“Native” is a misleading or misused term in the context of graph databases because a graph cannot be mapped in a maximally consistent way with the underlying hardware (the von Neumann architecture represents data in a sequential manner and it is much faster to access it this way rather than randomly).
There are generally two families in the graph database world: those which use underlying traditional tables of nodes and many-to-many edges; and index-free adjacency which just means each node in the graph knows the memory address of its connections (other side of the edges).
Distributed graphs necessarily end up using the former because it’s difficult if not impossible for a node to know the memory address of its connection when that crosses a physical boundary. So typically index-free adjacency graphs have a master-slave setup with multiple read replicas but a single one to write to.
So with a “native graph” you don’t rely on potentially expensive join operations to find neighbors of neighbors and can traverse complex paths easily.
This is spot on. Though, in Memgraph’s context, there is a combination of lock free techniques and raw pointer representation that can work exceptionally well for a large variety of use cases, and even better so compared to others on streaming / event driven / large volume of writes. Using raw pointers, there are also optimization opportunities when storing or rebalancing the graph to maximize the number of adjacent nodes in a cache line so benefiting from sequential representation too.
There are generally two families in the graph database world: those which use underlying traditional tables of nodes and many-to-many edges; and index-free adjacency which just means each node in the graph knows the memory address of its connections (other side of the edges).
Distributed graphs necessarily end up using the former because it’s difficult if not impossible for a node to know the memory address of its connection when that crosses a physical boundary. So typically index-free adjacency graphs have a master-slave setup with multiple read replicas but a single one to write to.
So with a “native graph” you don’t rely on potentially expensive join operations to find neighbors of neighbors and can traverse complex paths easily.