Locy Use Case: Infrastructure Blast Radius¶

Compute transitive downstream impact from a failing upstream service.

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.

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.Uni.open(DB_DIR)
session = db.session()

DB_DIR: /tmp/uni_locy_kb12iwiu

2) Define Schema (Recommended)¶

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

(
    db.schema()
    .label("Service")
        .property("name", "string")
    .done()
    .edge_type("CALLS", ["Service"], ["Service"])
    .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.

tx = session.tx()
tx.execute("CREATE (:Service {name: 'api'})")
tx.execute("CREATE (:Service {name: 'gateway'})")
tx.execute("CREATE (:Service {name: 'worker'})")
tx.execute("CREATE (:Service {name: 'db'})")
tx.execute("CREATE (:Service {name: 'cache'})")
tx.execute("MATCH (a:Service {name:'api'}), (g:Service {name:'gateway'}) CREATE (a)-[:CALLS]->(g)")
tx.execute("MATCH (g:Service {name:'gateway'}), (w:Service {name:'worker'}) CREATE (g)-[:CALLS]->(w)")
tx.execute("MATCH (w:Service {name:'worker'}), (d:Service {name:'db'}) CREATE (w)-[:CALLS]->(d)")
tx.execute("MATCH (w:Service {name:'worker'}), (c:Service {name:'cache'}) CREATE (w)-[:CALLS]->(c)")
tx.commit()
print('Seeded graph data')

Seeded graph data

4) Locy Program¶

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

program = r'''
CREATE RULE impacts AS
MATCH (a:Service)-[:CALLS]->(b:Service)
YIELD KEY a, KEY b

CREATE RULE impacts AS
MATCH (a:Service)-[:CALLS]->(mid:Service)
WHERE mid IS impacts TO b
YIELD KEY a, KEY b

QUERY impacts WHERE a.name = 'api' RETURN b.name AS impacted_service
'''
print(program)

CREATE RULE impacts AS
MATCH (a:Service)-[:CALLS]->(b:Service)
YIELD KEY a, KEY b

CREATE RULE impacts AS
MATCH (a:Service)-[:CALLS]->(mid:Service)
WHERE mid IS impacts TO b
YIELD KEY a, KEY b

QUERY impacts WHERE a.name = 'api' RETURN b.name AS impacted_service

5) Evaluate Locy Program¶

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

out = session.locy(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: ['impacts']
Iterations: 4
Strata: 1
Queries executed: 8

6) Inspect Command Results¶

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

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.command_type)
    rows = getattr(cmd, 'rows', None)
    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:
\nimpacts: 9 row(s)
[{'a': Node(id=0, labels=["Service"], properties={'name': 'api'}),
  'b': Node(id=1, labels=["Service"], properties={'name': 'gateway'})},
 {'a': Node(id=1, labels=["Service"], properties={'name': 'gateway'}),
  'b': Node(id=2, labels=["Service"], properties={'name': 'worker'})},
 {'a': Node(id=2, labels=["Service"], properties={'name': 'worker'}),
  'b': Node(id=3, labels=["Service"], properties={'name': 'db'})},
 {'a': Node(id=2, labels=["Service"], properties={'name': 'worker'}),
  'b': Node(id=4, labels=["Service"], properties={'name': 'cache'})},
 {'a': Node(id=0, labels=["Service"], properties={'name': 'api'}),
  'b': Node(id=2, labels=["Service"], properties={'name': 'worker'})},
 {'a': Node(id=1, labels=["Service"], properties={'name': 'gateway'}),
  'b': Node(id=3, labels=["Service"], properties={'name': 'db'})},
 {'a': Node(id=1, labels=["Service"], properties={'name': 'gateway'}),
  'b': Node(id=4, labels=["Service"], properties={'name': 'cache'})},
 {'a': Node(id=0, labels=["Service"], properties={'name': 'api'}),
  'b': Node(id=3, labels=["Service"], properties={'name': 'db'})},
 {'a': Node(id=0, labels=["Service"], properties={'name': 'api'}),
  'b': Node(id=4, labels=["Service"], properties={'name': 'cache'})}]
\nCommand results:
\nCommand #1: query
[{'impacted_service': 'gateway'},
 {'impacted_service': 'worker'},
 {'impacted_service': 'cache'},
 {'impacted_service': 'db'}]

7) What To Expect¶

Use these checks to validate output after evaluation: - For api, impacted services should include gateway, worker, db, and cache. - Rows should represent transitive reachability, not only direct neighbors. - This pattern is useful for outage simulation and dependency triage.

8) Cleanup¶

Delete the temporary database directory created in setup.

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

Cleaned up /tmp/uni_locy_kb12iwiu