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Bloom Filters: A History and Modern Applications Michael Mitzenmacher
The main point ,[object Object]
The Problem Solved by BF: Approximate Set Membership ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Bloom Filters Start with an  m  bit array, filled with 0s. Hash each item  x j   in  S k  times.  If  H i ( x j ) =  a , set  B [ a ] = 1 . To check if  y  is in  S , check  B  at  H i ( y ) .  All  k  values must be  1 . Possible to have a false positive;  all  k  values are  1 , but  y  is not in  S . n  items  m   =  cn  bits  k  hash functions 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 B 0 1 0 0 1 0 1 0 0 1 1 1 0 1 1 0 B 0 1 0 0 1 0 1 0 0 1 1 1 0 1 1 0 B 0 1 0 0 1 0 1 0 0 1 1 1 0 1 1 0 B
False Positive Probability ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],n  items  m   =  cn  bits  k  hash functions
Example m / n  = 8 Opt  k  = 8 ln 2 = 5.45... n  items  m   =  cn  bits  k  hash functions
False Positives -- Theory ,[object Object],[object Object]
Alternative Approach for   Bloom Filters ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Perfect Hashing Approach Element 1 Element 2 Element 3 Element 4 Element 5 Fingerprint(4) Fingerprint(5) Fingerprint(2) Fingerprint(1) Fingerprint(3)
Why Aren’t Bloom Filters Taught in Algorithms 101 ? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Bloom filters and  some related schemes are covered in  other  texts
Classic Uses of BF: Spell-Checking ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Classic Uses of BF: Data Bases ,[object Object],[object Object],[object Object]
Example ,[object Object],[object Object],[object Object],Chicago 30K Raul Chicago … 70K Alice Topeka … 25K Moe New York … 30K George New York … 60K John City Addr Salary Empl 30K Topeka 55K Chicago 60K New York Cost of living City COL City Addr Salary Empl
Mathematical and Practical Niceties ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
The main point (revised) ,[object Object],[object Object]
A Modern Application:  Distributed Web Caches Web Cache 1 Web Cache 2 Web Cache 3 The Web
Web Caching ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Bloom Filters and Deletions ,[object Object],[object Object],[object Object],[object Object],[object Object]
Handling Deletions ,[object Object],[object Object],0 1 0 0 1 0 1 0 0 1 1 1 0 1 1 0 B x i   x j
Counting Bloom Filters Start with an  m  bit array, filled with 0s. Hash each item  x j   in  S k  times.  If  H i ( x j ) =  a , add 1 to  B [ a ] . To delete  x j   decrement the corresponding counters. Can obtain a corresponding Bloom filter by reducing to 0/1. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 B 0 3 0 0 1 0 2 0 0 3 2 1 0 2 1 0 B 0 2 0 0 0 0 2 0 0 3 2 1 0 1 1 0 B 0 1 0 0 0 0 1 0 0 1 1 1 0 1 1 0 B
Counting Bloom Filters: Overflow ,[object Object],[object Object],[object Object],[object Object],[object Object]
Improved Counting Bloom Filter ,[object Object],[object Object],[object Object],[object Object],[object Object]
Compression ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Compressed Bloom Filters ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Compressed Bloom Filters n=10,000 0.0108 0.0177 0.0216 f False positive rate 1 2 6 k Hash functions 7.923 7.923 8 z/n Transmit bits per/el 92 14 8 m/n Array bits per/el
Modern applications ,[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
P2P Collaboration ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
2. Resource Location: Framework Queries sent to root. Each node keeps a list of resources reachable through it, through children. List = Bloom filter.
Resource Location: Examples ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
3. Packet routing ,[object Object],[object Object],[object Object]
Loop detection in Icarus ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Scalable Multicast Forwarding ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
4. Measurement infrastructure ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Measurement Infrastructure ,[object Object],[object Object],[object Object],[object Object],[object Object]
Conservative Update 0 3 4 1 8 1 1 0 3 2 5 4 2 0 y Increment +2 The flow associated with  y  can only have been responsible for 3 packets; counters should be updated to 5. 0 3 4 1 8 1 1 0 5 2 5 5 2 0
Hash-Based Approximate Counting ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Variations and Extensions ,[object Object],[object Object],[object Object]
Extension :  Distance-Sensitive Bloom Filters ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Extension:  Bloomier Filter ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Extension: Approximate Concurrent State Machines ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Motivation: Router State Problem ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Variation:  Simpler Hashing ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
The main point revised again ,[object Object],[object Object]

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New zealand bloom filter

  • 1. Bloom Filters: A History and Modern Applications Michael Mitzenmacher
  • 2.
  • 3.
  • 4. Bloom Filters Start with an m bit array, filled with 0s. Hash each item x j in S k times. If H i ( x j ) = a , set B [ a ] = 1 . To check if y is in S , check B at H i ( y ) . All k values must be 1 . Possible to have a false positive; all k values are 1 , but y is not in S . n items m = cn bits k hash functions 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 B 0 1 0 0 1 0 1 0 0 1 1 1 0 1 1 0 B 0 1 0 0 1 0 1 0 0 1 1 1 0 1 1 0 B 0 1 0 0 1 0 1 0 0 1 1 1 0 1 1 0 B
  • 5.
  • 6. Example m / n = 8 Opt k = 8 ln 2 = 5.45... n items m = cn bits k hash functions
  • 7.
  • 8.
  • 9. Perfect Hashing Approach Element 1 Element 2 Element 3 Element 4 Element 5 Fingerprint(4) Fingerprint(5) Fingerprint(2) Fingerprint(1) Fingerprint(3)
  • 10.
  • 11. Bloom filters and some related schemes are covered in other texts
  • 12.
  • 13.
  • 14.
  • 15.
  • 16.
  • 17. A Modern Application: Distributed Web Caches Web Cache 1 Web Cache 2 Web Cache 3 The Web
  • 18.
  • 19.
  • 20.
  • 21. Counting Bloom Filters Start with an m bit array, filled with 0s. Hash each item x j in S k times. If H i ( x j ) = a , add 1 to B [ a ] . To delete x j decrement the corresponding counters. Can obtain a corresponding Bloom filter by reducing to 0/1. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 B 0 3 0 0 1 0 2 0 0 3 2 1 0 2 1 0 B 0 2 0 0 0 0 2 0 0 3 2 1 0 1 1 0 B 0 1 0 0 0 0 1 0 0 1 1 1 0 1 1 0 B
  • 22.
  • 23.
  • 24.
  • 25.
  • 26. Compressed Bloom Filters n=10,000 0.0108 0.0177 0.0216 f False positive rate 1 2 6 k Hash functions 7.923 7.923 8 z/n Transmit bits per/el 92 14 8 m/n Array bits per/el
  • 27.
  • 28.
  • 29.
  • 30. 2. Resource Location: Framework Queries sent to root. Each node keeps a list of resources reachable through it, through children. List = Bloom filter.
  • 31.
  • 32.
  • 33.
  • 34.
  • 35.
  • 36.
  • 37. Conservative Update 0 3 4 1 8 1 1 0 3 2 5 4 2 0 y Increment +2 The flow associated with y can only have been responsible for 3 packets; counters should be updated to 5. 0 3 4 1 8 1 1 0 5 2 5 5 2 0
  • 38.
  • 39.
  • 40.
  • 41.
  • 42.
  • 43.
  • 44.
  • 45.