Add utf8view benchmark for aggregate topk (apache#15518)

* Fix

* fix fmt

Author: zhuqi-lucas

datafusion/core/benches/data_utils/mod.rs

@@ -19,7 +19,8 @@
 
 use arrow::array::{
     builder::{Int64Builder, StringBuilder},
-    Float32Array, Float64Array, RecordBatch, StringArray, UInt64Array,
+    ArrayRef, Float32Array, Float64Array, RecordBatch, StringArray, StringViewBuilder,
+    UInt64Array,
 };
 use arrow::datatypes::{DataType, Field, Schema, SchemaRef};
 use datafusion::datasource::MemTable;
@@ -158,13 +159,39 @@ pub fn create_record_batches(
         .collect::<Vec<_>>()
 }
 
+/// An enum that wraps either a regular StringBuilder or a GenericByteViewBuilder
+/// so that both can be used interchangeably.
+enum TraceIdBuilder {
+    Utf8(StringBuilder),
+    Utf8View(StringViewBuilder),
+}
+
+impl TraceIdBuilder {
+    /// Append a value to the builder.
+    fn append_value(&mut self, value: &str) {
+        match self {
+            TraceIdBuilder::Utf8(builder) => builder.append_value(value),
+            TraceIdBuilder::Utf8View(builder) => builder.append_value(value),
+        }
+    }
+
+    /// Finish building and return the ArrayRef.
+    fn finish(self) -> ArrayRef {
+        match self {
+            TraceIdBuilder::Utf8(mut builder) => Arc::new(builder.finish()),
+            TraceIdBuilder::Utf8View(mut builder) => Arc::new(builder.finish()),
+        }
+    }
+}
+
 /// Create time series data with `partition_cnt` partitions and `sample_cnt` rows per partition
 /// in ascending order, if `asc` is true, otherwise randomly sampled using a Pareto distribution
 #[allow(dead_code)]
 pub(crate) fn make_data(
     partition_cnt: i32,
     sample_cnt: i32,
     asc: bool,
+    use_view: bool,
 ) -> Result<(Arc<Schema>, Vec<Vec<RecordBatch>>), DataFusionError> {
     // constants observed from trace data
     let simultaneous_group_cnt = 2000;
@@ -177,11 +204,17 @@ pub(crate) fn make_data(
     let mut rng = rand::rngs::SmallRng::from_seed([0; 32]);
 
     // populate data
-    let schema = test_schema();
+    let schema = test_schema(use_view);
     let mut partitions = vec![];
     let mut cur_time = 16909000000000i64;
     for _ in 0..partition_cnt {
-        let mut id_builder = StringBuilder::new();
+        // Choose the appropriate builder based on use_view.
+        let mut id_builder = if use_view {
+            TraceIdBuilder::Utf8View(StringViewBuilder::new())
+        } else {
+            TraceIdBuilder::Utf8(StringBuilder::new())
+        };
+
         let mut ts_builder = Int64Builder::new();
         let gen_id = |rng: &mut rand::rngs::SmallRng| {
             rng.gen::<[u8; 16]>()
@@ -230,10 +263,19 @@ pub(crate) fn make_data(
     Ok((schema, partitions))
 }
 
-/// The Schema used by make_data
-fn test_schema() -> SchemaRef {
-    Arc::new(Schema::new(vec![
-        Field::new("trace_id", DataType::Utf8, false),
-        Field::new("timestamp_ms", DataType::Int64, false),
-    ]))
+/// Returns a Schema based on the use_view flag
+fn test_schema(use_view: bool) -> SchemaRef {
+    if use_view {
+        // Return Utf8View schema
+        Arc::new(Schema::new(vec![
+            Field::new("trace_id", DataType::Utf8View, false),
+            Field::new("timestamp_ms", DataType::Int64, false),
+        ]))
+    } else {
+        // Return regular Utf8 schema
+        Arc::new(Schema::new(vec![
+            Field::new("trace_id", DataType::Utf8, false),
+            Field::new("timestamp_ms", DataType::Int64, false),
+        ]))
+    }
 }

