Blocked Signatures

The interesting thing about BitFunnel, the search architecture used by Bing, is its unorthodoxy; it revisits many ideas ignored by contemporary search technologies. The paper’s references go back to the 80s, when one million records was considered an enormous database. Something about this appeals to me, that there might be long forgotten ideas waiting to be reinterpreted and recombined in the context of modern micro-architectures.

A bit-sliced signature arrangement reduces the number of words which must be processed to evaluate a query, but it’s not enough for BitFunnel’s purposes. The last piece of background in the BitFunnel paper is blocked signatures, which are discussed in the 1990 paper A signature file scheme based on multiple organizations for indexing very large text databases by Kent, Sacks-Davis, Ramamohanarao (KS-DR). Blocked signatures further reduce the amount of processing per query, at what can be an acceptable false positive rate. In this post I aim to piece their data structure together in modern Java.


The goal is to map documents into blocks consisting of a fixed number of documents (referred to as the blocking factor in the BitFunnel paper) so only bit sliced block signatures need be stored, where a block signature is a bloom filter of the terms in a block of documents. There are a variety of ways of doing this but they all start with assigning an integer label to each document prior to block assignment. This topic is covered at length in KS-DR.

The most obvious technique is to assign contiguous ranges of document IDs to blocks of size N, that is the function i -> Math.floorDiv(i, N). This is only useful if blocks of document signatures are also stored, acting as a top level index into those document signatures. Physically, there is a block index, which is the bit sliced signature of the terms in each block, and separate blocks of document signatures, again bit sliced by term. Queries can be evaluated by constructing a bloom filter from the query’s terms, specifying a set of bit slices in the block index to inspect and intersect. The result of the intersection gives the IDs of the document signature blocks to query. This is like a two level tree, and is better than a signature scan over all the document signatures, but why not just bit slice the document signatures? For rare terms, once a block is located, it does cut down the number of words in each slice to be intersected. However, the real problem with this technique, besides storage, is the cost of a false match at the block level: it results in a document level query, touching N bits, but yields nothing. The BitFunnel blocked signatures generalise this two level hierarchical arrangement for multiple levels.

This post goes on a tangent from BitFunnel here, focusing on the ideas put forward in KS-DR. An alternative is to choose a number M > N coprime to C, an estimate of the capacity of the index, and use the function i -> Math.floorDiv(M * i % C, N) to permute records prior to blocking, then make a copy of the block index for each of several values of M. If you choose, say, two values of M, when evaluating queries, you can map the query terms and get the matching blocks from each representation as before. There is no need for a document level query or storage though. If you have a bitmap of the document IDs (not the signatures) for each block, you can intersect the document bitmaps to get the document IDs matching the query (with false positives, the number of which reduces with the number of copies). In the KS-DR paper, this bitmap I assume the existence of is actually computed on the fly via an expensive reverse mapping with the help of a lookup table.

Java Implementation

The code is very similar to the bit sliced signature code, because a significant part of querying is a bit sliced lookup of block IDs, which requires storage of a bit matrix. The major difference is the requirement for block assignment and ultimately block intersection. I encapsulate this in a BlockSet which contains Blocks and is responsible for block assignment and intersection.

Details of block creation (blocking factor, bit matrix dimensions, hashing policy) can be hidden behind a supplier interface.

public class BlockFactory<D extends Supplier<Set<T>> & IntSupplier, T, Q extends Set<T>> implements Supplier<Block<D, T, Q>> {

    private final List<ToIntFunction<T>> hashes;
    private final int blockingFactor;
    private final int blockCapacity;

    public BlockFactory(List<ToIntFunction<T>> hashes, int blockingFactor, int blockCapacity) {
        this.hashes = hashes;
        this.blockingFactor = blockingFactor;
        this.blockCapacity = blockCapacity;

    public Block<D, T, Q> get() {
        return new Block<>(blockingFactor, blockCapacity, hashes);

This gives us blocks, which is really just a wrapper around a bit matrix of terms and a bit set of document IDs. It can do three things

  1. Index a document, this requires that it knows the blocking factor (the number of blocks it can index), the hash functions and the bloom filter size.
  2. Check if the block might contain at least one document matching all the terms
  3. Share its document IDs

The code looks quite similar to my previous bit sliced signature implementation.

