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);


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.

Confusing Sets and Lists

Optimisation is rarely free: often point improvements incur costs elsewhere. Sometimes we try to optimise away the constraints we observe, without questioning our underlying assumptions. I have often seen the roles of lists and sets confused. An application can be brought to its knees – that is, cease to deliver commercial value – if List.contains is called frequently enough on big enough lists. And then there is the workaround…

When I moved over to Java from C++ several years ago, it seemed utterly crazy that there was even a Collection interface – exactly what Scott Meier’s Effective STL said not to do. I still think it’s crazy. Sets and lists cannot be substituted, and when you add up the marginal costs, as well as the costs of any compensatory workarounds, confusing them is responsible for a lot of performance bugs. As an application developer, it is part of your job to choose. Here are a few simple examples of when to use each collection type.


Is an element in the collection?

Never ever do this with a List. This operation is often referred to as being \mathcal{O}(n). Which means in your worst case will touch every element in the list (technically, at least once). You have a choice between HashSet and a TreeSet, and both have costs and benefits.

If you choose a HashSet, your best case is \mathcal{O}(1): you evaluate a hash code, take its modulus with respect to the size of an array, and look up a bucket containing only one element. The worst case occurs with a degenerate hash code which maps all elements to the same bucket. This is again \mathcal{O}(n): you probe a linked list testing each element for equality. On average you get something between these two cases and it depends on the uniformity of your hash code implementation.

If you choose a TreeSet you get a worst case \mathcal{O}(\log n): this is effectively just a binary search through the nodes in a red black tree. Performance is limited by the cost of the comparator, and suffers systematically from cache misses for large sets (like any kind of pointer chasing, branch prediction and prefetching is difficult if not impossible).

If you’re working with numbers, and small to medium collections, a sorted primitive array may be the best approach, so long as it fits in cache. If you’re working with integers, you can do this in constant time in the worst case by using a BitSet.


What is the value of the element at a given index with respect to a sort order?

This is an obvious use case for a List: it’s \mathcal{O}(1) – this is just a lookup at an array offset.

You couldn’t even write the code with a HashSet without copying the data into an intermediate ordered structure, at which point you would probably think again. You see this sort of thing done in code written by inexpensive programmers at large outsourcing consultancies, who were perhaps just under unreasonable pressure to deliver to arbitrary deadlines.

SortedSet, and anything conceptually similar, is the wrong data structure for this operation. The only way to compute this is \mathcal{O}(n): you iterate through the set incrementing a counter until you reach the index, and then return the element you’ve iterated to. If you reach the end of the set, you throw. If you do this a lot, you’ll notice.


How many predecessors does an element have with respect to a sort order?

Another classic operation for List, so long as you keep it sorted. Use Collections.binarySearch to find the index of the element in the collection with complexity \mathcal{O}(\log n). This is its rank. If you can get away with it, primitive arrays will be much better here, especially if they are small enough to fit in cache.

Once again, there are creativity points available for the solution involving a HashSet, and they do exist in the wild, but a clean looking solution is at least possible with a SortedSet. However, it involves an iteration with another check against an incrementing counter. It’s \mathcal{O}(n) and if you do it a lot, you’ll blow your performance profile, so use a sorted list instead.

What if you had the source code?

Is this fundamental or just a limitation of the Collections framework? Maybe if you had the source code you could just make these data structures optimal for every use case, without having to choose the right one? Not without creating a Frankenstein, and not without a lot of memory. Optimisation isn’t free.

Roaring TreeMap (English Translation)

A presentation of the Roaring TreeMap data structure translated into English from French Nouveaux modèles d’index bitmap compressés à 64 bits authored by Samy Chambi, Daniel Lemire and Robert Godin.

Roaring TreeMap

The RoaringTreeMap model combines a Java TreeMap with the Roaring bitmap structure to index a set of 64-bit integers represented by the positions of the set bits of a bitmap. TreeMap is an implementation of a Red-Black Tree structure, a self-balancing binary search tree, and is defined amongst other collections in the java.util package. The various tree operations (insertion, search, etc.) are implemented using the algorithms proposed in  (Cormen et al., 2001).

To represent a 64-bit integer, the model splits the integer into two parts. The first part constitutes the 32 most significant bits, and the second part represents the 32 least significant bits. A node of a RoaringTreeMap is composed of a key, which is a 32-bit integer, and a RoaringBitmap. RoaringTreeMap arranges groups of 64-bit integers containing the same 32 most significant bits into the same node. The key of the node stores the 32 most significant bits for the entire group, and the Roaring bitmap associated with the node contains the 32 least significant bits of each integer. RoaringTreeMap utilises a form of prefix compression on the 32 most significant bits of each integer in a group, which can save up to 32 \times 2^{32}-1 bits for such a group.

