an attempt to create an asynchronously replicated append only performant split brain resistant mesh
I am a beginner at distributed systems.
I thought:
- if we had an append only system, that could accept writes as fast as it could
- but also replicated the data asynchonously behind the scenes
- resolve conflicts with a GUI
CAP theorem means that we sacrifice certain things for certain requirements.
node_lww.py uses timestamps to resolve conflicts, received broadcasted data is sorted when retrieving a value, so all servers that are synchronized shall agree on a value.
If there is a split brain, then a server shall report a different value for that split.
Creates a hash of the data and creates a trail of the data that originated from. I am yet to create a visualization of the trail of data.
Generates random withdrawals and deposit events and applies them to a balance. Every node in the cluster should agree on the final balance.
The problem is that any decision based on a balance could be cancellable.
This is used by the Jepsen tests.
The Jepsen tests test s for linearizability and the eventually consistent mesh fails the linearizability test.
I am working on Jepsen tests for the mesh. Currently the jepsen tests spin up 5 servers in AWS and deploy the mesh to them, then creates a connection to each server to prepare for streaming instructions.
The linearizability test in Jepsen fails, because this software is not linerarizable.
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