MongoDB

MongoDB has exhibited severe data loss issues including unexplained record disappearance, unsuccessful recovery from corruption, replication gaps causing missing records on slaves, and replication stopping without errors.

MongoDB exhibits non-linear performance degradation as data volume increases. Real-world cases show response times deteriorating from 5ms to over 1 second under load, and sharding provides only temporary relief while adding operational complexity. Query performance becomes unacceptable for high-throughput transactional applications.

MongoDB requires a global write lock for any write operation. Under write-heavy loads, this severely degrades performance, making it unsuitable for applications with balanced or write-heavy read/write ratios.

MongoDB's CUD (Create, Update, Delete) operations are inefficient at scale. Deleting all documents from a 50-million-document collection takes many hours, forcing developers to drop and recreate collections instead. MongoDB lacks a TRUNCATE TABLE equivalent.

Adding a shard to a MongoDB cluster under heavy load is problematic. MongoDB either migrates chunks so aggressively that it causes DoS conditions on production traffic, or refuses to move chunks at all, making it unsuitable for high-traffic sites with heavy write volumes.

The mongos routing layer is unreliable and crashes every few hours to days under load. Some crashes involve assertion failures that don't fully terminate the process, leaving it in a broken state even with restart supervision.

Replication on heavily loaded MongoDB servers either causes DoS on the master or replicates so slowly that the operation log is exhausted, requiring very large oplog sizes (e.g., 50GB) and still failing to keep up.

Choosing the wrong shard key can cause data imbalance, generate too many scattered queries across shards, and severely limit MongoDB's horizontal scaling capabilities. This is a critical architectural decision with lasting performance implications.

MongoDB consistently delivers 3-4ms read latency, which is insufficient for applications requiring sub-millisecond response times (e.g., real-time bidding systems). This creates a critical performance gap for latency-sensitive workloads.

Once MongoDB is upgraded from a replica set to a sharded configuration for horizontal scaling, it cannot revert to a single replica set. This is a strictly one-way operation, locking organizations into sharding architecture permanently.

Scaling MongoDB up or down requires replica set elections when the primary node is updated, causing service interruptions. This makes scaling operations disruptive in production environments.

MongoDB uses eventual consistency for replica sets, which can cause situations where different users read different data at the same time. Applications requiring strong consistency and real-time data accuracy face serious issues.

Operating MongoDB at scale requires significant ongoing operational effort including replica set management, version inconsistencies, sharding maintenance, and aggregation pipeline tuning. Organizations find themselves spending more engineering time maintaining the database than building product features. Migration case studies show 50% cost reductions when switching to relational alternatives.

MongoDB implements new capabilities (transactions, analytics, time-series, search, graph) as bolt-on features rather than core architectural improvements. These features lack the robustness of native implementations in purpose-built databases, requiring constant maintenance and tuning. The architecture wasn't designed for modern analytical and transactional workloads.

MongoDB's document-oriented model lacks complex join support compared to SQL databases. The $lookup operator provides only basic functionality, forcing developers to redesign data models and embed related data within documents, which results in significant data duplication and storage overhead.

MongoDB's ACID transaction capabilities are significantly weaker than traditional SQL databases. While multi-document transactions were added in version 4.0, they come with substantial performance overhead and remain difficult to use reliably for applications requiring strict consistency guarantees.

MongoDB's aggregation framework becomes brittle and unmaintainable for complex analytical queries. Pipelines require hundreds of lines of transformations that break easily when document structure changes. Teams often export data to SQL databases or data warehouses to handle reporting that would be simple SQL joins, adding operational overhead.

Oversized chunks in MongoDB sharding cannot move between shards, causing data imbalance and performance problems. This remains a persistent issue even with MongoDB 7.x automated chunk splitting improvements.

Companies spend around $200,000 annually on MongoDB analytics infrastructure, including ETL processes, duplicate data storage, broken pipeline maintenance, and developer time for constant schema adjustments.

MongoDB documents have a strict 16 MB size limit. Developers frequently hit this limit by appending unbounded arrays (logs, activities, comments) inside single documents, causing update failures and data loss.

Maintaining a large number of indexes in MongoDB degrades write performance because each write operation must update multiple indexes. The system forces developers to choose between query performance and write performance.

Using MongoDB alongside other systems creates architectural fragmentation, increasing engineering overhead and making it difficult to maintain data consistency across regions.

High network latency in distributed environments significantly impacts application performance and causes delays. Default timeout settings (30 seconds) are often too long for user experience expectations.

MongoDB faces challenges protecting data across distributed environments including cloud providers, edge devices, and on-premises systems. Implementing consistent encryption and security policies across AWS, Azure, and edge devices impacts performance and adds complexity.

Historical MongoDB deployments lacked adequate tools to monitor and manage production systems effectively. Context-aware metrics are now critical, but understanding which metrics to track and how to calculate working set sizes remains challenging.

As MongoDB databases grow in size and complexity, queries become slow and inefficient. Developers must fine-tune queries, indexes, and database architecture to ensure optimal performance, but this is time-consuming and error-prone.

Migrating large datasets from relational databases (e.g., MySQL) to MongoDB is difficult and time-consuming, requiring significant engineering effort to restructure data and handle schema differences.

MongoDB's flexible, schemaless design initially enables rapid iteration but becomes a liability at scale. The dynamic schema leads to data drift, type divergence, and loss of control over data consistency across teams. Proper data model design requires specialized knowledge and careful planning to avoid technical debt.

MongoDB Query Language (MQL) is a custom syntax that locks teams into MongoDB-specific knowledge and prevents cross-team collaboration. Unlike SQL, which is portable across databases, MQL expertise doesn't transfer to other database systems, making it difficult to migrate or use multiple databases in the same organization.

MongoDB's self-containing document paradigm and dynamic schema lead to larger storage requirements compared to normalized relational databases. Data redundancy and fragmentation further increase storage use and costs.

MongoDB's transaction feature introduces performance trade-offs between data consistency and transaction throughput. Developers must carefully design transaction boundaries to avoid bloating the transaction log, requiring complex optimization.

MongoDB's primary-secondary replica set model requires one node to be 'primary' while others are 'secondary', rather than treating all nodes equivalently. This makes rebalancing more complicated compared to peer-based architectures.

MongoDB does not support renaming collections. Developers must drop and recreate collections if restructuring is needed, making schema evolution cumbersome.

MongoDB stores frequently used data and indexes in RAM, making performance highly dependent on sufficient RAM availability. This can consume more memory resources and require more hardware than other databases, increasing operational costs.

MongoDB feels fast with small datasets even without indexes. As data grows, unindexed queries suddenly become slow, forcing full collection scans. Developers often ignore indexing until performance issues force attention.

Users report a disconnect between the MongoDB development team and user base. Concerns are not adequately addressed in a timely manner, and the team prioritizes new features over fixing existing problems.

Users have criticized the quality of MongoDB's documentation and support resources. Inadequate documentation hinders developers, especially those new to the platform, making onboarding difficult.

Developers often use MongoDB multi-document transactions for single-document updates, which are already atomic. This adds latency and resource overhead unnecessarily, as atomic operators are sufficient for single-document operations.