dtm VS dgraph

- using a distributed transaction framework (like https://github.com/dtm-labs/dtm)

But in this article, we introduce a workflow pattern in github.com/dtm-labs/dtm. Under this pattern, a mixture of XA, SAGA and TCC can be applied to different branches in a single distributed transactions, allowing users to customize most of the contents of a distributed transaction, providing great flexibility.

The open source distributed transaction framework https://github.com/dtm-labs/dtm has a two-stage message pattern inside that handles this problem very well. The following is an example of the use of an interbank transfer operation.

The above Sub-transaction Barrier technique, when used in conjunction with the distributed transaction framework https://github.com/dtm-labs/dtm, has been made available in several language SDKs, with the following example code in Go.

Maybe you can take a look at github.com/dtm-labs/dtm which provide built-in TCC pattern

Distributed XA transactions can solve the above business problem. This article presents a solution based on dtm-labs/dtm. DTM is a popular distributed transaction framework which supports XA, Saga, OutBox, and TCC patterns.

Github: https://github.com/dtm-labs/dtm

This article gives an example of implementing a distributed transaction across multiple store engines, Mysql, Redis and Mongo. This example is based on the Distributed Transaction Framework https://github.com/dtm-labs/dtm and will hopefully help to solve your problems in data consistency across microservices.

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.