These are my slides from the third year Advanced Algorithms course I used to give at the University of Bristol, UK.

1. Advanced Algorithms – COMS31900
Hashing part one
Chaining, true randomness and universal hashing
(based on slides by Markus Jalsenius)
Benjamin Sach

2. Dictionaries
Often we want to perform the following three operations:
In a dictionary data structure we store (key, value)-pairs
add(x, v) Add the the pair (x, v).
lookup(x) Return v if (x, v) is in dictionary, or NULL otherwise.
delete(x) Remove pair (x, v) (assuming (x, v) is in dictionary).
such that for any key there is at most one pair (key, value) in the dictionary.

3. Dictionaries
Often we want to perform the following three operations:
Linked lists
Binary search trees
(2,3,4)-trees
In a dictionary data structure we store (key, value)-pairs
add(x, v) Add the the pair (x, v).
lookup(x) Return v if (x, v) is in dictionary, or NULL otherwise.
delete(x) Remove pair (x, v) (assuming (x, v) is in dictionary).
There are many data structures that will do this job, e.g.:
Red-black trees
Skip lists
van Emde Boas trees (later in this course)
such that for any key there is at most one pair (key, value) in the dictionary.

4. Dictionaries
Often we want to perform the following three operations:
Linked lists
Binary search trees
(2,3,4)-trees
In a dictionary data structure we store (key, value)-pairs
add(x, v) Add the the pair (x, v).
lookup(x) Return v if (x, v) is in dictionary, or NULL otherwise.
delete(x) Remove pair (x, v) (assuming (x, v) is in dictionary).
There are many data structures that will do this job, e.g.:
Red-black trees
Skip lists
van Emde Boas trees (later in this course)
such that for any key there is at most one pair (key, value) in the dictionary.
these data structures all support extra operations beyond the three above

5. Dictionaries
Often we want to perform the following three operations:
Linked lists
Binary search trees
(2,3,4)-trees
In a dictionary data structure we store (key, value)-pairs
add(x, v) Add the the pair (x, v).
lookup(x) Return v if (x, v) is in dictionary, or NULL otherwise.
delete(x) Remove pair (x, v) (assuming (x, v) is in dictionary).
There are many data structures that will do this job, e.g.:
Red-black trees
Skip lists
van Emde Boas trees (later in this course)
such that for any key there is at most one pair (key, value) in the dictionary.
these data structures all support extra operations beyond the three above
but none of them take O(1) worst case time for all operations. . .

6. Dictionaries
Often we want to perform the following three operations:
Linked lists
Binary search trees
(2,3,4)-trees
In a dictionary data structure we store (key, value)-pairs
add(x, v) Add the the pair (x, v).
lookup(x) Return v if (x, v) is in dictionary, or NULL otherwise.
delete(x) Remove pair (x, v) (assuming (x, v) is in dictionary).
There are many data structures that will do this job, e.g.:
Red-black trees
Skip lists
van Emde Boas trees (later in this course)
such that for any key there is at most one pair (key, value) in the dictionary.
these data structures all support extra operations beyond the three above
but none of them take O(1) worst case time for all operations. . .
so maybe there is room for improvement?

7. Hash tables
Universe U containing u keys.
We want to store n elements from the universe, U in a dictionary.
Typically u = |U| is much, much larger than n.

8. Hash tables
Universe U containing u keys.
We want to store n elements from the universe, U in a dictionary.
Array T of size m.
Typically u = |U| is much, much larger than n.
m

9. Hash tables
Universe U containing u keys.
We want to store n elements from the universe, U in a dictionary.
Array T of size m.
Typically u = |U| is much, much larger than n.
T is called a hash table.
m

10. Hash tables
Universe U containing u keys.
We want to store n elements from the universe, U in a dictionary.
Array T of size m.
A hash function h : U → [m] maps a key to a position in T.
We write [m] to denote the set {0, . . . , m − 1}.
Typically u = |U| is much, much larger than n.
T is called a hash table.
m

11. Hash tables
Universe U containing u keys.
We want to store n elements from the universe, U in a dictionary.
Array T of size m.
A hash function h : U → [m] maps a key to a position in T.
x
h(x) (x, vx)
We write [m] to denote the set {0, . . . , m − 1}.
Typically u = |U| is much, much larger than n.
T is called a hash table.
m

12. Hash tables
Universe U containing u keys.
We want to store n elements from the universe, U in a dictionary.
Array T of size m.
A hash function h : U → [m] maps a key to a position in T.
x
h(x)
We want to avoid collisions, i.e. h(x) = h(y) for x = y.
(x, vx)
We write [m] to denote the set {0, . . . , m − 1}.
Typically u = |U| is much, much larger than n.
T is called a hash table.
m

13. Hash tables
Universe U containing u keys.
We want to store n elements from the universe, U in a dictionary.
Array T of size m.
A hash function h : U → [m] maps a key to a position in T.
x
h(x)
We want to avoid collisions, i.e. h(x) = h(y) for x = y.
y
w
z (x, vx) (y, vy) (z, vz) (w, vw)
Collisions can be resolved
with chaining, i.e. linked list.
We write [m] to denote the set {0, . . . , m − 1}.
Typically u = |U| is much, much larger than n.
T is called a hash table.
m

14. Time complexity
We cannot avoid collisions entirely since u m;
Operation Worst case time Comment
Simply add item to the list link if
necessary.
We might have to search through the
whole list containing x.
Only O(1) to perform the actual
delete. . . but you have to find x first
add(x, v) O(1)
O(length of chain containing x)lookup(x)
delete(x)
some keys from the universe are bound to be mapped to the same position.
O(length of chain containing x)
By building a hash table with chaining, we get the following time complexities:
(remember u is the size of the universe and m is the size of the table)