datafusion/core/benches/distinct_query_sql.rs

@@ -133,7 +133,8 @@ pub async fn create_context_sampled_data(
     partition_cnt: i32,
     sample_cnt: i32,
 ) -> Result<(Arc<dyn ExecutionPlan>, Arc<TaskContext>)> {
-    let (schema, parts) = make_data(partition_cnt, sample_cnt, false /* asc */).unwrap();
+    let (schema, parts) =
+        make_data(partition_cnt, sample_cnt, false /* asc */, false).unwrap();
     let mem_table = Arc::new(MemTable::try_new(schema, parts).unwrap());
 
     // Create the DataFrame

datafusion/core/benches/topk_aggregate.rs

@@ -33,8 +33,9 @@ async fn create_context(
     sample_cnt: i32,
     asc: bool,
     use_topk: bool,
+    use_view: bool,
 ) -> Result<(Arc<dyn ExecutionPlan>, Arc<TaskContext>)> {
-    let (schema, parts) = make_data(partition_cnt, sample_cnt, asc).unwrap();
+    let (schema, parts) = make_data(partition_cnt, sample_cnt, asc, use_view).unwrap();
     let mem_table = Arc::new(MemTable::try_new(schema, parts).unwrap());
 
     // Create the DataFrame
@@ -108,7 +109,7 @@ fn criterion_benchmark(c: &mut Criterion) {
         |b| {
             b.iter(|| {
                 let real = rt.block_on(async {
-                    create_context(limit, partitions, samples, false, false)
+                    create_context(limit, partitions, samples, false, false, false)
                         .await
                         .unwrap()
                 });
@@ -122,7 +123,7 @@ fn criterion_benchmark(c: &mut Criterion) {
         |b| {
             b.iter(|| {
                 let asc = rt.block_on(async {
-                    create_context(limit, partitions, samples, true, false)
+                    create_context(limit, partitions, samples, true, false, false)
                         .await
                         .unwrap()
                 });
@@ -140,7 +141,7 @@ fn criterion_benchmark(c: &mut Criterion) {
         |b| {
             b.iter(|| {
                 let topk_real = rt.block_on(async {
-                    create_context(limit, partitions, samples, false, true)
+                    create_context(limit, partitions, samples, false, true, false)
                         .await
                         .unwrap()
                 });
@@ -158,7 +159,45 @@ fn criterion_benchmark(c: &mut Criterion) {
         |b| {
             b.iter(|| {
                 let topk_asc = rt.block_on(async {
-                    create_context(limit, partitions, samples, true, true)
+                    create_context(limit, partitions, samples, true, true, false)
+                        .await
+                        .unwrap()
+                });
+                run(&rt, topk_asc.0.clone(), topk_asc.1.clone(), true)
+            })
+        },
+    );
+
+    // Utf8View schema，time-series rows
+    c.bench_function(
+        format!(
+            "top k={limit} aggregate {} time-series rows [Utf8View]",
+            partitions * samples
+        )
+        .as_str(),
+        |b| {
+            b.iter(|| {
+                let topk_real = rt.block_on(async {
+                    create_context(limit, partitions, samples, false, true, true)
+                        .await
+                        .unwrap()
+                });
+                run(&rt, topk_real.0.clone(), topk_real.1.clone(), false)
+            })
+        },
+    );
+
+    // Utf8View schema，worst-case rows
+    c.bench_function(
+        format!(
+            "top k={limit} aggregate {} worst-case rows [Utf8View]",
+            partitions * samples
+        )
+        .as_str(),
+        |b| {
+            b.iter(|| {
+                let topk_asc = rt.block_on(async {
+                    create_context(limit, partitions, samples, true, true, true)
                         .await
                         .unwrap()
                 });