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.

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_32753lx6

2) Define Schema (Recommended)¶

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

In [2]:

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

print('Schema created')

( 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.

In [3]:

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

db.execute("CREATE (:Service {name: 'api'})") db.execute("CREATE (:Service {name: 'gateway'})") db.execute("CREATE (:Service {name: 'worker'})") db.execute("CREATE (:Service {name: 'db'})") db.execute("CREATE (:Service {name: 'cache'})") db.execute("MATCH (a:Service {name:'api'}), (g:Service {name:'gateway'}) CREATE (a)-[:CALLS]->(g)") db.execute("MATCH (g:Service {name:'gateway'}), (w:Service {name:'worker'}) CREATE (g)-[:CALLS]->(w)") db.execute("MATCH (w:Service {name:'worker'}), (d:Service {name:'db'}) CREATE (w)-[:CALLS]->(d)") db.execute("MATCH (w:Service {name:'worker'}), (c:Service {name:'cache'}) CREATE (w)-[:CALLS]->(c)") 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 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)

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).

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: ['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.

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:
\nimpacts: 9 row(s)
[{'a': {'_id': '0', '_labels': ['Service'], 'name': 'api'},
  'b': {'_id': '1', '_labels': ['Service'], 'name': 'gateway'}},
 {'a': {'_id': '1', '_labels': ['Service'], 'name': 'gateway'},
  'b': {'_id': '2', '_labels': ['Service'], 'name': 'worker'}},
 {'a': {'_id': '2', '_labels': ['Service'], 'name': 'worker'},
  'b': {'_id': '4', '_labels': ['Service'], 'name': 'cache'}},
 {'a': {'_id': '2', '_labels': ['Service'], 'name': 'worker'},
  'b': {'_id': '3', '_labels': ['Service'], 'name': 'db'}},
 {'a': {'_id': '0', '_labels': ['Service'], 'name': 'api'},
  'b': {'_id': '2', '_labels': ['Service'], 'name': 'worker'}},
 {'a': {'_id': '1', '_labels': ['Service'], 'name': 'gateway'},
  'b': {'_id': '4', '_labels': ['Service'], 'name': 'cache'}},
 {'a': {'_id': '1', '_labels': ['Service'], 'name': 'gateway'},
  'b': {'_id': '3', '_labels': ['Service'], 'name': 'db'}},
 {'a': {'_id': '0', '_labels': ['Service'], 'name': 'api'},
  'b': {'_id': '4', '_labels': ['Service'], 'name': 'cache'}},
 {'a': {'_id': '0', '_labels': ['Service'], 'name': 'api'},
  'b': {'_id': '3', '_labels': ['Service'], 'name': 'db'}}]
\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.

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_32753lx6