15. Time complexity
We cannot avoid collisions entirely since u m;
Operation Worst case time Comment
Simply add item to the list link if
necessary.
We might have to search through the
whole list containing x.
Only O(1) to perform the actual
delete. . . but you have to find x first
add(x, v) O(1)
O(length of chain containing x)lookup(x)
delete(x)
some keys from the universe are bound to be mapped to the same position.
So how long are these chains?
O(length of chain containing x)
By building a hash table with chaining, we get the following time complexities:
(remember u is the size of the universe and m is the size of the table)

16. True randomness
THEOREM
Consider any n fixed inputs to the hash table (which has size m),
i.e. any sequence of n add/lookup/delete operations.
The expected run-time per operation is O(1 + n
m ), or simply O(1) if m n.
Pick h uniformly at random from the set of all functions U → [m].

17. True randomness
THEOREM
Consider any n fixed inputs to the hash table (which has size m),
PROOF
i.e. any sequence of n add/lookup/delete operations.
The expected run-time per operation is O(1 + n
m ), or simply O(1) if m n.
Pick h uniformly at random from the set of all functions U → [m].

18. True randomness
THEOREM
Consider any n fixed inputs to the hash table (which has size m),
PROOF
Let x, y be two distinct keys from U.
i.e. any sequence of n add/lookup/delete operations.
The expected run-time per operation is O(1 + n
m ), or simply O(1) if m n.
Pick h uniformly at random from the set of all functions U → [m].

19. True randomness
THEOREM
Consider any n fixed inputs to the hash table (which has size m),
PROOF
Let x, y be two distinct keys from U.
Let indicator r.v. Ix,y be 1 iff h(x) = h(y).
i.e. any sequence of n add/lookup/delete operations.
The expected run-time per operation is O(1 + n
m ), or simply O(1) if m n.
Pick h uniformly at random from the set of all functions U → [m].

20. True randomness
THEOREM
Consider any n fixed inputs to the hash table (which has size m),
PROOF
iff means if and only if.Let x, y be two distinct keys from U.
Let indicator r.v. Ix,y be 1 iff h(x) = h(y).
i.e. any sequence of n add/lookup/delete operations.
The expected run-time per operation is O(1 + n
m ), or simply O(1) if m n.
Pick h uniformly at random from the set of all functions U → [m].

21. True randomness
THEOREM
Consider any n fixed inputs to the hash table (which has size m),
PROOF
iff means if and only if.Let x, y be two distinct keys from U.
Let indicator r.v. Ix,y be 1 iff h(x) = h(y).
we have that, Pr h(x) = h(y) =
1
m
i.e. any sequence of n add/lookup/delete operations.
The expected run-time per operation is O(1 + n
m ), or simply O(1) if m n.
Pick h uniformly at random from the set of all functions U → [m].

22. True randomness
THEOREM
Consider any n fixed inputs to the hash table (which has size m),
PROOF
iff means if and only if.Let x, y be two distinct keys from U.
Let indicator r.v. Ix,y be 1 iff h(x) = h(y).
we have that, Pr h(x) = h(y) =
1
m
this is because h(x) and h(y) are chosen uniformly and independently from [m].
i.e. any sequence of n add/lookup/delete operations.
The expected run-time per operation is O(1 + n
m ), or simply O(1) if m n.
Pick h uniformly at random from the set of all functions U → [m].

23. True randomness
THEOREM
Consider any n fixed inputs to the hash table (which has size m),
PROOF
iff means if and only if.Let x, y be two distinct keys from U.
Let indicator r.v. Ix,y be 1 iff h(x) = h(y).
we have that, Pr h(x) = h(y) =
1
m
this is because h(x) and h(y) are chosen uniformly and independently from [m].
Therefore, E(Ix,y) = Pr(Ix,y = 1) = Pr h(x) = h(y) = 1
m .
i.e. any sequence of n add/lookup/delete operations.
The expected run-time per operation is O(1 + n
m ), or simply O(1) if m n.
Pick h uniformly at random from the set of all functions U → [m].

24. True randomness
THEOREM
Consider any n fixed inputs to the hash table (which has size m),
PROOF
iff means if and only if.Let x, y be two distinct keys from U.
Let indicator r.v. Ix,y be 1 iff h(x) = h(y).
we have that, Pr h(x) = h(y) =
1
m
this is because h(x) and h(y) are chosen uniformly and independently from [m].
Therefore, E(Ix,y) = Pr(Ix,y = 1) = Pr h(x) = h(y) = 1
m .
i.e. any sequence of n add/lookup/delete operations.
The expected run-time per operation is O(1 + n
m ), or simply O(1) if m n.
Pick h uniformly at random from the set of all functions U → [m].
We have that, E(Ix,y) = 1
m .

25. True randomness
THEOREM
Consider any n fixed inputs to the hash table (which has size m),
PROOF
iff means if and only if.Let x, y be two distinct keys from U.
Let indicator r.v. Ix,y be 1 iff h(x) = h(y).
i.e. any sequence of n add/lookup/delete operations.
The expected run-time per operation is O(1 + n
m ), or simply O(1) if m n.
Pick h uniformly at random from the set of all functions U → [m].
We have that, E(Ix,y) = 1
m .

