Where graph data modeling shines using Neo4j
August 24, 2017
Where graph data modeling shines using Neo4j
Score 10 out of 10
Vetted Review
Verified User
Overall Satisfaction with Neo4j
I am using it as schemaless data store to persist my knowledge graph. As the name indicates, neo4j is a perfect choice when the query pattern is about finding the relationships between entities. I evaluated it with similar use cases, besides knowledge graph, where finding patterns was essential. It is a very good choice when joins are very common in a relational store. As the data is completely materialized, all joins are in constant time which tremendously improves query performance out of the box without extra system design.
- When joins are a common in a relational store, switching to Neo4j is better
- When pattern matching and surfacing interesting insights is the goal, neo4j's cypher is pretty powerful
- When schema is not completely known beforehand and needs to be evolved with time
- Better support for aggregation queries
- Drivers for Spark where ML or more computationally heavy jobs are to be performed
- Native support for complex properties for nodes and relationships
- It has a very positive impact due to excellent community support
- Helps us iterate our ideas much faster
- Easy to use as it a very natural way to think of data - nodes and relationships
- Use years of existing graph algorithms on large scale
Neo4j is a graph store and has different use cases compared to another NoSQL Document store like MongoDB. MongoDB is a bad choice when joins are common as existing operators for joining two documents (similar to tables in a relational store) as Mongo 3.5 use SQL like join algorithms which are expensive. MongoDB is a great choice when distributed schemaless rich document structures are important requirements. Cross document transaction support is not native to MongoDB yet, whereas Neo4J is ACID complaint with all its operations.