An insertion or search operation for a 64 bit integer in a RoaringTreeMap starts by performing a random access in the tree to find a node such that the node’s key is equal to the 32 most significant bits of the integer in question. Using a self-balancing binary search tree allows such an operation to be performed with logarithmic time complexity with respect to the total number of nodes in the RoaringTreeMap. If such a node is found, a second insertion or search operation will be performed on the RoaringBitmap associated with the node, which will also have a logarithmic time complexity with respect to the number of entries in the first level index of the Roaring bitmap and the number of entries contained within the accessed container, given that the container is sorted.

Another advantage of the RoaringTreeMap comes from the property of self-balancing binary search trees, which guarantees that the nodes of the tree can always be ordered by the values of their keys in a linear time complexity with respect the number of nodes in the tree. Combined with the ascending sort order of the 32-bit integers maintained within the Roaring bitmaps, this allows the RoaringTreeMap to iterate over the set of 64-bit integers in ascending order in time linearly proportional to the size of set. This is very effective for the computation of basic set operations between two RoaringTreeMaps, such as: intersection, union, symmetric difference, which can be performed in time linearly proportional to the number of integers contained in the tree. Without this property, such an operation would execute with quadratic time complexity with respect to the number of elements in the two sets.

Union of two RoaringTreeMaps

A union takes two RoaringTreeMaps as input and produces a new RoaringTreeMap as output. The algorithm starts with the allocation of a new empty dynamic array which will be filled with the nodes which form the union. Next, the nodes of the two trees are traversed in the ascending order of their keys. During an iteration, if the two current nodes have keys with different values, the container of the node with the smallest key is copied and inserted into the dynamic array, then the algorithm advances to the position of this node in the tree. Otherwise, if the two nodes compared during an iteration have keys of the same value, a logical OR is computed between the Roaring bitmaps associated with each node, and the result is returned as a new Roaring bitmap. A new node containing the Roaring bitmap obtained and a key of equal value to the two nodes will be inserted into the dynamic array. These operations continue until each node in either tree is consumed. At the end, the nodes inserted in the dynamic array will be sorted in the order of their keys, after which a recursive algorithm constructs the RoaringTreeMap from the dynamic array. The algorithm requires time of O(n1 + n2) to traverse the two input trees, where n1 and n2 represent the number of nodes in the two trees respectively. The construction of the dynamic array requires time of O(n1+n2) because it could require up to O(n1+n2) insertions, and each insertion requires constant amortised time. The construction of the final RoaringTreeMap is implemented with an efficient recursive algorithm which runs in a time of O(n1 + n2). So the total execution time for computing the union consisting of the two algorithms is O(n1 + n2), plus the time necessary to compute the logical OR of the Roaring Bitmaps (Chambi et al., 2014, 2015).

Intersection of two RoaringTreeMaps

An intersection takes two RoaringTreeMaps as input and produces a new RoaringTreeMap as output. The algorithm starts by allocating an empty dynamic array  into which the nodes that form the intersection will be written. After, the algorithm iterates over the nodes of the two trees in the ascending order of their keys. In the case where the keys of the nodes differ in value, the algorithm advances to the position of the node with the smallest key. Otherwise, in the case where the two keys have the same value, a logical AND operation will be performed between the Roaring bitmaps associated with each node, which results in a new Roaring bitmap. Then a new node containing the key and the resultant Roaring bitmap is inserted into the next slot in the dynamic array. These operations continue until each node in either tree is consumed. After the iteration is complete, the dynamic array will be sorted in the order of the nodes’ keys. Afterwards a recursive algorithm builds the RoaringTreeMap from the node array. The execution time of the second intersection algorithm depends on the time necessary to traverse the two trees, for populating the dynamic array, and for constructing the tree of the RoaringTreeMap. The total time to complete these operations is of the order of O(n1 + n2) in the worst case, where n1 and n2 represent the number of nodes in the first and second trees respectively. In the best case, the algorithm executes in a time of min(n1, n2), when a traversal of one of the trees is sufficient to arrive at the final result. This does not account for the possible time to compute the logical ANDs between the Roaring bitmaps (Chambi et al., 2014, 2015).

Choosing the Right Radix: Measurement or Mathematics?

I recently wrote a post about radix sorting, and found that for large arrays of unsigned integers a handwritten implementation beats Arrays.sort. However, I paid no attention to the choice of radix and used a default of eight bits. It turns out this was a lucky choice: modifying my benchmark to parametrise the radix, I observed a maximum at one byte, regardless of the size of array.

