Here’s one way to turn that intuition into a concrete weight function + priority queue:
Build the directed graph G(V,E) where • V = all your entity classes • E = edges child→parent for every
@ManyToOne(plus parent→child for symmetry, if you like)Assign each edge e a weight w(e) based on its annotation: •
@ManyToOne(optional=false)→ w(e)=2 •@ManyToOne(optional=true)→ w(e)=1 • (Optional:@OneToOneor@ManyToManycould get their own weights)Compute per-node scores by summing incident edge weights: For each node v: score[v] = ∑{e out of v} w(e) + ∑{e into v} w(e) (Or, if you only want to bump both ends once per edge, just double the out-sum.)
Push all entities into a
PriorityQueue<Entity>ordered by descendingscore[v].Process in queue order: poll → do your ETL/load/whatever → then (optionally) re-compute neighbor scores or simply proceed.
Here’s a quick Java sketch:
// 1) Gather all entities and relationships
List<Class<?>> entities = ...;
Graph<Class<?>,Edge> graph = buildGraphFromMetamodel(entities);
// 2) Define edge weights
int weightOf(Edge e) {
if (e.isManyToOne() && ! e.isOptional()) return 2;
if (e.isManyToOne()) return 1;
return 0;
}
// 3) Score nodes
Map<Class<?>,Integer> score = new HashMap<>();
for (Class<?> v: entities) {
int sum = 0;
for (Edge out: graph.outgoingEdgesOf(v)) sum += weightOf(out);
for (Edge in : graph.incomingEdgesOf(v)) sum += weightOf(in);
score.put(v, sum);
}
// 4) Build PriorityQueue (highest score first)
PriorityQueue<Class<?>> queue =
new PriorityQueue<>((a,b) -> score.get(b) - score.get(a));
queue.addAll(entities);
// 5) Process in order
while (! queue.isEmpty()) {
Class<?> e = queue.poll();
// your “load this entity next” logic here
}
With this, every required Many-To-One gives +2 to both the child and the parent, making “Release” (which has many required links) bubble to the front ahead of “Medium,” “Recording,” etc.
You can tweak the weights (e.g. give parents more credit than children, or factor in One-To-Many edges differently) until the queue order matches your ideal ETL sequence.
Let me know if you’d like to refine the weighting further—say, penalizing optional links or rewarding deep-chain coverage.
Here’s a concrete, numerical breakdown of every entity’s incoming/outgoing @ManyToOne links, their assigned weights (2 for required, 1 for optional), and the resulting total score. We then rank them into a “priority queue” (highest score first).
1. Weight Rules
Every @ManyToOne(optional=false) edge ⇒ weight = 2
(we assume all shown ManyToOne in your Graphviz are required)
2. Edge Counts & Scores
Entity Outgoing M2O Edges In M2O Edges Score = 2×(out + in) Release 5 3 2×8 = 16 Recording 1 3 2×4 = 8 Artist 3 1 2×4 = 8 ReleaseGroup 2 1 2×3 = 6 ArtistCredit 0 3 2×3 = 6 Area 1 2 2×3 = 6 Label 2 1 2×3 = 6 Medium 2 0 2×2 = 4 RecordingAlias 2 0 2×2 = 4 ReleaseLabel 2 0 2×2 = 4 Isrc 1 0 2×1 = 2 ReleaseGroupPrimaryType 0 1 2×1 = 2 RecordingAliasType 0 1 2×1 = 2 MediumFormat 0 1 2×1 = 2 Work 1 0 2×1 = 2 ArtistAlias 2 0 2×2 = 4 (processed above) ArtistAliasType 0 1 2×1 = 2 Instrument 1 0 2×1 = 2 InstrumentType 0 1 2×1 = 2 Track 1 0 2×1 = 2 LabelType 0 1 2×1 = 2 ReleaseAlias 2 0 2×2 = 4 ReleaseAliasType 0 1 2×1 = 2 WorkType 0 1 2×1 = 2 Gender 0 1 2×1 = 2 Language 0 1 2×1 = 2 ReleasePackaging 0 1 2×1 = 2 ReleaseStatus 0 1 2×1 = 2
(Any entity not listed has zero required M2O edges, hence score = 0.)
3. Priority Queue Order
Release (score 16)
Recording, Artist (both 8)
ReleaseGroup, ArtistCredit, Area, Label (all 6)
Medium, RecordingAlias, ReleaseLabel, ArtistAlias, ReleaseAlias (all 4)
Isrc, ReleaseGroupPrimaryType, RecordingAliasType, MediumFormat, Work, ArtistAliasType, Instrument, InstrumentType, Track, LabelType, ReleaseAliasType, WorkType, Gender, Language, ReleasePackaging, ReleaseStatus (all 2)
The rest (score 0) last.
4. Sequence of Processing
text[1] Release
[2] Recording, Artist
[3] ReleaseGroup, ArtistCredit, Area, Label
[4] Medium, RecordingAlias, ReleaseLabel, ArtistAlias, ReleaseAlias
[5] (all score 2 group…)
[6] (all score 0 group…)
By turning each required ManyToOne into a +2 bump for both ends, you get a clear numeric ranking that respects referential integrity (parents rise to the top) and yields a deterministic, reproducible load or execution order.
