Data partitioning is a common concept in Database Management Systems (DBMS) to split data in chunks or fragments. In general, it can be distinguished between Replication, Vertical Partitioning and Horizontal Partitioning. Polypheny also supports combining these techniques. This allows selectively replicating parts of the data and therefore efficiently optimize for multiple application requirements at the same time.
Polypheny-DB allows storing an entity on multiple data stores. This allows optimizing for different workloads, since Polypheny-DB always selects the data store with the best characteristics for executing a query. By exploiting the advantages of the available data stores, Polypheny is able to provide the best possible performance for heterogeneous workloads.
In the Polypheny context, the assignment of a full replica to a data store is called Full Placement.
In addition to fully replicating an entity, Polypheny-DB also allows replicating only a subset of the fields. Every field needs to be assigned to at least one data store. In the Polypheny context, these assignments are called Column Placements. It is distinguished between automatic placements and manual placements. The former are created and removed automatically by Polypheny. There are always automatic placements for the primary key fields.
Refers to the partitioning of entities into a disjoint set of tuples that are stored separately. Polypheny-DB comes with support for multiple partition algorithms. These algorithms can be applied to any entity based on an arbitrary field (i.e. the partition column), which results in a fragmentation of the entity based on the values of the selected field.
Every partition is assigned to one or multiple data stores. In the Polypheny context, these assignments are called Partition Placements.
Polypheny supports the following partition functions:
- List - the entity is partitioned by explicitly specifying values for each partition.
- Hash - the entity is partitioned based on the hash of the values of the partition field.
- Range - the entity is partitioned into numerical “ranges”. There needs to be no overlap between the ranges.
- Temperature-aware Partitioning - Serves as an extension to the classical partition functions. It places data based on their “temperature” in either a HOT- or COLD partition group. This classification depends on certain cost metrics like the access frequency. It internally uses the hash partitioning function.