Is this an algorithmic or technical phenomenon? Is this something that could have been predicted on the back of an envelope without running a benchmark?

Extended Benchmark Results

Size Radix Score Score Error (99.9%) Unit
100 2 135.559923 7.72397 ops/ms
100 4 262.854579 37.372678 ops/ms
100 8 345.038234 30.954927 ops/ms
100 16 7.717496 1.144967 ops/ms
1000 2 13.892142 1.522749 ops/ms
1000 4 27.712719 4.057162 ops/ms
1000 8 52.253497 4.761172 ops/ms
1000 16 7.656033 0.499627 ops/ms
10000 2 1.627354 0.186948 ops/ms
10000 4 3.620869 0.029128 ops/ms
10000 8 6.435789 0.610848 ops/ms
10000 16 3.703248 0.45177 ops/ms
100000 2 0.144575 0.014348 ops/ms
100000 4 0.281837 0.025707 ops/ms
100000 8 0.543274 0.031553 ops/ms
100000 16 0.533998 0.126949 ops/ms
1000000 2 0.011293 0.001429 ops/ms
1000000 4 0.021128 0.003137 ops/ms
1000000 8 0.037376 0.005783 ops/ms
1000000 16 0.031053 0.007987 ops/ms


To model the execution time of the algorithm, we can write t = f(r, n), where n \in \mathbb{N} is the length of the input array, and r \in [1, 32) is the size in bits of the radix. We can inspect if the model predicts non-monotonic execution time with a minimum (corresponding to maximal throughput), or if t increases indefinitely as a function of r. If we find a plausible model predicting a minimum, temporarily treating r as continuous, we can solve \frac{\partial f}{\partial r}|_{n=N, r \in [1,32)} = 0 to find the theoretically optimal radix. This pre-supposes we derive a non-monotonic model.

Constructing a Model

We need to write down an equation before we can do any calculus, which requires two dangerous assumptions.

  1. Each operation has the same cost, making the execution time proportional to the number of operation.
  2. The costs of operations do not vary as a function of n or r.

This means all we need to do is find a formula for the number of operations, and then vary n and r. The usual pitfall in this approach relates to the first assumption, in that memory accesses are modelled as uniform cost; memory access can vary widely in cost ranging from registers to RAM on another socket. We are about to fall foul of both assumptions constructing an intuitive model of the algorithm’s loop.

        while (shift < Integer.SIZE) {
            Arrays.fill(histogram, 0);
            for (int i = 0; i < data.length; ++i) {
                ++histogram[((data[i] & mask) >> shift) + 1];
            for (int i = 0; i < 1 << radix; ++i) {
                histogram[i + 1] += histogram[i];
            for (int i = 0; i < data.length; ++i) {
                copy[histogram[(data[i] & mask) >> shift]++] = data[i];
            for (int i = 0; i < data.length; ++i) {
                data[i] = copy[i];
            shift += radix;
            mask <<= radix;

The outer loop depends on the choice of radix while the inner loops depend on the size of the array and the choice of radix. There are five obvious aspects to capture:

  • The first inner loop takes time proportional to n
  • The third and fourth inner loops take time proportional to n
  • We can factor the per-element costs of loops 1, 3 and 4 into a constant a
  • The second inner loop takes time proportional to 2^r, modeled with by the term b2^r
  • The body of the loop executes 32/r times

This can be summarised as the formula:

f(r, n) = 32\frac{(3an + b2^r)}{r}

It was claimed the algorithm had linear complexity in n and it only has a linear term in n. Good. However, the exponential r term in the numerator dominates the linear term in the denominator, making the function monotonic in r. The model fails to predict the observed throughput maximising radix. There are clearly much more complicated mechanisms at play than can be captured counting operations.

Sorting Unsigned Integers Faster in Java

I discovered a curious resource for audio-visualising sort algorithms, which is exciting for two reasons. The first is that I finally feel like I understand Alexander Scriabin: he was not a composer. He discovered Tim Sort 80 years before Tim Peters and called it Black Mass. (If you aren’t familiar with the piece, fast-forward to 1:40 to hear the congruence.)

The second reason was that I noticed Radix Sort (LSD). While it was an affront to my senses, it used a mere 800 array accesses and no comparisons! I was unaware of this algorithm so delved deeper and implemented it for integers, and benchmarked my code against Arrays.sort.

Radix Sort

It is taken as given by many (myself included, or am I just projecting my thoughts on to others?) that O(n \log n) is the best you can do in a sort algorithm. But this is actually only true for sort algorithms which depend on comparison. If you can afford to restrict the data types your sort algorithm supports to types with a positional interpretation (java.util can’t because it needs to be ubiquitous and maintainable), you can get away with a linear time algorithm.

