adding benchmark for extracting arrow statistics from parquet (apache…

…#10610)

* adding benchmark for extracting arrow statistics from parquet

* fix clippy

* fix clippy

datafusion/core/Cargo.toml

@@ -209,3 +209,7 @@ name = "sort"
 [[bench]]
 harness = false
 name = "topk_aggregate"
+
+[[bench]]
+harness = false
+name = "parquet_statistic"

datafusion/core/benches/parquet_statistic.rs

@@ -0,0 +1,205 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements.  See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership.  The ASF licenses this file
+// to you 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.
+
+//! Benchmarks of benchmark for extracting arrow statistics from parquet
+
+use arrow::array::{ArrayRef, DictionaryArray, Float64Array, StringArray, UInt64Array};
+use arrow_array::{Int32Array, RecordBatch};
+use arrow_schema::{
+    DataType::{self, *},
+    Field, Schema,
+};
+use criterion::{criterion_group, criterion_main, BenchmarkId, Criterion};
+use datafusion::datasource::physical_plan::parquet::{
+    RequestedStatistics, StatisticsConverter,
+};
+use parquet::arrow::{arrow_reader::ArrowReaderBuilder, ArrowWriter};
+use parquet::file::properties::WriterProperties;
+use std::sync::Arc;
+use tempfile::NamedTempFile;
+#[derive(Debug, Clone)]
+enum TestTypes {
+    UInt64,
+    F64,
+    String,
+    Dictionary,
+}
+
+use std::fmt;
+
+impl fmt::Display for TestTypes {
+    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
+        match self {
+            TestTypes::UInt64 => write!(f, "UInt64"),
+            TestTypes::F64 => write!(f, "F64"),
+            TestTypes::String => write!(f, "String"),
+            TestTypes::Dictionary => write!(f, "Dictionary(Int32, String)"),
+        }
+    }
+}
+
+fn create_parquet_file(dtype: TestTypes, row_groups: usize) -> NamedTempFile {
+    let schema = match dtype {
+        TestTypes::UInt64 => {
+            Arc::new(Schema::new(vec![Field::new("col", DataType::UInt64, true)]))
+        }
+        TestTypes::F64 => Arc::new(Schema::new(vec![Field::new(
+            "col",
+            DataType::Float64,
+            true,
+        )])),
+        TestTypes::String => {
+            Arc::new(Schema::new(vec![Field::new("col", DataType::Utf8, true)]))
+        }
+        TestTypes::Dictionary => Arc::new(Schema::new(vec![Field::new(
+            "col",
+            DataType::Dictionary(Box::new(Int32), Box::new(Utf8)),
+            true,
+        )])),
+    };
+
+    let props = WriterProperties::builder().build();
+    let file = tempfile::Builder::new()
+        .suffix(".parquet")
+        .tempfile()
+        .unwrap();
+    let mut writer =
+        ArrowWriter::try_new(file.reopen().unwrap(), schema.clone(), Some(props))
+            .unwrap();
+
+    for _ in 0..row_groups {
+        let batch = match dtype {
+            TestTypes::UInt64 => make_uint64_batch(),
+            TestTypes::F64 => make_f64_batch(),
+            TestTypes::String => make_string_batch(),
+            TestTypes::Dictionary => make_dict_batch(),
+        };
+        writer.write(&batch).unwrap();
+    }
+    writer.close().unwrap();
+    file
+}
+
+fn make_uint64_batch() -> RecordBatch {
+    let array: ArrayRef = Arc::new(UInt64Array::from(vec![
+        Some(1),
+        Some(2),
+        Some(3),
+        Some(4),
+        Some(5),
+    ]));
+    RecordBatch::try_new(
+        Arc::new(arrow::datatypes::Schema::new(vec![
+            arrow::datatypes::Field::new("col", UInt64, false),
+        ])),
+        vec![array],
+    )
+    .unwrap()
+}
+
+fn make_f64_batch() -> RecordBatch {
+    let array: ArrayRef = Arc::new(Float64Array::from(vec![1.0, 2.0, 3.0, 4.0, 5.0]));
+    RecordBatch::try_new(
+        Arc::new(arrow::datatypes::Schema::new(vec![
+            arrow::datatypes::Field::new("col", Float64, false),
+        ])),
+        vec![array],
+    )
+    .unwrap()
+}
+
+fn make_string_batch() -> RecordBatch {
+    let array: ArrayRef = Arc::new(StringArray::from(vec!["a", "b", "c", "d", "e"]));
+    RecordBatch::try_new(
+        Arc::new(arrow::datatypes::Schema::new(vec![
+            arrow::datatypes::Field::new("col", Utf8, false),
+        ])),
+        vec![array],
+    )
+    .unwrap()
+}
+
+fn make_dict_batch() -> RecordBatch {
+    let keys = Int32Array::from(vec![0, 1, 2, 3, 4]);
+    let values = StringArray::from(vec!["a", "b", "c", "d", "e"]);
+    let array: ArrayRef =
+        Arc::new(DictionaryArray::try_new(keys, Arc::new(values)).unwrap());
+    RecordBatch::try_new(
+        Arc::new(Schema::new(vec![Field::new(
+            "col",
+            Dictionary(Box::new(Int32), Box::new(Utf8)),
+            false,
+        )])),
+        vec![array],
+    )
+    .unwrap()
+}
+
+fn criterion_benchmark(c: &mut Criterion) {
+    let row_groups = 100;
+    use TestTypes::*;
+    let types = vec![UInt64, F64, String, Dictionary];
+
+    for dtype in types {
+        let file = create_parquet_file(dtype.clone(), row_groups);
+        let file = file.reopen().unwrap();
+        let reader = ArrowReaderBuilder::try_new(file).unwrap();
+        let metadata = reader.metadata();
+
+        let mut group =
+            c.benchmark_group(format!("Extract statistics for {}", dtype.clone()));
+        group.bench_function(
+            BenchmarkId::new("extract_statistics", dtype.clone()),
+            |b| {
+                b.iter(|| {
+                    let _ = StatisticsConverter::try_new(
+                        "col",
+                        RequestedStatistics::Min,
+                        reader.schema(),
+                    )
+                    .unwrap()
+                    .extract(metadata)
+                    .unwrap();
+
+                    let _ = StatisticsConverter::try_new(
+                        "col",
+                        RequestedStatistics::Max,
+                        reader.schema(),
+                    )
+                    .unwrap()
+                    .extract(reader.metadata())
+                    .unwrap();
+
+                    let _ = StatisticsConverter::try_new(
+                        "col",
+                        RequestedStatistics::NullCount,
+                        reader.schema(),
+                    )
+                    .unwrap()
+                    .extract(reader.metadata())
+                    .unwrap();
+
+                    let _ = StatisticsConverter::row_counts(reader.metadata()).unwrap();
+                })
+            },
+        );
+        group.finish();
+    }
+}
+
+criterion_group!(benches, criterion_benchmark);
+criterion_main!(benches);