Optimizing MongoDB Performance and Scalability for a Global Medical Technology Company
Strengthening Cluster Health Through Monitoring, Remediation, and Smarter Data Architecture
For a global medical technology company with 5,000 employees, worldwide manufacturing operations, and more than $2 billion in revenue, database performance and scalability were essential to maintaining stable operations.
As data volumes continued to grow, the company’s MongoDB environment began to experience a combination of storage pressure, balancing issues, and deeper application-level problems that affected cluster health and performance.
Delbridge was engaged to help stabilize the environment, improve visibility, and put in place a more sustainable long-term strategy.
The Delbridge Approach
Delbridge addressed the engagement through a combination of operational monitoring, architecture improvements, and deep technical remediation.
The work began with better visibility into cluster behavior. To support continuous micro-monitoring, Delbridge developed a Python script that extracted key metrics from MongoDB Atlas clusters and populated Excel dashboards. This made it easier to detect developing issues early, including those caused by relatively small but persistent daily data buildup.
At the same time, Delbridge worked to resolve underlying storage and balancing problems affecting the environment. The engagement combined short-term corrective action with longer-term structural improvements, helping the customer move toward a healthier and more scalable MongoDB deployment.
Improving visibility into cluster performance and growth patterns
Restoring balance and scalability across the sharded environment
Resolving root-cause data issues to support long-term stability
The Challenge: Growth , Imbalance, and Hidden Application Issues
The customer’s MongoDB environment was under pressure from a range of interconnected issues.
Over time, more than 10 TB of uncompressed data had accumulated in shards, pushing the environment beyond MongoDB’s optimal operating thresholds and contributing to critical performance issues. As data volumes continued to increase, the existing architecture struggled to distribute load efficiently.
Compounding the challenge, shard balancing had been blocked by application-level issues that generated duplicate unique IDs. These duplicates led to cluster instability and failures, making it difficult to maintain healthy data distribution or scale cleanly.
The customer needed more than a one-time fix. It needed a practical path to restore performance, accommodate growth, and address the root causes that had been undermining cluster health.
Delivery and Execution
Delbridge implemented a set of coordinated improvements across monitoring, storage, and cluster architecture.
To improve operational visibility, the team developed a Python-based monitoring solution that extracted key MongoDB Atlas metrics and fed them into Excel dashboards for continuous tracking. This gave the customer a more detailed view of cluster behavior and helped surface issues early, including the impact of daily data buildup at relatively small increments.
To address scale and storage pressure, Delbridge performed initial syncs and added new shards to support growing data volumes. Although there were temporary performance trade-offs during this work, the changes established a more scalable foundation for the environment.
The team then migrated data from overloaded shards to newly added ones with minimal disruption to operations, reducing pressure on the most heavily impacted parts of the cluster. At the same time, Delbridge investigated and resolved the application-level issue behind duplicate unique IDs. To do this, the team created custom JavaScript tools to detect and remove duplicates, clearing the path for shard balancing and helping prevent further cluster failures.
The engagement also supported a broader long-term data strategy by improving data distribution and introducing online archiving to help maintain healthier cluster performance over time.
Results
Through this engagement, the medical technology company strengthened the performance, scalability, and long-term health of its MongoDB environment.
By introducing automated micro-monitoring, Delbridge gave the customer better visibility into early warning signs and ongoing cluster behavior. By addressing overloaded shards, adding capacity, and migrating data with minimal disruption, the team improved the cluster’s ability to scale as data volumes grew.
Most importantly, Delbridge resolved the deeper duplication issue that had blocked balancing and contributed to cluster instability. With those root causes addressed and online archiving introduced as part of the longer-term strategy, the customer was left with a more stable and sustainable MongoDB foundation.
This case demonstrates how complex database issues are often not isolated to infrastructure alone. When monitoring, architecture, and application behavior are addressed together, it becomes possible to restore stability and create a platform better suited for long-term growth.
Outcomes
- Automated monitoring of MongoDB Atlas cluster metrics
- Excel dashboards for continuous micro-monitoring
- Resolution of performance issues tied to 10+ TB of uncompressed shard data
- New shards added to support continued data growth
- Data migrated from overloaded shards with minimal disruption
- Duplicate unique ID issues identified and resolved
- Custom JavaScript tools created for duplicate detection and cleanup
- Online archiving introduced to support long-term cluster health
Delbridge owns the largest number of hands-on DBA professionals outside of MongoDB, and a growing capability outside of MongoDB as well.
We have assembled a global team to provide follow-the-sun coverage and a regular reporting process that helps document current state operation, areas of weakness, and metrics over time.
What do we deliver ?
“Always-on” Monitoring
1Timely Incident Response
2Cluster Provisioning
3Backup and Recovery Management
4Upgrades and Change Management
5Risk Mitigation
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