feat(spark): Add SessionStateBuilderSpark to datafusion-spark

datafusion/spark/Cargo.toml

@@ -29,6 +29,10 @@ edition = { workspace = true }
 [package.metadata.docs.rs]
 all-features = true
 
+[features]
+default = []
+core = ["datafusion"]
+
 # Note: add additional linter rules in lib.rs.
 # Rust does not support workspace + new linter rules in subcrates yet
 # https://github.com/rust-lang/cargo/issues/13157
@@ -43,6 +47,8 @@ arrow = { workspace = true }
 bigdecimal = { workspace = true }
 chrono = { workspace = true }
 crc32fast = "1.4"
+# Optional dependency for SessionStateBuilderSpark extension trait
+datafusion = { workspace = true, optional = true, default-features = false }
 datafusion-catalog = { workspace = true }
 datafusion-common = { workspace = true }
 datafusion-execution = { workspace = true }
@@ -59,6 +65,8 @@ url = { workspace = true }
 [dev-dependencies]
 arrow = { workspace = true, features = ["test_utils"] }
 criterion = { workspace = true }
+# for SessionStateBuilderSpark tests
+datafusion = { workspace = true, default-features = false }
 
 [[bench]]
 harness = false

datafusion/spark/src/lib.rs

@@ -93,10 +93,42 @@
 //! ```
 //!
 //![`Expr`]: datafusion_expr::Expr
+//!
+//! # Example: enabling Apache Spark features with SessionStateBuilder
+//!
+//! The recommended way to enable Apache Spark compatibility is to use the
+//! `SessionStateBuilderSpark` extension trait. This registers all
+//! Apache Spark functions (scalar, aggregate, window, and table) as well as the Apache Spark
+//! expression planner.
+//!
+//! Enable the `core` feature in your `Cargo.toml`:
+//! ```toml
+//! datafusion-spark = { version = "X", features = ["core"] }
+//! ```
+//!
+//! Then use the extension trait:
+//! ```ignore
+//! use datafusion_core::execution::SessionStateBuilder;
+//! use datafusion_spark::SessionStateBuilderSpark;
+//!
+//! // Create a SessionState with Apache Spark features enabled
+//! // note: the order matters here, `with_spark_features` should be
+//! // called after `with_default_features` to overwrite any existing functions
+//! let state = SessionStateBuilder::new()
+//!     .with_default_features()
+//!     .with_spark_features()
+//!     .build();
+//! ```
 
 pub mod function;
 pub mod planner;
 
+#[cfg(feature = "core")]
+mod session_state;
+
+#[cfg(feature = "core")]
+pub use session_state::SessionStateBuilderSpark;
+
 use datafusion_catalog::TableFunction;
 use datafusion_common::Result;
 use datafusion_execution::FunctionRegistry;

datafusion/spark/src/session_state.rs

@@ -0,0 +1,96 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements.  See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership.  The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License.  You may obtain a copy of the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied.  See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+use std::collections::HashMap;
+use std::sync::Arc;
+
+use datafusion::execution::session_state::SessionStateBuilder;
+
+use crate::planner::SparkFunctionPlanner;
+use crate::{
+    all_default_aggregate_functions, all_default_scalar_functions,
+    all_default_table_functions, all_default_window_functions,
+};
+
+/// Extension trait for adding Apache Spark features to [`SessionStateBuilder`].
+///
+/// This trait provides a convenient way to register all Apache Spark-compatible
+/// functions and planners with a DataFusion session.
+pub trait SessionStateBuilderSpark {
+    /// Adds all expr_planners, scalar, aggregate, window and table functions
+    /// compatible with Apache Spark.
+    ///
+    /// Note: This overwrites any previously registered items with the same name.
+    fn with_spark_features(self) -> Self;
+}
+
+impl SessionStateBuilderSpark for SessionStateBuilder {
+    fn with_spark_features(mut self) -> Self {
+        self.expr_planners()
+            .get_or_insert_with(Vec::new)
+            // planners are evaluated in order of insertion. Push Apache Spark function planner to the front
+            // to take precedence over others
+            .insert(0, Arc::new(SparkFunctionPlanner));
+
+        self.scalar_functions()
+            .get_or_insert_with(Vec::new)
+            .extend(all_default_scalar_functions());
+
+        self.aggregate_functions()
+            .get_or_insert_with(Vec::new)
+            .extend(all_default_aggregate_functions());
+
+        self.window_functions()
+            .get_or_insert_with(Vec::new)
+            .extend(all_default_window_functions());
+
+        self.table_functions()
+            .get_or_insert_with(HashMap::new)
+            .extend(
+                all_default_table_functions()
+                    .into_iter()
+                    .map(|f| (f.name().to_string(), f)),
+            );
+
+        self
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_session_state_with_spark_features() {
+        let state = SessionStateBuilder::new().with_spark_features().build();
+
+        assert!(
+            state.scalar_functions().contains_key("sha2"),
+            "Apache Spark scalar function 'sha2' should be registered"
+        );
+
+        assert!(
+            state.aggregate_functions().contains_key("try_sum"),
+            "Apache Spark aggregate function 'try_sum' should be registered"
+        );
+
+        assert!(
+            !state.expr_planners().is_empty(),
+            "Apache Spark expr planners should be registered"
+        );
+    }
+}

datafusion/sqllogictest/Cargo.toml

@@ -47,7 +47,7 @@ bytes = { workspace = true, optional = true }
 chrono = { workspace = true, optional = true }
 clap = { version = "4.5.53", features = ["derive", "env"] }
 datafusion = { workspace = true, default-features = true, features = ["avro"] }
-datafusion-spark = { workspace = true, default-features = true }
+datafusion-spark = { workspace = true, features = ["core"] }
 datafusion-substrait = { workspace = true, default-features = true }
 futures = { workspace = true }
 half = { workspace = true, default-features = true }

datafusion/sqllogictest/src/test_context.rs

@@ -46,6 +46,7 @@ use datafusion::{
     datasource::{MemTable, TableProvider, TableType},
     prelude::{CsvReadOptions, SessionContext},
 };
+use datafusion_spark::SessionStateBuilderSpark;
 
 use crate::is_spark_path;
 use async_trait::async_trait;
@@ -84,21 +85,14 @@ impl TestContext {
 
         let mut state_builder = SessionStateBuilder::new()
             .with_config(config)
-            .with_runtime_env(runtime);
+            .with_runtime_env(runtime)
+            .with_default_features();
 
         if is_spark_path(relative_path) {
-            state_builder = state_builder.with_expr_planners(vec![Arc::new(
-                datafusion_spark::planner::SparkFunctionPlanner,
-            )]);
+            state_builder = state_builder.with_spark_features();
         }
 
-        let mut state = state_builder.with_default_features().build();
-
-        if is_spark_path(relative_path) {
-            info!("Registering Spark functions");
-            datafusion_spark::register_all(&mut state)
-                .expect("Can not register Spark functions");
-        }
+        let state = state_builder.build();
 
         let mut test_ctx = TestContext::new(SessionContext::new_with_state(state));