Bloom Filters in Java Using Redis

Bloom filters are the in-memory data structures used in applications to test whether a key belongs to a particular set or not, it's like a Redis cache but where the Redis cache stores the entire data as it is rather than any optimization, but in Bloom filters the bitset or bitmap implementation where it provides a good memory optimization and find operation is very fast here each bit represents the presence of a particular element where each bit is hashed by different hash functions.

Bloom filters are much faster and more efficient for some use cases like membership testing i.e. particular elements belong to a set or not in the memory, compared to normal Redis caching.

Steps for Starting the Redis Stack to Start the Redis Bloom Server Using Docker

• Install the Docker from the official website.
• When the server starts the code will start working

Step 1: Adding the dependency on pom.xml after starting the Redis bloom server:

<dependency>
 <groupId>com.redislabs</groupId>
 <artifactId>jrebloom</artifactId>
 <version>2.1.0</version>
 </dependency>

Step 2: Initializing Bloom Filter Client in Java

Client clientVariable = new Client(hostAddress, port, timeOut , poolSize );

The parameters that can be given while initializing:

• host address: The server IP where the application is deployed.
• port: The port it is pointing to default is 6379.
• timeOut: The maximum time it will wait to get the response from the server.
• poolSize: The pool size of the Jedis pool.

Basic operations performed on Redis Bloom

Program of Bloom Filters in Java using Redis

Below is the implementation for Creating and Checking info Redis Bloom filter:

Example 1:

Output:

Below in the output we can see the Redis Bloom Filter info has created.

👁 Information of Redis Bloom filter Created

Example 2:

Below is the implementation for Adding and Checking keys into the Filter:

Output :

Below in the output we can see the keys added into the filter.

👁 Keys added into the filter

Example 3:

Below is the implementation of Deleting the bloomFilter:

Output :

Below in the output we can see the bloom filter has deleted.

👁 Bloom Filter deleted

Limitations of Bloom Filter:

• They may produce false positives and false negatives very rarely.
• Limited operations can only be performed insertion and exists (membership check whether a particular key exists or not in a particular filter).
• The size of the bloom filter should be pre estimated.
• It cannot store exact data and also, we can't retrieve.
• It doesn't have the feature of single key deletion in a bloomFilter.