26. True randomness
THEOREM
Consider any n fixed inputs to the hash table (which has size m),
PROOF
iff means if and only if.Let x, y be two distinct keys from U.
Let indicator r.v. Ix,y be 1 iff h(x) = h(y).
i.e. any sequence of n add/lookup/delete operations.
The expected run-time per operation is O(1 + n
m ), or simply O(1) if m n.
Pick h uniformly at random from the set of all functions U → [m].
We have that, E(Ix,y) = 1
m .

27. True randomness
THEOREM
Consider any n fixed inputs to the hash table (which has size m),
PROOF
iff means if and only if.Let x, y be two distinct keys from U.
Let indicator r.v. Ix,y be 1 iff h(x) = h(y).
i.e. any sequence of n add/lookup/delete operations.
The expected run-time per operation is O(1 + n
m ), or simply O(1) if m n.
Pick h uniformly at random from the set of all functions U → [m].
We have that, E(Ix,y) = 1
m .

28. True randomness
THEOREM
Consider any n fixed inputs to the hash table (which has size m),
PROOF
iff means if and only if.Let x, y be two distinct keys from U.
Let indicator r.v. Ix,y be 1 iff h(x) = h(y).
We have that, E(Ix,y) = 1
m .
i.e. any sequence of n add/lookup/delete operations.
The expected run-time per operation is O(1 + n
m ), or simply O(1) if m n.
Pick h uniformly at random from the set of all functions U → [m].

29. True randomness
THEOREM
Consider any n fixed inputs to the hash table (which has size m),
PROOF
iff means if and only if.Let x, y be two distinct keys from U.
Let indicator r.v. Ix,y be 1 iff h(x) = h(y).
We have that, E(Ix,y) = 1
m .
Let Nx be the number of keys stored in T that are hashed to h(x)
i.e. any sequence of n add/lookup/delete operations.
The expected run-time per operation is O(1 + n
m ), or simply O(1) if m n.
Pick h uniformly at random from the set of all functions U → [m].

30. True randomness
THEOREM
Consider any n fixed inputs to the hash table (which has size m),
PROOF
iff means if and only if.Let x, y be two distinct keys from U.
Let indicator r.v. Ix,y be 1 iff h(x) = h(y).
We have that, E(Ix,y) = 1
m .
Let Nx be the number of keys stored in T that are hashed to h(x)
so, in the worst case it takes Nx time to look up x in T.
i.e. any sequence of n add/lookup/delete operations.
The expected run-time per operation is O(1 + n
m ), or simply O(1) if m n.
Pick h uniformly at random from the set of all functions U → [m].

31. True randomness
THEOREM
Consider any n fixed inputs to the hash table (which has size m),
PROOF
Observe that Nx =
y∈T
Ix,y
iff means if and only if.Let x, y be two distinct keys from U.
Let indicator r.v. Ix,y be 1 iff h(x) = h(y).
We have that, E(Ix,y) = 1
m .
Let Nx be the number of keys stored in T that are hashed to h(x)
so, in the worst case it takes Nx time to look up x in T.
the keys in T
i.e. any sequence of n add/lookup/delete operations.
The expected run-time per operation is O(1 + n
m ), or simply O(1) if m n.
Pick h uniformly at random from the set of all functions U → [m].

32. True randomness
THEOREM
Consider any n fixed inputs to the hash table (which has size m),
PROOF
Observe that Nx =
y∈T
Ix,y
iff means if and only if.
linearity of expectation.
Let x, y be two distinct keys from U.
Let indicator r.v. Ix,y be 1 iff h(x) = h(y).
We have that, E(Ix,y) = 1
m .
Let Nx be the number of keys stored in T that are hashed to h(x)
so, in the worst case it takes Nx time to look up x in T.
Finally, we have that E(Nx) = E


y∈T
Ix,y

 =
y∈T
E(Ix,y) = n·
1
m
=
n
m
the keys in T
i.e. any sequence of n add/lookup/delete operations.
The expected run-time per operation is O(1 + n
m ), or simply O(1) if m n.
Pick h uniformly at random from the set of all functions U → [m].

33. Specifying the hash function
Problem: how do we specify an arbitrary (e.g. a truely random) hash function?

34. Specifying the hash function
Problem: how do we specify an arbitrary (e.g. a truely random) hash function?
For each key in U we need to specify an arbitrary position in T,
this is a number in [m], so requires log2 m bits.

35. Specifying the hash function
Problem: how do we specify an arbitrary (e.g. a truely random) hash function?
For each key in U we need to specify an arbitrary position in T,
this is a number in [m], so requires log2 m bits.
So in total we need u log2 m bits, which is a ridiculous amount of space!

36. Specifying the hash function
Problem: how do we specify an arbitrary (e.g. a truely random) hash function?
For each key in U we need to specify an arbitrary position in T,
this is a number in [m], so requires log2 m bits.
So in total we need u log2 m bits, which is a ridiculous amount of space!
(in particular, it’s much bigger than the table :s)

37. Specifying the hash function
Problem: how do we specify an arbitrary (e.g. a truely random) hash function?
For each key in U we need to specify an arbitrary position in T,
Why not pick the hash function as we go?
this is a number in [m], so requires log2 m bits.
So in total we need u log2 m bits, which is a ridiculous amount of space!
(in particular, it’s much bigger than the table :s)

38. Specifying the hash function
Problem: how do we specify an arbitrary (e.g. a truely random) hash function?
For each key in U we need to specify an arbitrary position in T,
Why not pick the hash function as we go?
this is a number in [m], so requires log2 m bits.
So in total we need u log2 m bits, which is a ridiculous amount of space!
(in particular, it’s much bigger than the table :s)
Couldn’t we generate h(x) when we first see x?

