This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Part I covers elementary data structures, sorting, and searching algorithms. Part II focuses on graph- and string-processing algorithms. All the features of this course are available for free. It does not offer a certificate upon completion.
2−3 Search Trees
Loading...
來自 Princeton University 的課程
Algorithms, Part I
4817 個評分
從本節課中
Balanced Search Trees
In this lecture, our goal is to develop a symbol table with guaranteed logarithmic performance for search and insert (and many other operations). We begin with 2−3 trees, which are easy to analyze but hard to implement. Next, we consider red−black binary search trees, which we view as a novel way to implement 2−3 trees as binary search trees. Finally, we introduce B-trees, a generalization of 2−3 trees that are widely used to implement file systems.
與講師見面
Kevin WaynePhillip Y. Goldman '86 Senior Lecturer
Computer Science
Robert SedgewickWilliam O. Baker *39 Professor of Computer Science
Computer Science
Welcome back, today we're going to talk about balance search trees,
Which will lead us to an ultimate symbol table implementation that can provide fast
performance for all the simulative options we've looked at, guaranteed.
So here's the review of where we were with single tables.
We took a look at the last time at the binary search tree,
Which if things are well modeled by random exertions, have a great performance.
They get search and insert on in time proportion for log base two of N and they
support ordered operations. But really our goal is to have these
operations be guaranteed to take time proportional to log N,
Because we don't have control over the order of operations and they may not be
random at all. And in fact, in many real applications,
they're not very random. So that's what were going to look at now
is try to find an implementation that can guarantee to be fast for all the symbol
table operations. .
That's our challenge. So what we're going to talk about to do
it, is an algorithm, that actually pretty old algorithm called 2-3 trees, and a
particular implementation that requires very little code, called left leaning red
black BSTs and then we'll talk about a generalization called B-trees. And these
methods are all widely used throughout our computational infrastructure.
To start, we'll talk about 2-3 search trees, which is a model that underlies the
concise and efficient implementation that we're going to look at.
So, the 2-3 tree is a way to generalize BSTs to provide the flexibility that we
need to guarantee fast performance. And the idea is very simple,
We allow one or two keys per node. That is, we allow for the possibility of
something called a 3-node that can hold two keys,
But then it has to have three children. In a regular BST node, the 2-node, we have
one link for the keys that are less than the key in the node, and one link for the
keys that are greater. In a 3-node, we need three links, one for
less, one for between and one for greater. Another property of these 2-3 trees is
that we are going to have perfect balance, That is every path from the route to a
null link is going to have the same link in the 2-3 tree.
So, as I mentioned, the symmetric order is part of the definition of a 2-3 tree.
Every 3-node has three links and two keys. The left link is for the keys that are,
points to a 2-3 tree with the keys that are smaller than the smaller of the two
keys in the 3-node. The middle link points to a 2-3 tree that
contains all the keys that are between the two keys.
And the right link points to all, 2-3 tree containing all the keys that are larger
than the larger of the two keys in the 3-node.
Okay, let's take a look at a demo of searching in a 2-3 tree.
So say we have this 2-3 tree here and we want to search for whether or not H is one
of the keys in the tree. So we start at the root,
Compare the search key against the key or keys in the node.
And follow the link corresponding to the interval that we know must contain the
search key by definition of the tree and then we recursively continue the search.
So, if we're looking for H, it's less than M, so the only place it could be in this
2-3 tree is in the 2-3 tree on the left link,
So we follow the left link. Now, we compare H with E and J, and in
this case it's between, so now we are going to take the middle link, that's the
only place that H possibly could be. In this case, that node, one node 2-3 tree
contains H, so that's a search hit. Let's take another example for
unsuccessful search, a key that's not in the tree.
As usual, we start at the root. It's less, so we go left.
And it's less than both keys, So, if it's in the tree, it would have to
be in the left link and it's between those two keys.
So if it's in the tree, it would have to be on the middle link.
And that link is null, so that's a search miss.
So the search is a natural generalization of the search in, binary search trees.
