NGA-TRAN commented on code in PR #10537:
URL: https://github.com/apache/datafusion/pull/10537#discussion_r1603501230
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datafusion/core/src/datasource/physical_plan/parquet/arrow_statistics.rs:
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@@ -0,0 +1,43 @@
+use arrow_array::ArrayRef;
+use arrow_schema::DataType;
+use datafusion_common::Result;
+use parquet::file::statistics::Statistics as ParquetStatistics;
+
+/// statistics extracted from `Statistics` as Arrow `ArrayRef`s
+///
+/// # Note:
+/// If the corresponding `Statistics` is not present, or has no information for
+/// a column, a NULL is present in the corresponding array entry
+pub struct ArrowStatistics {
+ /// min values
+ min: ArrayRef,
+ /// max values
+ max: ArrayRef,
+ /// Row counts (UInt64Array)
+ row_count: ArrayRef,
+ /// Null Counts (UInt64Array)
+ null_count: ArrayRef,
+}
+
+/// Extract `ArrowStatistics` from the parquet [`Statistics`]
+pub fn parquet_stats_to_arrow<'a>(
+ arrow_datatype: &DataType,
+ statistics: impl IntoIterator<Item = Option<&'a ParquetStatistics>>,
+) -> Result<ArrowStatistics> {
+ todo!() // MY TODO next
+}
Review Comment:
Yeah, it is very easy to get `FileMetaData` from the parquet reader. I agree
the sort order is not needed (yet) but I will see what we needs we we go and
add them in
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datafusion/core/tests/parquet/arrow_statistics.rs:
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@@ -0,0 +1,528 @@
+// 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.
+
+//! This file contains an end to end test of extracting statitics from parquet
files.
+//! It writes data into a parquet file, reads statistics and verifies they are
correct
+
+use std::sync::Arc;
+
+// use arrow::json::reader;
+use arrow_array::{make_array, Array, Int64Array, RecordBatch};
+use arrow_schema::{DataType, Field, Schema};
+use parquet::arrow::arrow_reader::ArrowReaderBuilder;
+use parquet::arrow::ArrowWriter;
+use parquet::file::properties::{EnabledStatistics, WriterProperties};
+use parquet::file::statistics::Statistics as ParquetFileStatistics;
+use parquet::format::Statistics as ParquetFormatStatistics;
+
+// TEST HELPERS
+
+/// Return a record batch with i64 with Null values
+fn make_int64_batches_with_null(
+ null_values: usize,
+ no_null_values_start: i64,
+ no_null_values_end: i64,
+) -> RecordBatch {
+ let schema = Arc::new(Schema::new(vec![Field::new("i64", DataType::Int64,
true)]));
+
+ let v64: Vec<i64> = (no_null_values_start as _..no_null_values_end as
_).collect();
+
+ RecordBatch::try_new(
+ schema,
+ vec![make_array(
+ Int64Array::from_iter(
+ v64.into_iter()
+ .map(Some)
+ .chain(std::iter::repeat(None).take(null_values)),
+ )
+ .to_data(),
+ )],
+ )
+ .unwrap()
+}
+
+// TODO: Eventually, we will need only read_statistics and read_row_count,
+// but for my understading, we will keep both write and read statistics.
+// Their values should be the same
+pub struct RowGroupsStatistics {
+ // Statistics from the file writer
+ pub write_statistics: Vec<WriteRowGroupStatistics>,
+
+ // Statistics from the file reader
+ pub read_statistics: Vec<ReadRowGroupStatistics>,
+}
+
+pub struct WriteRowGroupStatistics {
+ pub statistics: ParquetFormatStatistics,
+ pub row_count: i64,
+}
+
+pub struct ReadRowGroupStatistics {
+ pub statistics: ParquetFileStatistics,
+ pub row_count: i64,
+}
+
+// Create a parquet file with one column for data type i64
+// Data of the file include
+// . Number of null rows is the given num_null
+// . There are non-null values in the range [no_null_values_start,
no_null_values_end], one value each row
+// . The file is divided into row groups of size row_per_group
+pub fn parquet_row_group_statistics(
+ num_null: usize,
+ no_null_values_start: i64,
+ no_null_values_end: i64,
+ row_per_group: usize,
+) -> RowGroupsStatistics {
+ let mut output_file = tempfile::Builder::new()
+ .prefix("parquert_statistics_test")
+ .suffix(".parquet")
+ .tempfile()
+ .expect("tempfile creation");
+
+ let props = WriterProperties::builder()
+ .set_max_row_group_size(row_per_group)
+ .set_statistics_enabled(EnabledStatistics::Chunk)
+ .build();
+
+ let batches = vec![
+ make_int64_batches_with_null(num_null, no_null_values_start,
