Project Name

Optimizing Data Scalability and Performance: A Case Study on Implementing Apache Cassandra as a Large-Scale Enterprise solution

Industry

Telecommunication

Technology

Apache Cassandra, Apache Cassandra Reaper

Overview

Our client belongs to the Telecom and Broadband industry in the American region and works on leveraging the concept of Network Maintenance solutions. They play a key role in helping cable operators identify any impairments in their cable plants. The main aim of our client is to get a solution for the overall maintenance without compromising the subscriber’s quality of experience.

Challenges

Since the client was managing the millions of modems on the production system they were facing issues handling the inefficient database through RDBMS and they want to opt for Cassandra Cluster to store the entire database and this data getting refreshed daily.
Managing the massive volume of data became quite impossible for our client.
Keeping the data consistent across the cluster customers doing manual repair becomes quite uneasy.
Deploying a NoSQL database system was not enough, the need for a decentralized system became very crucial at that time.

Our Solution

We have given a comprehensive approach to our client that includes:

Our team had implemented Cassandra Reaper which is an open-source tool for designing the automated repair and maintenance of Cassandra databases.
With this, we automated the identification and repair of inconsistencies in data across the nodes to ensure data integrity.
We had set up a single instance of repair service per customer environment using both Podman containers and as a systemd service on port 8080 -> 8081. Our team then successfully configured the clusters of each of the customers and scheduled weekly repairs on each of the Cassandra clusters optimization.
Moreover, we highlighted the scalability of Cassandra Reaper which was capable of managing repairs on multiple Cassandra clusters.
At last, we suggested the deployment of several instances for high availability and adaptability to network restrictions.

Data Flow Diagram

Conclusion

At last, with our solution, we efficiently maintained the customer’s high-load Casandra clusters through scheduled repair jobs using Cassandra-reaper. This helped our client to ensure data consistency, minimizing the repair time, and providing insights for optimal database performance. With Cassandra’s implementation, it becomes possible for our client to manage and maintain the large-scale and distributed Cassandra database in a telecommunications environment.