Let’s line up those two weight-rankings side by side and then pinpoint exactly why they diverge:
Entity With Local visited Without visited Δ ArtistCredit 28 28 0 ReleaseGroup 26 7 +19 AreaType 24 24 0 Recording 15 16 –1 Area 15 15 0 Release 14 14 0 MediumFormat 13 11 +2 ReleaseAliasType 10 3 +7 Label 9 5 +4 RecordingAliasType 9 3 +6 ArtistType 8 8 0 Artist 7 5 +2 Isrc 6 11 –5 ArtistAlias 4 2 +2 ReleaseLabel 3 6 –3 ArtistAliasType 3 3 0 Track 2 4 –2 InstrumentType 2 2 0 WorkType 2 2 0 Work 1 1 0 ReleaseAlias 1 13 –12 Medium 1 1 0 Instrument 1 1 0 RecordingAlias 1 2 –1 (Zero-weight group) 0 0 0
Key takeaways:
ReleaseGroup explodes when visited is per-root (+19).
Why? ReleaseGroup sits two hops from many roots. With a fresh visited for each root, every path that reaches it adds new weight. When you don’t reset, only the first root claims those edges.
ReleaseAlias, Isrc, ReleaseLabel, Track drop under local-visited.
These live in small “subtrees” under one or two BFS roots. With local-visited you skip intra-root cycles but still re-count per root; without any visited you end up re-counting the same cycles in a single BFS, wildly inflating those leaf-level entities.
Stable nodes (ArtistCredit, AreaType, ArtistType, …)
Their scores don’t move because their position in the DAG means each BFS root sees them exactly once—no cycles, no alternate paths.
What’s going on “under the hood”?
No visited at all: your BFSIterator’s own vertex-visited set prevents infinite loops, but edges get processed every time they appear in the queue. If there’s a cycle or multiple routes, you’ll add weight on each traversal.
Local visited: you stop double-counting an edge per BFS root, pruning out cycles within that one search. But across different starts, everything is live again.
Bottom line:
• Use per-root visited if you want “each edge counts once for each BFS origin.”
• Use global visited if you want “each edge counts exactly one time total.”
• Or switch to summing static in/out-degree weights (or a Dijkstra/priority-queue approach) if you need a cycle-resistant, order-independent measure.
That explains exactly why ReleaseGroup rockets to the top under one regime but ReleaseAlias soars under the other.
Below is a concrete, numeric check for your ReleaseGroup class that folds in both its JPA relations and its “native” (scalar) fields marked non-nullable. Think of weight as a sum of:
• Required scalar fields (each +1)
• Required @ManyToOne(optional=false) (each +3)
• Optional @ManyToOne(optional=true) (each +1)
• Incoming required edges (each +2)
• (We ignore @OneToMany and optional incoming links for simplicity)
1. ReleaseGroup Anatomy
java// Scalar, non-null columns:
@NotNull @Column(nullable=false) Long releaseGroupId; // +1
@NotNull @Column(nullable=false) UUID gid; // +1
@NotBlank @Column(nullable=false) String releaseGroupName; // +1
// Outgoing relations:
@ManyToOne(optional=false) ArtistCredit<?> artistCredit; // +3
@ManyToOne(optional=true) ReleaseGroupPrimaryType<?> type; // +1
// Incoming relations (from other entities):
// • Release → ReleaseGroup (default optional=true) // +2
2. Score Breakdown
Source of Weight Count Unit Weight Subtotal Required scalar fields 3 +1 3 Required M2O (artistCredit, optional=false) 1 +3 3 Optional M2O (type, optional=true) 1 +1 1 Incoming required M2O (from Release) 1 +2 2 Total 9
So ReleaseGroup gets a score of 9 under this scheme.
3. What This Buys You
You now factor in both schema-level constraints (non-nullable columns) and referential dependencies.
Every entity’s score reflects its intrinsic data “weight” (how many required fields it has) plus how many and what type of relationships it participates in.
You can enqueue classes in descending score order—so the “heaviest” tables (the ones with the most mandatory data and links) get processed first.
Next Steps
• If you like this scoring model, we can roll it out across all your entities and produce a full ranked list.
• Or we can tweak the weights (e.g. give scalar fields more/less weight, include @OneToMany cardinality, etc.).
Let me know how you’d like to proceed!
Thanks for sending the per-root visited results—this log is rich with signal. When you reset the visited set for each BFS traversal, you allow every edge to contribute multiple times if it's reachable from multiple roots. This leads to accumulative priority rather than strictly structural weighting. Let's break it down:
🔄 Behavior of Per-Vertex visited
Each BFS traversal explores edges afresh from its root. If multiple paths lead to the same edge across different roots, it will:
✅ Fire once per root traversal
✅ Increment priority of both endpoints repeatedly
✅ Inflate scores for "shared" or "popular" entities like ArtistCredit
This explains why:
Entity Reason for High Score ArtistCreditConnected to multiple entities via required M2O links; reachable from many roots AreaTypeLikely touched indirectly from several paths (e.g., via Area → AreaType) RecordingAppears in multiple chains: Track → Recording, RecordingAlias → Recording, Isrc → Recording, etc. ReleaseGroupReached from Release → ReleaseGroup → ArtistCredit, so several root traversals touch it Isrc, ReleaseAliasLeaf nodes under Recording and Release that are repeatedly reached
Meanwhile, entities like Gender, Language, and ReleaseStatus log 0 because they’re either:
Only reachable via a single root, or
Not referenced through traversable edges under your current ruleset
🧠 What This Tells You
You're not just measuring structure; you're capturing traversal popularity.
Entities deeply embedded in relational chains accumulate weight from multiple perspectives.
This mirrors how often they're “relied upon” across graph paths—very useful for execution planning, prefetching, or ETL batching.
Compared to the global visited approach (which gives a "structural fingerprint"), this is more akin to a “heat map” of centrality.
If you want, I can help translate these traversal counts into a ranked ETL queue, or visualize the graph with node sizing proportional to visit frequency. You’re very close to deriving a traversal-weighted index plan. Want to push it further?
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