The complexity of MongoDB's...

## The issue of scaling with MongoDB ... However, at a server level, it’s a different story. You are advised to scale vertically, but there will be a limit to how far this can take you. Once you get beyond this, MongoDB allows you to upgrade a single replica set to a sharded configuration. However, once you have a sharded configuration, you cannot go back to a replica set; this is a strictly one-way operation. There’s also the issue of how MongoDB handles replication. It does this through a replica set, a group of processes that maintain the same data set. While this helps with redundancy and availability, MongoDB requires one node to be the “primary” node, while other nodes are considered “secondary,” rather than treating all nodes as equivalent. That makes rebalancing more complicated. MongoDB experiences service interruptions while you scale up or down or adjust tiers, as this requires a replica set election, as the primary node in a replica set is removed and updated. This primary-secondary replica set model and its reliance on sharding for horizontal scaling introduce complexity and cost as data volumes increase. While MongoDB is capable of handling large datasets, scaling horizontally often requires substantial reconfiguration, leading to service interruptions and operational challenges. For many enterprises, these difficulties become more pronounced as their data requirements increase. … ### Case study: Nativo’s shift from MongoDB to unified, real-time scale ... As traffic surged, this dual-system setup became increasingly complex and created performance bottlenecks. Nativo required sub-millisecond read latency to meet auction deadlines, but MongoDB consistently delivered only 3-4ms reads, creating a critical performance gap. The fragmented architecture also added engineering overhead and made it difficult to keep data consistent across regions. … ## High costs of scaling and performance trade-offs One of MongoDB's most common pain points is the rising cost associated with scaling. As enterprises grow, the need for more hardware, often driven by the need to maintain performance because of sharding, leads to skyrocketing infrastructure expenses. Moreover, MongoDB's approach to keeping secondary indexes in DRAM for faster queries further adds to these costs. … ## The complexity of MongoDB’s sharding architecture MongoDB's sharding model, while powerful, introduces a layer of complexity that can be difficult to manage, particularly as the number of shards grows. Poor sharding strategies lead to data hot spots and inefficient data distribution, exacerbating performance issues and complicating maintenance. … ## Lessons learned from MongoDB’s limitations These companies' experiences highlight important lessons for any enterprise evaluating its database options. While MongoDB offers many benefits, its limitations in scaling, cost, and operational complexity can make it less suitable for high-performance, large-scale environments. Companies that anticipate data growth and require consistent low latency, high availability, and predictable costs should consider alternatives better optimized for these demands.