dgraph VS NebulaGraph Database

Dgraph: A distributed and scalable graph database known for high performance. It's a good fit for large-scale graph processing, offering a GraphQL-like query language and gRPC API support.

Dgraph (GraphQL, DQL)

Have you tried dgraph.io?

DGraph – A distributed GraphQL database with a graph backend.

How does it compare to, say grakn (renamed https://vaticle.com/, I think?), or draph (https://dgraph.io/), or Ontotext's GraphDB (https://www.ontotext.com/products/graphdb/), or Datomic?

Consul Connect service mesh has a higher memory footprint, so on a small cluster with e5-medium nodes (2 vCPUs, 4 GB memory), you will only be able to support a maximum of 6 side-car proxies. In order to get an application like Dgraph working, which will have 6 nodes (3 Dgraph Alpha pods and 3 Dgraph Zero pods) for high availability along with at least one client, a larger footprint with more robust Kubernetes worker nodes were required.

A NoSQL graph database is a type of non-relational, distributed database which employs a graph model. NoSQL stands for “Not only SQL” and refers to a new breed of databases that differ from traditional relational databases in their data model and performance. Graph databases are especially useful for data associated with relationships—everything from friendships on social netwo#rks to equipment supply chains or business processes. They can quickly traverse vast amounts of linked data points to discover insights and hidden connections between entities, making them ideal for network analysis– such as financial fraud detection, recommendation engines and many other use cases– all while performing at scale.

An open source graph database is always the best place to start as they come with a supportive community that ultimately creates the perfect ecosystem.

When we first came across NebulaGraph, because the ecology was not perfect, and only some businesses migrated to Nebula, we used to import NebulaGraph data, whether full or incremental, by pushing Hive tables to Kafka and consuming Kafka to write NebulaGraph in batch. Later, as more and more data and businesses switched to NebulaGraph, the problem of importing data efficiency became more and more serious. The increase in import time made it unacceptable to still be importing data at full volume during peak business hours.

NebulaGraph NebulaGraph is a popular open-source graph database that can handle large volumes of data with milliseconds of latency, scale up quickly, and have the ability to perform fast graph analytics. Official website https://www.nebula-graph.io/ GitHub projects https://github.com/orgs/vesoft-inc/repositories https://github.com/vesoft-inc/nebula

NebulaGraph, a leading open source graph database, announced it raised tens of millions of US dollars in Series A funding. Investors in the round are led by Jeneration Capital, with participation from the previous investors - Matrix Partner China, Redpoint China Ventures, and Source Code Capital. China Renaissance served as the exclusive financial advisor in this financing round.

Support backup and restore. https://github.com/vesoft-inc/nebula/pull/3469 https://github.com/vesoft-inc/nebula-agent/pull/1 https://github.com/vesoft-inc/nebula-br/pull/22

One option is NebulaGraph if your looking for low latency, scalability, and HA.

After the step-by-step decomposition of the patterns, the expected execution plan for the MATCH clause is finally generated. As you can see, it takes a lot of effort to transform a complicated pattern into the underlying interfaces for a traversal. Of course, the execution plan can be optimized, such as the multi-step traversal can be encapsulated by using the Loop operator and the sub-plan of a one-step traversal can be reused, which will not be detailed in this article. If you are interested, please refer to the source code of Nebula Graph.