Locy Use Case: Supply Chain Provenance¶

Trace multi-hop upstream supplier lineage for a finished component.

This notebook uses schema-first mode (recommended): labels, edge types, and typed properties are defined before ingest.

How To Read This Notebook¶

• Step 1 initializes an isolated local database.
• Step 2 defines schema (the recommended production path).
• Step 3 seeds a minimal graph for this use case.
• Step 4 declares Locy rules and query statements.
• Steps 5-6 evaluate and inspect command/query outputs.
• Step 7 tells you what to look for in the results.

1) Setup¶

Creates a temporary database directory so the example is reproducible and leaves no state behind.

In [1]:

import os
import shutil
import tempfile
from pprint import pprint

import uni_db

DB_DIR = tempfile.mkdtemp(prefix="uni_locy_")
print("DB_DIR:", DB_DIR)

db = uni_db.Database(DB_DIR)

import os import shutil import tempfile from pprint import pprint import uni_db DB_DIR = tempfile.mkdtemp(prefix="uni_locy_") print("DB_DIR:", DB_DIR) db = uni_db.Database(DB_DIR)

DB_DIR: /tmp/uni_locy_lndvxd_9

2) Define Schema (Recommended)¶

Define labels, property types, and edge types before inserting data.

In [2]:

(
    db.schema()
    .label("Part")
        .property("sku", "string")
        .property("kind", "string")
    .done()
    .edge_type("SOURCED_FROM", ["Part"], ["Part"])
    .done()
    .apply()
)

print('Schema created')

( db.schema() .label("Part") .property("sku", "string") .property("kind", "string") .done() .edge_type("SOURCED_FROM", ["Part"], ["Part"]) .done() .apply() ) print('Schema created')

Schema created

3) Seed Graph Data¶

Insert only the entities/relationships needed for this scenario so rule behavior stays easy to inspect.

In [3]:

db.execute("CREATE (:Part {sku: 'C1', kind: 'finished'})")
db.execute("CREATE (:Part {sku: 'B1', kind: 'subassembly'})")
db.execute("CREATE (:Part {sku: 'B2', kind: 'subassembly'})")
db.execute("CREATE (:Part {sku: 'R1', kind: 'raw'})")
db.execute("CREATE (:Part {sku: 'R2', kind: 'raw'})")
db.execute("MATCH (c:Part {sku:'C1'}), (b1:Part {sku:'B1'}) CREATE (c)-[:SOURCED_FROM]->(b1)")
db.execute("MATCH (c:Part {sku:'C1'}), (b2:Part {sku:'B2'}) CREATE (c)-[:SOURCED_FROM]->(b2)")
db.execute("MATCH (b1:Part {sku:'B1'}), (r1:Part {sku:'R1'}) CREATE (b1)-[:SOURCED_FROM]->(r1)")
db.execute("MATCH (b2:Part {sku:'B2'}), (r2:Part {sku:'R2'}) CREATE (b2)-[:SOURCED_FROM]->(r2)")
print('Seeded graph data')

db.execute("CREATE (:Part {sku: 'C1', kind: 'finished'})") db.execute("CREATE (:Part {sku: 'B1', kind: 'subassembly'})") db.execute("CREATE (:Part {sku: 'B2', kind: 'subassembly'})") db.execute("CREATE (:Part {sku: 'R1', kind: 'raw'})") db.execute("CREATE (:Part {sku: 'R2', kind: 'raw'})") db.execute("MATCH (c:Part {sku:'C1'}), (b1:Part {sku:'B1'}) CREATE (c)-[:SOURCED_FROM]->(b1)") db.execute("MATCH (c:Part {sku:'C1'}), (b2:Part {sku:'B2'}) CREATE (c)-[:SOURCED_FROM]->(b2)") db.execute("MATCH (b1:Part {sku:'B1'}), (r1:Part {sku:'R1'}) CREATE (b1)-[:SOURCED_FROM]->(r1)") db.execute("MATCH (b2:Part {sku:'B2'}), (r2:Part {sku:'R2'}) CREATE (b2)-[:SOURCED_FROM]->(r2)") print('Seeded graph data')

Seeded graph data

4) Locy Program¶

CREATE RULE defines derived relations. QUERY ... WHERE ... RETURN ... reads from those relations.

In [4]:

program = r'''
CREATE RULE upstream AS
MATCH (a:Part)-[:SOURCED_FROM]->(b:Part)
YIELD KEY a, KEY b

CREATE RULE upstream AS
MATCH (a:Part)-[:SOURCED_FROM]->(mid:Part)
WHERE mid IS upstream TO b
YIELD KEY a, KEY b

QUERY upstream WHERE a.sku = 'C1' RETURN b.sku AS supplier_sku, b.kind AS supplier_kind
'''
print(program)

program = r''' CREATE RULE upstream AS MATCH (a:Part)-[:SOURCED_FROM]->(b:Part) YIELD KEY a, KEY b CREATE RULE upstream AS MATCH (a:Part)-[:SOURCED_FROM]->(mid:Part) WHERE mid IS upstream TO b YIELD KEY a, KEY b QUERY upstream WHERE a.sku = 'C1' RETURN b.sku AS supplier_sku, b.kind AS supplier_kind ''' print(program)

CREATE RULE upstream AS
MATCH (a:Part)-[:SOURCED_FROM]->(b:Part)
YIELD KEY a, KEY b

CREATE RULE upstream AS
MATCH (a:Part)-[:SOURCED_FROM]->(mid:Part)
WHERE mid IS upstream TO b
YIELD KEY a, KEY b

QUERY upstream WHERE a.sku = 'C1' RETURN b.sku AS supplier_sku, b.kind AS supplier_kind

5) Evaluate Locy Program¶

Run the program, then inspect materialization stats (iterations, strata, and executed queries).

