  Real world graphs are often dynamic and evolve over time. It is crucial for
storing and querying graph evolution in graph databases. However, existing
works either suffer from high storage overhead or lack efficient temporal query
support, or both. In this paper, we propose AeonG, a new graph database with
built-in temporal support. AeonG is based on a novel temporal graph model. To
fit this model, we design a storage engine and a query engine. Our storage
engine is hybrid, with one current storage to manage the most recent versions
of graph objects, and another historical storage to manage the previous
versions of graph objects. This separation makes the performance degradation of
querying the most recent graph object versions as slight as possible. To reduce
the historical storage overhead, we propose a novel anchor+delta strategy, in
which we periodically create a complete version (namely anchor) of a graph
object, and maintain every change (namely delta) between two adjacent anchors
of the same object. To boost temporal query processing, we propose an
anchor-based version retrieval technique in the query engine to skip
unnecessary historical version traversals. Extensive experiments are conducted
on both real and synthetic datasets. The results show that AeonG achieves up to
5.73X lower storage consumption and 2.57X lower temporal query latency against
state-of-the-art approaches, while introducing only 9.74% performance
degradation for supporting temporal features