39. Specifying the hash function
Problem: how do we specify an arbitrary (e.g. a truely random) hash function?
For each key in U we need to specify an arbitrary position in T,
Why not pick the hash function as we go?
this is a number in [m], so requires log2 m bits.
So in total we need u log2 m bits, which is a ridiculous amount of space!
(in particular, it’s much bigger than the table :s)
Couldn’t we generate h(x) when we first see x?
Wouldn’t we only use n log2 m bits? (one per key we actually store)

40. Specifying the hash function
Problem: how do we specify an arbitrary (e.g. a truely random) hash function?
For each key in U we need to specify an arbitrary position in T,
Why not pick the hash function as we go?
this is a number in [m], so requires log2 m bits.
So in total we need u log2 m bits, which is a ridiculous amount of space!
(in particular, it’s much bigger than the table :s)
Couldn’t we generate h(x) when we first see x?
Wouldn’t we only use n log2 m bits? (one per key we actually store)
The problem with this approach is recalling h(x) the next time we see x

41. Specifying the hash function
Problem: how do we specify an arbitrary (e.g. a truely random) hash function?
For each key in U we need to specify an arbitrary position in T,
Why not pick the hash function as we go?
this is a number in [m], so requires log2 m bits.
So in total we need u log2 m bits, which is a ridiculous amount of space!
(in particular, it’s much bigger than the table :s)
Couldn’t we generate h(x) when we first see x?
Wouldn’t we only use n log2 m bits? (one per key we actually store)
The problem with this approach is recalling h(x) the next time we see x
Essentially we’d need to build a dictionary to solve the dictionary problem!

42. Specifying the hash function
Problem: how do we specify an arbitrary (e.g. a truely random) hash function?
For each key in U we need to specify an arbitrary position in T,
Why not pick the hash function as we go?
this is a number in [m], so requires log2 m bits.
So in total we need u log2 m bits, which is a ridiculous amount of space!
(in particular, it’s much bigger than the table :s)
Couldn’t we generate h(x) when we first see x?
Wouldn’t we only use n log2 m bits? (one per key we actually store)
The problem with this approach is recalling h(x) the next time we see x
Essentially we’d need to build a dictionary to solve the dictionary problem!
This has become rather cyclic... let’s try something else!

43. Specifying the hash function
Problem: how do we specify an arbitrary (e.g. a truely random) hash function?
For each key in U we need to specify an arbitrary position in T,
this is a number in [m], so requires log2 m bits.
So in total we need u log2 m bits, which is a ridiculous amount of space!
(in particular, it’s much bigger than the table :s)

44. Specifying the hash function
Problem: how do we specify an arbitrary (e.g. a truely random) hash function?
For each key in U we need to specify an arbitrary position in T,
Instead, we define a set, or family of hash functions: H = {h1, h2, . . . }.
this is a number in [m], so requires log2 m bits.
So in total we need u log2 m bits, which is a ridiculous amount of space!
(in particular, it’s much bigger than the table :s)

45. Specifying the hash function
Problem: how do we specify an arbitrary (e.g. a truely random) hash function?
For each key in U we need to specify an arbitrary position in T,
Instead, we define a set, or family of hash functions: H = {h1, h2, . . . }.
this is a number in [m], so requires log2 m bits.
So in total we need u log2 m bits, which is a ridiculous amount of space!
(in particular, it’s much bigger than the table :s)
As part of initialising the hash table,
we choose the hash function h from H randomly.

46. Specifying the hash function
Problem: how do we specify an arbitrary (e.g. a truely random) hash function?
For each key in U we need to specify an arbitrary position in T,
Instead, we define a set, or family of hash functions: H = {h1, h2, . . . }.
How should we specify the hash functions in H and how do we pick one at random?
this is a number in [m], so requires log2 m bits.
So in total we need u log2 m bits, which is a ridiculous amount of space!
(in particular, it’s much bigger than the table :s)
As part of initialising the hash table,
we choose the hash function h from H randomly.

47. Weakly universal hashing
A set H of hash functions is weakly universal if for any two distinct keys x, y ∈ U,
Pr h(x) = h(y)
1
m
where h is chosen uniformly at random from H.

48. Weakly universal hashing
A set H of hash functions is weakly universal if for any two distinct keys x, y ∈ U,
Pr h(x) = h(y)
1
m
where h is chosen uniformly at random from H.
OBSERVE
The randomness here comes from the fact that h is picked randomly.

49. Weakly universal hashing
A set H of hash functions is weakly universal if for any two distinct keys x, y ∈ U,
Pr h(x) = h(y)
1
m
where h is chosen uniformly at random from H.
OBSERVE
The randomness here comes from the fact that h is picked randomly.
THEOREM
Consider any n fixed inputs to the hash table (which has size m),
i.e. any sequence of n add/lookup/delete operations.
The expected run-time per operation is O(1) if m n.
Pick h uniformly at random from a weakly universal set H of hash functions.

50. Weakly universal hashing
A set H of hash functions is weakly universal if for any two distinct keys x, y ∈ U,
Pr h(x) = h(y)
1
m
where h is chosen uniformly at random from H.
PROOF
The proof we used for true randomness works here too (which is nice)
OBSERVE
The randomness here comes from the fact that h is picked randomly.
THEOREM
Consider any n fixed inputs to the hash table (which has size m),
i.e. any sequence of n add/lookup/delete operations.
The expected run-time per operation is O(1) if m n.
Pick h uniformly at random from a weakly universal set H of hash functions.

