Advanced SearchTechniques for Large Scale Data Analytics
Pavel Zezula and Jan Sedmidubsky
Masaryk University
http://disa.fi.muni.cz
 In many data mining situations, we do not
know the entire data set in advance
 Stream Management is important when the
input rate is controlled externally:
▪ Google queries
▪ Twitter or Facebook status updates
 We can think of the data as infinite and
non-stationary (the distribution changes
over time)
Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212) 2
3
 Input elements enter at a rapid rate,
at one or more input ports (i.e., streams)
▪ We call elements of the stream tuples
 The system cannot store the entire stream
accessibly
 Q: How do you make critical calculations
about the stream using a limited amount of
(secondary) memory?
Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212)
Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212) 4
Processor
Limited
Working
Storage
. . . 1, 5, 2, 7, 0, 9, 3
. . . a, r, v, t, y, h, b
. . . 0, 0, 1, 0, 1, 1, 0
time
Streams Entering.
Each is stream is
composed of
elements/tuples
Ad-Hoc
Queries
Output
Archival
Storage
Standing
Queries
 Types of queries one wants on answer on
a data stream:
▪ Sampling data from a stream
▪ Construct a random sample
▪ Queries over sliding windows
▪ Number of items of type x in the last k elements
of the stream
▪ Filtering a data stream
▪ Select elements with property x from the stream
▪ Counting distinct elements
▪ Number of distinct elements in the last k elements
of the stream
Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212) 5
 Mining query streams
▪ Google wants to know what queries are
more frequent today than yesterday
 Mining click streams
▪ Yahoo wants to know which of its pages are
getting an unusual number of hits in the past hour
 Mining social network news feeds
▪ E.g., look for trending topics on Twitter, Facebook
Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212) 6
 Sensor Networks
▪ Many sensors feeding into a central controller
 Telephone call records
▪ Data feeds into customer bills as well as
settlements between telephone companies
 IP packets monitored at a switch
▪ Gather information for optimal routing
▪ Detect denial-of-service attacks
Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212) 7
As the stream grows the sample
also gets bigger
 Since we can not store the entire stream,
one obvious approach is to store a sample
 Two different problems:
▪ (1) Sample a fixed proportion of elements
in the stream (say 1 in 10)
▪ (2) Maintain a random sample of fixed size
over a potentially infinite stream
▪ At any “time” k we would like a random sample
of s elements
▪ What is the property of the sample we want to maintain?
For all time steps k, each of k elements seen so far has
equal prob. of being sampled
Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212) 9
 Problem 1: Sampling fixed proportion
 Scenario: Search engine query stream
▪ Stream of tuples: (user, query, time)
▪ Answer questions such as: How often did a user
run the same query in a single days
▪ Have space to store 1/10th of query stream
 Naïve solution:
▪ Generate a random integer in [0..9] for each query
▪ Store the query if the integer is 0, otherwise
discard
Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212) 10
 Simple question: What fraction of queries by an
average search engine user are duplicates?
▪ Suppose each user issues x queries once and d queries
twice (total of x+2d queries)
▪ Correct answer: d/(x+d)
▪ Proposed solution: We keep 10% of the queries
▪ Sample will contain x/10 of the singleton queries and
2d/10 of the duplicate queries at least once
▪ But only d/100 pairs of duplicates
▪ d/100 = 1/10 ∙ 1/10 ∙ d
▪ Of d “duplicates” 18d/100 appear exactly once
▪ 18d/100 = ((1/10 ∙ 9/10)+(9/10 ∙ 1/10)) ∙ d
▪ So the sample-based answer is
𝑑
100
𝑥
10
+
𝑑
100
+
18𝑑
100
=
𝒅
𝟏𝟎𝒙+𝟏𝟗𝒅
Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212) 11
Solution:
 Pick 1/10th of users and take all their
searches in the sample
 Use a hash function that hashes the
user name or user id uniformly into 10
buckets
Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212) 12
 Stream of tuples with keys:
▪ Key is some subset of each tuple’s components
▪ e.g., tuple is (user, search, time); key is user
▪ Choice of key depends on application
 To get a sample of a/b fraction of the stream:
▪ Hash each tuple’s key uniformly into b buckets
▪ Pick the tuple if its hash value is at most a
Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212) 13
Hash table with b buckets, pick the tuple if its hash value is at most a.
How to generate a 30% sample?
Hash into b=10 buckets, take the tuple if it hashes to one of the first 3 buckets
As the stream grows, the sample is of
fixed size
 Problem 2: Fixed-size sample
 Suppose we need to maintain a random
sample S of size exactly s tuples
▪ E.g., main memory size constraint
 Why? Don’t know length of stream in advance
 Suppose at time n we have seen n items
▪ Each item is in the sample S with equal prob. s/n
Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212) 15
How to think about the problem: say s = 2
Stream: a x c y z k c d e g…
At n= 5, each of the first 5 tuples is included in the sample S with equal prob.
