feat: Unify the execute and explain APIs

python/python/tests/test_explain.py

@@ -1,4 +1,4 @@
-"""Tests for explain_datafusion API."""
+"""Tests for explain API."""
 
 import pyarrow as pa
 import pytest
@@ -25,7 +25,7 @@ def test_explain_simple_query(person_data):
     """Test explain output contains all expected sections."""
     config, people = person_data
     query = CypherQuery("MATCH (p:Person) RETURN p.name, p.age").with_config(config)
-    plan = query.explain_datafusion({"Person": people})
+    plan = query.explain({"Person": people})
 
     # Verify the plan is a non-empty string
     assert isinstance(plan, str)
@@ -48,7 +48,7 @@ def test_explain_with_clauses(person_data):
     query = CypherQuery(
         "MATCH (p:Person) WHERE p.age > 30 RETURN p.name ORDER BY p.age LIMIT 2"
     ).with_config(config)
-    plan = query.explain_datafusion({"Person": people})
+    plan = query.explain({"Person": people})
 
     assert isinstance(plan, str)
     assert "WHERE p.age > 30" in plan
@@ -63,11 +63,11 @@ def test_explain_error_handling(person_data):
     # Missing config
     query_no_config = CypherQuery("MATCH (p:Person) RETURN p.name")
     with pytest.raises(ValueError, match="Graph configuration is required"):
-        query_no_config.explain_datafusion({"Person": people})
+        query_no_config.explain({"Person": people})
 
     # Missing datasets
     query_with_config = CypherQuery("MATCH (p:Person) RETURN p.name").with_config(
         config
     )
     with pytest.raises(ValueError, match="No input datasets provided"):
-        query_with_config.explain_datafusion({})
+        query_with_config.explain({})

python/python/tests/test_graph.py

@@ -62,41 +62,38 @@ def graph_env(tmp_path):
     return config, datasets, people_table
 
 
-@pytest.mark.parametrize("execute_method", ["execute", "execute_datafusion"])
-def test_basic_node_selection(graph_env, execute_method):
+def test_basic_node_selection(graph_env):
     config, datasets, _ = graph_env
     query = CypherQuery("MATCH (p:Person) RETURN p.name, p.age").with_config(config)
-    result = getattr(query, execute_method)({"Person": datasets["Person"]})
+    result = query.execute({"Person": datasets["Person"]})
     data = result.to_pydict()
 
     assert set(data.keys()) == {"p.name", "p.age"}
     assert len(data["p.name"]) == 4
     assert "Alice" in set(data["p.name"])
 
 
-@pytest.mark.parametrize("execute_method", ["execute", "execute_datafusion"])
-def test_filtered_query(graph_env, execute_method):
+def test_filtered_query(graph_env):
     config, datasets, _ = graph_env
     query = CypherQuery(
         "MATCH (p:Person) WHERE p.age > 30 RETURN p.name, p.age"
     ).with_config(config)
-    result = getattr(query, execute_method)({"Person": datasets["Person"]})
+    result = query.execute({"Person": datasets["Person"]})
     data = result.to_pydict()
 
     assert len(data["p.name"]) == 2
     assert set(data["p.name"]) == {"Bob", "David"}
     assert all(age > 30 for age in data["p.age"])
 
 
-@pytest.mark.parametrize("execute_method", ["execute", "execute_datafusion"])
-def test_relationship_query(graph_env, execute_method):
+def test_relationship_query(graph_env):
     config, datasets, _ = graph_env
     query = CypherQuery(
         "MATCH (p:Person)-[:WORKS_FOR]->(c:Company) "
         "RETURN p.person_id AS person_id, p.name AS name, c.company_id AS company_id"
     ).with_config(config)
 
-    result = getattr(query, execute_method)(
+    result = query.execute(
         {
             "Person": datasets["Person"],
             "Company": datasets["Company"],
@@ -109,8 +106,7 @@ def test_relationship_query(graph_env, execute_method):
     assert data["company_id"] == [101, 101, 102, 103]
 
