There are several levels of data abstraction in PSL to help manage and isolate data:

DataStore

The DataStore represents the physical place that all the data is stored. It matches one-to-one with an actual RDBMS database instance (either H2 or PostgreSQL).

All data is stored in tables organized by predicate (one predicate to a table).

Databases are created using their constructor.

In this diagram, you can see how the data resides in the DataStore:
DataStore

Database

The Database is like a view onto a DataStore where subsets of the data are assigned to be read/write, read-only, or inaccessible. This makes it easy to do things like have observations and truth in the same database without worrying about one leaking into the other.

To get a database, you call DataStore.getDatabase() on a DataStore.
getDatabase() takes two required arguments and one variadic argument:

  1. The write partition (this partition will be marked as read/write).
  2. A set of predicates to be considered “closed”.
  3. Any number of read partition (these partitions will all be marked as read-only).

In this diagram, you can see what a Database set up for inference looks like:
Database - Inference

In this diagram, you can see what a Database set up as a truth for weight learning or evaluation looks like:
Database - Truth

Partition

A Partition is the most fine-grained collection of data in PSL. Every ground atom (piece of data) belongs to exactly one partition. Within a partition, all data must be unique (an exception will be thrown during data loading if this is broken).

In most cases, you will want two or three partitions for inference:

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