Radix sort, along with the closely related counting sort, does not use comparisons. Instead, the data is interpreted as a fixed length string of symbols. For each position, the cumulative histogram of symbols is computed to calculate sort indices. While the data needs to be scanned several times, the algorithm scales linearly and the overhead of the multiple scans is amortised for large arrays.

As you can see on Wikipedia, there are two kinds of radix sort: Least Significant Digit and Most Significant Digit. This dichotomy relates to the order the (representational) string of symbols is traversed in. I implemented and benchmarked the LSD version for integers.


The implementation interprets an integer as the concatenation of n bit string symbols of fixed size size 32/n. It performs n passes over the array, starting with the least significant bits, which it modifies in place. For each pass the data is scanned three times, in order to:

  1. Compute the cumulative histogram over the symbols in their natural sort order
  2. Copy the value with symbol k to the mth position in a buffer, where m is defined by the cumulative density of k.
  3. Copy the buffer back into the original array

The implementation, which won’t work unless the chunks are proper divisors of 32, is below. The bonus (or caveat) is that it automatically supports unsigned integers. The code could be modified slightly to work with signed integers at a performance cost.

import java.util.Arrays;

public class RadixSort {

    private final int radix;

    public RadixSort() {

    public RadixSort(int radix) {
        assert 32 % radix== 0;
        this.radix= radix;

    public void sort(int[] data) {
        int[] histogram = new int[(1 << radix) + 1];
        int shift = 0;
        int mask = (1 << radix) - 1;
        int[] copy = new int[data.length];
        while (shift < Integer.SIZE) {
            Arrays.fill(histogram, 0);
            for (int i = 0; i < data.length; ++i) {
                ++histogram[((data[i] & mask) >> shift) + 1];
            for (int i = 0; i < 1 << radix; ++i) {
                histogram[i + 1] += histogram[i];
            for (int i = 0; i < data.length; ++i) {
                copy[histogram[(data[i] & mask) >> shift]++] = data[i];
            for (int i = 0; i < data.length; ++i) {
                data[i] = copy[i];
            shift += radix;
            mask <<= radix;

The time complexity is obviously linear, a temporary buffer is allocated, but in comparison to Arrays.sort it looks fairly spartan. Instinctively, cache locality looks fairly poor because the second inner loop of the three jumps all over the place. Will this implementation beat Arrays.sort (for integers)?


The algorithm is measured using arrays of random positive integers, for which both algorithms are equivalent, from a range of sizes. This isn’t always the best idea (the Tim Sort algorithm comes into its own on nearly sorted data), so take the result below with a pinch of salt. Care must be taken to copy the array in the benchmark since both algorithms are in-place.

public void launchBenchmark(String... jvmArgs) throws Exception {
        Options opt = new OptionsBuilder()
                .include(this.getClass().getName() + ".*")

        new Runner(opt).run();

    public void Arrays_Sort(Data data, Blackhole bh) {
        int[] array = Arrays.copyOf(, data.size);

    public void Radix_Sort(Data data, Blackhole bh) {
        int[] array = Arrays.copyOf(, data.size);

    public static class Data {

        @Param({"100", "1000", "10000", "100000", "1000000"})
        int size;

        int[] data;
        RadixSort radixSort = new RadixSort();

        public void init() {
            data = createArray(size);

