fix: Improve TopK filter pushdown

ballista/executor/src/execution_engine.rs

@@ -27,10 +27,13 @@ use ballista_core::execution_plans::sort_shuffle::SortShuffleWriterExec;
 use ballista_core::serde::protobuf::ShuffleWritePartition;
 use ballista_core::utils;
 use datafusion::common::tree_node::{Transformed, TreeNode};
+use datafusion::config::ConfigOptions;
 use datafusion::datasource::physical_plan::{FileScanConfigBuilder, ParquetSource};
 use datafusion::datasource::source::DataSourceExec;
 use datafusion::error::{DataFusionError, Result};
 use datafusion::execution::context::TaskContext;
+use datafusion::physical_optimizer::PhysicalOptimizerRule;
+use datafusion::physical_optimizer::filter_pushdown::FilterPushdown;
 use datafusion::physical_plan::ExecutionPlan;
 use datafusion::physical_plan::metrics::MetricsSet;
 use std::fmt::{Debug, Display};
@@ -136,6 +139,13 @@ impl ExecutionEngine for DefaultExecutionEngine {
         plan: Arc<dyn ExecutionPlan>,
         work_dir: &str,
     ) -> Result<Arc<dyn QueryStageExecutor>> {
+        // Re-run FilterPushdown(Post) to re-establish dynamic filter links
+        // (e.g., TopK → DataSourceExec) that are lost during protobuf
+        // serialization/deserialization between scheduler and executor.
+        let filter_pushdown = FilterPushdown::new_post_optimization();
+        let config = ConfigOptions::default();
+        let plan = filter_pushdown.optimize(plan, &config)?;
+
         // Fix ParquetSource metadata_size_hint lost during serialization
         let plan = fix_parquet_metadata_size_hint(plan)?;
 

ballista/scheduler/Cargo.toml

@@ -80,3 +80,4 @@ tonic-prost-build = { workspace = true }
 
 [dev-dependencies]
 rstest = { workspace = true }
+tempfile = { workspace = true }

ballista/scheduler/src/planner.rs

@@ -169,23 +169,51 @@ impl DefaultDistributedPlanner {
                 with_new_children_if_necessary(execution_plan, vec![unresolved_shuffle])?,
                 stages,
             ))
-        } else if let Some(_sort_preserving_merge) = execution_plan
+        } else if let Some(sort_preserving_merge) = execution_plan
             .as_any()
             .downcast_ref::<SortPreservingMergeExec>(
         ) {
-            let shuffle_writer = create_shuffle_writer_with_config(
-                job_id,
-                self.next_stage_id(),
-                children[0].clone(),
-                None,
-                config,
-            )?;
-            let unresolved_shuffle = create_unresolved_shuffle(shuffle_writer.as_ref());
-            stages.push(shuffle_writer);
-            Ok((
-                with_new_children_if_necessary(execution_plan, vec![unresolved_shuffle])?,
-                stages,
-            ))
+            // For TopK queries (SortPreservingMergeExec with a small fetch/limit),
+            // skip the stage break and keep the merge in the same stage as its children.
+            // This avoids the overhead of shuffle write/read for a small number of rows,
+            // which dominates execution time for TopK queries in distributed mode.
+            //
+            // Note on parallelism: because SortPreservingMergeExec has an output
+            // partitioning of 1, the entire stage becomes a single task assigned to
+            // one executor (ShuffleWriterExec::input_partition_count() == 1).
+            // This does sacrifice cluster-level parallelism (no cross-executor
+            // distribution). However, within that executor the child partitions
+            // still execute as parallel async streams, so intra-executor parallelism
+            // is preserved. For small fetch values this trade-off is worthwhile as
+            // the shuffle coordination overhead far exceeds the merge cost.
+            const TOPK_FETCH_THRESHOLD: usize = 1000;
+            if sort_preserving_merge
+                .fetch()
+                .is_some_and(|f| f <= TOPK_FETCH_THRESHOLD)
+            {
+                Ok((
+                    with_new_children_if_necessary(execution_plan, children)?,
+                    stages,
+                ))
+            } else {
+                let shuffle_writer = create_shuffle_writer_with_config(
+                    job_id,
+                    self.next_stage_id(),
+                    children[0].clone(),
+                    None,
+                    config,
+                )?;
+                let unresolved_shuffle =
+                    create_unresolved_shuffle(shuffle_writer.as_ref());
+                stages.push(shuffle_writer);
+                Ok((
+                    with_new_children_if_necessary(
+                        execution_plan,
+                        vec![unresolved_shuffle],
+                    )?,
+                    stages,
+                ))
+            }
         } else if let Some(repart) =
             execution_plan.as_any().downcast_ref::<RepartitionExec>()
         {
@@ -940,6 +968,91 @@ order by
         Ok(result_exec_plan)
     }
 