At n= 7, each of the first 7 tuples is included in the sample S with equal prob.
Impractical solution would be to store all the n tuples seen
so far and out of them pick s at random
 Algorithm (a.k.a. Reservoir Sampling)
▪ Store all the first s elements of the stream to S
▪ Suppose we have seen n-1 elements, and now
the nth element arrives (n > s)
▪ With probability s/n, keep the nth element, else discard it
▪ If we picked the nth element, then it replaces one of the
s elements in the sample S, picked uniformly at random
 Claim: This algorithm maintains a sample S
with the desired property:
▪ After n elements, the sample contains each
element seen so far with probability s/nPavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212) 16
 We prove this by induction:
▪ Assume that after n elements, the sample contains
each element seen so far with probability s/n
▪ We need to show that after seeing element n+1
the sample maintains the property
▪ Sample contains each element seen so far with
probability s/(n+1)
 Base case:
▪ After we see n=s elements the sample S has the
desired property
▪ Each out of n=s elements is in the sample with
probability s/s = 1
Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212) 17
 Inductive hypothesis: After n elements, the sample
S contains each element seen so far with prob. s/n
 Now element n+1 arrives
 Inductive step: For elements already in S,
probability that the algorithm keeps it in S is:
 So, at time n, tuples in S were there with prob. s/n
 Time nn+1, tuple stayed in S with prob. n/(n+1)
 So prob. tuple is in S at time n+1 =
𝒔
𝒏
⋅
𝒏
𝒏+𝟏
=
𝒔
𝒏+𝟏
Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212) 18
1
1
11
1






 















n
n
s
s
n
s
n
s
Element n+1 discarded Element n+1
not discarded
Element in the
sample not picked
 A useful model of stream processing is that
queries are about a window of length N –
the N most recent elements received
 Interesting case: N is so large that the data
cannot be stored in memory, or even on disk
▪ Or, there are so many streams that windows
for all cannot be stored
 Amazon example:
▪ For every product X we keep 0/1 stream of whether
that product was sold in the n-th transaction
▪ We want answer queries, how many times have we
sold X in the last k sales
Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212) 20
 Sliding window on a single stream:
Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212) 21
q w e r t y u i o p a s d f g h j k l z x c v b n m
q w e r t y u i o p a s d f g h j k l z x c v b n m
q w e r t y u i o p a s d f g h j k l z x c v b n m
q w e r t y u i o p a s d f g h j k l z x c v b n m
Past Future
N = 6
22
 Problem:
▪ Given a stream of 0s and 1s
▪ Be prepared to answer queries of the form
How many 1s are in the last k bits? where k ≤ N
 Obvious solution:
Store the most recent N bits
▪ When new bit comes in, discard the N+1st bit
0 1 0 0 1 1 0 1 1 1 0 1 0 1 0 1 1 0 1 1 0 1 1 0
Past Future
Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212)
Suppose N=6
 You can not get an exact answer without
storing the entire window
 Real Problem:
What if we cannot afford to store N bits?
▪ E.g., we’re processing 1 billion streams and
N = 1 billion
 But we are happy with an approximate
answer
23Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212)
0 1 0 0 1 1 0 1 1 1 0 1 0 1 0 1 1 0 1 1 0 1 1 0
Past Future
 Q: How many 1s are in the last N bits?
 A simple solution that does not really solve
our problem: Uniformity assumption
 Maintain 2 counters:
▪ S: number of 1s from the beginning of the stream
▪ Z: number of 0s from the beginning of the stream
 How many 1s are in the last N bits? 𝑵 ∙
𝑺
𝑺+𝒁
 But, what if stream is non-uniform?
▪ What if distribution changes over time?
Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212) 24
0 1 0 0 1 1 1 0 0 0 1 0 1 0 0 1 0 0 0 1 0 1 1 0 1 1 0 1 1 1 0 0 1 0 1 0 1 1 0 0 1 1 0 1 0
N
Past Future
 DGIM solution that does not assume
uniformity
 We store 𝑶(log 𝟐 𝑵) bits per stream
 Solution gives approximate answer,
never off by more than 50%
▪ Error factor can be reduced to any fraction > 0,
with more complicated algorithm and
proportionally more stored bits
Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212) 25
[Datar, Gionis, Indyk, Motwani]
 Solution that doesn’t (quite) work:
▪ Summarize exponentially increasing regions
of the stream, looking backward
▪ Drop small regions if they begin at the same point
as a larger region
Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212) 26
0 1 0 0 1 1 1 0 0 0 1 0 1 0 0 1 0 0 0 1 0 1 1 0 1 1 0 1 1 1 0 0 1 0 1 0 1 1 0 0 1 1 0 1 0
N
?
