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Abstract
En 中文
Distributed database systems are widely used to meet the demands of storing and managing computation-heavy workloads. To boost performance and minimize resource and data contention, these systems require selecting a
distributed physical design
that determines
where
to place data, and
which
data items to
replicate
and
partition.
Deciding on a physical design is difficult as each choice poses a trade-off in the design space, and a poor choice can significantly degrade performance. Current design decisions are typically static and cannot adapt to workload changes or are unable to combine multiple design choices such as data replication
and
data partitioning integrally. This paper presents
MorphoSys
, a distributed database system that dynamically chooses, and alters, its physical design based on the workload. MorphoSys makes integrated design decisions for all of the data partitioning, replication and placement decisions on-the-fly using a learned cost model. MorphoSys provides efficient transaction execution in the face of design changes via a novel concurrency control and update propagation scheme. Our experimental evaluation, using several benchmark workloads and state-of-the-art comparison systems, shows that MorphoSys delivers excellent system performance through effective and efficient physical designs.
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