Approaching Infrastructure Thresholds: The database size swelled to nearly 14 TB, reaching the physical storage limits of their AWS EC2 instances.
Severe Resource Strains: High-frequency write operations caused heavy CPU spikes, limiting the system’s processing headroom.
Query Latency: Overloaded tables resulted in slower query responses, threatening the speed and reliability of the platform's user-facing services.
Lack of Automated Scale:The team lacked an automated system to manage long-term database growth while maintaining continuous database stability.
→ Build a structured partition model to automatically archive historical data to cost-effective AWS S3 storage.
→ Lower CPU utilization on write operations to ensure predictable performance.
→ Transition the legacy database version to a modern, supported release to enable parallel database replication without service interruption.
Designed and executed a custom database partitioning approach to separate active data from historical records.
Upgraded the core database environment from MySQL 5.5 to the latest stable release. This allowed the platform to leverage advanced parallel and crash-safe replication capabilities.
All structural database changes were completed on a parallel environment. The final cutover to the new system was executed with minimal downtime, protecting business continuity.
Deployed Prometheus and Grafana dashboards to monitor internal database performance and prevent operational issues.
Archiving historical partition files directly to AWS S3 reduced the active database footprint by 57% (down to 6TB from 14TB), eliminating the risk of hitting EC2 storage limits and optimizing long-term hosting expenses.
Slashing database CPU consumption by 45% cleared processing queues, ensuring the platform remains stable during unexpected call traffic spikes.
By organizing active data into lean, partitioned sets, query response times improved, allowing internal teams and support agents to pull up records faster.
By optimizing the open-source MySQL and ProxySQL stack, the team achieved a 4X write performance boost and a 5X improvement in data handling capacity without committing to expensive proprietary database vendors.
Exotel manages cloud communications for over 7000 enterprise customers, orchestrating over 25 billion customer interactions annually. As transaction volumes soared, their primary database grew to nearly 14 TB, hitting the physical limits of their cloud servers. This data strain caused severe CPU load during peak hours, threatening the fast, dependable query response times required to support global business communications. To sustain momentum, Exotel needed a long-term scaling strategy that avoided expensive hardware upgrades or proprietary software lock-in.
Exotel partnered with Mydbops, integrating database consultants directly into their workflow. Acting as a remote extension of the team, Mydbops designed an automated partitioning lifecycle, moving older records to cost-effective S3 storage and safely shrinking the active database size to a lean 6 TB. By upgrading the core architecture in a parallel environment, they managed the final cutover with minimal downtime. Mydbops continues to support Exotel with 24/7 remote DBA services and real-time monitoring, ensuring a highly available platform ready to absorb future global growth.
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