public class Block<D extends Supplier<Set<T>> & IntSupplier, T, Q extends Set<T>> {

    private final BitSet documentIds;
    private final long[][] bitMatrix;
    private final int capacity;
    private final List<ToIntFunction<T>> hashFunctions;
    private int docIndex = 0;

    public Block(int blockingFactor, int capacity, List<ToIntFunction<T>> hashFunctions) {
        assert Integer.bitCount(capacity) == 1;
        this.documentIds = new BitSet();
        this.bitMatrix = new long[capacity >> 6][blockingFactor];
        this.capacity = capacity;
        this.hashFunctions = hashFunctions;

    public void add(D doc) {
        int docIndex = this.docIndex++;
        int docWordIndex = docIndex >>> 6;
        long docWord = 1L << docIndex;
        mapTerms(doc.get()).forEach(r -> bitMatrix[r][docWordIndex] |= docWord);

    public void contribute(BitSet result) {

    public boolean matches(Q query) {
        int[] rows = mapTerms(query).distinct().toArray();
        return IntStream.range(0, capacity >> 6)
                        .filter(i -> hasMatch(rows, i))

    private boolean hasMatch(int[] rows, int offset) {
        long word = 0L;
        for (int i = 0; i < rows.length && word == 0; ++i) {
            word |= bitMatrix[rows[i]][offset];
        return word != 0;

    private IntStream mapTerms(Set<T> terms) {
        return -> -> mapHash(f.applyAsInt(t))));

    private int mapHash(int hash) {
        return hash & -hash & (capacity - 1);

Now a level up. A BlockIndex has a BlockSet for each relatively prime factor. When evaluating a query, it passes the query to each of its BlockSets, retrieving all blocks which probably match the query.

public class BlockSet<D extends Supplier<Set<T>> & IntSupplier, T, Q extends Set<T>> {

    private final Block[] blocks;
    private final Supplier<Block<D, T, Q>> newBlock;
    private final int blockingFactor;
    private final int estimatedCapacity;
    private final int prime;

    public BlockSet(Supplier<Block<D, T, Q>> newBlock, int blockingFactor, int estimatedCapacity, int prime) {
        assert Integer.bitCount(blockingFactor) == 1 && Integer.bitCount(estimatedCapacity) == 1;
        this.newBlock = newBlock;
        this.blocks = new Block[estimatedCapacity/blockingFactor];
        for (int i = 0; i < blocks.length; ++i) {
            blocks[i] = newBlock.get();
        this.blockingFactor = blockingFactor;
        this.estimatedCapacity = estimatedCapacity; = prime;

    public void add(D doc) {
        int blockIndex = blockIndex(doc.getAsInt());
        Block<D, T, Q> block = (Block<D, T, Q>)blocks[blockIndex];

    public BitSet query(Q query) {
        BitSet result = new BitSet();
              .filter(b -> b.matches(query))
              .forEach(b -> b.contribute(result));
        return result;

    private int blockIndex(int value) {
        return ((value * prime) & (estimatedCapacity - 1)) / blockingFactor;

With a BlockIndex as the tip of an iceberg – it just needs to intersect the bit sets of document IDs.

public class BlockIndex<D extends Supplier<Set<T>> & IntSupplier, T, Q extends Set<T>> {

    private final List<BlockSet<D, T, Q>> blockSets;

    public BlockIndex(List<BlockSet<D, T, Q>> blockSets) {
        this.blockSets = blockSets;

    public IntStream query(Q query) {
        BitSet result = null;
        for (BlockSet<D, T, Q> blockSet : blockSets) {
            BitSet docIds = blockSet.query(query);
            if (null == result) {
                result = docIds;
            } else {
        return null == result ? IntStream.of() :;

This code is obviously experimental, but a problem with it as it stands is memory consumption with the temporary bit sets. A better, but less Java 8+ compliant bit set is RoaringBitmap.

Blocked Signatures in BitFunnel

Blocked Signatures are a very old idea, naturally it is reported that there are a few innovations in the BitFunnel data structure. BitFunnel uses multiple levels with blocking factors, each of which must be a proper power of 2, rather than multiple factors coprime to estimated capacity at the same level. Each level has rank = log(blockingFactor). The effect in BitFunnel is having several levels of blocking density. Blocks from different levels can be intersected efficiently by transforming dense blocks to rank zero (the least dense representation) prior to intersection.

