Ladybug openCypher#

Ladybug follows openCypher where possible. Schema, DDL, some clauses, and MATCH semantics differ from Neo4j. Overview: Differences between Ladybug and Neo4j. DDL: Create table.

Ladybug is embedded (in-process) — no server URI; open a file path or :memory: via real_ladybug.

Core principles#

1. Schema first — node and relationship tables must exist before insert. One label per node/rel table; every node table needs a primary key.
2. Walk vs trail — patterns use walk semantics (edges may repeat). Use is_trail() / is_acyclic() when you need Neo4j-like trail checks.
3. Variable-length paths — require an upper bound for termination; if omitted, default upper bound is 30.
4. Catalog — prefer CALL procedure(...) instead of Neo4j SHOW … for many introspection tasks.

Execute from Python (quick start)#

Import real_ladybug (Ladybug Python bindings). Full docs: Python API, generated reference.

python

import real_ladybug as lb

db = lb.Database("path/to/db.lbug")
conn = lb.Connection(db)
rows = conn.execute("""
    MATCH (a:User)-[f:Follows]->(b:User)
    RETURN a.name, b.name, f.since;
""")
for row in rows:
    print(row)

• conn.execute / await conn.execute per statement unless the API documents batching.
• Multiple statements (semicolon-separated) return a list of results; a single statement returns one result.
• COPY / LOAD FROM paths resolve relative to the process CWD unless absolute.

For async, result helpers, UDFs, and Parquet/DataFrame import — see references/api-reference.md.

Schema snippet (DDL)#

cypher

CREATE NODE TABLE User(name STRING PRIMARY KEY, age INT64);
CREATE NODE TABLE City(name STRING PRIMARY KEY, population INT64);
CREATE REL TABLE Follows(FROM User TO User, since INT64);
CREATE REL TABLE LivesIn(FROM User TO City, MANY_ONE);

Optional IF NOT EXISTS. Multiplicity: MANY_ONE, ONE_MANY, MANY_MANY, ONE_ONE. CREATE NODE TABLE AS / CREATE REL TABLE AS — infer schema from LOAD FROM or MATCH … RETURN.

Import#

• COPY NodeTable FROM "file.csv" (Parquet and other formats per Import data).
• Neo4j’s LOAD CSV FROM → LOAD FROM in Ladybug.
• In Python: LOAD FROM df / COPY Table FROM df for Pandas/Polars/Arrow without an intermediate file.

Full-text search (FTS)#

Load the FTS extension first. Index STRING columns on node tables only; query with CALL QUERY_FTS_INDEX; list with CALL SHOW_INDEXES() RETURN *. Full procedure signatures: references/api-reference.md.

When results differ from Neo4j#

Use the checklist and clause table in references/workflow-patterns.md: walk vs trail, variable-length defaults, unsupported clauses ( FOREACH, REMOVE, FINISH, SET +=, …), and CALL vs SHOW.

Utility scripts#

Bundled helpers (optional — require real_ladybug on PYTHONPATH):

• scripts/run_cypher.py — run a Cypher string or .cypher file against a .lbug path.
• scripts/check_env.py — verify import real_ladybug and print basic info.

Additional resources#

• Detailed Python API, FTS CALL syntax, and DDL/import tables: references/api-reference.md
• Debugging workflows, Neo4j comparison table, query habits: references/workflow-patterns.md

Doc links#

• Ladybug vs Neo4j
• DDL / create table
• Import data
• Full-text search
• MATCH
• CALL / functions