Question

I have store 111 million key-value pairs (one key can have multiple values – maximum 2/3) whose key are 50 bit Integers and values are 32 bit (maximum) Integers. Now, my requirements are:

1. Fast Insertion of (Key, Value) pair [allowing duplicates]
2. Fast retrieving of value/values based on key.

A nice solution of it is given here based on MultiMap. However, I want to store more key-values pairs in main memory with no/little bit performance penalty. I studied from web articles that B+ Tree, R+ Tree, B Tree, Compact Multimap etc. can be a nice solution for that. Can anybody help me:

Is there any Java library which satisfies my all those needs properly
(above mentioned/other ds also acceptable. no issue with that) ?
Actually, I want an efficient java library data structure to store/retrieve
key-value/values pairs which takes less memory footprint and must be
built in-memory.

NB: I have tried with HashMultiMap (Guava with some modification with trove) as mentioned by Louis Wasserman, Kyoto/Tokyo Cabinet etc etc.My experience is not good with disk-baked solutions. So please avoid that :). Another point is that, for choosing library/ds one important point is: keys are 50 bit (so if we assign 64bit) 14 bit will lost and values are 32 bit Int (maximum)- mostly they are 10-12-14 bits. So, we can save space there also.

Answer 1

I don’t think there’s anything in the JDK which will do this.

However, implementing such a thing is a simple matter of programming. Here is an open-addressed hashtable with linear probing, with keys and values stored in parallel arrays:

public class LongIntParallelHashMultimap {

    private static final long NULL = 0L;

    private final long[] keys;
    private final int[] values;
    private int size;

    public LongIntParallelHashMultimap(int capacity) {
        keys = new long[capacity];
        values = new int[capacity];
    }

    public void put(long key, int value) {
        if (key == NULL) throw new IllegalArgumentException("key cannot be " + NULL);
        if (size == keys.length) throw new IllegalStateException("map is full");

        int index = indexFor(key);
        while (keys[index] != NULL) {
            index = successor(index);
        }
        keys[index] = key;
        values[index] = value;
        ++size;
    }

    public int[] get(long key) {
        if (key == NULL) throw new IllegalArgumentException("key cannot be " + NULL);

        int index = indexFor(key);
        int count = countHits(key, index);

        int[] hits = new int[count];
        int hitIndex = 0;

        while (keys[index] != NULL) {
            if (keys[index] == key) {
                hits[hitIndex] = values[index];
                ++hitIndex;
            }
            index = successor(index);
        }

        return hits;
    }

    private int countHits(long key, int index) {
        int numHits = 0;
        while (keys[index] != NULL) {
            if (keys[index] == key) ++numHits;
            index = successor(index);
        }
        return numHits;
    }

    private int indexFor(long key) {
        // the hashing constant is (the golden ratio * Long.MAX_VALUE) + 1
        // see The Art of Computer Programming, section 6.4
        // the constant has two important properties:
        // (1) it is coprime with 2^64, so multiplication by it is a bijective function, and does not generate collisions in the hash
        // (2) it has a 1 in the bottom bit, so it does not add zeroes in the bottom bits of the hash, and does not generate (gratuitous) collisions in the index
        long hash = key * 5700357409661598721L;
        return Math.abs((int) (hash % keys.length));
    }

    private int successor(int index) {
        return (index + 1) % keys.length;
    }

    public int size() {
        return size;
    }

}

Note that this is a fixed-size structure. You will need to create it big enough to hold all your data – 110 million entries for me takes up 1.32 GB. The bigger you make it, in excess of what you need to store the data, the faster that insertions and lookups will be. I found that for 110 million entries, with a load factor of 0.5 (2.64 GB, twice as much space as needed), it took on average 403 nanoseconds to look up a key, but with a load factor of 0.75 (1.76 GB, a third more space than is needed), it took 575 nanoseconds. Decreasing the load factor below 0.5 usually doesn’t make much difference, and indeed, with a load factor of 0.33 (4.00 GB, three times more space than needed), i get an average time of 394 nanoseconds. So, even though you have 5 GB available, don’t use it all.

Note also that zero is not allowed as a key. If this is a problem, change the null value to be something else, and pre-fill the keys array with that on creation.