Implementation of MongoDB Sharded Cluster
Industry
Telecommunications
Project
Implementation of MongoDB Sharded Cluster for CDR project
Client
Top Telecom service operator in Spain
Our Role
Development & Implementation
Technologies
OS: Ubuntu 22.04 LTS
Database: MongoDB version: 6.0.4
Cloud: AWS
Challenge
A leading telecom provider faces challenges due to the rapid growth of Call Detail Records (CDRs), generating billions of records daily. Their reliance on a single MongoDB server and traditional RDBMS (MySQL) has led to critical storage limitations, with the MongoDB server nearing capacity and risking data loss. Additionally, the RDBMS experiences performance bottlenecks during peak usage, slowing insert speeds and hindering real-time analytics.
The single-server setup poses risks to high availability and disaster recovery, as any downtime leads to data unavailability for customers. Moreover, the RDBMS struggles with complex queries on large datasets, causing inefficient retrieval. Considering the impractical costs of vertical scaling, transitioning to a MongoDB-sharded cluster is essential for optimizing data management and enhancing performance to meet future demands.
Solution
Sharding in MongoDB is a key process that facilitates horizontal scaling by dividing a large dataset into smaller, more manageable subsets known as shards. Each shard is implemented as a replica set, meaning that data is partitioned across multiple servers for enhanced performance and storage capacity and replicated to ensure high availability. This architecture allows the system to handle an increasing volume of data efficiently, as shards can be distributed across different machines. The cluster configuration is managed by config servers, which store crucial metadata and configuration settings, ensuring that the system operates cohesively.
The query router, known as Mongos, acts as an intermediary between client applications and the sharded cluster. It intelligently routes queries to the appropriate shards based on the shard key, which is a predefined value that determines how data is distributed. When a shard receives a query, it processes it and returns the results to the query router, which then aggregates the responses and sends them back to the client. This system not only optimizes data retrieval and insertion speeds but also alleviates performance bottlenecks typically experienced in traditional single-server setups, enabling MongoDB to support large-scale applications with efficiency and resilience.
Key Benefits
Maximize Storage for Data Growth
Dynamic storage scaling accommodates growing data volumes without capacity constraints. This is essential for managing billions of CDRs generated daily.
Horizontal Scaling Enhances Performance
Distributing workloads across multiple servers enhances performance and prevents bottlenecks. This approach efficiently handles peak loads and large datasets.
High Availability & Fault Tolerance
Built-in redundancy and automatic failover ensure continuous operation during failures. This resilience is crucial for maintaining service uptime in telecom.
High-Speed Inserts & Queries
Shard clusters enable rapid data ingestion and query performance, critical for real-time processing. This capability meets escalating data demands effectively.
