I need ideas for an architecture formed by a Causal Cluster with the main graph and 10K machines in which to consult the graph and that can work automatically if communications with the main graph in the Cluster are ever broken.
There should be periodic updates of these mini graphs if the main graph changes.
It is not necessary for the machines to have a front, all the queries are from the back
- number of licenses to contract
- the 10K machines do not have a large storage or processing capacity
- install the 10k neo4j and use the Neo4j Streams CDC to keep them updated
- install an Elastic on the 10K machines and keep it updated with Neo4j Transaction Handler
- I don't know what else ...
Thanks in advance
All the best
for the licensing etc. questions please reach out to: Contact Us - Neo4j Graph Data Platform
in general you can use Neo4j Fabric for federating many different databases and then select in your fabric queries dynamically which ones you want to address:
thanks for answering.
I have some doubts about the solution you propose.
The image is this:
1)The different machines can be updated with the cluster, so that any change in the cluster is reflected in the 30K machines automatically? like a CDC
2)What is the main difference between this configuration and the default configuration, connecting the different neo4j instances of each machine with the Causal Cluster with kafka + cdc neo4j streams?