fix

datafusion/core/tests/memory_limit/mod.rs

@@ -26,7 +26,6 @@ mod memory_limit_validation;
 use arrow::array::{ArrayRef, DictionaryArray, Int32Array, RecordBatch, StringViewArray};
 use arrow::compute::SortOptions;
 use arrow::datatypes::{Int32Type, SchemaRef};
-use arrow::util::pretty;
 use arrow_schema::{DataType, Field, Schema};
 use datafusion::assert_batches_eq;
 use datafusion::datasource::memory::MemorySourceConfig;
@@ -46,7 +45,7 @@ use datafusion_execution::memory_pool::{
     FairSpillPool, GreedyMemoryPool, MemoryPool, TrackConsumersPool,
 };
 use datafusion_execution::TaskContext;
-use datafusion_expr::{col, Expr, TableType};
+use datafusion_expr::{Expr, TableType};
 use datafusion_physical_expr::{LexOrdering, PhysicalSortExpr};
 use datafusion_physical_optimizer::join_selection::JoinSelection;
 use datafusion_physical_optimizer::PhysicalOptimizerRule;
@@ -466,7 +465,7 @@ async fn test_stringview_external_sort() {
         .await
         .unwrap();
 
-    let results = df.collect().await.expect("Query execution failed");
+    let _ = df.collect().await.expect("Query execution failed");
 }
 
 /// Run the query with the specified memory limit,

datafusion/physical-plan/src/sorts/sort.rs

@@ -461,7 +461,7 @@ impl ExternalSorter {
                     new_columns.push(Arc::new(new_array));
                     arr_mutated = true;
                 } else {
-                    new_columns.push(array.clone());
+                    new_columns.push(Arc::clone(array));
                 }
             }
 
@@ -1478,7 +1478,7 @@ mod tests {
         // Processing 840 KB of data using 400 KB of memory requires at least 2 spills
         // It will spill roughly 18000 rows and 800 KBytes.
         // We leave a little wiggle room for the actual numbers.
-        assert!((2..=10).contains(&spill_count));
+        assert!((12..=18).contains(&spill_count));
         assert!((15000..=20000).contains(&spilled_rows));
         assert!((700000..=900000).contains(&spilled_bytes));