    public static int[] createArray(int size) {
        int[] array = new int[size];
        Random random = new Random(0);
        for (int i = 0; i < size; ++i) {
            array[i] = Math.abs(random.nextInt());
        return array;
Benchmark Mode Threads Samples Score Score Error (99.9%) Unit Param: size
Arrays_Sort thrpt 1 10 1304.687189 147.380334 ops/ms 100
Arrays_Sort thrpt 1 10 78.518664 9.339994 ops/ms 1000
Arrays_Sort thrpt 1 10 1.700208 0.091836 ops/ms 10000
Arrays_Sort thrpt 1 10 0.133835 0.007146 ops/ms 100000
Arrays_Sort thrpt 1 10 0.010560 0.000409 ops/ms 1000000
Radix_Sort thrpt 1 10 404.807772 24.930898 ops/ms 100
Radix_Sort thrpt 1 10 51.787409 4.881181 ops/ms 1000
Radix_Sort thrpt 1 10 6.065590 0.576709 ops/ms 10000
Radix_Sort thrpt 1 10 0.620338 0.068776 ops/ms 100000
Radix_Sort thrpt 1 10 0.043098 0.004481 ops/ms 1000000
Arrays_Sort sample 1 3088586 0.000902 0.000018 ms/op 100
Arrays_Sort·p0.00 sample 1 1 0.000394 ms/op 100
Arrays_Sort·p0.50 sample 1 1 0.000790 ms/op 100
Arrays_Sort·p0.90 sample 1 1 0.000791 ms/op 100
Arrays_Sort·p0.95 sample 1 1 0.001186 ms/op 100
Arrays_Sort·p0.99 sample 1 1 0.001974 ms/op 100
Arrays_Sort·p0.999 sample 1 1 0.020128 ms/op 100
Arrays_Sort·p0.9999 sample 1 1 0.084096 ms/op 100
Arrays_Sort·p1.00 sample 1 1 4.096000 ms/op 100
Arrays_Sort sample 1 2127794 0.011876 0.000037 ms/op 1000
Arrays_Sort·p0.00 sample 1 1 0.007896 ms/op 1000
Arrays_Sort·p0.50 sample 1 1 0.009872 ms/op 1000
Arrays_Sort·p0.90 sample 1 1 0.015408 ms/op 1000
Arrays_Sort·p0.95 sample 1 1 0.024096 ms/op 1000
Arrays_Sort·p0.99 sample 1 1 0.033920 ms/op 1000
Arrays_Sort·p0.999 sample 1 1 0.061568 ms/op 1000
Arrays_Sort·p0.9999 sample 1 1 0.894976 ms/op 1000
Arrays_Sort·p1.00 sample 1 1 4.448256 ms/op 1000
Arrays_Sort sample 1 168991 0.591169 0.001671 ms/op 10000
Arrays_Sort·p0.00 sample 1 1 0.483840 ms/op 10000
Arrays_Sort·p0.50 sample 1 1 0.563200 ms/op 10000
Arrays_Sort·p0.90 sample 1 1 0.707584 ms/op 10000
Arrays_Sort·p0.95 sample 1 1 0.766976 ms/op 10000
Arrays_Sort·p0.99 sample 1 1 0.942080 ms/op 10000
Arrays_Sort·p0.999 sample 1 1 2.058273 ms/op 10000
Arrays_Sort·p0.9999 sample 1 1 7.526102 ms/op 10000
Arrays_Sort·p1.00 sample 1 1 46.333952 ms/op 10000
Arrays_Sort sample 1 13027 7.670135 0.021512 ms/op 100000
Arrays_Sort·p0.00 sample 1 1 6.356992 ms/op 100000
Arrays_Sort·p0.50 sample 1 1 7.634944 ms/op 100000
Arrays_Sort·p0.90 sample 1 1 8.454144 ms/op 100000
Arrays_Sort·p0.95 sample 1 1 8.742502 ms/op 100000
Arrays_Sort·p0.99 sample 1 1 9.666560 ms/op 100000
Arrays_Sort·p0.999 sample 1 1 12.916883 ms/op 100000
Arrays_Sort·p0.9999 sample 1 1 28.037900 ms/op 100000
Arrays_Sort·p1.00 sample 1 1 28.573696 ms/op 100000
Arrays_Sort sample 1 1042 96.278673 0.603645 ms/op 1000000
Arrays_Sort·p0.00 sample 1 1 86.114304 ms/op 1000000
Arrays_Sort·p0.50 sample 1 1 94.896128 ms/op 1000000
Arrays_Sort·p0.90 sample 1 1 104.293990 ms/op 1000000
Arrays_Sort·p0.95 sample 1 1 106.430464 ms/op 1000000
Arrays_Sort·p0.99 sample 1 1 111.223767 ms/op 1000000
Arrays_Sort·p0.999 sample 1 1 134.172770 ms/op 1000000
Arrays_Sort·p0.9999 sample 1 1 134.742016 ms/op 1000000
Arrays_Sort·p1.00 sample 1 1 134.742016 ms/op 1000000
Radix_Sort sample 1 2240042 0.002941 0.000033 ms/op 100
Radix_Sort·p0.00 sample 1 1 0.001578 ms/op 100
Radix_Sort·p0.50 sample 1 1 0.002368 ms/op 100
Radix_Sort·p0.90 sample 1 1 0.003556 ms/op 100
Radix_Sort·p0.95 sample 1 1 0.004344 ms/op 100
Radix_Sort·p0.99 sample 1 1 0.011056 ms/op 100
Radix_Sort·p0.999 sample 1 1 0.027232 ms/op 100
Radix_Sort·p0.9999 sample 1 1 0.731127 ms/op 100
Radix_Sort·p1.00 sample 1 1 5.660672 ms/op 100
Radix_Sort sample 1 2695825 0.018553 0.000038 ms/op 1000
Radix_Sort·p0.00 sample 1 1 0.013424 ms/op 1000
Radix_Sort·p0.50 sample 1 1 0.016576 ms/op 1000
Radix_Sort·p0.90 sample 1 1 0.025280 ms/op 1000
Radix_Sort·p0.95 sample 1 1 0.031200 ms/op 1000
Radix_Sort·p0.99 sample 1 1 0.050944 ms/op 1000
Radix_Sort·p0.999 sample 1 1 0.082944 ms/op 1000
Radix_Sort·p0.9999 sample 1 1 0.830295 ms/op 1000
Radix_Sort·p1.00 sample 1 1 6.660096 ms/op 1000
Radix_Sort sample 1 685589 0.145695 0.000234 ms/op 10000
Radix_Sort·p0.00 sample 1 1 0.112512 ms/op 10000
Radix_Sort·p0.50 sample 1 1 0.128000 ms/op 10000
Radix_Sort·p0.90 sample 1 1 0.196608 ms/op 10000
Radix_Sort·p0.95 sample 1 1 0.225792 ms/op 10000
Radix_Sort·p0.99 sample 1 1 0.309248 ms/op 10000
Radix_Sort·p0.999 sample 1 1 0.805888 ms/op 10000
Radix_Sort·p0.9999 sample 1 1 1.818141 ms/op 10000
Radix_Sort·p1.00 sample 1 1 14.401536 ms/op 10000
Radix_Sort sample 1 60843 1.641961 0.005783 ms/op 100000
Radix_Sort·p0.00 sample 1 1 1.251328 ms/op 100000
Radix_Sort·p0.50 sample 1 1 1.542144 ms/op 100000
Radix_Sort·p0.90 sample 1 1 2.002944 ms/op 100000
Radix_Sort·p0.95 sample 1 1 2.375680 ms/op 100000
Radix_Sort·p0.99 sample 1 1 3.447030 ms/op 100000
Radix_Sort·p0.999 sample 1 1 5.719294 ms/op 100000
Radix_Sort·p0.9999 sample 1 1 8.724165 ms/op 100000
Radix_Sort·p1.00 sample 1 1 13.074432 ms/op 100000
Radix_Sort sample 1 4846 20.640787 0.260926 ms/op 1000000
Radix_Sort·p0.00 sample 1 1 14.893056 ms/op 1000000
Radix_Sort·p0.50 sample 1 1 18.743296 ms/op 1000000
Radix_Sort·p0.90 sample 1 1 26.673152 ms/op 1000000
Radix_Sort·p0.95 sample 1 1 30.724915 ms/op 1000000
Radix_Sort·p0.99 sample 1 1 40.470446 ms/op 1000000
Radix_Sort·p0.999 sample 1 1 63.016600 ms/op 1000000
Radix_Sort·p0.9999 sample 1 1 136.052736 ms/op 1000000
Radix_Sort·p1.00 sample 1 1 136.052736 ms/op 1000000

