it.

If we compile this and run it a few times, we can see that the execution time is just long enough to be interesting, while the timing variation from run to run is small. We have created a plausible-looking microbenchmark.

$ ghc -O2  --make WordTest
[1 of 1] Compiling Main             ( WordTest.hs, WordTest.o )
Linking WordTest ...
$ ./WordTest
0.196347s to read words
479829 words
1.063537s to construct filter
4602978 bits
0.766899s to query every element
$ ./WordTest
0.179284s to read words
479829 words
1.069363s to construct filter
4602978 bits
0.780079s to query every element

Profile-driven performance tuning 

To understand where our program might benefit from some tuning, we rebuild it and run it with profiling enabled.

Since we already built WordTest and have not subsequently changed it, if we rerun ghc to enable profiling support, it will quite reasonably decide to do nothing. We must force it to rebuild, which we accomplish by updating the filesystem’s idea of when we last edited the source file.

$ touch WordTest.hs
$ ghc -O2 -prof -auto-all --make WordTest
[1 of 1] Compiling Main             ( WordTest.hs, WordTest.o )
Linking WordTest ...

$ ./WordTest +RTS -p
0.322675s to read words
479829 words
suggested sizings: Right (4602978,7)
2.475339s to construct filter
1.964404s to query every element

$ head -20 WordTest.prof
total time  =          4.10 secs   (205 ticks @ 20 ms)
total alloc = 2,752,287,168 bytes  (excludes profiling overheads)

COST CENTRE                    MODULE               %time %alloc

doubleHash                     BloomFilter.Hash      48.8   66.4
indices                        BloomFilter.Mutable   13.7   15.8
elem                           BloomFilter            9.8    1.3
hashByteString                 BloomFilter.Hash       6.8    3.8
easyList                       BloomFilter.Easy       5.9    0.3
hashIO                         BloomFilter.Hash       4.4    5.3
main                           Main                   4.4    3.8
insert                         BloomFilter.Mutable    2.9    0.0
len                            BloomFilter            2.0    2.4
length                         BloomFilter.Mutable    1.5    1.0

Our doubleHash function immediately leaps out as a huge time and memory sink.

Recall that the body of doubleHash is an innocuous list comprehension.

doubleHash :: Hashable a => Int -> a -> [Word32]
doubleHash numHashes value = [h1 + h2 * i | i <- [0..num]]
    where h   = hashSalt 0x9150a946c4a8966e value
          h1  = fromIntegral (h `shiftR` 32) .&. maxBound
          h2  = fromIntegral h
          num = fromIntegral numHashes

Since the function returns a list, it makes some sense that it allocates so much memory, but when code this simple performs so badly, we should be suspicious.

Faced with a performance mystery, the suspicious mind will naturally want to inspect the output of the compiler. We don’t need to start scrabbling through assembly language dumps: it’s best to start at a higher level.

GHC’s -ddump-simpl option prints out the code that it produces after performing all of its high-level optimisations.

$ ghc -O2 -c -ddump-simpl --make BloomFilter/Hash.hs > dump.txt
[1 of 1] Compiling BloomFilter.Hash ( BloomFilter/Hash.hs )

The file thus produced is about a thousand lines long. Most of the names in it are mangled somewhat from their original Haskell representations. Even so, searching for doubleHash will immediately drop us at the definition of the function. For example, here is how we might start exactly at the right spot from a Unix shell.

$ less +/doubleHash dump.txt

It can be difficult to start reading the output of GHC’s simplifier. There are many automatically generated names, and the code has many obscure annotations. We can make substantial progress by ignoring things that we do not understand, focusing on those that look familiar. The Core language shares some features with regular Haskell, notably type signatures; let for variable binding; and case for pattern matching.

If we skim through the definition of doubleHash, we will arrive at a section that looks something like this.

__letrec { -- 1
  go_s1YC :: [GHC.Word.Word32] -> [GHC.Word.Word32] -- 2
  [Arity 1
   Str: DmdType S]
  go_s1YC =
    \ (ds_a1DR :: [GHC.Word.Word32]) ->
      case ds_a1DR of wild_a1DS {
    [] -> GHC.Base.[] @ GHC.Word.Word32; -- 3
    : y_a1DW ys_a1DX -> -- 4
      GHC.Base.: @ GHC.Word.Word32 -- 5
        (case h1_s1YA of wild1_a1Mk { GHC.Word.W32# x#_a1Mm -> -- 6
         case h2_s1Yy of wild2_a1Mu { GHC.Word.W32# x#1_a1Mw ->
         case y_a1DW of wild11_a1My { GHC.Word.W32# y#_a1MA ->
         GHC.Word.W32# -- 7
           (GHC.Prim.narrow32Word#
          (GHC.Prim.plusWord# -- 8
             x#_a1Mm (GHC.Prim.narrow32Word#
                              (GHC.Prim.timesWord# x#1_a1Mw y#_a1MA))))
         }
         }
         })
        (go_s1YC ys_a1DX) -- 9
      };
} in
  go_s1YC - 10
    (GHC.Word.$w$dmenumFromTo2
       __word 0 (GHC.Prim.narrow32Word# (GHC.Prim.int2Word# ww_s1X3)))

This is the body of the list comprehension. It may seem daunting, but we can look through it piece by piece and find that it is not, after all, so complicated.