01
12
23
4
106
We can reconstruct the count of the last N bits, except we
are not sure how many of the last 6 1s are included in the N
Window of
width 16
has 6 1s
 Stores only O(log2N ) bits
▪ 𝑶(log 𝑵) counts of log 𝟐 𝑵 bits each
 Easy update as more bits enter
 Error in count no greater than the number
of 1s in the “unknown” area
Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212) 27
28
 As long as the 1s are fairly evenly distributed,
the error due to the unknown region is small
– no more than 50%
 But it could be that all the 1s are in the
unknown area at the end
 In that case, the error is unbounded!
Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212)
0 1 0 0 1 1 1 0 0 0 1 0 1 0 0 1 0 0 0 1 0 1 1 0 1 1 0 1 1 1 0 0 1 0 1 0 1 1 0 0 1 1 0 1 0
01
12
23
4
106
N
?
 Idea: Instead of summarizing fixed-length
blocks, summarize blocks with specific
number of 1s:
▪ Let the block sizes (number of 1s) increase
exponentially
 When there are few 1s in the window, block
sizes stay small, so errors are small
29Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212)
1001010110001011010101010101011010101010101110101010111010100010110010
N
[Datar, Gionis, Indyk, Motwani]
30
 Each bit in the stream has a timestamp,
starting 1, 2, …
 Record timestamps modulo N (the window
size), so we can represent any relevant
timestamp in 𝑶(𝒍𝒐𝒈 𝟐 𝑵) bits
Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212)
 A bucket in the DGIM method is a record
consisting of:
▪ (A) The timestamp of its end [O(log N) bits]
▪ (B) The number of 1s between its beginning and
end [O(log log N) bits]
 Constraint on buckets:
Number of 1s must be a power of 2
▪ That explains the O(log log N) in (B) above
31Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212)
1001010110001011010101010101011010101010101110101010111010100010110010
N
 Either one or two buckets with the same
power-of-2 number of 1s
 Buckets do not overlap in timestamps
 Buckets are sorted by size
▪ Earlier buckets are not smaller than later buckets
 Buckets disappear when their
end-time is > N time units in the past
Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212) 32
Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212) 33
N
1 of
size 2
2 of
size 4
2 of
size 8
At least 1 of
size 16. Partially
beyond window.
2 of
size 1
1001010110001011010101010101011010101010101110101010111010100010110010
Three properties of buckets that are maintained:
- Either one or two buckets with the same power-of-2 number of 1s
- Buckets do not overlap in timestamps
- Buckets are sorted by size
 When a new bit comes in, drop the last
(oldest) bucket if its end-time is prior to N
time units before the current time
 2 cases: Current bit is 0 or 1
 If the current bit is 0:
no other changes are needed
34Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212)
 If the current bit is 1:
▪ (1) Create a new bucket of size 1, for just this bit
▪ End timestamp = current time
▪ (2) If there are now three buckets of size 1,
combine the oldest two into a bucket of size 2
▪ (3) If there are now three buckets of size 2,
combine the oldest two into a bucket of size 4
▪ (4) And so on …
35Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212)
36
1001010110001011010101010101011010101010101110101010111010100010110010
0010101100010110101010101010110101010101011101010101110101000101100101
0010101100010110101010101010110101010101011101010101110101000101100101
0101100010110101010101010110101010101011101010101110101000101100101101
0101100010110101010101010110101010101011101010101110101000101100101101
0101100010110101010101010110101010101011101010101110101000101100101101
Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212)
Current state of the stream:
Bit of value 1 arrives
Two orange buckets get merged into a yellow bucket
Next bit 1 arrives, new orange bucket is created, then 0 comes, then 1:
Buckets get merged…
State of the buckets after merging
37
 To estimate the number of 1s in the most
recent N bits:
1. Sum the sizes of all buckets but the last
(note “size” means the number of 1s in the bucket)
2. Add half the size of the last bucket
 Remember: We do not know how many 1s
of the last bucket are still within the wanted
window
Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212)
Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212) 38
N
1 of
size 2
2 of
size 4
2 of
size 8
At least 1 of
size 16. Partially
beyond window.