51. Constructing a weakly universal family of hash functions
Suppose U = [u], i.e. the keys in the universe are integers 0 to u−1.
Let p be any prime bigger than u.
For a, b ∈ [p], let
ha,b(x) = (ax + b mod p) mod m,
Hp,m = {ha,b | a ∈ {1, . . . , p − 1}, b ∈ {0, . . . , p − 1}}.

52. Constructing a weakly universal family of hash functions
Suppose U = [u], i.e. the keys in the universe are integers 0 to u−1.
Let p be any prime bigger than u.
For a, b ∈ [p], let
ha,b(x) = (ax + b mod p) mod m,
Hp,m = {ha,b | a ∈ {1, . . . , p − 1}, b ∈ {0, . . . , p − 1}}.
THEOREM
Hp,m is a weakly universal set of hash functions.

53. Constructing a weakly universal family of hash functions
Suppose U = [u], i.e. the keys in the universe are integers 0 to u−1.
Let p be any prime bigger than u.
For a, b ∈ [p], let
ha,b(x) = (ax + b mod p) mod m,
Hp,m = {ha,b | a ∈ {1, . . . , p − 1}, b ∈ {0, . . . , p − 1}}.
THEOREM
Hp,m is a weakly universal set of hash functions.
PROOF
See CLRS, Theorem 11.5, (page 267 in 3rd edition).

54. Constructing a weakly universal family of hash functions
Suppose U = [u], i.e. the keys in the universe are integers 0 to u−1.
Let p be any prime bigger than u.
For a, b ∈ [p], let
ha,b(x) = (ax + b mod p) mod m,
Hp,m = {ha,b | a ∈ {1, . . . , p − 1}, b ∈ {0, . . . , p − 1}}.
THEOREM
Hp,m is a weakly universal set of hash functions.
PROOF
See CLRS, Theorem 11.5, (page 267 in 3rd edition).
OBSERVE
ax + b is a linear transformation which “spreads the keys” over p values when
taken modulo p. This does not cause any collisions.
Only when taken modulo m do we get collisions.

55. True randomness vs. weakly universal hashing
the expected lookup time in the hash table is O(1).
we have seen that when m n,
For both,
true randomness
(h is picked uniformly from the set of all possible hash functions)
and weakly universal hashing
(h is picked uniformly from a weakly universal set of hash functions)

56. True randomness vs. weakly universal hashing
Since constructing a weakly universal set of hash functions seems much easier
the expected lookup time in the hash table is O(1).
we have seen that when m n,
than obtaining true randomness, this is all good news!
For both,
true randomness
(h is picked uniformly from the set of all possible hash functions)
and weakly universal hashing
(h is picked uniformly from a weakly universal set of hash functions)

57. True randomness vs. weakly universal hashing
Since constructing a weakly universal set of hash functions seems much easier
isn’t it?
the expected lookup time in the hash table is O(1).
we have seen that when m n,
than obtaining true randomness, this is all good news!
For both,
true randomness
(h is picked uniformly from the set of all possible hash functions)
and weakly universal hashing
(h is picked uniformly from a weakly universal set of hash functions)

58. True randomness vs. weakly universal hashing
Since constructing a weakly universal set of hash functions seems much easier
What about the length of the longest chain? (the longest linked list)
isn’t it?
the expected lookup time in the hash table is O(1).
we have seen that when m n,
than obtaining true randomness, this is all good news!
For both,
true randomness
(h is picked uniformly from the set of all possible hash functions)
and weakly universal hashing
(h is picked uniformly from a weakly universal set of hash functions)

59. True randomness vs. weakly universal hashing
Since constructing a weakly universal set of hash functions seems much easier
What about the length of the longest chain? (the longest linked list)
isn’t it?
If it is very long, some lookups could take a very long time. . .
the expected lookup time in the hash table is O(1).
we have seen that when m n,
than obtaining true randomness, this is all good news!
For both,
true randomness
(h is picked uniformly from the set of all possible hash functions)
and weakly universal hashing
(h is picked uniformly from a weakly universal set of hash functions)

60. Longest chain – true randomness
If h is selected uniformly at random from all functions U → [m] then,
Pr (any chain has length 3 log m )
1
m
.
LEMMA
over m fixed inputs,

61. Longest chain – true randomness
If h is selected uniformly at random from all functions U → [m] then,
Pr (any chain has length 3 log m )
1
m
.
LEMMA
OBSERVE
In this lemma we insert m keys, i.e. n = m.
over m fixed inputs,

62. Longest chain – true randomness
If h is selected uniformly at random from all functions U → [m] then,
Pr (any chain has length 3 log m )
1
m
.
LEMMA
OBSERVE
In this lemma we insert m keys, i.e. n = m.
over m fixed inputs,

63. Longest chain – true randomness
If h is selected uniformly at random from all functions U → [m] then,
Pr (any chain has length 3 log m )
1
m
.
LEMMA
OBSERVE
In this lemma we insert m keys, i.e. n = m.
PROOF
The problem is equivalent to showing that if we randomly throw m balls into m bins, the
probability of having a bin with at least 3 log m balls is at most 1
m .
· · ·
over m fixed inputs,

64. Longest chain – true randomness
PROOF
continued. . .
Let X1 be the number of balls in the first bin.

65. Longest chain – true randomness
PROOF
continued. . .
Let X1 be the number of balls in the first bin.
Choose any k of the m balls (we’ll pick k in a bit)

66. Longest chain – true randomness
PROOF
continued. . .
Let X1 be the number of balls in the first bin.
Choose any k of the m balls (we’ll pick k in a bit)
the probability at all of these k balls go into the first bin is 1
mk .

