Temperature-aware Data Management in Polypheny-DB (Master Project, Finished)
In the data management community it is common practice to describe the access frequency of a certain data set by a “temperature”. Hot means that the data is currently very frequently accessed while cold means that it hasn’t been accessed for a long time. In between, there can be multiple nuances of warm.
Multi-temperature data management refers to the approach of storing hot data on fast (and expensive), warm data on a slightly slower and cold data on slow, but also very cheap
The goal of this project is to
- extend Polypheny-DB to allow user-defined partition functions,
- develop a cost model which classifies data entities with a temperature depending on certain parameters, for example, the access frequency, the storage cost, the read / write latency, etc., and
- implement a temperature-aware partition function which places data based on its access frequency.
Start / End Dates
2021/02/08 - 2021/07/25