backward logic - JoseCanova/brainz GitHub Wiki

You're asking to expand on the concept of "backward logic" in the context of the relational graph and prioritization. This is an excellent point, as many database operations (like data loading or deletion) have inherent directionality that might run counter to simple "forward" dependency.

Let's break down "backward logic" with scientific foundations, particularly in relation to your graph model and the goal of batch execution.

Backward Logic in Relational Graphs and Batch Processing "Backward logic" in this context refers to processing dependencies in the reverse order of their conventional relational (foreign key) dependencies, or considering the implications of actions that "clean up" or "delete" data, which often flow from dependent to parent entities.

1. The "Forward" (Creation/Insertion) Logic: Topological Sort

Foundation: When dealing with data insertion or creation, the standard approach is based on "forward" dependencies. If Table B has a foreign key to Table A, then Table A must exist before Table B can be inserted. This forms a directed graph where edges point from the referenced table (parent) to the referencing table (child).

Graph Traversal: A topological sort (as discussed before) gives you a valid order for inserting data. It processes nodes that have no incoming dependencies first, then those that depend only on already processed nodes, and so on. Your processGraphByBreadthFirst and generatePriorityForElement aim to identify these "root" or independent elements and prioritize elements that are less dependent.

2. The "Backward" (Deletion/Cleanup) Logic: Reverse Topological Sort

Foundation: When dealing with data deletion or cleanup, the dependency flow is reversed. If you want to delete a record from Table A, and Table B has records referencing it (via a foreign key), you often cannot delete from Table A directly due to referential integrity constraints. You must first delete the referencing records in Table B, and then you can delete from Table A. This implies a "backward" flow of deletion.

Graph Traversal for Deletion: This scenario requires a reverse topological sort. You process nodes that have no outgoing dependencies first (leaf nodes), then those whose dependencies have already been deleted, and so on. In the context of your graph, an edge from A to B (A -> B) means B depends on A. For deletion, you would want to delete B before A.

Example:

Recording has a foreign key to ArtistCredit. (Edge: ArtistCredit -> Recording for insertion)

To delete an ArtistCredit, you must first delete all Recording records that reference it. (Deletion flow: Recording -> ArtistCredit)

3. "Backward Logic" in Your Graph Model and Prioritization

Your current generatePriorityForElement method calculates a priority based on the number of outgoing edges (pe.getSource().equals(element) implies counting edges from the element). This is a metric of how many other entities depend on this one in a "forward" sense. A higher count implies more downstream dependencies.

If your batch execution needs to handle scenarios like:

Cascading Deletions: If your database has ON DELETE CASCADE rules, the database handles the "backward logic" automatically. You delete the parent, and the children are automatically deleted.

Manual Deletion Ordering: If you need to manually delete data across tables without cascading rules (or to control the order explicitly), then you would need to:

Construct a graph that represents the deletion order (i.e., reverse the direction of foreign key edges). So, if TableB references TableA, the deletion dependency is TableB -> TableA.

Perform a topological sort on this reversed graph. This would identify tables that can be deleted first (those with no entities depending on them, or whose dependents have already been removed).

Specific Business Logic for Cleanup/Archiving: Sometimes, "backward logic" isn't strictly about foreign keys but about business rules. For example, if you archive old Release data, you might first need to archive related Tracks, then Mediums, then ReleaseLabels, before finally archiving the Release itself. This defines a processing flow that is logically "backward" from creation.

How to Implement "Backward Logic" in JGraphT:

Reverse the Graph Edges (Conceptually):

If your entityDirectedGraph currently represents Parent -> Child for insertion, you would need to either:

Create a new graph by iterating through all edges in entityDirectedGraph and adding them in the reverse direction (target -> source) to the new graph.

Utilize a reverse iterator or algorithm if JGraphT offers one for your specific need (though explicitly reversing the graph is clearer).

Apply Topological Sort on the Reversed Graph:

Once you have the "reversed dependency" graph, apply a topological sort on it. This will give you the correct processing order for "backward" operations like deletion or specific cleanup.

Nodes appearing earlier in this reverse topological sort would be higher priority for deletion/cleanup.

Scientific Foundations for Backward Logic:

Dependency Theory: This is a core concept in database theory. Dependencies are not just for insertion; they dictate the order of all operations, including updates and deletions.

Graph Theory (Dual Graphs/Transpose Graphs): Creating a graph with reversed edges is known as forming the transpose or reverse graph. Algorithms applied to the transpose graph can effectively solve problems that require traversing dependencies "backward."

Referential Integrity Constraints: The rules enforced by RDBMS (e.g., ON DELETE RESTRICT, ON DELETE CASCADE, ON DELETE SET NULL) are direct manifestations of backward logic. Understanding these constraints is crucial when determining the actual deletion order.

In summary, "backward logic" is about understanding and ordering operations (especially deletion, archiving, or cleanup) in a way that respects the reverse flow of data dependencies. While your current prioritization likely focuses on "forward" insertion, adapting your graph processing to a reversed graph structure would allow you to implement robust "backward logic" for other batch operations.