Bit-Sliced Signatures and Bloom Filters

While the inverted index is a familiar indexing structure for many with a casual interest in information retrieval, the concept of a document signature may not be. The idea seems to have a long history, having been popular until it lost favour in the late 90s, owing to file sizes and performance empirically inferior to inverted indices. However, there has been new research into the use of signatures for search problems in the last decade, for instance, TopSig, and they are an important building block of the BitFunnel data structure.

Information Retrieval with Signatures

An m bit signature of a document consists of a bit array of fixed length m containing the (not guaranteed to be disjoint) union of the signatures of the terms found in the document. The signature of a term t is the m bit array with each jth bit set where j = hash(k, t) mod m, for each of k hash functions. This is obviously a Bloom filter by another name. In a recent post I noted that a Bloom filter’s behaviour can be implemented efficiently in less than ten lines of Java code.

In an information retrieval setting, a document signature would be computed for each document in a corpus by tokenising each document and building a Bloom filter from the extracted terms. To retrieve the documents matching a given query, the query signature (which is the union of the signatures of the query’s terms) is computed. In what I will call the signature scan phase of the retrieval, the document signatures are iterated over; each document being included if its signature contains all the bits in the query signature. The result set will contain false positives but should also be quite small, so a cheap filter for correctness is executed after the signature scan is complete.

While the hash function and ordering of the documents could be controlled creatively to cut down the number of signatures to be inspected for a given query, this approach is obviously inefficient, despite the appeal of low level bit-wise instructions. What happens when there are several billion documents in a corpus?

Parallelism and Bit-Slicing

Threads and SIMD instructions could be thrown at the signature scan to speed it up, but one approach to parallelising the retrieval is to increase the number of documents represented by each processed word. If there are n documents with m bit signatures, the signature list is an n * m bit matrix. Its transpose, an m * n bit matrix, is referred to as a bit sliced signature. When querying the bit sliced signature, only the rows specified in the query need be accessed and intersected, and each word represents the presence of a term in up to 64 documents. This is a very old technique – the the earliest formulation of a variant of this data structure I could find, where it was referred to as superimposed coding, was published in 1984, but references implementations from the 60s. An accessible evaluation was published in 1994.

Java Implementation

An implementation of such a structure for an immutable corpus is trivial but informative. Typically terms will be strings but needn’t be, whereas documents can be reduced to a set of terms. Using ToIntFunction to abstract hash functions again, we just need to map all term-document tuples into the bits of a long[][]. When querying the structure, we need to map the query’s terms into a sorted sequence of integers, determining which rows of the bit matrix to access.

On the way in, rather than using the hash functions to compute the bit to set (this is constant for each document), the row index is calculated. For each term, for each hash function, the appropriate row index is calculated and the document’s bit is set in the corresponding array. Clean Java would do this outside of the constructor, of course.

public class BitSlicedSignature<D extends Supplier<Set<T>>, T, Q extends Set<T>> {

    private final long[][] bitMatrix;
    private final int width;
    private final int height;
    private final List<ToIntFunction<T>> hashFunctions;

    public BitSlicedSignature(List<D> documents,
                              List<ToIntFunction<T>> hashFunctions,
                              int height) {
        this.hashFunctions = hashFunctions;
        this.width = (documents.size() + 63) / 64;
        this.height = height;
        this.bitMatrix = new long[height][width];
        int docIndex = 0;
        for (D doc : documents) {
            int docWordIndex = docIndex >> 6;
            long docWord = 1L << docIndex;
            for (T term : doc.get()) {
                for (ToIntFunction<T> hash : hashFunctions) {
                    int row = mapHash(hash.applyAsInt(term));
                    bitMatrix[row][docWordIndex] |= docWord;

    private int mapHash(int hash) {
        return Math.abs(hash % height);

To query the structure, the query is mapped into row indices and the corresponding rows are intersected word by word, matching document IDs are emitted lazily as an IntStream. The appeal of doing this lazily is that we should expect there to be a lot of documents, this way the bit-wise intersections can be done in chunks as and when the caller wants more documents. This can be achieved with the help of two utility methods:

    public IntStream query(Q query) {
        int[] rows =
                          .flatMapToInt(t -> -> mapHash(h.applyAsInt(t))))
        return IntStream.range(0, width).flatMap(i -> bitsOf(intersection(rows, i), i));

    private long intersection(int[] rows, int offset) {
        long word = -1L;
        for (int i = 0; i < rows.length && word != 0; ++i) {
            word &= bitMatrix[rows[i]][offset];
        return word;

    private static IntStream bitsOf(long word, int offset) {
        return IntStream.range(0, Long.SIZE)
                        .filter(i -> (1L << i & word) != 0)
                        .map(i -> Long.SIZE * offset + i);

As you can probably see, you can leave vast swathes of the long[][] untouched, assuming the query is for a small number of terms. A more sophisticated implementation might partition the documents into several bit matrices.