67. Longest chain – true randomness
PROOF
continued. . .
Let X1 be the number of balls in the first bin.
Pr(X1 k)
m
k
·
1
mk
1
k!
.
Choose any k of the m balls (we’ll pick k in a bit)
the probability at all of these k balls go into the first bin is 1
mk .
So, the union bound gives us

68. Longest chain – true randomness
PROOF
continued. . .
Let X1 be the number of balls in the first bin.
Pr(X1 k)
m
k
·
1
mk
1
k!
.
Number of subsets of size k.
Choose any k of the m balls (we’ll pick k in a bit)
the probability at all of these k balls go into the first bin is 1
mk .
So, the union bound gives us

69. Longest chain – true randomness
PROOF
continued. . .
Let X1 be the number of balls in the first bin.
Pr(X1 k)
m
k
·
1
mk
1
k!
.
Number of subsets of size k.
Choose any k of the m balls (we’ll pick k in a bit)
the probability at all of these k balls go into the first bin is 1
mk .
So, the union bound gives us
Let V1, . . . , Vq be q events. Then
THEOREM
Pr
q
i=1
Vi
q
i=1
Pr(Vi).

70. Longest chain – true randomness
PROOF
continued. . .
Let X1 be the number of balls in the first bin.
Pr(X1 k)
m
k
·
1
mk
1
k!
.
Number of subsets of size k.
Choose any k of the m balls (we’ll pick k in a bit)
the probability at all of these k balls go into the first bin is 1
mk .
So, the union bound gives us

71. Longest chain – true randomness
PROOF
continued. . .
Let X1 be the number of balls in the first bin.
Pr(X1 k)
m
k
·
1
mk
1
k!
.
Number of subsets of size k.
Choose any k of the m balls (we’ll pick k in a bit)
the probability at all of these k balls go into the first bin is 1
mk .
So, the union bound gives us
m
k
=
m!
k!(m − k)!

72. Longest chain – true randomness
PROOF
continued. . .
Let X1 be the number of balls in the first bin.
Pr(X1 k)
m
k
·
1
mk
1
k!
.
Number of subsets of size k.
Choose any k of the m balls (we’ll pick k in a bit)
the probability at all of these k balls go into the first bin is 1
mk .
So, the union bound gives us
m
k
=
m!
k!(m − k)!
=
m · (m − 1) · (m − 2) · . . . (m − k + 1) · (m − k)!
k!(m − k)!

73. Longest chain – true randomness
PROOF
continued. . .
Let X1 be the number of balls in the first bin.
Pr(X1 k)
m
k
·
1
mk
1
k!
.
Number of subsets of size k.
Choose any k of the m balls (we’ll pick k in a bit)
the probability at all of these k balls go into the first bin is 1
mk .
So, the union bound gives us
m
k
=
m!
k!(m − k)!
=
m · (m − 1) · (m − 2) · . . . (m − k + 1) · (m − k)!
k!(m − k)!

74. Longest chain – true randomness
PROOF
continued. . .
Let X1 be the number of balls in the first bin.
Pr(X1 k)
m
k
·
1
mk
1
k!
.
Number of subsets of size k.
Choose any k of the m balls (we’ll pick k in a bit)
the probability at all of these k balls go into the first bin is 1
mk .
So, the union bound gives us
m · (m) · (m) · . . . (m)
k!
m
k
=
m!
k!(m − k)!
=
m · (m − 1) · (m − 2) · . . . (m − k + 1) · (m − k)!
k!(m − k)!

75. Longest chain – true randomness
PROOF
continued. . .
Let X1 be the number of balls in the first bin.
Pr(X1 k)
m
k
·
1
mk
1
k!
.
Number of subsets of size k.
Choose any k of the m balls (we’ll pick k in a bit)
the probability at all of these k balls go into the first bin is 1
mk .
So, the union bound gives us
m · (m) · (m) · . . . (m)
k!
m
k
=
m!
k!(m − k)!
=
m · (m − 1) · (m − 2) · . . . (m − k + 1) · (m − k)!
k!(m − k)!

76. Longest chain – true randomness
PROOF
continued. . .
Let X1 be the number of balls in the first bin.
Pr(X1 k)
m
k
·
1
mk
1
k!
.
Number of subsets of size k.
Choose any k of the m balls (we’ll pick k in a bit)
the probability at all of these k balls go into the first bin is 1
mk .
So, the union bound gives us
m · (m) · (m) · . . . (m)
k!
k
m
k
=
m!
k!(m − k)!
=
m · (m − 1) · (m − 2) · . . . (m − k + 1) · (m − k)!
k!(m − k)!

77. Longest chain – true randomness
PROOF
continued. . .
Let X1 be the number of balls in the first bin.
Pr(X1 k)
m
k
·
1
mk
1
k!
.
Number of subsets of size k.
Choose any k of the m balls (we’ll pick k in a bit)
the probability at all of these k balls go into the first bin is 1
mk .
So, the union bound gives us
m · (m) · (m) · . . . (m)
k!
mk
k!
k
m
k
=
m!
k!(m − k)!
=
m · (m − 1) · (m − 2) · . . . (m − k + 1) · (m − k)!
k!(m − k)!