Now what about inserting? Well, it's a similar type of strategy as
with regular binary search trees, except that we manipulate the two and 3-node to
keep perfect balance in the tree. So the easy case is if the key winds up in
a 2-node at the bottom, like this one. Suppose we're inserting K. K is less than
M so we go left. K is greater than both the keys, so we go
right. K is less than L, so the search ends at
the left link of L. And to perform an insertion all we need to
do is replace that 2-node with a 3-node containing K.
Now, K is inserted into the 2-3 tree and it satisfies all the rules.
Now if we're inserting into a 3-node at the bottom, we have to do more work.
And specifically, the work we do is, we add the key to a 3-node to create a
temporary 4-node and then split up that four node and move it's middle key into
the parent. So, let's see an example.
If we are going to insert Z into this tree, it's greater than N, so we go to the
right. It's greater than R, so we go to the right. And now it's greater than x,
and that's a null link to the right of x, so the search ends there and are, what we
want to do is insert Z into that 3-node. And the way we do it is, first make a
temporary 4-node that replaces that 3-node.
And then that's not a 2-3 tree cuz it's got that one 4-node with three keys and
four links. But what we can do is split that 4-node
and pass the middle key up to its parent. So split into two 2-node and pass the
middle key to the parent. That's kind of a magical operation and
believe me, it's easier to get done in the implementation than the graphics.
But now you can see that, that local transformation on the 2-3 tree completes
the insertion. Now, if that parent were a 3-node, it
would become a temporary 4-node and would continue the process moving up the tree.
That's a demo of search and insertion in a 2-3 tree.
So, let's look at a double split like that.
So, say we're inserting L in, into this tree.
So, it goes down to the middle, and winds up needing to be inserted in the, 3-node
in the middle. So we're going to convert that into a
4-node. Now, L is the middle key of that one,
So we're going to split that 4-node into, two 2-nodes and move L to the parent.
The 4-node had four links, and the two 2-nodes have four lengths, so nothing has
to be changed below. And then this insertion into the parent
changed it from a two, a 3-node into a 4-node essentially adding a length cuz of
the split with the two 2-nodes where there was only one 3-node before.
But now, that's not a 2-3 tree, so we have to split again.
And in this case there is no paren, so we create a new one and the height of the
tree increases by one. That's the only time the height of a 2-3
tree changes, when the roots splits the height introduces increases by one.
So, that's a demo of insertion into a 3-node at the bottom, in a 2-3 tree that
percolates all the way to the top. Now let's look at constructing a 2-3 tree
from an initially empty tree. So if we start by just inserting a key,
well, that just creates a 2-node containing that key, and that's legal 2-3
tree, so we're fine. Now, inserting E into that well, it's
going to B if it's in the tree left of S, that's a null lin. So we need to convert
that 2-node into a 3-node. Okay? And that's the legal of 2-3 trees,
So we stop inserting A into that. We convert that 3-node into a temporary
4-node, but then we need to split that 4-node moving E to the parent and that
creates a new, root node and increases the size of the tree by one, But now that's a
legal 2-3 tree so we stop. Insert R into that, it goes to the right
of E. Convert into a 3-node,
Now insert C into that. it goes to the left of E, has to be joined with A into a
new 3-node. Again, that's a legal 2-3 tree and we
stop. Now we insert H, that kind of goes to the
right of E. That 3-node gets converted into a 4-node.
That's a temporary 4-node and we split and move R to the parent,
Now that parent's a legal and there's nothing more to be done.
We, have a legal three tree, 2-3 tree. Insert X,
It's bigger than R, Goes to the right.
There's a 2-node, there's room for the X. Insert P,
That goes between E and R. The 2-node containing H,
Right link is null, So we convert that 2-node into a 3-node
and now we have a legal 2-3 tree. Now, you, you can see this next insertion
is going to cause some splitting wherever it is.
So insert L, That's between E and R.
So it goes in the, 3-node containing H and P and we convert that into a temporary
4-node. Split that 4-node, moving L to the parent.
Now that parents of 4-node and that has to be split,
And we create a new root node. And then the height of the tree grows by
one. And that's a legal 2-3 tree, so we stop.
So, those local transformations, converting a 2-node to a 3-node or
converting a three to a four, and then splitting and passing a node up.
