Countmin sketch in Java with Examples
The Countmin sketch is a probabilistic data structure. The CountMin sketch is a simple technique to summarize large amounts of frequency data. Countmin sketch algorithm talks about keeping track of the count of things. i.e, How many times an element is present in the set. Finding the count of an item could be easily achieved in Java using HashTable or Map.
Trying MultiSet as an alternative to Countmin sketch
Let’s try to implement this data structure using MultiSet with the below source code and try to find out issues with this approach.
// Java program to try MultiSet as an // alternative to Countmin sketch import com.google.common.collect.HashMultiset; import com.google.common.collect.Multiset; public class MultiSetDemo { public static void main(String[] args) { Multiset<String> blackListedIPs = HashMultiset.create(); blackListedIPs.add( "192.170.0.1" ); blackListedIPs.add( "75.245.10.1" ); blackListedIPs.add( "10.125.22.20" ); blackListedIPs.add( "192.170.0.1" ); System.out.println(blackListedIPs .count( "192.170.0.1" )); System.out.println(blackListedIPs .count( "10.125.22.20" )); } } 
Understanding the problem of using MultiSet
Now let’s look at the time and space consumed with this type of approach.
 Number of UUIDs Insertion Time(ms)   10 <25  100 <25  1, 000 30  10, 000 257  100, 000 1200  1, 000, 000 4244  
Let’s have a look at the memory (space) consumed:
 Number of UUIDs JVM heap used(MB)   10 <2  100 <2  1, 000 3  10, 000 9  100, 000 39  1, 000, 000 234  
We can easily understand that as data grows, the above approach is consuming a lot of memory and time to process the data. This can be optimized if we use countmin sketch algorithm.
What is Countmin sketch and how does it work?
Countmin sketch approach was proposed by Graham Cormode and S. Muthukrishnan. in the paper approximating data with the countmin sketch published in 2011/12. Countmin sketch is used to count the frequency of the events on the streaming data. Like Bloom filter, Countmin sketch algorithm also works with hash codes. It uses multiple hash functions to map these frequencies on to the matrix (Consider sketch here a two dimensional array or matrix).
Let’s look at the below example step by step.
 Consider below 2D array with 4 rows and 16 columns, also the number of rows is equal to the number of hash functions. That means we are taking four hash functions for our example. Initialize/mark each cell in the matrix with zero.
Note: The more accurate result you want, the more number of hash function to be used.
 Now let’s add some element to it. To do so we have to pass that element with all four hash functions which will may result as follows.
Input : 192.170.0.1
hashFunction1(192.170.0.1): 1 hashFunction2(192.170.0.1): 6 hashFunction3(192.170.0.1): 3 hashFunction4(192.170.0.1): 1
Now visit to the indexes retrieved above by all four hash function and mark them as 1.
 Similarly process second input by passing it to all four hash functions.
Input : 75.245.10.1
hashFunction1(75.245.10.1): 1 hashFunction2(75.245.10.1): 2 hashFunction3(75.245.10.1): 4 hashFunction4(75.245.10.1): 6
Now, take these indexes and visit the matrix, if the given index has already been marked as 1. This is called collision which means that, the index of that row was already marked by some previous inputs and in this case just increament the index value by 1. In our case, since we have already marked index 1 of row 1 i.e., hashFunction1() as 1 by previous input, so this time it will be increamented by 1 and now this cell entry will be 2, but for rest of the index of rest rows it will be 0, since there was no collision.
 Let’s process next input
Input : 10.125.22.20
hashFunction1(10.125.22.20): 3 hashFunction2(10.125.22.20): 4 hashFunction3(10.125.22.20): 1 hashFunction4(10.125.22.20): 6
Lets, represent it on matrix, do remember to increment the count by 1 if already some entry exist.
 Similarly process next input.
Input : 192.170.0.1
hashFunction1(192.170.0.1): 1 hashFunction2(192.170.0.1): 6 hashFunction3(192.170.0.1): 3 hashFunction4(192.170.0.1): 1
Now let’s test some element and check how many time are they present.

