Ordering Entity Processing as an ETL Pipeline

Ordering Entity Processing as an ETL Pipeline

When you talk about “loading the classes with @OneToMany before those with @ManyToOne,” you’re really describing the classic dimension-before-fact pattern in ETL:

1. Extract Pull in all your entity definitions and their relationship metadata (the JPA metamodel).

2. Transform Compute a loading order or “weight” for each class based on its dependencies:

   • Classes that only have outgoing one-to-many edges (no incoming many-to-one) are roots or dimensions.

   • Classes with many-to-one edges point back to those roots and act like fact tables.

   • You can assign each node a level equal to the length of the longest path from any root.

3. Load Process in topologically sorted order:

   • First all level-0 (pure one-to-many) classes

   • Then level-1 classes (those that depend on level-0)

   • And so on, up to the deepest level.

That’s a true ETL: you’ve extracted the graph, transformed it into a dependency-driven load sequence, then you “load” (process) each class in that order.

Computing “Correct” Weights

Rather than ad-hoc BFS counters, consider these approaches:

1. Topological Leveling • Run a topological sort on your DAG (since JPA relationships form no real cycles once all Transients are removed). • Assign each node a weight = its topological index or its longest dependency chain length.

2. Degree-Based Priority • In-degree = number of many-to-one edges (how many things depend on you). • Out-degree = number of one-to-many edges (how many things you depend on). • You can define weight = in-degree × α + out-degree × β to favor loading dimensions first (α>β) or facts first (β>α).

3. Graph-Centrality Metrics If you want a more nuanced “importance” score, use algorithms like PageRank or eigenvector centrality. Those capture not just raw counts but also the importance of your neighbors.

Why Dimension-Before-Fact Works

• Referential Integrity: you’ll never process a child before its parent exists.

• Stability: there’s a unique topological order for any DAG.

• Predictability: no oscillation—each node’s level is fixed once you transform the graph.

Putting It Into Code

1. Build your directed graph of entities (no global edge-visited set needed).

2. Use a library or simple DFS to compute each node’s longest path length from any root.

3. Sort classes by that length ascending, then process in that exact order.

That way, one-to-many classes (roots) have level 0, many-to-one (leaves/facts) have higher levels, and your “load” phase is a pure walk through a sorted list.