The table tells an interesting story. Arrays.sort is vastly superior for small arrays (the arrays most people have), but for large arrays the custom implementation comes into its own. Interestingly, this is consistent with the computer science. If you need to sort large arrays of (unsigned) integers and care about performance, think about implementing radix sort.

Microsecond Latency Rules Engine with RoaringBitmap

Implementing a rules engine can shorten development time and remove a lot of tedious if statements from your business logic. Unfortunately they are almost always slow and often bloated. Simple rules engines can be implemented by assigning integer salience to each line in a truth table, with rule resolution treated as an iterative intersection of ordered sets of integers. Implemented in terms of sorted sets, it would be remiss not to consider RoaringBitmap for the engine’s core. The code is at github.

Classification Table and Syntax

This rules engine builds on the simple idea of a truth table usually used to teach predicate logic and computer hardware. Starting with a table and some attributes, interpreting one attribute as a classification, we get a list of rules. It is trivial to load such a table from a database. Since classifications can overlap, we prioritise by putting the rules we care about most – or the most salient rules – at the top of the table. When multiple rules match a fact, we take the last in the set ordered by salience. So we don’t always have to specify all of the attributes to get a classification, we can rank attributes by their importance left to right, where it’s required that all attributes to the left of a specified attribute are also specified when matching a fact against a rule set.

It’s possible to define rules containing wildcards. Wildcard rules will match any query (warning: if these are marked as high salience they will hide more specific rules with lower salience). It’s also possible to specify a prefix with a wildcard, which will match any query that matches at least the prefix.

Below is an example table consisting of rules for classification of regional English accents by phonetic feature.