 
-@pytest.mark.parametrize("execute_method", ["execute", "execute_datafusion"])
-def test_friendship_direct_and_network(graph_env, execute_method):
+def test_friendship_direct_and_network(graph_env):
     config, datasets, _ = graph_env
     # Direct friends of Alice (person_id = 1)
     query_direct = CypherQuery(
@@ -119,7 +115,7 @@ def test_friendship_direct_and_network(graph_env, execute_method):
         "RETURN b.person_id AS friend_id"
     ).with_config(config)
 
-    result_direct = getattr(query_direct, execute_method)(
+    result_direct = query_direct.execute(
         {
             "Person": datasets["Person"],
             "FRIEND_OF": datasets["FRIEND_OF"],
@@ -134,7 +130,7 @@ def test_friendship_direct_and_network(graph_env, execute_method):
         "RETURN f.person_id AS person1_id, t.person_id AS person2_id"
     ).with_config(config)
 
-    result_edges = getattr(query_edges, execute_method)(
+    result_edges = query_edges.execute(
         {
             "Person": datasets["Person"],
             "FRIEND_OF": datasets["FRIEND_OF"],
@@ -145,16 +141,15 @@ def test_friendship_direct_and_network(graph_env, execute_method):
     assert got == {(1, 2), (1, 3), (2, 4), (3, 4)}
 
 
-@pytest.mark.parametrize("execute_method", ["execute", "execute_datafusion"])
-def test_two_hop_friends_of_friends(graph_env, execute_method):
+def test_two_hop_friends_of_friends(graph_env):
     config, datasets, _ = graph_env
     query = CypherQuery(
         "MATCH (a:Person)-[:FRIEND_OF]->(b:Person)-[:FRIEND_OF]->(c:Person) "
         "WHERE a.person_id = 1 "
         "RETURN a.person_id AS a_id, b.person_id AS b_id, c.person_id AS c_id"
     ).with_config(config)
 
-    result = getattr(query, execute_method)(
+    result = query.execute(
         {
             "Person": datasets["Person"],
             "FRIEND_OF": datasets["FRIEND_OF"],
@@ -164,29 +159,31 @@ def test_two_hop_friends_of_friends(graph_env, execute_method):
     assert set(data["c_id"]) == {4}
 
 
-@pytest.mark.parametrize("execute_method", ["execute", "execute_datafusion"])
-def test_variable_length_path(graph_env, execute_method):
+def test_variable_length_path(graph_env):
     config, datasets, _ = graph_env
     query = CypherQuery(
         "MATCH (p1:Person)-[:FRIEND_OF*1..2]-(p2:Person) "
         "RETURN p1.person_id AS p1, p2.person_id AS p2"
     ).with_config(config)
-    _ = getattr(query, execute_method)(
+
+    result = query.execute(
         {
             "Person": datasets["Person"],
             "FRIEND_OF": datasets["FRIEND_OF"],
         }
     )
+    data = result.to_pydict()
+    got = set(zip(data["p1"], data["p2"]))
+    assert got == {(1, 2), (1, 3), (2, 4), (3, 4), (1, 4)}
 
 
-@pytest.mark.parametrize("execute_method", ["execute", "execute_datafusion"])
-def test_distinct_clause(graph_env, execute_method):
+def test_distinct_clause(graph_env):
     config, datasets, _ = graph_env
     query = CypherQuery(
         "MATCH (p:Person)-[:WORKS_FOR]->(c:Company) RETURN DISTINCT c.company_name"
     ).with_config(config)
 
