style: apply make format

Author: andygrove

native/core/src/execution/planner.rs

@@ -122,11 +122,10 @@ use datafusion_comet_proto::{
     spark_partitioning::{partitioning::PartitioningStruct, Partitioning as SparkPartitioning},
 };
 use datafusion_comet_spark_expr::{
-    jvm_udf::JvmScalarUdfExpr,
-    ArrayInsert, Avg, AvgDecimal, Cast, CheckOverflow, Correlation, Covariance, CreateNamedStruct,
-    DecimalRescaleCheckOverflow, GetArrayStructFields, GetStructField, IfExpr, ListExtract,
-    NormalizeNaNAndZero, SparkCastOptions, Stddev, SumDecimal, ToJson, UnboundColumn, Variance,
-    WideDecimalBinaryExpr, WideDecimalOp,
+    jvm_udf::JvmScalarUdfExpr, ArrayInsert, Avg, AvgDecimal, Cast, CheckOverflow, Correlation,
+    Covariance, CreateNamedStruct, DecimalRescaleCheckOverflow, GetArrayStructFields,
+    GetStructField, IfExpr, ListExtract, NormalizeNaNAndZero, SparkCastOptions, Stddev, SumDecimal,
+    ToJson, UnboundColumn, Variance, WideDecimalBinaryExpr, WideDecimalOp,
 };
 use itertools::Itertools;
 use jni::objects::{Global, JObject};
@@ -708,9 +707,10 @@ impl PhysicalPlanner {
                     .iter()
                     .map(|e| self.create_expr(e, Arc::clone(&input_schema)))
                     .collect::<Result<Vec<_>, _>>()?;
-                let return_type = to_arrow_datatype(udf.return_type.as_ref().ok_or_else(|| {
-                    GeneralError("JvmScalarUdf missing return_type".to_string())
-                })?);
+                let return_type =
+                    to_arrow_datatype(udf.return_type.as_ref().ok_or_else(|| {
+                        GeneralError("JvmScalarUdf missing return_type".to_string())
+                    })?);
                 Ok(Arc::new(JvmScalarUdfExpr::new(
                     udf.class_name.clone(),
                     args,

native/spark-expr/src/lib.rs

@@ -55,9 +55,9 @@ pub use cast::{spark_cast, Cast, SparkCastOptions};
 mod bloom_filter;
 pub use bloom_filter::{BloomFilterAgg, BloomFilterMightContain};
 
-pub mod jvm_udf;
 mod conditional_funcs;
 mod conversion_funcs;
+pub mod jvm_udf;
 mod map_funcs;
 pub use map_funcs::spark_map_sort;
 mod math_funcs;

spark/src/test/scala/org/apache/comet/CometRegExpJvmSuite.scala

@@ -19,13 +19,14 @@
 
 package org.apache.comet
 
+import org.apache.spark.SparkConf
 import org.apache.spark.sql.CometTestBase
 import org.apache.spark.sql.comet.CometProjectExec
 import org.apache.spark.sql.execution.adaptive.AdaptiveSparkPlanHelper
 
 class CometRegExpJvmSuite extends CometTestBase with AdaptiveSparkPlanHelper {
 
-  override protected def sparkConf =
+  override protected def sparkConf: SparkConf =
     super.sparkConf.set("spark.comet.exec.regexp.useJVM", "true")
 
   test("rlike: Java regex semantics, with flag on") {