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I'm trying to get Snap and MongoDB to work together and I need an instance for MonadBaseControl IO Snap and I'm having trouble understanding why the following code typechecks but won't run correctly (it times out, but if I run the database query inside the snap monad, rather than wrapping the Snap monad in MongoDB's Action transformer, it works fine, so it's not the db connection):

note that you need FlexibleInstances. As a bonus, if someone could explain why replacing liftBaseWith above with liftIO . f $ return . STio doesn't typecheck, I would appreciate it.

Binary search trees are used quite often for storing or finding values. Like a binary search, they essentially work by sorting the items.

In this post I will describe a search tree that does not require that the items be sorted. Hence, the tree can support some interesting queries. The queries will always be correct, but they will only be fast in some cases.

Usually, to make searching fast, each branch in a search tree stores information that helps to decide whether to go left or right. But if we want to be able to construct a tree for any possible type of query, then that is not always possible. Instead, we can still aim to eliminate large parts of the search space, by storing bounds.

Suppose we have a tree that stores integers, and we want to find the first item in the tree that is greater or equal to some query integer. In each branch of the tree, we could store the maximum of all values in that subtree. Call it the upper bound of the subtree. If this upper bound is less than the query, then we can eliminate the entire subtree from consideration.

Now let's generalize that. The maximum value is an example of a semilattice. That is just a fancy way of saying that for a pair of values we can get some kind of bound. As a typeclass it looks like

The queries we perform on the tree should of course work together with the bounds. That means that if a bound for a branch in the tree doesn't satisfy the query, then none of the values in the subtree do. In haskell terms:

Note that a semilattice always gives a partial order, and hence a satisfy function by

because

However, I keep the distinction between the query and value type for more flexibility and for more descriptive types.

Given the Satisfy and Semilattice typeclasses, the search tree datastructure is straight forward. A search tree can be empty, a single value, or a branch. In each branch we store the bound of that subtree.

If we have a SearchTree, then we can find the first element that satisfies a query, simply by searching both sides of each branch. The trick to making the search faster is to only continue as long as the bound satisfies the query:

Completely analogously, we can find the last satisfied item instead:

Or we can even generalize this search to any Monoid, where the above are for the First and Last monoids respectively. I will this leave as an exercise for the reader.

The basis of each tree are branches. We will always construct branches with a smart constructor that calculates the bound as the meet of the bounds of its two arguments. That way, the stored bound is always correct.

A search will always take time at least linear in the depth of the tree. So, for fast searches we need a balanced tree, where each subtree has roughly the same size. Here is arguably the most tricky part of the code, which converts a list to a balanced search tree.

And that's it. I use this data structure for finding rectangles (more about that in a future post), and there I only needed to build the search structure once, and use it multiple times. So, in this post I am not going to talk about updates at all. If you wanted to do updates efficiently, then you would need to worry about updating bounds, rebalancing etc.

Here is an example of the search tree in action. The query will be to find a value (>= q) for a given q. The bounds will be maximum values.

First, check the satisfy law:

So indeed, satisfy q a || satisfy q b ==> satisfy q (meet a b). And this bound is in fact tight, so also the other way around satisfy q (meet a b) ==> satisfy q a || satisfy q b. This will become important later.

Now here are some example queries:

Semilattices and queries can easily be combined into tuples. For a tree of pairs, and queries of pairs, you could use.

Now we can not only questions like "What is the first/last/smallest element that is greater than some given query?". But also "What is the first/last/smallest element greater than a given query that also satisfies some other property?".

It's nice that we now have a search tree that always gives correct answers. But is it also efficient?

As hinted in the introduction, that is not always the case. First of all, meet could give a really bad bound. For example, if meet a b = Bottom for all a /= b, and Bottom satisfies everything, then we really can do no better than a brute force search.

On the other hand, suppose that meet gives 'perfect' information, like the Ge example above,

That is equivalent to saying that

Then for any Branch, we only have to search either the left or the right subtree. Because, if a subtree doesn't contain the value, we know can see so from the bound. For a balanced tree, that means the search takes O(log n) time.

Another efficient case is when the items are sorted. By that I mean that, if an item satisfies the query, then all items after it also satisfy that query. We actually need something slightly more restrictive: namely that if a query is satisfied for the meet of some items, then all items after them also satisfy the query. In terms of code:

Now suppose that we are searching a tree of the form st = mkBranch a b with findFirst q. Then there are three cases:

1. not (satisfy q (bound st)).
2. not (satisfy q (bound a)).
3. satisfy q (bound a).

In the first case the search fails, and we are done. In the second case, we only have to search b, which by induction can be done efficiently. The third case is not so clear. In fact, there are two sub cases:

• 3a. findFirst q a = Just someResult
• 3b. findFirst q b = Nothing

In case 3a we found something in the left branch. Since we are only interested in the first result, that means we are done. In case 3b, we get to use the fact that the items are sorted. Since we have satisfy q (bound a), that means that all items in b will satisfy the query. So when searching b, in all cases we take the left branch.

Overall, the search time will be at most twice the depth of the tree, which is O(log n).

The really cool thing is that we can combine the two conditions. If satisfy can be written as

where satisfy1 has exact bounds, and the tree is sorted for satisfy2, then queries still take O(log n) time.

Finally, here is an example that makes use of efficient searching with the two conditions. I make use of the Semilattice and Satisfy instances for pairs which I defined above.

The tree is ordered by year of election, and the Max semilattice gives tight bounds for names. So we can efficiently search for the first US presidents elected after 1850 who's name comes starts with a letter after "P":

And with the following query type we can search on just one of the elements of the tuple. Note that we need the type parameter in Any because of the functional dependency in the Satisfy class.

I don't know of a simple way to recreate the following function without doing it manually and I feel a bit daft.

I'm gonna kick myself, I know it.

It's from Twitter, It's open source, It's about Scala. What's not to like?