-    result = getattr(query, execute_method)(
+    result = query.execute(
         {
             "Person": datasets["Person"],
             "Company": datasets["Company"],

python/src/graph.rs

@@ -22,7 +22,8 @@ use arrow::ffi_stream::ArrowArrayStreamReader;
 use arrow_array::{RecordBatch, RecordBatchReader};
 use arrow_schema::Schema;
 use lance_graph::{
-    CypherQuery as RustCypherQuery, GraphConfig as RustGraphConfig, GraphError as RustGraphError,
+    ExecutionStrategy as RustExecutionStrategy, CypherQuery as RustCypherQuery,
+    GraphConfig as RustGraphConfig, GraphError as RustGraphError,
 };
 use pyo3::{
     exceptions::{PyNotImplementedError, PyRuntimeError, PyValueError},
@@ -34,6 +35,28 @@ use serde_json::Value as JsonValue;
 
 use crate::RT;
 
+/// Execution strategy for Cypher queries
+#[pyclass(name = "ExecutionStrategy", module = "lance.graph")]
+#[derive(Clone, Copy)]
+pub enum ExecutionStrategy {
+    /// Use DataFusion query planner (default, full feature support)
+    DataFusion,
+    /// Use simple single-table executor (legacy, limited features)
+    Simple,
+    /// Use Lance native executor (not yet implemented)
+    LanceNative,
+}
+
+impl From<ExecutionStrategy> for RustExecutionStrategy {
+    fn from(strategy: ExecutionStrategy) -> Self {
+        match strategy {
+            ExecutionStrategy::DataFusion => RustExecutionStrategy::DataFusion,
+            ExecutionStrategy::Simple => RustExecutionStrategy::Simple,
+            ExecutionStrategy::LanceNative => RustExecutionStrategy::LanceNative,
+        }
+    }
+}
+
 /// Convert GraphError to PyErr
 fn graph_error_to_pyerr(err: RustGraphError) -> PyErr {
     match &err {
@@ -267,6 +290,8 @@ impl CypherQuery {
     /// ----------
     /// datasets : dict
     ///     Dictionary mapping table names to Lance datasets
+    /// strategy : ExecutionStrategy, optional
+    ///     Execution strategy to use (defaults to DataFusion)
     ///
     /// Returns
     /// -------
@@ -277,56 +302,40 @@ impl CypherQuery {
     /// ------
     /// RuntimeError
     ///     If query execution fails
-    fn execute(&self, py: Python, datasets: &Bound<'_, PyDict>) -> PyResult<PyObject> {
-        // Convert datasets to Arrow batches while holding the GIL - same as before
-        let arrow_datasets = python_datasets_to_batches(datasets)?;
-
-        // Clone the inner query for use in the async block
-        let inner_query = self.inner.clone();
-
-        // Use RT.block_on with Some(py) like the scanner to_pyarrow method
-        let result_batch = RT
-            .block_on(Some(py), inner_query.execute(arrow_datasets))?
-            .map_err(graph_error_to_pyerr)?;
-
-        record_batch_to_python_table(py, &result_batch)
-    }
-
-    /// Execute query using the DataFusion planner with in-memory datasets
-    ///
-    /// Parameters
-    /// ----------
-    /// datasets : dict
-    ///     Dictionary mapping table names to in-memory tables (pyarrow.Table, LanceDataset, etc.)
-    ///     Keys should match node labels and relationship types in the graph config.
     ///
-    /// Returns
-    /// -------
-    /// pyarrow.Table
-    ///     Query results as Arrow table
+    /// Examples
+    /// --------
+    /// >>> # Default strategy (DataFusion)
+    /// >>> result = query.execute(datasets)
     ///
-    /// Raises
-    /// ------
-    /// ValueError
-    ///     If the query is invalid or datasets are missing
-    /// RuntimeError
-    ///     If query execution fails
-    fn execute_datafusion(&self, py: Python, datasets: &Bound<'_, PyDict>) -> PyResult<PyObject> {
-        // Convert datasets to Arrow RecordBatch map
+    /// >>> # Explicit strategy
+    /// >>> from lance.graph import ExecutionStrategy
+    /// >>> result = query.execute(datasets, strategy=ExecutionStrategy.Simple)
+    #[pyo3(signature = (datasets, strategy=None))]
+    fn execute(
+        &self,
+        py: Python,
+        datasets: &Bound<'_, PyDict>,
+        strategy: Option<ExecutionStrategy>,
+    ) -> PyResult<PyObject> {
+        // Convert datasets to Arrow batches while holding the GIL
         let arrow_datasets = python_datasets_to_batches(datasets)?;
 
