refactor: convert to mysql values directly from arrow

src/common/recordbatch/Cargo.toml

@@ -27,4 +27,9 @@ snafu.workspace = true
 tokio.workspace = true
 
 [dev-dependencies]
+criterion = "0.7.0"
 tokio.workspace = true
+
+[[bench]]
+name = "iter_record_batch_rows"
+harness = false

src/common/recordbatch/benches/iter_record_batch_rows.rs

@@ -0,0 +1,179 @@
+// Copyright 2023 Greptime Team
+//
+// Licensed 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::hint::black_box;
+use std::sync::Arc;
+
+use criterion::{BenchmarkId, Criterion, criterion_group, criterion_main};
+use datafusion::arrow::array::{Int32Array, TimestampMillisecondArray};
+use datafusion::arrow::datatypes::{DataType, Field, TimeUnit};
+use datafusion_common::arrow::array::{ArrayRef, RecordBatch, StringArray};
+use datafusion_common::arrow::datatypes::Schema;
+use datafusion_common::{ScalarValue, utils};
+use datatypes::arrow::array::AsArray;
+use datatypes::arrow::datatypes::{
+    Int32Type, TimestampMicrosecondType, TimestampMillisecondType, TimestampNanosecondType,
+    TimestampSecondType,
+};
+use datatypes::schema::SchemaRef;
+
+fn prepare_record_batch(rows: usize) -> RecordBatch {
+    let schema = Schema::new(vec![
+        Field::new(
+            "ts",
+            DataType::Timestamp(TimeUnit::Millisecond, None),
+            false,
+        ),
+        Field::new("i", DataType::Int32, true),
+        Field::new("s", DataType::Utf8, true),
+    ]);
+
+    let columns: Vec<ArrayRef> = vec![
+        Arc::new(TimestampMillisecondArray::from_iter_values(
+            (0..rows).map(|x| (1760313600000 + x) as i64),
+        )),
+        Arc::new(Int32Array::from_iter_values((0..rows).map(|x| x as i32))),
+        Arc::new(StringArray::from_iter((0..rows).map(|x| {
+            if x % 2 == 0 {
+                Some(format!("s_{x}"))
+            } else {
+                None
+            }
+        }))),
+    ];
+
+    RecordBatch::try_new(Arc::new(schema), columns).unwrap()
+}
+
+fn iter_by_greptimedb_values(schema: SchemaRef, record_batch: RecordBatch) {
+    let record_batch =
+        common_recordbatch::RecordBatch::try_from_df_record_batch(schema, record_batch).unwrap();
+    for row in record_batch.rows() {
+        black_box(row);
+    }
+}
+
+fn iter_by_loop_rows_and_columns(record_batch: RecordBatch) {
+    for i in 0..record_batch.num_rows() {
+        for column in record_batch.columns() {
+            match column.data_type() {
+                DataType::Timestamp(time_unit, _) => {
+                    let v = match time_unit {
+                        TimeUnit::Second => {
+                            let array = column.as_primitive::<TimestampSecondType>();
+                            array.value(i)
+                        }
+                        TimeUnit::Millisecond => {
+                            let array = column.as_primitive::<TimestampMillisecondType>();
+                            array.value(i)
+                        }
+                        TimeUnit::Microsecond => {
+                            let array = column.as_primitive::<TimestampMicrosecondType>();
+                            array.value(i)
+                        }
+                        TimeUnit::Nanosecond => {
+                            let array = column.as_primitive::<TimestampNanosecondType>();
+                            array.value(i)
+                        }
+                    };
+                    black_box(v);
+                }
+                DataType::Int32 => {
+                    let array = column.as_primitive::<Int32Type>();
+                    let v = array.value(i);
+                    black_box(v);
+                }
+                DataType::Utf8 => {
+                    let array = column.as_string::<i32>();
+                    let v = array.value(i);
+                    black_box(v);
+                }
+                _ => unreachable!(),
+            }
+        }
+    }
+}
+
+fn iter_by_datafusion_scalar_values(record_batch: RecordBatch) {
+    let columns = record_batch.columns();
+    for i in 0..record_batch.num_rows() {
+        let row = utils::get_row_at_idx(columns, i).unwrap();
+        black_box(row);
+    }
+}
+
+fn iter_by_datafusion_scalar_values_with_buf(record_batch: RecordBatch) {
+    let columns = record_batch.columns();
+    let mut buf = vec![ScalarValue::Null; columns.len()];
+    for i in 0..record_batch.num_rows() {
+        utils::extract_row_at_idx_to_buf(columns, i, &mut buf).unwrap();
+    }
+}
+
+pub fn criterion_benchmark(c: &mut Criterion) {
+    let mut group = c.benchmark_group("iter_record_batch");
+
+    for rows in [1usize, 10, 100, 1_000, 10_000] {
+        group.bench_with_input(
+            BenchmarkId::new("by_greptimedb_values", rows),
+            &rows,
+            |b, rows| {
+                let record_batch = prepare_record_batch(*rows);
+                let schema =
+                    Arc::new(datatypes::schema::Schema::try_from(record_batch.schema()).unwrap());
+                b.iter(|| {
+                    iter_by_greptimedb_values(schema.clone(), record_batch.clone());
+                })
+            },
+        );
+
+        group.bench_with_input(
+            BenchmarkId::new("by_loop_rows_and_columns", rows),
+            &rows,
+            |b, rows| {
+                let record_batch = prepare_record_batch(*rows);
+                b.iter(|| {
+                    iter_by_loop_rows_and_columns(record_batch.clone());
+                })
+            },
+        );
+
+        group.bench_with_input(
+            BenchmarkId::new("by_datafusion_scalar_values", rows),
+            &rows,
+            |b, rows| {
+                let record_batch = prepare_record_batch(*rows);
+                b.iter(|| {
+                    iter_by_datafusion_scalar_values(record_batch.clone());
+                })
+            },
+        );
+
+        group.bench_with_input(
+            BenchmarkId::new("by_datafusion_scalar_values_with_buf", rows),
+            &rows,
+            |b, rows| {
+                let record_batch = prepare_record_batch(*rows);
+                b.iter(|| {
+                    iter_by_datafusion_scalar_values_with_buf(record_batch.clone());
+                })
+            },
+        );
+    }
+
+    group.finish();
+}
+
+criterion_group!(benches, criterion_benchmark);
+criterion_main!(benches);

src/servers/Cargo.toml

@@ -35,6 +35,7 @@ catalog.workspace = true
 chrono.workspace = true
 common-base.workspace = true
 common-catalog.workspace = true
+common-decimal.workspace = true
 common-error.workspace = true
 common-frontend.workspace = true
 common-grpc.workspace = true