English Accent Rules

thought cloth lot palm plant bath trap accent
/ɔ/ /ɒ/ /ɑ/ /ɑː/ /ɑː/ /ɑː/

/æ/ Received Pronunciation (UK)
/ɔ/ /ɔ/ /ɑ/ /ɑ/ /æ/ /æ/

/æ/ Georgian (US)
/ɑ/ /ɑ/ /ɑ/ /ɑ/ /æ/ /æ/

/æ/ Canadian
* * /ɑ/ /ɑ/ /æ/ /æ/

/æ/ North American
* * * * * *

/æ/ Non Native
* * * * * *

* French


In the example above, the vowel sounds used in words differentiating speakers of several English accents are configured as a classification table. The accent column is the classification of any speaker exhibiting the properties specified in the six leftmost columns. UK Received Pronunciation is the most specific rule and has high salience, whereas various North American accents differ from RP in their use of short A vowels. A catch all for North American accents would wild card the sounds in thought and caught (contrast Boston pronunciations with Texas). So long as trap has been pronounced with a short A (which all English speakers do), and no other rule would recognise the sounds used in the first six words, the rule engine would conclude the speaker is using English as a second language. If not even the word trap is recognisable, then the speaker is probably unintelligible, or could be French.


A rule with a given salience can be represented by creating a bitmap index on salience by the attribute values of the rules. For instance, to store the rule \{foo, bar\} \rightarrow 42, with salience 10, create a bitmap index on the first attribute of the rule, and set the 10th bit of the “foo” bitmap; likewise for the “bar” bitmap of the second index. Finding rules which match both attributes is a bitwise intersection, and since we rank by salience, the rule that wins is the first in the set. An obvious choice for fast ordered sets is RoaringBitmap.

RoaringBitmap consists of containers, which are fast, cache-friendly sorted sets of integers, and can contain up to 2^{16} - 1 shorts. In RoaringBitmap, containers are indexed by keys consisting of the most significant 16 bits of the integer. For a rules engine, if you have more than 2^{16} - 1 rules you have a much bigger problem anyway, so a container could index all the rules you could ever need, so RoaringBitmap itself would be overkill. While RoaringBitmap indexes containers by shorts (it does so for the sake of compression), we can implement wildcard and prefix matching by associating containers with Strings rather than shorts. As the core data structure of the rules engine, a RoaringBitmap container is placed at each node of an Apache commons PatriciaTrie. It’s really that simple – see the source at github.

When the rules engine is queried, a set consisting of all the rules that match is intersected with the container found at the node in the trie matching the value specified for each attribute. When more than one rule matches, the rule with the highest salience is accessed via the Container.first() method, one of the features I have contributed to RoaringBitmap. See example usage at github.



A Quick Look at RoaringBitmap

This article is an introduction to the data structures found in the RoaringBitmap library, which I have been making extensive use of recently. I wrote some time ago about the basic idea of bitmap indices, which are used in various databases and search engines, with the caveat that no traditional implementation is optimal across all data scenarios (in terms of size of the data set, sparsity, cardinalities of attributes and global sort orders of data sets with respect to specific attributes). RoaringBitmap is a dynamic data structure which aims to be that one-size-fits-all solution across all scenarios.


A RoaringBitmap should be thought of as a set of unsigned integers, consisting of containers which cover disjoint subsets. Each subset can contain values from a range of size 2^{16}-1, and the subset is indexed by a 16 bit key. This means that in the worst case it only takes 16 bits to represent a single 32 bit value, so unsigned 32 bit integers can be stored as Java shorts. The choice of container size also means that in the worst case, the container will still fit in L1 cache on a modern processor.

The implementation of the container covering a disjoint subset is free to vary between RunContainer, BitmapContainer and ArrayContainer, depending entirely on properties of the subset. When inserting data into a RoaringBitmap, it is decided whether to create a new container, or to mutate an existing container, depending on whether the values fit in the range covered by the container’s key. When performing a set operation, for instance by intersecting two bitmaps or computing their symmetric difference, a new RoaringBitmap is created by performing operations container by container, and it is decided dynamically which container implementation is best suited for the result. For cases where it is too difficult to determine the best implementation automatically, the method runOptimize is available to the programmer to make sure.

When querying a RoaringBitmap, the query can be executed container by container (which incidentally makes the query naturally parallelisable, but it hasn’t been done yet), and each pair from the cartesian product of combinations of container implementations must be implemented separately. This is manageable because there are only three implementations, and there won’t be any more. There is less work to do for symmetric operations, such as union and intersection, than with asymmetric operations such as contains.