In [5]:

out = db.locy_evaluate(program)

print("Derived relations:", list(out["derived"].keys()))
stats = out["stats"]
print("Iterations:", stats.total_iterations)
print("Strata:", stats.strata_evaluated)
print("Queries executed:", stats.queries_executed)

out = db.locy_evaluate(program) print("Derived relations:", list(out["derived"].keys())) stats = out["stats"] print("Iterations:", stats.total_iterations) print("Strata:", stats.strata_evaluated) print("Queries executed:", stats.queries_executed)

Derived relations: ['upstream']
Iterations: 3
Strata: 1
Queries executed: 6

6) Inspect Command Results¶

Each command result can contain rows; this is the easiest way to verify your rule outputs and query projections.

In [6]:

print("Derived relation snapshots:")
for rel_name, rel_rows in out["derived"].items():
    print(f"\\n{rel_name}: {len(rel_rows)} row(s)")
    pprint(rel_rows)

if out["command_results"]:
    print("\\nCommand results:")
for i, cmd in enumerate(out["command_results"], start=1):
    print(f"\\nCommand #{i}:", cmd.get("type"))
    rows = cmd.get("rows")
    if rows is not None:
        pprint(rows)
if not out["command_results"]:
    print("\\nNo QUERY/EXPLAIN/ABDUCE command outputs in this program.")

print("Derived relation snapshots:") for rel_name, rel_rows in out["derived"].items(): print(f"\\n{rel_name}: {len(rel_rows)} row(s)") pprint(rel_rows) if out["command_results"]: print("\\nCommand results:") for i, cmd in enumerate(out["command_results"], start=1): print(f"\\nCommand #{i}:", cmd.get("type")) rows = cmd.get("rows") if rows is not None: pprint(rows) if not out["command_results"]: print("\\nNo QUERY/EXPLAIN/ABDUCE command outputs in this program.")

Derived relation snapshots:
\nupstream: 6 row(s)
[{'a': {'_id': '0', '_labels': ['Part'], 'kind': 'finished', 'sku': 'C1'},
  'b': {'_id': '2', '_labels': ['Part'], 'kind': 'subassembly', 'sku': 'B2'}},
 {'a': {'_id': '0', '_labels': ['Part'], 'kind': 'finished', 'sku': 'C1'},
  'b': {'_id': '1', '_labels': ['Part'], 'kind': 'subassembly', 'sku': 'B1'}},
 {'a': {'_id': '1', '_labels': ['Part'], 'kind': 'subassembly', 'sku': 'B1'},
  'b': {'_id': '3', '_labels': ['Part'], 'kind': 'raw', 'sku': 'R1'}},
 {'a': {'_id': '2', '_labels': ['Part'], 'kind': 'subassembly', 'sku': 'B2'},
  'b': {'_id': '4', '_labels': ['Part'], 'kind': 'raw', 'sku': 'R2'}},
 {'a': {'_id': '0', '_labels': ['Part'], 'kind': 'finished', 'sku': 'C1'},
  'b': {'_id': '3', '_labels': ['Part'], 'kind': 'raw', 'sku': 'R1'}},
 {'a': {'_id': '0', '_labels': ['Part'], 'kind': 'finished', 'sku': 'C1'},
  'b': {'_id': '4', '_labels': ['Part'], 'kind': 'raw', 'sku': 'R2'}}]
\nCommand results:
\nCommand #1: query
[{'supplier_kind': 'subassembly', 'supplier_sku': 'B1'},
 {'supplier_kind': 'subassembly', 'supplier_sku': 'B2'},
 {'supplier_kind': 'raw', 'supplier_sku': 'R1'},
 {'supplier_kind': 'raw', 'supplier_sku': 'R2'}]

7) What To Expect¶

Use these checks to validate output after evaluation:

• For C1, output should include both subassemblies (B1, B2) and raw parts (R1, R2).
• supplier_kind helps separate immediate suppliers vs deeper upstream tiers.
• This same pattern scales to provenance and recall workflows.

8) Cleanup¶

Delete the temporary database directory created in setup.

In [7]:

shutil.rmtree(DB_DIR, ignore_errors=True)
print("Cleaned up", DB_DIR)

shutil.rmtree(DB_DIR, ignore_errors=True) print("Cleaned up", DB_DIR)

Cleaned up /tmp/uni_locy_lndvxd_9