gsql2rsql

Query your Delta Tables as a Graph

No need for a separate graph database. Write intuitive OpenCypher queries, get Databricks SQL automatically.

Why Databricks?

Databricks provides tables designed for massive scale, enabling efficient storage and querying of tens of billions of triples with features like time travel No ETL or migration needed—just query your data lake as a graph. Recently, Databricks released support for recursive queries, unlocking the use of SQL warehouses for graph-type queries.

Why gsql2rsql?

See It in Action

pip install gsql2rsql

from gsql2rsql import GraphContext

# Point to your existing Delta tables - no migration needed
graph = GraphContext(
    nodes_table="catalog.fraud.nodes",
    edges_table="catalog.fraud.edges",
)

# Write graph queries with familiar Cypher syntax
sql = graph.transpile("""
    MATCH path = (origin:Person {id: 12345})-[:TRANSACTION*1..4]->(dest:Person)
    WHERE dest.risk_score > 0.8
    RETURN dest.id, dest.name, dest.risk_score, length(path) AS depth
    ORDER BY depth, dest.risk_score DESC
    LIMIT 3
""")

print(sql)

5 lines of Cypher → optimized Databricks SQL with recursive CTEs

WITH RECURSIVE
  paths_1 AS (
-- Base case: direct edges (depth = 1)
SELECT
  e.src AS start_node,
  e.dst AS end_node,
  1 AS depth,
  ARRAY(e.src, e.dst) AS path,
  ARRAY(NAMED_STRUCT('src', e.src, 'dst', e.dst, 'amount', e.amount, 'timestamp', e.timestamp)) AS path_edges,
  ARRAY(e.src) AS visited
FROM catalog.fraud.edges e
JOIN catalog.fraud.nodes src ON src.id = e.src
WHERE (relationship_type = 'TRANSACTION') AND (src.id) = (12345)

UNION ALL

-- Recursive case: extend paths
SELECT
  p.start_node,
  e.dst AS end_node,
  p.depth + 1 AS depth,
  CONCAT(p.path, ARRAY(e.dst)) AS path,
  ARRAY_APPEND(p.path_edges, NAMED_STRUCT('src', e.src, 'dst', e.dst, 'amount', e.amount, 'timestamp', e.timestamp)) AS path_edges,
  CONCAT(p.visited, ARRAY(e.src)) AS visited
FROM paths_1 p
JOIN catalog.fraud.edges e
  ON p.end_node = e.src
WHERE p.depth < 4
  AND NOT ARRAY_CONTAINS(p.visited, e.dst)
  AND (relationship_type = 'TRANSACTION')
  )
SELECT
   _gsql2rsql_dest_id AS id
  ,_gsql2rsql_dest_name AS name
  ,_gsql2rsql_dest_risk_score AS risk_score
  ,(SIZE(_gsql2rsql_path_id) - 1) AS depth
FROM (
  SELECT
 sink.id AS _gsql2rsql_dest_id
,sink.name AS _gsql2rsql_dest_name
,sink.risk_score AS _gsql2rsql_dest_risk_score
,source.id AS _gsql2rsql_origin_id
,source.name AS _gsql2rsql_origin_name
,source.risk_score AS _gsql2rsql_origin_risk_score
,p.start_node
,p.end_node
,p.depth
,p.path AS _gsql2rsql_path_id
,p.path_edges AS _gsql2rsql_path_edges
  FROM paths_1 p
  JOIN catalog.fraud.nodes sink
ON sink.id = p.end_node
  JOIN catalog.fraud.nodes source
ON source.id = p.start_node
  WHERE p.depth >= 1 AND p.depth <= 4 AND (sink.risk_score) > (0.8)
) AS _proj
ORDER BY depth ASC, _gsql2rsql_dest_risk_score DESC
LIMIT 3

Early Stage Project — Not for OLTP or end-user queries

This project is in early development. APIs may change, features may be incomplete, and bugs are expected. Contributions and feedback are welcome!

This transpiler is for internal analytics and exploration (data science, engineering, analysis). It obviously makes no sense for OLTP! If you plan to expose transpiled queries to end users, be careful: implement validation, rate limiting, and security. Use common sense.

Real-World Examples

Fraud Detection

```cypher
-- Find fraud rings: accounts connected through suspicious transactions
MATCH (a:Account)-[:TRANSFER*2..4]->(b:Account)
WHERE a.flagged = true AND b.flagged = true
RETURN DISTINCT a.id, b.id, length(path) AS hops
```

[See more fraud detection queries →](examples/fraud.md)

Credit Analysis

```cypher
-- Analyze credit exposure through guarantor chains
MATCH path = (borrower:Customer)-[:GUARANTEES*1..3]->(guarantor:Customer)
WHERE borrower.credit_score < 600
RETURN borrower.id, COLLECT(guarantor.id) AS chain
```

[See more credit analysis queries →](examples/credit.md)

Social Network

```cypher
-- Friends of friends who work at tech companies
MATCH (me:Person {id: 123})-[:KNOWS*1..2]->(friend)-[:WORKS_AT]->(c:Company)
WHERE c.industry = 'Technology'
RETURN DISTINCT friend.name, c.name
```

[See all feature examples →](examples/features.md)

That's it! No schema boilerplate, no complex setup.

Full User Guide →

Low-Level API (Without GraphContext)

For advanced use cases or non-Triple-Store schemas, use the components directly:

from gsql2rsql import OpenCypherParser, LogicalPlan, SQLRenderer
from gsql2rsql.common.schema import NodeSchema, EdgeSchema, EntityProperty
from gsql2rsql.renderer.schema_provider import SimpleSQLSchemaProvider, SQLTableDescriptor

# 1. Define schema (SimpleSQLSchemaProvider)
schema = SimpleSQLSchemaProvider()

person = NodeSchema(
    name="Person",
    node_id_property=EntityProperty("id", int),
    properties=[EntityProperty("name", str)],
)
schema.add_node(
    person,
    SQLTableDescriptor(table_name="people", node_id_columns=["id"]),
)

knows = EdgeSchema(
    name="KNOWS",
    source_node_id="Person",
    sink_node_id="Person",
)
schema.add_edge(
    knows,
    SQLTableDescriptor(table_name="friendships"),
)

# 2. Transpile
parser = OpenCypherParser()
ast = parser.parse("MATCH (p:Person)-[:KNOWS]->(f:Person) RETURN p.name, f.name")
plan = LogicalPlan.process_query_tree(ast, schema)
plan.resolve(original_query="...")

renderer = SQLRenderer(db_schema_provider=schema)
sql = renderer.render_plan(plan)

Key Features

Architecture

gsql2rsql uses a 4-phase pipeline for correctness:

OpenCypher → Parser → Planner → Resolver → Renderer → SQL

1. Parser: Cypher → AST (syntax only, no schema)
2. Planner: AST → Logical operators (semantics)
3. Resolver: Validate columns & types against schema
4. Renderer: Operators → Databricks SQL

This separation ensures each phase has clear responsibilities and can be tested independently.

Documentation

Project Status

Research Project

Contributions welcome!

• GitHub Repository
• Issue Tracker
• Contributing Guide

License

MIT License - see LICENSE