-        // Clone for async move
+        // Convert Python strategy to Rust strategy
+        let rust_strategy = strategy.map(|s| s.into());
+
+        // Clone the inner query for use in the async block
         let inner_query = self.inner.clone();
 
-        // Execute via runtime
+        // Use RT.block_on with Some(py) like the scanner to_pyarrow method
         let result_batch = RT
-            .block_on(Some(py), inner_query.execute_datafusion(arrow_datasets))?
+            .block_on(Some(py), inner_query.execute(arrow_datasets, rust_strategy))?
             .map_err(graph_error_to_pyerr)?;
 
         record_batch_to_python_table(py, &result_batch)
     }
 
-    /// Explain query uusing the DataFusion planner with in-memory datasets
+    /// Explain query using the DataFusion planner with in-memory datasets
     ///
     /// Parameters
     /// ----------
@@ -345,7 +354,7 @@ impl CypherQuery {
     ///     If the query is invalid or datasets are missing
     /// RuntimeError
     ///     If query explain fails
-    fn explain_datafusion(&self, py: Python, datasets: &Bound<'_, PyDict>) -> PyResult<String> {
+    fn explain(&self, py: Python, datasets: &Bound<'_, PyDict>) -> PyResult<String> {
         // Convert datasets to Arrow RecordBatch map
         let arrow_datasets = python_datasets_to_batches(datasets)?;
 
@@ -354,7 +363,7 @@ impl CypherQuery {
 
         // Execute via runtime
         let plan = RT
-            .block_on(Some(py), inner_query.explain_datafusion(arrow_datasets))?
+            .block_on(Some(py), inner_query.explain(arrow_datasets))?
             .map_err(graph_error_to_pyerr)?;
 
         Ok(plan)
@@ -562,6 +571,7 @@ fn record_batch_to_python_table(
 pub fn register_graph_module(py: Python, parent_module: &Bound<'_, PyModule>) -> PyResult<()> {
     let graph_module = PyModule::new(py, "graph")?;
 
+    graph_module.add_class::<ExecutionStrategy>()?;
     graph_module.add_class::<GraphConfig>()?;
     graph_module.add_class::<GraphConfigBuilder>()?;
     graph_module.add_class::<CypherQuery>()?;

rust/lance-graph/benches/graph_execution.rs

@@ -22,7 +22,7 @@ use arrow_schema::{DataType, Field, Schema as ArrowSchema};
 use criterion::{black_box, criterion_group, criterion_main, BenchmarkId, Criterion, Throughput};
 use futures::TryStreamExt;
 use lance::dataset::{Dataset, WriteMode, WriteParams};
-use lance_graph::{CypherQuery, GraphConfig};
+use lance_graph::{CypherQuery, ExecutionStrategy, GraphConfig};
 use tempfile::TempDir;
 
 fn create_people_batch() -> RecordBatch {
@@ -71,7 +71,11 @@ fn execute_cypher_query(
     q: &CypherQuery,
     datasets: HashMap<String, RecordBatch>,
 ) -> RecordBatch {
-    rt.block_on(async move { q.execute(datasets).await.unwrap() })
+    rt.block_on(async move {
+        q.execute(datasets, Some(ExecutionStrategy::Simple))
+            .await
+            .unwrap()
+    })
 }
 
 fn make_people_batch(n: usize) -> RecordBatch {

rust/lance-graph/src/lib.rs

@@ -53,4 +53,4 @@ pub const MAX_VARIABLE_LENGTH_HOPS: u32 = 20;
 
 pub use config::{GraphConfig, NodeMapping, RelationshipMapping};
 pub use error::{GraphError, Result};
-pub use query::CypherQuery;
+pub use query::{CypherQuery, ExecutionStrategy};