no_null_values_end), // TODO: likely make this more general using
+
// create_data_batch(scenario); where scenario is the respective
enum data type
+ ];
+
+ let schema = batches[0].schema();
+
+ let mut writer = ArrowWriter::try_new(&mut output_file, schema,
Some(props)).unwrap();
+
+ for batch in batches {
+ writer.write(&batch).expect("writing batch");
+ }
+
+ //////////////// WRITE STATISTICS ///////////////////////
+ let file_meta = writer.close().unwrap();
+
+ // meta data of the row groups
+ let rg_metas = file_meta.row_groups;
+
+ // Statstics of each grow group
+ let mut write_rg_statistics = Vec::new();
+ for rg_meta in rg_metas {
+ let column = &rg_meta.columns;
+ assert_eq!(column.len(), 1, "expected 1 column");
+ let col_metadata = &column[0].meta_data;
+ let col_metadata = col_metadata.as_ref().unwrap();
+ let stats = &col_metadata.statistics;
+ let stats = stats.as_ref().unwrap();
+
+ let rg_stats = WriteRowGroupStatistics {
+ row_count: rg_meta.num_rows,
+ statistics: stats.clone(),
+ };
+ write_rg_statistics.push(rg_stats);
+ }
+
+ //////////////// READ STATISTICS ///////////////////////
+ let file = output_file.reopen().unwrap();
+
+ let reader = ArrowReaderBuilder::try_new(file).unwrap();
+ let parquet_meta = reader.metadata().clone();
+
+ // meta data of the row groups
+ let rg_metas = parquet_meta.row_groups();
+
+ // Statstics of each grow group
+ let mut read_rg_statistics = Vec::new();
+ for rg_meta in rg_metas {
+ let column = &rg_meta.columns();
+ assert_eq!(column.len(), 1, "expected 1 column");
+ let stats = &column[0].statistics().unwrap();
+
+ let rg_stats = ReadRowGroupStatistics {
+ row_count: rg_meta.num_rows() as i64,
+ statistics: (*stats).clone(),
+ };
+ read_rg_statistics.push(rg_stats);
+ }
+
+ RowGroupsStatistics {
+ write_statistics: write_rg_statistics,
+ read_statistics: read_rg_statistics,
+ }
+}
+
+// TESTS
+
+#[tokio::test]
+async fn test_one_row_group_without_null() {
+ let row_per_group = 20;
+ let rg_statistics = parquet_row_group_statistics(0, 4, 7, row_per_group);
+
+ //////////// Verify write row group statistics ////////////
+ let write_rg_stats = &rg_statistics.write_statistics;
+
+ // only 1 row group
+ assert_eq!(write_rg_stats.len(), 1, "expected 1 row group");
+ let rg = &write_rg_stats[0];
+
+ // 3 rows
+ assert_eq!(rg.row_count, 3, "expected 3 rows");
+
+ // no nulls
+ assert!(rg.statistics.null_count.is_none(), "expected no nulls");
+
+ let statistics = &rg.statistics;
+
+ // min is [4, 0, 0, 0, 0, 0, 0, 0]
+ assert_eq!(
+ *statistics.min_value.as_ref().unwrap(),
+ [4, 0, 0, 0, 0, 0, 0, 0]
+ );
+ // max is [6, 0, 0, 0, 0, 0, 0, 0]
+ assert_eq!(
+ *statistics.max_value.as_ref().unwrap(),
+ [6, 0, 0, 0, 0, 0, 0, 0]
+ );
+
+ println!("RG statistics:\n {statistics:#?}");
+ // This will display:
+ // RG statistics:
+ // Statistics {
+ // max: Some(
+ // [
+ // 6,
+ // 0,
+ // 0,
+ // 0,
+ // 0,
+ // 0,
+ // 0,
+ // 0,
+ // ],
+ // ),
+ // min: Some(
+ // [
+ // 4,
+ // 0,
+ // 0,
+ // 0,
+ // 0,
+ // 0,
+ // 0,
+ // 0,
+ // ],
+ // ),
+ // null_count: None,
+ // distinct_count: None,
+ // max_value: Some(
+ // [
+ // 6,
+ // 0,
+ // 0,
+ // 0,
+ // 0,
+ // 0,
+ // 0,
+ // 0,
+ // ],
+ // ),
+ // min_value: Some(
+ // [
+ // 4,
+ // 0,
+ // 0,
+ // 0,
+ // 0,
+ // 0,
+ // 0,
+ // 0,
+ // ],
+ // ),
+ // is_max_value_exact: Some(
+ // true,
+ // ),
+ // is_min_value_exact: Some(
+ // true,
+ // ),
+ // }
+
+ //////////// Verify read row group statistics ////////////
+ let read_rg_stats = &rg_statistics.read_statistics;
+
+ // only 1 row group
+ assert_eq!(read_rg_stats.len(), 1, "expected 1 row group");
+ let rg = &read_rg_stats[0];
+
+ // 3 rows
+ assert_eq!(rg.row_count, 3, "expected 3 rows");
+
+ // rg.statistics is Int64
+ let stats = &rg.statistics;
+ match stats {
+ ParquetFileStatistics::Int64(int64_stats) => {
+ // no nulls
+ assert_eq!(int64_stats.null_count(), 0, "expected no nulls");
+ // min is 4
+ assert_eq!(*int64_stats.min(), 4);
+ // max is 6
+ assert_eq!(*int64_stats.max(), 6);
+ }
+ _ => panic!("expected Int64"),
+ }
Review Comment:
Yes, that is the plan. These tests are mostly for me to understand what the
parquet stats looks like before I convert it into the arrow stats
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