When there are lots of clean words in a section of a bitmap, the best choice of container is run length encoding. The implementation of RunContainer is simple and very compact. It consists of an array of shorts (not ints, the most significant 16 bits are in the key) where the values at the even indices are the starts of runs, and the values at the odd indices are the lengths of the respective runs. Membership queries can be implemented simply using a binary search, and quantile queries can be implemented in constant time. Computing container cardinality requires a pass over the entire run array.


When data is sparse within a section of the bitmap, the best implementation is an array (short[]).  For very sparse data, this isn’t theoretically optimal, but for most cases it is very good and the array for the container will fit in L1 cache for mechanical sympathy. Cardinality is very fast because it is precomputed, and operations would be fast in spite of their precise implementation by virtue of the small size of the set (that being said, the actual implementations are fast). Often when creating a new container, it is necessary to convert to a bitmap for better compression as the container fills up.


BitmapContainer is the classic implementation of a bitset. There is a fixed length long[] which should be interpreted bitwise, and a precomputed cardinality. Operations on BitmapContainers tend to be very fast, despite typically touching each element in the array, because they fit in L1 cache and make extensive use of Java intrinsics. If you find a method name in here and run your JVM on a reasonably modern processor, your code will quickly get optimised by the JVM, sometimes even to a single instruction. A much hackneyed example, explained better by Michael Barker quite some time ago, would be Long.bitCount, which translates to the single instruction popcnt and has various uses when operating on BitmapContainers. When intersecting with another container, the cardinality can only decrease or remain the same, so there is a chance an ArrayContainer will be produced.


There is a really nice Scala project on github which functions as a DSL for creating RoaringBitmaps – it allows you to create an equality encoded (see my previous bitmap index post) RoaringBitmap in a very fluid way. The project is here.

I have implemented bit slice indices, both equality and range encoded, in a data quality tool I am building. That project is hosted here. Below is an implementation of a range encoded bit slice index as an example of how to work with RoaringBitmaps.

public class RangeEncodedOptBitSliceIndex implements RoaringIndex {

  private final int[] basis;
  private final int[] cumulativeBasis;
  private final RoaringBitmap[][] bitslice;

  public RangeEncodedOptBitSliceIndex(ProjectionIndex projectionIndex, int[] basis) {
    this.basis = basis;
    this.cumulativeBasis = accumulateBasis(basis);
    this.bitslice = BitSlices.createRangeEncodedBitSlice(projectionIndex, basis);

  public RoaringBitmap whereEqual(int code, RoaringBitmap existence) {
    RoaringBitmap result = existence.clone();
    int[] expansion = expand(code, cumulativeBasis);
    for(int i = 0; i < cumulativeBasis.length; ++i) {
      int component = expansion[i];
      if(component == 0) {
      else if(component == basis[i] - 1) {
        result.andNot(bitslice[i][basis[i] - 2]);
      else {
        result.and(FastAggregation.xor(bitslice[i][component], bitslice[i][component - 1]));
    return result;

  public RoaringBitmap whereNotEqual(int code, RoaringBitmap existence) {
    RoaringBitmap inequality = existence.clone();
    inequality.andNot(whereEqual(code, existence));
    return inequality;

  public RoaringBitmap whereLessThan(int code, RoaringBitmap existence) {
    return whereLessThanOrEqual(code - 1, existence);

  public RoaringBitmap whereLessThanOrEqual(int code, RoaringBitmap existence) {
    final int[] expansion = expand(code, cumulativeBasis);
    final int firstIndex = cumulativeBasis.length - 1;
    int component = expansion[firstIndex];
    int threshold = basis[firstIndex] - 1;
    RoaringBitmap result = component < threshold ? bitslice[firstIndex][component].clone() : existence.clone();     for(int i = firstIndex - 1; i >= 0; --i) {
      component = expansion[i];
      threshold = basis[i] - 1;
      if(component != threshold) {
      if(component != 0) {
        result.or(bitslice[i][component - 1]);
    return result;

  public RoaringBitmap whereGreaterThan(int code, RoaringBitmap existence) {
    RoaringBitmap result = existence.clone();
    result.andNot(whereLessThanOrEqual(code, existence));
    return result;

  public RoaringBitmap whereGreaterThanOrEqual(int code, RoaringBitmap existence) {
    RoaringBitmap result = existence.clone();
    result.andNot(whereLessThan(code, existence));
    return result;

Further Reading

The library has been implemented under an Apache License by several contributors, the most significant contributions coming from computer science researcher Daniel Lemire, who presented RoaringBitmap at Spark Summit 2017. The project site is here and the research paper behind the library is freely available.