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I have \$k\$ sorted Lists, each of size \$n\$. Currently I have hard-coded 5 sorted Lists each of size 3, but in general that is configurable.

I would like to search for single element in each of the \$k\$ Lists (or its predecessor, if it doesn't exist).

Obviously I can binary search each List individually, resulting in a \$O(k*log(n))\$ runtime. But I think we can do better than that: after all, we're doing the same search \$k\$ times.

Could this be improved?

private static TreeSet<Integer> tree = new TreeSet<Integer>();

public SearchItem(final List<List<Integer>> inputs) {
    tree = new TreeSet<Integer>();
    for (List<Integer> input : inputs) {

public Integer getItem(final Integer x) {
    if(tree.contains(x)) {
        return x;
    } else {
        return tree.higher(x);

public static void main(String[] args) {
    List<List<Integer>> lists = new ArrayList<List<Integer>>();

    List<Integer> list1 = new ArrayList<Integer>(Arrays.asList(3, 4, 6));
    List<Integer> list2 = new ArrayList<Integer>(Arrays.asList(1, 2, 3));
    List<Integer> list3 = new ArrayList<Integer>(Arrays.asList(2, 3, 6));
    List<Integer> list4 = new ArrayList<Integer>(Arrays.asList(1, 2, 3));
    List<Integer> list5 = new ArrayList<Integer>(Arrays.asList(4, 8, 13));


    SearchItem search = new SearchItem(lists);

    Integer dataOuput = search.getItem(5);

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Question: In your text you say: I would like to search for single element in each of the k Lists (or its predecessor, if it doesn't exist). but in your code you get the `*successor* not the predecessor. Which one is right? – rolfl Feb 28 '14 at 2:00
@rolfl: I just updated the question.. I miss couple of important points.. I have rewrote most of the things.. Now take a look.. It will make sense now.. I guess now you will be angry as in the question lot of things might have change as corresponding to what you have suggested :( – david Feb 28 '14 at 3:05
up vote 8 down vote accepted

I think you have some misguided assumptions here.

For a start, what you have now is not \$O(k \log(n))\$, it first scans and sorts all the data in to the tree, which is a \$O(N \log(N))\$ operation, where \$N\$ is the cumulative data size (sum of input-array sizes). Then, the check on it, is a \$O(\log(N))\$ check, so, your algorithm is in the order of \$O(N\log(N))\$.

The complexity you are worried about \$O(k\log(n))\$ is really quite trivial. \$k\$ is a small number (5), and log(n) is always small... in many ways, after n = 128 it is effectively a constant....

So, I would do the following:

  1. binary search each List
  2. keep the 'min' value

with the code:

Integer result = null;
for (List<Integer> data : lists) {
    int pos = Collections.binarySearch(data, input);
    if (pos >= 0) {
        //exact match, return
        return data.get(pos);
    pos = -pos - 1;
    if (pos < data.size()) {
        Integer found = data.get(pos);
        if (result == null || result.compareTo(found) > 0) {
            result = found;
return result;

Now, if you wanted to be fancy, and you wanted the fastest response times, you could put each binary search in to a Callable<Integer>, and run them in parallel....

Then rank the results, and teturn it all in time complexity \$O(\log(n))\$ assuming \$k\$ is less than your hardware CPU count.

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Two quick notes about the code in the question:

  1. A bug: a client could create more than one instance of the class but all of them will use the same TreeSet instance since it's static.

  2. public Integer getItem(final Integer x) {
        if(tree.contains(x)) {
            return x;
        } else {
            return tree.higher(x);

    The following is the same:

    public Integer getItem(final Integer x) {
        return tree.ceiling(x);
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