Efficient Use of Bloom Filters in Microservices

Introduction

Reliable and efficient data processing is a crucial issue in the banking industry. Microservice architecture plays a significant role in this, as it ensures system modularity, scalability, and resilience. In this context, probabilistic data structures, such as Bloom filters, are effectively used to reduce the load when processing data-intensive tasks.

What is a Bloom Filter?

A Bloom filter is a space-optimized probabilistic data structure used to check the membership of an element in a set. It always correctly says "no" if the element is not present, while it may say "yes" to an element that is not actually in the set (false positive).

Use Cases in Banking:

1. Checking blocked bank accounts - During a transaction, it is necessary to check if the recipient's account is blocked or not. Bloom filters can be used to quickly check blocked accounts without directly connecting to the database.

2. Detecting fraudulent transactions - Bloom filters can be used to quickly check if a transaction belongs to a set of fraudulent transactions.

3. Blacklist checking - Banks have blacklists for suspicious individuals and companies. Bloom filters will help optimize these checks.

C# Code Example:

Here's how it can be implemented in C#:

Here, the Bloom filter is used to check if the transaction's recipient account is in the set of blocked accounts. If yes, only then do we perform additional checks by connecting to the database. This reduces the load on the database.

Conclusion:

Bloom filters are a very useful data structure in banking industry microservices, where processing large volumes of data is common. It helps us optimize performance, reduce database load, and increase system scalability. However, it is necessary to optimally adjust the filter parameters (size, number of hash functions) to the specific use case to minimize the probability of false positives.

Optimizing a bank's IT infrastructure with probabilistic structures contributes to the creation of client-oriented, reliable, and scalable services.