Those are the, only operations we need to consider to get balance in our search
trees. Alright.
So as I've mentioned and this diagram shows, the splitting of 4-node and a 2-3
tree is a local transformation. It only involves changing a constant
number of links. So, in this example, it shows the general
situation, when the 4-node to be split is the middle length,
But the same is true if it's a left or right.
And those six subtrees drawn could be huge.
They could contain millions of keys, But it doesn't matter what they contain.
We don't touch them at all, Nor do we touch anything above this node
in the tree until the split happens. So the transformation that splits that b,
c, d, node and inserts the C into the 3-node at the root, just involves, making
that 3-node into a temporary 4-node. And making that, 4-node into two 2-nodes
and adjusting the lengths appropriately. Just a constant number of operations and
that's why, this operation, is, in general, efficient. so let's look at the
just the global properties that these manipulations preserve.
The two things that are critical is that the, in a, in a 2-3 tree, we always have
symmetric order. That is the word that we defined, for
2-nodes and 3-nodes, and we also have the perfect balance.
The distance from the root to the bottom is always the same.
And to prove that, we just need to show that each transformation maintains
symmetric order and perfect balance, and these are all the possible transformations
that we could do. If we split the root, then, that's the,
what happens at the root, and if there was perfect balance before, there's perfect
balance after, with the height one bigger. If the parent was a 2-node then the
transformation is a local transformation and if you look at where the links are,
then it's easy to see by induction that if there was perfect balance before there's
perfect balance afterward, Because we didn't change anything about
the perfect balance in any of those subtrees.
And that's true in every case. If the 3-nodes at the right and this one
is one higher and those four are one lower and afterwards it's the same.
If there was perfect balance before there's perfect balance afterwards,
because we didn't change the height of any nodes.
We just moved things around locally within nodes.
And this is when this parent is a 3-node, then there's the tree cases,
If we split up the last split at the middle and split at the right,
And again, changing the four node to, to a 2-nodes and adding links.
If there was perfect balance before, there's perfect balance after, because we
didn't change the heights of anything else in the tree.
So our operations maintain symmetric order and perfect balance in a 2-3 tree.
So, that's going to give us, a very easy way to describe a performance.
The call, or operations have costs that's proportional to the path link from the,
height to the bottom, and every path from the root to a null link has the same
length. How long can those paths be?
Well, it's not hard to see that the, in the worst case, if they're all 2-nodes,
that's the longest they can be is log base two of N.
Now, and if they're all 3-nodes, it would be log base three of N, which is less,
It's about 0.63 log base two of N. So, all the paths in a 2-3 tree with N
nodes have to have length between those two bounds and those are pretty small
numbers. For a million nodes, that's between twelve
and twenty. And, if, if it's a billion nodes, that's
between eighteen and 30. Those are remarkably small numbers, so
we're going to have guaranteed performance, even for huge databases,
We're going to be able to guarantee that we can get search and insert them with
just eighteen to 30 operations and it's quite remarkable, really.
So, here's what our table, will look like, when we finish the implementation of 2-3
trees. Every operation is guaranteed to be a
constant times log n. Now, the constant depends on the
implementation, exactly what kind of manipulations we need to do to convert,
3-nodes to 4-nodes and so forth. But it's, easy to see from demo and from
the diagrams that those are going to be constant, guaranteed logarithmic
performance for all operations, which is certainly what we want in a symbol table
implementation Now what about the implementation?
Well, we're actually not going to talk about a direct implementation of 2-3
trees, because it's kind of complicated. It's cumbersome to maintain multiple node
types. You might need, a multiple compares to
move down the tree. If there's a two, a 3-node, it takes more
compares than a 2-node, So, it's complicated to analyze.
We have to take track, keep track the links as we go up and down the tree to
take, handle the splitting, And there's, and there's a lot of cases.
I drew all the cases and, and, there's a, whether you're splitting into the middle
of a 4-node or the right of a 2-node, there's just a lot of cases.
So, you could do it but we're not going to because there's a much easier way.
So that's 2-3 trees, A, a model for implementing balanced trees
in guaranteed logarithmic time.