78. Longest chain – true randomness
PROOF
continued. . .
Let X1 be the number of balls in the first bin.
Pr(X1 k)
m
k
·
1
mk
1
k!
.
By using the union bound again, we have that
Number of subsets of size k.
Choose any k of the m balls (we’ll pick k in a bit)
the probability at all of these k balls go into the first bin is 1
mk .
So, the union bound gives us

79. Longest chain – true randomness
PROOF
continued. . .
Let X1 be the number of balls in the first bin.
Pr(X1 k)
m
k
·
1
mk
1
k!
.
Pr(at least one bin receives at least k balls) m · Pr(X1 k)
m
k!
.
By using the union bound again, we have that
Number of subsets of size k.
Choose any k of the m balls (we’ll pick k in a bit)
the probability at all of these k balls go into the first bin is 1
mk .
So, the union bound gives us

80. Longest chain – true randomness
PROOF
continued. . .
Let X1 be the number of balls in the first bin.
Pr(X1 k)
m
k
·
1
mk
1
k!
.
Now we set k = 3 log m and observe that
m
k!
1
m
for m 2,
and we are done.
Pr(at least one bin receives at least k balls) m · Pr(X1 k)
m
k!
.
By using the union bound again, we have that
Number of subsets of size k.
Choose any k of the m balls (we’ll pick k in a bit)
the probability at all of these k balls go into the first bin is 1
mk .
So, the union bound gives us

81. Longest chain – true randomness
PROOF
continued. . .
Let X1 be the number of balls in the first bin.
Pr(X1 k)
m
k
·
1
mk
1
k!
.
Now we set k = 3 log m and observe that
m
k!
1
m
for m 2,
and we are done.
Pr(at least one bin receives at least k balls) m · Pr(X1 k)
m
k!
.
By using the union bound again, we have that
Number of subsets of size k.
Choose any k of the m balls (we’ll pick k in a bit)
the probability at all of these k balls go into the first bin is 1
mk .
So, the union bound gives us
Why is m
k!
1
m ? (when k = 3 log m)
k! = k × (k − 1) × (k − 2) . . . × 2 × 1
k terms
k! > 2 × 2 × 2 . . . × 2 × 1 = 2k−1
Let k = 3 log m . . .
k! > 2(3 log m−1) 22 log m = (2log m)2 = m2
so m
k!
m
m2 = 1
m

82. Longest chain – true randomness
If h is selected uniformly at random from all functions U → [m] then,
Pr (any chain has length 3 log m )
1
m
.
LEMMA
OBSERVE
In this lemma we insert m keys, i.e. n = m.
PROOF
The problem is equivalent to showing that if we randomly throw m balls into m bins, the
probability of having a bin with at least 3 log m balls is at most 1
m .
· · ·
over m fixed inputs,

83. Longest chain – weakly universal hashing
The conclusion from previous slides is that with true randomness,
the longest chain is very short (at most 3 log m) with high probability.

84. Longest chain – weakly universal hashing
The conclusion from previous slides is that with true randomness,
If h is picked uniformly at random from a weakly universal set of hash functions then,
over m fixed inputs,
Pr any chain has length 1 +
√
2m
1
2
.
LEMMA
the longest chain is very short (at most 3 log m) with high probability.

85. Longest chain – weakly universal hashing
The conclusion from previous slides is that with true randomness,
If h is picked uniformly at random from a weakly universal set of hash functions then,
over m fixed inputs,
Pr any chain has length 1 +
√
2m
1
2
.
LEMMA
OBSERVE
This rubbish upper bound of 1
2 does not necessarily rule out the possibility that the
tightest upper bound is indeed very small. However, the upper bound of 1
2 is in fact tight!
the longest chain is very short (at most 3 log m) with high probability.

86. Longest chain – weakly universal hashing
PROOF
For any two keys x, y, let indicator r.v. Ix,y be 1 iff h(x) = h(y).

87. Longest chain – weakly universal hashing
PROOF
For any two keys x, y, let indicator r.v. Ix,y be 1 iff h(x) = h(y).
Let r.v. C be the total number of collisions: C = x,y∈T, x<y Ix,y.

88. Longest chain – weakly universal hashing
PROOF
For any two keys x, y, let indicator r.v. Ix,y be 1 iff h(x) = h(y).
Let r.v. C be the total number of collisions: C = x,y∈T, x<y Ix,y.
Using linearity of expectation and E(Ix,y) = 1
m (h is weakly universal),
E(C) = E
x,y∈T, x<y
Ix,y =
x,y∈T, x<y
E(Ix,y) =
m
2
·
1
m
m
2
.

89. Longest chain – weakly universal hashing
PROOF
For any two keys x, y, let indicator r.v. Ix,y be 1 iff h(x) = h(y).
Let r.v. C be the total number of collisions: C = x,y∈T, x<y Ix,y.
Using linearity of expectation and E(Ix,y) = 1
m (h is weakly universal),
E(C) = E
x,y∈T, x<y
Ix,y =
x,y∈T, x<y
E(Ix,y) =
m
2
·
1
m
m
2
.
by Markov’s inequality, Pr(C m)
E(C)
m
1
2 .

90. Longest chain – weakly universal hashing
PROOF
For any two keys x, y, let indicator r.v. Ix,y be 1 iff h(x) = h(y).
Let r.v. C be the total number of collisions: C = x,y∈T, x<y Ix,y.
Using linearity of expectation and E(Ix,y) = 1
m (h is weakly universal),
E(C) = E
x,y∈T, x<y
Ix,y =
x,y∈T, x<y
E(Ix,y) =
m
2
·
1
m
m
2
.
by Markov’s inequality, Pr(C m)
E(C)
m
1
2 .
Let r.v. L be the length of the longest chain. Then C L
2 .

