CSE 326: Data Structures Splay Trees Ben Lerner Summer 2007.

CSE 326: Data StructuresSplay Trees

Ben Lerner

Summer 2007

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Administrivia

• Midterm July 16 during lecture– Topics posted, Review next week

• Project 2c posted early next week

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Self adjustment for better living

• Ordinary binary search trees have no balance conditions– what you get from insertion order is it

• Balanced trees like AVL trees enforce a balance condition when nodes change– tree is always balanced after an insert or delete

• Self-adjusting trees get reorganized over time as nodes are accessed

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Splay Trees

• Blind adjusting version of AVL trees– Why worry about balances? Just rotate

anyway!

• Amortized time per operations is O(log n)• Worst case time per operation is O(n)

– But guaranteed to happen rarely

Insert/Find always rotate node to the root!

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Amortized ComplexityIf a sequence of M operations takes O(M f(n)) time,we say the amortized runtime is O(f(n)).

Amortized complexity is worst-case guarantee over sequences of operations.

• Worst case time per operation can still be large, say O(n)

• Worst case time for any sequence of M operations is O(M f(n))

Average time per operation for any sequence is O(f(n))

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Amortized Complexity• Is amortized guarantee any weaker than

worstcase?

• Is amortized guarantee any stronger than averagecase?

• Is average case guarantee good enough in practice?

• Is amortized guarantee good enough in practice?

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The Splay Tree Idea

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92

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If you’re forced to make a really deep access:

Since you’re down there anyway,fix up a lot of deep nodes!

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1. Find or insert a node k2. Splay k to the root using:

zig-zag, zig-zig, or plain old zig rotation

Why could this be good??

1. Helps the new root, ko Great if k is accessed again

2. And helps many others!o Great if many others on the path are accessed

Find/Insert in Splay Trees

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Splaying node k to the root:Need to be careful!

One option (that we won’t use) is to repeatedly use AVL single rotation until k becomes the root: (see Section 4.5.1 for details)

s

Ak

B C

r

D

q

E

p

F

r

D

q

E

p

F

C

s

A B

k

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Splaying node k to the root:Need to be careful!

What’s bad about this process?

s

Ak

B C

r

D

q

E

p

F

r

D

q

E

p

F

C

s

A B

k

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• Let X be a non-root node with 2 ancestors.• P is its parent node.• G is its grandparent node.

P

G

X

G

P

X

G

P

X

G

P

X

Splay Tree Terminology

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Zig-Zig and Zig-Zag

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G 5

1 P Zig-zag

G

P 5

X 2

Zig-zig

X

Parent and grandparentin same direction.

Parent and grandparentin different directions.

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Zig-Zag operation

• “Zig-Zag” consists of two rotations of the opposite direction (assume R is the node that was accessed)

(RotateFromRight) (RotateFromLeft)

ZigZagFromLeft

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Splay: Zig-Zag*

g

Xp

Y

k

Z

W

*Just like an…

k

Y

g

W

p

ZX

Which nodes improve depth?

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Zig-Zig operation

• “Zig-Zig” consists of two single rotations of the same direction (R is the node that was accessed)

(RotateFromLeft) (RotateFromLeft)

ZigZigFromLeft

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Splay: Zig-Zig*

k

Z

Y

p

X

g

W

g

W

X

p

Y

k

Z

*Is this just two AVL single rotations in a row?

Why does this help?

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Special Case for Root: Zigp

X

k

Y

Z

root k

Z

p

Y

X

root

Relative depth of p, Y, Z? Relative depth of everyone else?

Why not drop zig-zig and just zig all the way?

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Splaying Example: Find(6)

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1

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6

Find(6)

2

1

3

6

5

4

?

Think of as if created by inserting 6,5,4,3,2,1 – each took constant time – a LOT of savings so far to amortize those bad accesses over

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Still Splaying 6

2

1

3

6

5

4

1

6

3

2 5

4

?

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Finally…

1

6

3

2 5

4

6

1

3

2 5

4

?

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Another Splay: Find(4)

Find(4)

6

1

3

2 5

4

6

1

4

3 5

2

?

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Example Splayed Out

6

1

4

3 5

2

61

4

3 5

2

?

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Practice Finding

• Find 2…

• Then how long would it take to find 6? 4?

• Will the tree ever look like what we started with again?

61

4

3 5

2

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But Wait…

What happened here?

Didn’t two find operations take linear timeinstead of logarithmic?

What about the amortized O(log n) guarantee?

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Why Splaying Helps

• If a node n on the access path is at depth d before the splay, it’s at about depth d/2 after the splay

• Overall, nodes which are low on the access path tend to move closer to the root

• Splaying gets amortized O(log n) performance. (Maybe not now, but soon, and for the rest of the operations.)

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Practical Benefit of Splaying• No heights to maintain, no imbalance to

check for– Less storage per node, easier to code

• Often data that is accessed once, is soon accessed again!– Splaying does implicit caching by bringing it to

the root

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Splay Operations: Find

• Find the node in normal BST manner

• Splay the node to the root– if node not found, splay what would have

been its parent

What if we didn’t splay?

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Splay Operations: Insert

• Insert the node in normal BST manner

• Splay the node to the root

What if we didn’t splay?

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Example Insert

• Inserting in order 1,2,3,…,8

• Without self-adjustment

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2

3

4

5

6

7

8

O(n2) time

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With Self-Adjustment

1

2

1 2

1

ZigFromRight

2

1 3

ZigFromRight2

1

3

1

2

3

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With Self-Adjustment

ZigFromRight2

1

34

4

2

1

3

4

O(n) time!!

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Splay Operations: Remove

find(k)

L R

k

L R

> k< k

delete k

Now what?

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Example Deletion

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201382

96

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15

5

2013

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2 96

splay

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15

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2013

2 96

remove

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15

5

2013

2 9

6splay

attach

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Practice Delete

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96

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JoinJoin(L, R): given two trees such that (stuff in L) < (stuff in R), merge them:

Splay on the maximum element in L, then attach R

L R R

L

Does this work to join any two trees?

splay

max

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Splay Tree Summary• All operations are in amortized O(log n) time

• Splaying can be done top-down; this may be better because:– only one pass– no recursion or parent pointers necessary– we didn’t cover top-down in class

• Splay trees are very effective search trees– Relatively simple– No extra fields required– Excellent locality properties: frequently accessed keys are

cheap to find