2 of
size 1
1001010110001011010101010101011010101010101110101010111010100010110010
 Each element of data stream is a tuple
 Given a list of keys S
 Determine which tuples of stream are in S
 Obvious solution: Hash table
▪ But suppose we do not have enough memory to
store all of S in a hash table
▪ E.g., we might be processing millions of filters
on the same stream
40Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212)
 Example: Email spam filtering
▪ We know 1 billion “good” email addresses
▪ If an email comes from one of these, it is NOT
spam
 Publish-subscribe systems
▪ You are collecting lots of messages (news articles)
▪ People express interest in certain sets of keywords
▪ Determine whether each message matches user’s
interest
41Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212)
 Given a set of keys S that we want to filter
 Create a bit array B of n bits, initially all 0s
 Choose a hash function h with range [0,n)
 Hash each member of s S to one of
n buckets, and set that bit to 1, i.e., B[h(s)]=1
 Hash each element a of the stream and
output only those that hash to bit that was
set to 1
▪ Output a if B[h(a)] == 1
42Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212)
 Creates false positives but no false negatives
▪ If the item is in S we surely output it, if not we may
still output it
43
Item
0010001011000
Output the item since it may be in S.
Item hashes to a bucket that at least
one of the items in S hashed to.
Hash
func h
Drop the item.
It hashes to a bucket set
to 0 so it is surely not in S.
Bit array B
Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212)
 |S| = 1 billion email addresses
|B|= 1GB = 8 billion bits
 If the email address is in S, then it surely
hashes to a bucket that has the big set to 1,
so it always gets through (no false negatives)
 Approximately 1/8 of the bits are set to 1, so
about 1/8th of the addresses not in S get
through to the output (false positives)
▪ Actually, less than 1/8th, because more than one
address might hash to the same bit
44Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212)
 Consider: |S| = m, |B| = n
 Use k independent hash functions h1 ,…, hk
 Initialization:
▪ Set B to all 0s
▪ Hash each element s S using each hash function hi,
set B[hi(s)] = 1 (for each i = 1,.., k)
 Run-time:
▪ When a stream element with key x arrives
▪ If B[hi(x)] = 1 for all i = 1,..., k then declare that x is in S
▪ That is, x hashes to a bucket set to 1 for every hash function hi(x)
▪ Otherwise discard the element x
Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212) 45
(note: we have a
single array B!)
 m = 1 billion, n = 8 billion
▪ k = 1: (1 – e-1/8) = 0.1175
▪ k = 2: (1 – e-1/4)2 = 0.0493
 What happens as we
keep increasing k?
 “Optimal” value of k: n/m ln(2)
▪ In our case: Optimal k = 8 ln(2) = 5.54 ≈ 6
▪ Error at k = 6: (1 – e-1/6)2 = 0.0235
Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212) 46
0 2 4 6 8 10 12 14 16 18 20
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
Number of hash functions, k
Falsepositiveprob.
 Bloom filters guarantee no false negatives,
and use limited memory
▪ Great for pre-processing before more
expensive checks
 Suitable for hardware implementation
▪ Hash function computations can be parallelized
 Is it better to have 1 big B or k small Bs?
▪ It is the same: (1 – e-km/n)k vs. (1 – e-m/(n/k))k
▪ But keeping 1 big B is simpler
Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212) 47
 Problem:
▪ Data stream consists of a universe of elements
chosen from a set of size N
▪ Maintain a count of the number of distinct
elements seen so far
 Obvious approach:
Maintain the set of elements seen so far
▪ That is, keep a hash table of all the distinct
elements seen so far
49Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212)
 How many different words are found among
the Web pages being crawled at a site?
▪ Unusually low or high numbers could indicate
artificial pages (spam?)
 How many different Web pages does each
customer request in a week?
 How many distinct products have we sold in
the last week?
50Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212)
 Real problem: What if we do not have space
to maintain the set of elements seen so far?
 Estimate the count in an unbiased way
 Accept that the count may have a little error,
but limit the probability that the error is large
51Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212)
 Pick a hash function h that maps each of the
N elements to at least log2 N bits
 For each stream element a, let r(a) be the
number of trailing 0s in h(a)
▪ r(a) = position of first 1 counting from the right
▪ E.g., say h(a) = 12, then 12 is 1100 in binary, so r(a) = 2
 Record R = the maximum r(a) seen
▪ R = maxa r(a), over all the items a seen so far
 Estimated number of distinct elements = 2R
52Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212)
 Very very rough and heuristic intuition why
Flajolet-Martin works:
▪ h(a) hashes a with equal prob. to any of N values
▪ Then h(a) is a sequence of log2 N bits,
where 2-r fraction of all as have a tail of r zeros
▪ About 50% of as hash to ***0
▪ About 25% of as hash to **00
▪ So, if we saw the longest tail of r=2 (i.e., item hash
ending *100) then we have probably seen
about 4 distinct items so far
▪ So, it takes to hash about 2r items before we
see one with zero-suffix of length r
Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212) 53