Test Input #1: 192.170.0.1
Pass above input to all four hash functions, and take the index numbers generated by hash functions.
hashFunction1(192.170.0.1): 1 hashFunction2(192.170.0.1): 6 hashFunction3(192.170.0.1): 3 hashFunction4(192.170.0.1): 1
Now visit to each index and take note down the entry present on that index
So the final entry on each index was 3, 2, 2, 2. Now take the minimum count among these entries and that is the result. So min(3, 2, 2, 2) is 2, that means the above test input is processed two times in the above list. 
Test Input #1: 10.125.22.20
Pass above input to all four hash functions, and take the index numbers generated by hash functions.
hashFunction1(10.125.22.20): 3 hashFunction2(10.125.22.20): 4 hashFunction3(10.125.22.20): 1 hashFunction4(10.125.22.20): 6
Now visit to each index and take note down the entry present on that index
So the final entry on each index was 1, 1, 1, 2. Now take the minimum count among these entries and that is the result. So min(1, 1, 1, 2) is 1, that means the above test input is processed only once in the above list.
Issue with Countmin sketch and its solution:
What if one or more elements got the same hash values and then they all incremented. So, in that case, the value would have been increased because of the hash collision. Thus sometimes (in very rare cases) Countmin sketch overcounts the frequencies because of the hash functions. So the more hash function we take there will be less collision. The fewer hash functions we take there will be a high probability of collision. Hence it always recommended taking more number of hash functions.
Applications of Countmin sketch:
 Compressed Sensing
 Networking
 NLP
 Stream Processing
 Frequency tracking
 Extension: Heavyhitters
 Extension: Rangequery
Implementation of Countmin sketch using Guava library in Java:
We can implement the Countmin sketch using Java library provided by Guava. Below is the step by step implementation:
 Use below maven dependency.
<
dependency
>
<
groupId
>com.clearspring.analytics</
groupId
>
<
artifactId
>stream</
artifactId
>
<
version
>2.9.5</
version
>
</
dependency
>
chevron_rightfilter_none  The detailed Java code is as follows:
import
com.clearspring.analytics
.stream.frequency.CountMinSketch;
public
class
CountMinSketchDemo {
public
static
void
main(String[] args)
{
CountMinSketch countMinSketch
=
new
CountMinSketch(
// epsilon
0.001
,
// delta
0.99
,
// seed
1
);
countMinSketch.add(
"75.245.10.1"
,
1
);
countMinSketch.add(
"10.125.22.20"
,
1
);
countMinSketch.add(
"192.170.0.1"
,
2
);
System.out.println(
countMinSketch
.estimateCount(
"192.170.0.1"
));
System.out.println(
countMinSketch
.estimateCount(
"999.999.99.99"
));
}
}
chevron_rightfilter_none  Above example takes three arguments in the constructor which are
 0.001 = the epsilon i.e., error rate  0.99 = the delta i.e., confidence or accuracy rate  1 = the seed
 Now lets have a look at the time and space consumed with this approach.
 Number of UUIDs  Multiset Insertion Time(ms)  CMS Insertion Time(ms)   10 <25 35  100 <25 30  1, 000 30 69  10, 000 257 246  100, 000 1200 970  1, 000, 000 4244 4419  
 Now, Let’s have a look of the memory consumed:
 Number of UUIDs  Multiset JVM heap used(MB)  CMS JVM heap used(MB)   10 <2 N/A  100 <2 N/A  1, 000 3 N/A  10, 000 9 N/A  100, 000 39 N/A  1, 000, 000 234 N/A  
 Suggestions:
 Epsilon  Delta  width/Row (hash functions) Depth/column CMS JVM heap used(MB)   0.1 0.99  7  20  0.009  0.01 0.999  10  100  0.02  0.001 0.9999  14  2000  0.2  0.0001 0.99999  17  20000  2  
Conclusion:
We have observed that the Countmin sketch is a good choice in a situation where we have to process a large data set with low memory consumption. We also saw that the more accurate result we want the number of hash functions(rows/width) has to be increased.
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