91. Longest chain – weakly universal hashing
PROOF
For any two keys x, y, let indicator r.v. Ix,y be 1 iff h(x) = h(y).
Let r.v. C be the total number of collisions: C = x,y∈T, x<y Ix,y.
Using linearity of expectation and E(Ix,y) = 1
m (h is weakly universal),
E(C) = E
x,y∈T, x<y
Ix,y =
x,y∈T, x<y
E(Ix,y) =
m
2
·
1
m
m
2
.
by Markov’s inequality, Pr(C m)
E(C)
m
1
2 .
Let r.v. L be the length of the longest chain. Then C L
2 .
This is because a chain of length L causes
L
2 collisions!

92. Longest chain – weakly universal hashing
PROOF
For any two keys x, y, let indicator r.v. Ix,y be 1 iff h(x) = h(y).
Let r.v. C be the total number of collisions: C = x,y∈T, x<y Ix,y.
Using linearity of expectation and E(Ix,y) = 1
m (h is weakly universal),
E(C) = E
x,y∈T, x<y
Ix,y =
x,y∈T, x<y
E(Ix,y) =
m
2
·
1
m
m
2
.
by Markov’s inequality, Pr(C m)
E(C)
m
1
2 .
Let r.v. L be the length of the longest chain. Then C L
2 .

93. Longest chain – weakly universal hashing
PROOF
For any two keys x, y, let indicator r.v. Ix,y be 1 iff h(x) = h(y).
Let r.v. C be the total number of collisions: C = x,y∈T, x<y Ix,y.
Using linearity of expectation and E(Ix,y) = 1
m (h is weakly universal),
E(C) = E
x,y∈T, x<y
Ix,y =
x,y∈T, x<y
E(Ix,y) =
m
2
·
1
m
m
2
.
by Markov’s inequality, Pr(C m)
E(C)
m
1
2 .
Let r.v. L be the length of the longest chain. Then C L
2 .
Now, Pr
(L−1)2
2 m Pr L
2 m Pr (C m) 1
2 .

94. Longest chain – weakly universal hashing
PROOF
For any two keys x, y, let indicator r.v. Ix,y be 1 iff h(x) = h(y).
Let r.v. C be the total number of collisions: C = x,y∈T, x<y Ix,y.
Using linearity of expectation and E(Ix,y) = 1
m (h is weakly universal),
E(C) = E
x,y∈T, x<y
Ix,y =
x,y∈T, x<y
E(Ix,y) =
m
2
·
1
m
m
2
.
by Markov’s inequality, Pr(C m)
E(C)
m
1
2 .
Let r.v. L be the length of the longest chain. Then C L
2 .
Now, Pr
(L−1)2
2 m Pr L
2 m Pr (C m) 1
2 .
this is because
L
2 =
L!
2!(L − 2)!
=
L · (L − 1)
2
(L − 1)2
2

95. Longest chain – weakly universal hashing
PROOF
For any two keys x, y, let indicator r.v. Ix,y be 1 iff h(x) = h(y).
Let r.v. C be the total number of collisions: C = x,y∈T, x<y Ix,y.
Using linearity of expectation and E(Ix,y) = 1
m (h is weakly universal),
E(C) = E
x,y∈T, x<y
Ix,y =
x,y∈T, x<y
E(Ix,y) =
m
2
·
1
m
m
2
.
by Markov’s inequality, Pr(C m)
E(C)
m
1
2 .
Let r.v. L be the length of the longest chain. Then C L
2 .
Now, Pr
(L−1)2
2 m Pr L
2 m Pr (C m) 1
2 .

96. Longest chain – weakly universal hashing
PROOF
For any two keys x, y, let indicator r.v. Ix,y be 1 iff h(x) = h(y).
Let r.v. C be the total number of collisions: C = x,y∈T, x<y Ix,y.
Using linearity of expectation and E(Ix,y) = 1
m (h is weakly universal),
E(C) = E
x,y∈T, x<y
Ix,y =
x,y∈T, x<y
E(Ix,y) =
m
2
·
1
m
m
2
.
by Markov’s inequality, Pr(C m)
E(C)
m
1
2 .
Let r.v. L be the length of the longest chain. Then C L
2 .
Now, Pr
(L−1)2
2 m Pr L
2 m Pr (C m) 1
2 .

97. Longest chain – weakly universal hashing
PROOF
For any two keys x, y, let indicator r.v. Ix,y be 1 iff h(x) = h(y).
Let r.v. C be the total number of collisions: C = x,y∈T, x<y Ix,y.
Using linearity of expectation and E(Ix,y) = 1
m (h is weakly universal),
E(C) = E
x,y∈T, x<y
Ix,y =
x,y∈T, x<y
E(Ix,y) =
m
2
·
1
m
m
2
.
by Markov’s inequality, Pr(C m)
E(C)
m
1
2 .
Let r.v. L be the length of the longest chain. Then C L
2 .
Now, Pr
(L−1)2
2 m Pr L
2 m Pr (C m) 1
2 .
By rearranging, we have that Pr L 1 +
√
2m 1
2 , and we are done.

98. Conclusions
the expected lookup time in a hash table with chaining is O(1).
we have seen that when m n,
For both,
true randomness (h is picked uniformly from the set of all possible hash functions)
and weakly universal hashing
(h is picked uniformly from a weakly universal set of hash functions)
If h is selected uniformly at random from all functions U → [m] then,
Pr (any chain has length 3 log m )
1
m
.
LEMMA
LEMMA
(both Lemmas hold for m any fixed inputs)
If h is picked uniformly at random from a weakly universal set of hash functions,
Pr any chain has length 1 +
√
2m
1
2
.