Neo4j vs. VictoriaMetrics Community

Neo4J is great for creating network graphs or illustrating how things are related. It is also good for finding individuals or things that have greater influence than others in a system. It is not appropriate if you have standard data sets that can be analyzed using conventional methods or visualized using Tableau, for example.

Incentivized

Best suited, where your data is highly cardinal since it does a better job at maintaining it than other competitors. It is also well suited if you are using Prometheus and are looking for something that is less hungry for resources in comparison since the migration would be easier. But in case the company is small and wants a solution which is cheap and relies on built-in visualizations, it is not something that is suited. Although it takes fewer resources than Prometheus, it is still resource-intensive and attracts a high cost for maintenance.

Incentivized

• Mature Query language, I found Cypher QL to be mature in handling all sorts of problems we throw at it. Its expressive enough to be intuitive while providing rich features for various scenarios.
• Native support for REST API, that makes interacting with Neo4J intuitive and easy.
• Support for Procedures in Java, procedures are custom code that could be added to the Neo4J to write custom querying of data. The best part about the procedures is it could be invoked using the REST API. This allows us to overcome any shortcomings from their Cypher query language.
• Nice UI and interface for executing the Query and visualizing the response.
• UI access controlled by User credentials allows for neat access controls.
• Awesome free community edition for small-scale projects.

Incentivized

• Easy to configure and manage
• Ready to be configured at cloud and microservices
• Easy to scale vertically and horizontally

Incentivized

• One of the hardest challenges that Neo4j had to solve was the horizontal scaling problem. I am not updated on recent developments, but at the time of my use, I couldn't find a viable solution.
• Neo4j does not play with other open source APIs like Blueprint. You have to use the native Neo4j API.
• There wasn't a visual tool to see your data. Of course, third party tools are always available, but I would have loved something which came with the Neo4j bundle. I love that Docker comes bundled with Kitematic, so it's not wrong to hope that Neo4j could also ship with some default visualization software.

Incentivized

• Community and Eco system -> Although it supports the ecosystem of Prometheus, it is still troublesome for someone who's not using Prometheus
• Configuration is not so easy, especially if you are a beginner, you will struggle a bit. Even the documentation is not so welcoming for beginners
• Built-in Visualisation is bare minimal, leading almost everyone to use some other extension/tool

Incentivized

[Based on] Query Language, Performance on small and large data sets, integration and deployment, analysis, API support, Interactive UI.

Incentivized

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.

Incentivized

Prometheus only support PromQL and it is very complex with different exporter required for different requirement like Windowsexporter,linixexporter,sqlexporter etc but VictoriaMetrics is very simple comapred to it. VictoriaMetrics support both PromQL and MetricQL and can be integrated with Graphana easily. It is very easy to setup and learn compared to mutiple Prometheus exporters

Incentivized

• Positive: Less complex queries on graph structures, than in relational databases.
• Negative: maintenance is a huge deal, things doesn't work and break, requiring lengthy restore operations.

• Lowered our infra costs in comparison with our older tools
• Need minimal effort for migration since it already supports Prometheus ecosystem
• Saved on Storage costs

Incentivized