+    /// Verifies that TopK queries (ORDER BY ... LIMIT N, where N is small)
+    /// do NOT create a stage break at SortPreservingMergeExec, avoiding
+    /// shuffle overhead for small result sets.
+    #[tokio::test]
+    async fn test_topk_avoids_stage_break() -> Result<(), BallistaError> {
+        use datafusion::prelude::{CsvReadOptions, SessionConfig, SessionContext};
+        use std::io::Write;
+
+        let tmp_dir = tempfile::tempdir().unwrap();
+        let schema = "id,value\n";
+        for i in 0..4 {
+            let path = tmp_dir.path().join(format!("part{i:02}.csv"));
+            let mut f = std::fs::File::create(&path).unwrap();
+            write!(f, "{schema}").unwrap();
+            for j in 0..10 {
+                writeln!(f, "{},{}", i * 10 + j, (i * 10 + j) * 100).unwrap();
+            }
+        }
+
+        let config = SessionConfig::new().with_target_partitions(4);
+        let ctx = SessionContext::new_with_config(config);
+        ctx.register_csv(
+            "test_table",
+            tmp_dir.path().to_str().unwrap(),
+            CsvReadOptions::new(),
+        )
+        .await?;
+
+        // TopK query with small LIMIT — should produce a single stage
+        let df = ctx
+            .sql("SELECT id, value FROM test_table ORDER BY value DESC LIMIT 10")
+            .await?;
+        let plan = df.into_optimized_plan()?;
+        let plan = ctx.state().create_physical_plan(&plan).await?;
+
+        let mut planner = DefaultDistributedPlanner::new();
+        let stages = planner.plan_query_stages(
+            "job-topk",
+            plan,
+            ctx.state().config().options(),
+        )?;
+
+        for (i, stage) in stages.iter().enumerate() {
+            println!(
+                "TopK Stage {i}:\n{}",
+                displayable(stage.as_ref()).indent(false)
+            );
+        }
+
+        // Should be a single stage (no shuffle for TopK with small limit)
+        assert_eq!(
+            1,
+            stages.len(),
+            "TopK with small LIMIT should produce 1 stage, got {}",
+            stages.len()
+        );
+
+        // The single stage should contain SortPreservingMergeExec
+        let root = stages[0].children()[0].clone();
+        let _merge = downcast_exec!(root, SortPreservingMergeExec);
+
+        // Without LIMIT, the same query should produce 2 stages (with shuffle)
+        let df_no_limit = ctx
+            .sql("SELECT id, value FROM test_table ORDER BY value DESC")
+            .await?;
+        let plan_no_limit = df_no_limit.into_optimized_plan()?;
+        let plan_no_limit = ctx.state().create_physical_plan(&plan_no_limit).await?;
+
+        let mut planner2 = DefaultDistributedPlanner::new();
+        let stages_no_limit = planner2.plan_query_stages(
+            "job-no-limit",
+            plan_no_limit,
+            ctx.state().config().options(),
+        )?;
+
+        assert_eq!(
+            2,
+            stages_no_limit.len(),
+            "ORDER BY without LIMIT should produce 2 stages, got {}",
+            stages_no_limit.len()
+        );
+
+        Ok(())
+    }
+
     fn memory_exec(
         schema: Arc<Schema>,
         partition_count: usize,