1. A __letrec is equivalent to a normal Haskell let.

2. GHC compiled the body of our list comprehension into a loop named go_s1YC.

3. If our case expression matches the empty list, we return the empty list. This is reassuringly familiar.

4. This pattern would read in Haskell as (y_a1DW:ys_a1DX). The (:) constructor appears before its operands because the Core language uses prefix notation exclusively for simplicity.

5. This is an application of the (:) constructor. The @ notation indicates that the first operand will have type Word32.

6. Each of the three case expressions unboxes a Word32value, to get at the primitive value inside. First to be unboxed is h1 (named h1_s1YA here), then h2, then the current list element, y.

   The unboxing occurs via pattern matching: W32# is the constructor that boxes a primitive value. By convention, primitive types and values, and functions that use them, always contains a # somewhere in their name.

7. Here, we apply the W32# constructor to a value of the primitive type Word32#, to give a normal value of type Word32.

8. The plusWord# and timesWord# functions add and multiply primitive unsigned integers.

9. This is the second argument to the (:) constructor, in which the go_s1YC function applies itself recursively.

10. Here, we apply our list comprehension loop function. Its argument is the Core translation of the expression [0..n].

From reading the Core for this code, we can see two interesting behaviours.

• We are creating a list, then immediately deconstructing it in the go_s1YC loop.

  GHC can often spot this pattern of production followed immediately by consumption, and transform it into a loop in which no allocation occurs. This class of transformation is called fusion, because the producer and consumer become fused together. Unfortunately, it is not occurring here.

• The repeated unboxing of h1 and h2 in the body of the loop is wasteful.

To address these problems, we make a few tiny changes to our doubleHash function.

doubleHash :: Hashable a => Int -> a -> [Word32]
doubleHash numHashes value = go 0
    where go n | n == num  = []
               | otherwise = h1 + h2 * n : go (n + 1)

          !h1 = fromIntegral (h `shiftR` 32) .&. maxBound
          !h2 = fromIntegral h

          h   = hashSalt 0x9150a946c4a8966e value
          num = fromIntegral numHashes

We have manually fused the [0..num] expression and the code that consumes it into a single loop. We have added strictness annotations to h1 and h2. And nothing more. This has turned a 6-line function into an 8-line function. What effect does our change have on Core output?

__letrec {
  $wgo_s1UH :: GHC.Prim.Word# -> [GHC.Word.Word32]
  [Arity 1
   Str: DmdType L]
  $wgo_s1UH =
    \ (ww2_s1St :: GHC.Prim.Word#) ->
      case GHC.Prim.eqWord# ww2_s1St a_s1T1 of wild1_X2m {
    GHC.Base.False ->
      GHC.Base.: @ GHC.Word.Word32
        (GHC.Word.W32#
         (GHC.Prim.narrow32Word#
          (GHC.Prim.plusWord#
           ipv_s1B2
           (GHC.Prim.narrow32Word#
        (GHC.Prim.timesWord# ipv1_s1AZ ww2_s1St)))))
        ($wgo_s1UH (GHC.Prim.narrow32Word#
                        (GHC.Prim.plusWord# ww2_s1St __word 1)));
    GHC.Base.True -> GHC.Base.[] @ GHC.Word.Word32
      };
} in  $wgo_s1UH __word 0

Our new function has compiled down to a simple counting loop. This is very encouraging, but how does it actually perform?

$ touch WordTest.hs
$ ghc -O2 -prof -auto-all --make WordTest
[1 of 1] Compiling Main             ( WordTest.hs, WordTest.o )
Linking WordTest ...

$ ./WordTest +RTS -p
0.304352s to read words
479829 words
suggested sizings: Right (4602978,7)
1.516229s to construct filter
1.069305s to query every element
~/src/darcs/book/examples/ch27/examples $ head -20 WordTest.prof
total time  =        3.68 secs    (184 ticks @ 20 ms)
total alloc = 2,644,805,536 bytes (excludes profiling overheads)

COST CENTRE                    MODULE               %time %alloc

doubleHash                     BloomFilter.Hash      45.1   65.0
indices                        BloomFilter.Mutable   19.0   16.4
elem                           BloomFilter           12.5    1.3
insert                         BloomFilter.Mutable    7.6    0.0
easyList                       BloomFilter.Easy       4.3    0.3
len                            BloomFilter            3.3    2.5
hashByteString                 BloomFilter.Hash       3.3    4.0
main                           Main                   2.7    4.0
hashIO                         BloomFilter.Hash       2.2    5.5
length                         BloomFilter.Mutable    0.0    1.0

Our tweak has improved performance by about 11%. This is a good result for such a small change.

Exercises 

1. Our use ofgenericLength in easyList will cause our function to loop infinitely if we supply an infinite list. Fix this.
2. Difficult. Write a QuickCheck property that checks whether the observed false positive rate is close to the requested false positive rate.