Bad Horizontal Scaling: Distributing data and queries across cluster shards is complex and not fully supported, less mature, and less easier-to-manage in distributed architectures, problems for very large datasets and high throughput workloads
Memory Limitations: Support is mainly for in-memory where majority portion of graph data is in RAM, for large graphs that exceed the available memory the performance degrades
Query Performance and Tuning: Optimizing queries is challenging and requires understanding the entire query plan and indexes which is counter-intuitive, why not than just use a relational database like postgres?
Commerical Licensing Costs: Expensive for large deployments and advanced features
Community Edition Limitations: Limited features, scalability, and support
Limited Sharding Capabilities: Sharding is not fully supported, setup and management can be problematic and complex
Focus on Property Graphs: Does not support any other type of graph schemas and paradigms like RDF
Full-Text Search Limitations: Lacks advanced and dedicated search capabilities
Backup and Recovery: Limited and complex backup and recovery especially for clustered environments and very large datasets, problematic for point-in-time recovery or restoring from a distributed backup
Monitoring and Management: Requires specialized tools and can be complex
Vendor Lock-in: Cypher is tightly coupled to Neo4J which may lead to vendor lock-in
Data Import/Export: Import/Export of very large datasets is problematic and time-consuming
Integration: In many cases custom development with other systems may be required
Driver Maturity and Consistency: Maturity of language drivers and feature parity can vary which may lead to inconsistencies and limitations
Limited Support for Some Languages: Less common languages may be less mature which may lead to maintenance and feature lag
Cypher Quirks: Frustrating quirks and edge cases for developers that may lead to unexpected behavior, requires understanding the query plan and execution
Stored Procedures: These can add complexity in development process
Schema Evolution: Evolving data model like new properties and relationships can be problematic especially in data migration
Data Validation: Ensuring data query and consistency requires careful planning and implementation of validation logic at application level
Integration with other Graph Systems: Differences in data models and query languages can be problematic
Deployment Complexity: Setting up and management of a clustered Neo4J deployment can be complex and require careful configuration
Security Hardening: Requires careful configuration and maintenance especially against specific settings and potential vulnerabilities
Tooling: Less mature for monitoring, profiling, and management
Resource Consumption: Very resource-intensive especially for large graphs and complex queries requires capacity planning and resource management
Reasoning: Being mainly a property graph database it lacks inference and reasoning ability, additional RDF support can be achieved via tools like neosemantics but they also lack reasoning functionality, difficult to optimize for SPARQL queries, significant custom development is required for semantic and linked data
Generative AI: Terribly slow for generative AI, integration with LLMs, poor query performances for specific query tasks in GraphRAG, best to use alternatives that can handle large datasets and more flexible queries, requires careful consideration of chunking strategy on branches