There are some obvious problems with this data structure. Firstly, a long[][] uses the same amount of memory whether its bits are set or not. What happens when you have some small documents and lots of terms? You have a column in the bit matrix where most of the bits are zero – it’s likely that a compressed bit set would be preferable. Similarly with very common terms you will have long horizontal runs of set bits.

Even worse, what happens when a term is very rare? If you are providing a search service, it’s likely you only ever need to provide a page of results at a time. If the term is rare enough, you may need to scan the entire row to fill a page, which could take a long time. To get around that, BitFunnel uses bit-sliced block signatures, which I will write about in the next post.

Building a Bloom Filter from Scratch

The Bloom filter is an interesting data structure for modelling approximate sets of objects. It can tell you with certainty that an object is not in a collection, but may give false positives. If you write Java for a living, I would not suggest you implement your own, because there is a perfectly good implementation in Guava. It can be illuminating to write one from scratch, however.


You can do two things with a Bloom filter: put things in it, and check if the filter probably contains items. This leads to the following interface:

public class BloomFilter<T> {
    void add(T value);

    boolean mightContain(T value);

Java Bloom Filter Implementation

A Bloom filter represents a set of objects quite succinctly as a bit array of finite length. Unlike a bitset, the bit array stores hashes of the objects, rather than object identities. In practice, you need multiple hash functions, which, modulo the capacity of the bit array, will collide only with low probability. The more hashes you have, the slower insertions and look ups will be. There is also a space trade off for improved accuracy: the larger the array, the less likely collisions of hashes modulo the capacity will be.

Since you probably want to be able to control the hash functions you use, the interface ToIntFunction fits in nicely as the perfect abstraction. You can set this up simply with a builder.

    public static <T> Builder<T> builder() {
        return new Builder<>();

    public static class Builder<T> {
        private int size;
        private List<ToIntFunction<T>> hashFunctions;

        public Builder<T> withSize(int size) {
            this.size = size;
            return this;

        public Builder<T> withHashFunctions(List<ToIntFunction<T>> hashFunctions) {
            this.hashFunctions = hashFunctions;
            return this;

        public BloomFilter<T> build() {
            return new BloomFilter<>(new long[(size + 63) / 64], size, hashFunctions);

    private final long[] array;
    private final int size;
    private final List<ToIntFunction<T>> hashFunctions;

    public BloomFilter(long[] array, int logicalSize, List<ToIntFunction<T>> hashFunctions) {
        this.array = array;
        this.size = logicalSize;
        this.hashFunctions = hashFunctions;

    private int mapHash(int hash) {
        return Math.abs(hash % size);

Adding Values

To add a value, you need to take an object, and for each hash function hash it modulo the capacity of the bit array. Using a long[] as the substrate of the bit set you must locate the word each hash belongs to, and set the bit corresponding to the remainder of the hash modulo 64. You can do this quickly as follows:

   public void add(T value) {
        for (ToIntFunction<T> function : hashFunctions) {
            int hash = mapHash(function.applyAsInt(value));
            array[hash >> 6] |= 1L << hash;

Note that each bit may already be set.

Querying the bit set

To check if an object is contained in the bitset, you need to evaluate the hashes again, and find the corresponding words. You can return false if the intersection of the appropriate word and the bit corresponding to the remainder modulo 64 is zero. That looks like this:

    public boolean mightContain(T value) {
        for (ToIntFunction<T> function : hashFunctions) {
            int hash = mapHash(function.applyAsInt(value));
            if ((array[hash >> 6] & (1L << hash)) == 0) {
                return false;
        return true;

Note that this absolutely does not mean the object is contained within the set, but you can get more precise results if you are willing to perform more hash evaluations, and if you are willing to use a larger bit array. Modeling the precise false positive rate is not clear cut.


Just as is the case for bitmap indices, bit slicing is a useful enhancement for Bloom filters, forming the basis of BitFunnel. In a follow up post I will share a simplified implementation of a bit sliced Bloom filter.